Month: April 2023
Global NoSQL Software Market Size, Analysis, Industry Trends, Top Suppliers and COVID …
MMS • RSS
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New Jersey, United States – The report offers an in-depth analysis of the Global NoSQL Software Market taking into account market dynamics, segmentation, geographical expansion, competitive landscape, and various other key aspects. The market analysts who have prepared the report have thoroughly studied the Global NoSQL Software market and have offered reliable and accurate data. The report analyses the current trends, growth opportunities, competitive pricing, restraining factors, and boosters that may have an impact on the overall dynamics of the Global NoSQL Software market. The report analytically studies the microeconomic and macroeconomic factors affecting the Global NoSQL Software market growth. New and emerging technologies that may influence the Global NoSQL Software market growth are also being studied in the report.
Both leading and emerging players of the Global NoSQL Software market are comprehensively looked at in the report. The analysts authoring the report deeply studied each and every aspect of the business of key players operating in the Global NoSQL Software market. In the company profiling section, the report offers exhaustive company profiling of all the players covered. The players are studied on the basis of different factors such as market share, growth strategies, new product launch, recent developments, future plans, revenue, gross margin, sales, capacity, production, and product portfolio.
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Key Players Mentioned in the Global NoSQL Software Market Research Report:
Amazon, Couchbase, MongoDB Inc., Microsoft, Marklogic, OrientDB, ArangoDB, Redis, CouchDB, DataStax.
Key companies operating in the Global NoSQL Software market are also comprehensively studied in the report. The Global NoSQL Software report offers definite understanding into the vendor landscape and development plans, which are likely to take place in the coming future. This report as a whole will act as an effective tool for the market players to understand the competitive scenario in the Global NoSQL Software market and accordingly plan their strategic activities.
Global NoSQL Software Market Segmentation:
NoSQL Software Market, By Type
• Document Databases
• Key-vale Databases
• Wide-column Store
• Graph Databases
• Others
NoSQL Market, By Application
• Social Networking
• Web Applications
• E-Commerce
• Data Analytics
• Data Storage
• Others
Players can use the report to gain sound understanding of the growth trend of important segments of the Global NoSQL Software market. The report offers separate analysis of product type and application segments of the Global NoSQL Software market. Each segment is studied in great detail to provide a clear and thorough analysis of its market growth, future growth potential, growth rate, growth drivers, and other key factors. The segmental analysis offered in the report will help players to discover rewarding growth pockets of the Global NoSQL Software market and gain a competitive advantage over their opponents.
Key regions including but not limited to North America, Asia Pacific, Europe, and the MEA are exhaustively analyzed based on market size, CAGR, market potential, economic and political factors, regulatory scenarios, and other significant parameters. The regional analysis provided in the report will help market participants to identify lucrative and untapped business opportunities in different regions and countries. It includes a special study on production and production rate, import and export, and consumption in each regional Global NoSQL Software market considered for research. The report also offers detailed analysis of country-level Global NoSQL Software markets.
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What to Expect in Our Report?
(1) A complete section of the Global NoSQL Software market report is dedicated for market dynamics, which include influence factors, market drivers, challenges, opportunities, and trends.
(2) Another broad section of the research study is reserved for regional analysis of the Global NoSQL Software market where important regions and countries are assessed for their growth potential, consumption, market share, and other vital factors indicating their market growth.
(3) Players can use the competitive analysis provided in the report to build new strategies or fine-tune their existing ones to rise above market challenges and increase their share of the Global NoSQL Software market.
(4) The report also discusses competitive situation and trends and sheds light on company expansions and merger and acquisition taking place in the Global NoSQL Software market. Moreover, it brings to light the market concentration rate and market shares of top three and five players.
(5) Readers are provided with findings and conclusion of the research study provided in the Global NoSQL Software Market report.
Key Questions Answered in the Report:
(1) What are the growth opportunities for the new entrants in the Global NoSQL Software industry?
(2) Who are the leading players functioning in the Global NoSQL Software marketplace?
(3) What are the key strategies participants are likely to adopt to increase their share in the Global NoSQL Software industry?
(4) What is the competitive situation in the Global NoSQL Software market?
(5) What are the emerging trends that may influence the Global NoSQL Software market growth?
(6) Which product type segment will exhibit high CAGR in future?
(7) Which application segment will grab a handsome share in the Global NoSQL Software industry?
(8) Which region is lucrative for the manufacturers?
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Verified Market Research® is a leading Global Research and Consulting firm that has been providing advanced analytical research solutions, custom consulting and in-depth data analysis for 10+ years to individuals and companies alike that are looking for accurate, reliable and up to date research data and technical consulting. We offer insights into strategic and growth analyses, Data necessary to achieve corporate goals and help make critical revenue decisions.
Our research studies help our clients make superior data-driven decisions, understand market forecast, capitalize on future opportunities and optimize efficiency by working as their partner to deliver accurate and valuable information. The industries we cover span over a large spectrum including Technology, Chemicals, Manufacturing, Energy, Food and Beverages, Automotive, Robotics, Packaging, Construction, Mining & Gas. Etc.
We, at Verified Market Research, assist in understanding holistic market indicating factors and most current and future market trends. Our analysts, with their high expertise in data gathering and governance, utilize industry techniques to collate and examine data at all stages. They are trained to combine modern data collection techniques, superior research methodology, subject expertise and years of collective experience to produce informative and accurate research.
Having serviced over 5000+ clients, we have provided reliable market research services to more than 100 Global Fortune 500 companies such as Amazon, Dell, IBM, Shell, Exxon Mobil, General Electric, Siemens, Microsoft, Sony and Hitachi. We have co-consulted with some of the world’s leading consulting firms like McKinsey & Company, Boston Consulting Group, Bain and Company for custom research and consulting projects for businesses worldwide.
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Java News Roundup: New OpenJDK JEPs, Payara Platform, Spring and Tomcat Updates, WildFly 28
MMS • Michael Redlich
Article originally posted on InfoQ. Visit InfoQ
This week’s Java roundup for April 17th, 2023 features news from OpenJDK, JDK 21, JMC 8.3.1, BellSoft, Spring Boot, Spring Security, Spring Session, Spring Authorization Server, Spring Integration, Spring for GraphQL and Spring Shell, WildFly 28, Payara Platform, Open Liberty 23.0.0.4-beta, Micronaut 3.9, Apache Tomcat updates, Ktor 2.3, JHipster Lite 0.32, JBang 0.106.3 and Gradle 8.1.1.
OpenJDK
JEP 446, Scoped Values (Preview), has been promoted from its JEP Draft 8304357 to Candidate status. Formerly known as Extent-Local Variables (Incubator), this JEP is now a preview feature following JEP 429, Scoped Values (Incubator), delivered in JDK 20. This JEP proposes to enable sharing of immutable data within and across threads. This is preferred to thread-local variables, especially when using large numbers of virtual threads.
JEP 447, Statements before super(), has been promoted from its JEP Draft 8300786 to Candidate status. This JEP, under the auspices of Project Amber, proposes to: allow statements that do not reference an instance being created to appear before the this() or super() calls in a constructor; and preserve existing safety and initialization guarantees for constructors. Gavin Bierman, consulting member of technical staff at Oracle, has provided an initial specification of this JEP for the Java community to review and provide feedback.
JEP 448, Vector API (Sixth Incubator), has been promoted from its JEP Draft 8305868 to Candidate status. This JEP, under the auspices of Project Panama, incorporates enhancements in response to feedback from the previous five rounds of incubation: JEP 438, Vector API (Fifth Incubator), delivered in JDK 20; JEP 426, Vector API (Fourth Incubator), delivered in JDK 19; JEP 417, Vector API (Third Incubator), delivered in JDK 18; JEP 414, Vector API (Second Incubator), delivered in JDK 17; and JEP 338, Vector API (Incubator), delivered as an incubator module in JDK 16. This feature proposes to enhance the Vector API to load and store vectors to and from a MemorySegment as defined by JEP 424, Foreign Function & Memory API (Preview).
JEP 449, Deprecate the Windows 32-bit x86 Port for Removal, has been promoted from its JEP Draft 8303167 to Candidate status. This feature JEP, introduced by George Adams, Senior Program Manager at Microsoft, proposes to deprecate the Windows x86-32 port with the intent to remove it in a future release. With no intent to implement JEP 436, Virtual Threads (Second Preview), in 32-bit platforms, removing support for this port will enable OpenJDK developers to accelerate development of new features.
JEP Draft 8305968, Integrity and Strong Encapsulation, and JEP Draft 8306275, Disallow the Dynamic Loading of Agents by Default, have been submitted by Ron Pressler, architect and technical lead for Project Loom at Oracle.
Integrity and Strong Encapsulation proposes to assure the integrity of code and data with a variety of features, such as strong encapsulation, that are enabled by default. Goals of this draft include: allow the Java platform to robustly maintain invariants required for maintainability, security and performance; and differentiate use cases where breaking encapsulation is convenient from use cases where disabling encapsulation is essential.
Disallow the Dynamic Loading of Agents by Default, following the approach of Integrity and Strong Encapsulation, proposes to disallow the dynamic loading of agents into a running JVM by default. Goals of this draft include: reassess the balance between serviceability and integrity; and ensure that a majority of tools, which do not need to dynamically load agents, are unaffected.
JDK Mission Control (JMC) 8.3.1 has been released with notable fixes such as: unable to open JMX Console after installing plugins in macOS and Linux; unable to edit Eclipse project run configurations after installing JMC plugins on Linux; and unable to perform flight recording on jLinked applications; More details on this release may be found in the release notes.
JDK 21
Build 19 of the JDK 21 early-access builds was also made available this past week featuring updates from Build 18 that include fixes to various issues. Further details on this build may be found in the release notes.
For JDK 21, developers are encouraged to report bugs via the Java Bug Database.
JDK 20
JDK 20.0.1, the first maintenance release of JDK 20, along with security updates for JDK 17.0.7, JDK 11.0.19 and JDK 8u371 were made available as part of Oracle’s Releases Critical Patch Update for April 2023.
BellSoft
Also concurrent with Oracle’s Critical Patch Update (CPU) for April 2023, BellSoft has released CPU patches for versions 17.0.6.0.1, 11.0.18.0.1 and 8u371 of Liberica JDK, their downstream distribution of OpenJDK. In addition, Patch Set Update (PSU) versions 20.0.1, 17.0.7, 11.0.19 and 8u372, containing CPU and non-critical fixes, have also been released.
Spring Framework
The first release candidate of Spring Boot 3.1.0 delivers notable new features: improved Testcontainers support including support at development time; support for Docker Compose; enhancements to SSL configuration; and improvements for building Docker images. More details on this release may be found in the release notes.
The release of Spring Boot 3.0.6 primarily addresses CVE-2023-20873, Security Bypass With Wildcard Pattern Matching on Cloud Foundry, a vulnerability in which an application that is deployed to Spring Cloud for Cloud Foundry could be susceptible to a security bypass. Along with improvements in documentation and dependency upgrades, this release also provides notable bug fixes such as: integration of Spring Cloud for Cloud Foundry does not use endpoint path mappings; the ApplicationAvailability bean is auto-configured even if a custom one already exists; and default configuration substitutions in Apache Cassandra don’t resolve against configuration derived from the spring.data.cassandra properties file. More details on this release may be found in the release notes.
Similarly, the release of Spring Boot 2.7.11 also addresses the aforementioned CVE-2023-20873 and provides improvements in documentation, dependency upgrades and the same bug fixes as Spring Boot 3.0.6. More details on this release may be found in the release notes.
Versions 6.1.0-RC1, 6.0.3, 5.8.3 and 5.7.8 of Spring Security have been released to primarily address CVE-2023-20862, Empty SecurityContext Is Not Properly Saved Upon Logout, a vulnerability in which serialized versions of logout does not: properly clean the security context; and unable to explicitly save an empty security context to the HttpSessionSecurityContextRepository class. This results in users still being authenticated even after logout. More details on these releases may be found in the release notes for version 6.1.0-RC1, version 6.0.3, version 5.8.3 and version 5.7.8.
The first release candidate of Spring Session 3.1.0 delivers dependency upgrades and a new feature in which an instance of the StringRedisSerializer class is reused to eliminate the need to instantiate additional serializer instances. More details on this release may be found in the release notes.
The first release candidate of Spring Authorization Server 1.1.0 provides dependency upgrades and new features such as: support for device code and user code in the JdbcOAuth2AuthorizationService class; improvements in the OAuth 2.0 Device Authorization Grant that include adding tests and reference documentation; and improvements in the OpenID Connect 1.0 logout endpoint. More details on this release may be found in the release notes.
Similarly, versions 1.0.2 and 0.4.2 of Spring Authorization Server have also been released featuring dependency upgrades and notable bug fixes: return of an incorrect INVALID_CLIENT token error code to the correct INVALID_GRANT token error code; a broken support link; the authentication secret should be saved after encoding upon registration of the client; and a consideration that would allow the use of localhost in redirect URIs. More details on these releases may be found in the release notes for version 1.0.2 and version 0.4.2.
Version 6.1.0-RC1 and 6.0.5 of Spring Integration have been released that share notable changes such as: remove a trailing space in the IntegrationWebSocketContainer class; and improvements to the BaseWsInboundGatewaySpec and TailAdapterSpec classes that didn’t override super methods and threw instances of NullPointerException due to target field not populated. More details on these releases may be found in the release notes for version 6.1.0-RC1 and version 6.0.5.
The first release candidate of Spring for GraphQL 1.2.0 delivers new features such as: update the SchemaMappingInspector class to support Connection types; support for pagination with Querydsl and Query By Example; and overall support for pagination and sorting. More details on this release may be found in the release notes.
Versions 3.1.0-M2, 3.0.2 and 2.1.8 of Spring Shell have been released featuring shared notable changes such as: builds upon Spring Boot 3.1.0-M2, 3.0.5 and 2.7.10, respectively; a backport of bug fixes; and a significant fix for custom type handling with positional arguments. More details on these releases may be found in the release notes for version 3.1.0-M2, version 3.0.2 and version 2.1.8.
WildFly
Red Hat has released WildFly 28 that ships with improved support for observability and full support for Jakarta EE 10. WildFly has added support for Micrometer and the MicroProfile Telemetry specification, but has removed support for MicroProfile Metrics. JDK 17 is recommended for production applications, but Red Hat has seen good results on JDK 20. More details on this release may be found in the release notes and InfoQ will follow up with a more detailed news story.
Payara
Payara has released their April 2023 edition of the Payara Platform that includes Community Edition 6.2023.4, Enterprise Edition 6.1.0 and Enterprise Edition 5.50.0.
Community Edition 6.2023.4 delivers:a fix for a Payara 6 deployment error with JDK17 and Records; improvements in the SameSite cookie attributes in the Application Deployment Descriptor and a global HTTP network listener; and dependency upgrades to EclipseLink 4.0.1, EclipseLink ASM 9.4.0, Hazelcast 5.2.2 and ASM 9.4. More details on this release may be found in the release notes.
Similarly, Enterprise Edition 6.1.0 features: a fix for a Payara 6 deployment error with JDK17 and Records; improvements in the SameSite cookie attributes in the Application Deployment Descriptor; and dependency upgrades to EclipseLink 4.0.1, EclipseLink ASM 9.4.0, Hazelcast 5.2.2 and ASM 9.4 More details on this release may be found in the release notes.
Enterprise Edition 5.50.0 ships with: a resolution for CVE-2023-24998, a vulnerability in Apache Commons FileUpload in which an attacker can trigger a denial-of-service with malicious uploads due to the number of processed request parts is not limited; a fix for a Hazelcast NoDataMemberInClusterException; an improvement in the SameSite cookie attribute in the Application Deployment Descriptor; and a dependency upgrade to Hazelcast 5.2.2. More details on this release may be found in the release notes.
Open Liberty
IBM has released Open Liberty 23.0.0.4-beta featuring updated support for the Jakarta Data specification such that developers may now combine multiple ways of specifying ordering and sorting, defining a precedence. Sorting that is defined by the @OrderBy annotation or a query-by-method keyword is applied first, followed by the parameters from the Sort record on the method or the Pageable interface.
Micronaut
The Micronaut Foundation has released Micronaut Framework 3.9.0 that delivers new features such as: the ability to customize a package to write introspection with the targetPackage field of the @Introspected annotation; the ability to enable Cross Origin Resource Sharing (CORS) configuration via the @CrossOrigin annotation; a breaking change in which the configuration property, micronaut.server.cors.*.configurations.allowed-origins, does not support regular expressions to prevent an accidental exposure of a user’s API; and updates to modules such as: Micronaut Kubernetes, Micronaut Security, Micronaut CRaC, Micronaut Maven and Micronaut Launch. More details on this release may be found in the release notes.
Apache Software Foundation
The Apache Tomcat team has provided point releases for versions 11.0.0-M5, 10.1.8, 9.0.74 and 8.5.88. All four versions share notable changes such as: reduce the default value of maxParameterCount from 10000 to 1000; correct a regression in the fix for bug 66442 that meant that streams without a response body did not decrement the active stream count when completing, leading to an ERR_HTTP2_SERVER_REFUSED_STREAM for some connections; implementation of RFC 9239, Updates to ECMAScript Media Types, in which the MIME types for JavaScript has changed to text/javascript. More details on these releases may be found in the release notes for version 11.0.0-M5, version 10.1.8, version 9.0.74 and version 8.5.88.
Ktor
JetBrains has released version 2.3.0 of Ktor, the asynchronous framework for creating microservices and web applications, that include improvements and fixes such as: support for regular expressions when defining routes; drop support for the legacy JS compiler that will be removed in the upcoming release of Kotlin 1.9.0; support for Apache 5 and Jetty 11; and support for Structured Concurrency for Sockets. More details on this release may be found in the release notes.
JHipster
The JHipster team has released version 0.32.0 of JHipster Lite with many dependency upgrades and notable changes such as: support for Hibernate second-level cache by setting the spring.jpa.properties.hibernate.cache.use_second_level_cache property to true; remove an unnecessary warning upon executing the npm run lint command; and remove an unnecessary stack trace upon running the npm t command. More details on this release may be found in the release notes.
JBang
The release of JBang 0.106.3 fixes formatting for an issue where ChatGPT errors on bad keys or usage limits.
Gradle
Gradle 8.1.1 has been released that ships with bug fixes: a MethodTooLargeException when instrumenting a class with significant number of lambdas for the configuration cache; the Kotlin DSL precompiled script plugins built with Gradle 8.1 cannot be used with other versions of Gradle; and Gradle 8.1 configuration of the freeCompilerArgs method for Kotlin in buildSrc breaks a build with errors that are not useful. More details on this release may be found in the release notes.
Global NoSQL Database Market to Witness Exponential Rise in Revenue Share … – Coleman News
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New Jersey, United States – “Global NoSQL Database Market Insight, Forecast 2030” is recently published by verified Market Research. The analysts and researchers have performed primary as well as secondary research on a large scale with the help of various methodologies like Porter’s Five Forces and PESTLE Analysis. Key trends and opportunities that may emerge in the near future have been discussed in the Global NoSQL Database Market Report. A detailed analysis of the factors positively influencing the growth has been done by the professionals. Besides, factors that may act as key challenges for the participants are examined in the Global NoSQL Database Market Report. The Global NoSQL Database research report lays emphasis on the key trends and opportunities that may emerge in the near future and positively impact the overall industry growth. Key drivers that are fuelling the growth are also discussed in the Global NoSQL Database report. Additionally, challenges and restraining factors that are likely to curb growth in the years to come are put forth by the analysts to prepare the manufacturers for future challenges in advance.
In addition, market revenues based on region and country are provided in the Global NoSQL Database report. The authors of the report have also shed light on the common business tactics adopted by players. The leading players of the Global NoSQL Database market and their complete profiles are included in the report. Besides that, investment opportunities, recommendations, and trends that are trending at present in the Global NoSQL Database market are mapped by the report. With the help of this report, the key players of the Global NoSQL Database market will be able to make sound decisions and plan their strategies accordingly to stay ahead of the curve.
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Key Players Mentioned in the Global NoSQL Database Market Research Report:
Objectivity Inc, Neo Technology Inc, MongoDB Inc, MarkLogic Corporation, Google LLC, Couchbase Inc, Microsoft Corporation, DataStax Inc, Amazon Web Services Inc & Aerospike Inc.
The competitive landscape is a critical aspect every key player needs to be familiar with. The report throws light on the competitive scenario of the Global NoSQL Database market to know the competition at both the domestic and global levels. Market experts have also offered the outline of every leading player of the Global NoSQL Database market, considering the key aspects such as areas of operation, production, and product portfolio. Additionally, companies in the report are studied based on the key factors such as company size, market share, market growth, revenue, production volume, and profits.
Global NoSQL Database Market Segmentation:
NoSQL Database Market, By Type
• Graph Database
• Column Based Store
• Document Database
• Key-Value Store
NoSQL Database Market, By Application
• Web Apps
• Data Analytics
• Mobile Apps
• Metadata Store
• Cache Memory
• Others
NoSQL Database Market, By Industry Vertical
• Retail
• Gaming
• IT
• Others
Our market analysts are experts in deeply segmenting the Global NoSQL Database market and thoroughly evaluating the growth potential of each and every segment studied in the report. Right at the beginning of the research study, the segments are compared on the basis of consumption and growth rate for a review period of nine years. The segmentation study included in the report offers a brilliant analysis of the Global NoSQL Database market, taking into consideration the market potential of different segments studied. It assists market participants to focus on high-growth areas of the Global NoSQL Database market and plan powerful business tactics to secure a position of strength in the industry.
Global NoSQL Database market research study is incomplete without regional analysis, and we are well aware of it. That is why the report includes a comprehensive and all-inclusive study that solely concentrates on the geographical growth of the Global NoSQL Database market. The study also includes accurate estimations about market growth at the global, regional, and country levels. It empowers you to understand why some regional markets are flourishing while others are seeing a decline in growth. It also allows you to focus on geographies that hold the potential to create lucrative prospects in the near future.
Inquire for a Discount on this Premium Report @ https://www.verifiedmarketresearch.com/ask-for-discount/?rid=129411
What to Expect in Our Report?
(1) A complete section of the Global NoSQL Database market report is dedicated for market dynamics, which include influence factors, market drivers, challenges, opportunities, and trends.
(2) Another broad section of the research study is reserved for regional analysis of the Global NoSQL Database market where important regions and countries are assessed for their growth potential, consumption, market share, and other vital factors indicating their market growth.
(3) Players can use the competitive analysis provided in the report to build new strategies or fine-tune their existing ones to rise above market challenges and increase their share of the Global NoSQL Database market.
(4) The report also discusses competitive situation and trends and sheds light on company expansions and merger and acquisition taking place in the Global NoSQL Database market. Moreover, it brings to light the market concentration rate and market shares of top three and five players.
(5) Readers are provided with findings and conclusion of the research study provided in the Global NoSQL Database Market report.
Key Questions Answered in the Report:
(1) What are the growth opportunities for the new entrants in the Global NoSQL Database industry?
(2) Who are the leading players functioning in the Global NoSQL Database marketplace?
(3) What are the key strategies participants are likely to adopt to increase their share in the Global NoSQL Database industry?
(4) What is the competitive situation in the Global NoSQL Database market?
(5) What are the emerging trends that may influence the Global NoSQL Database market growth?
(6) Which product type segment will exhibit high CAGR in future?
(7) Which application segment will grab a handsome share in the Global NoSQL Database industry?
(8) Which region is lucrative for the manufacturers?
For More Information or Query or Customization Before Buying, Visit @ https://www.verifiedmarketresearch.com/product/nosql-database-market/
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Verified Market Research® is a leading Global Research and Consulting firm that has been providing advanced analytical research solutions, custom consulting and in-depth data analysis for 10+ years to individuals and companies alike that are looking for accurate, reliable and up to date research data and technical consulting. We offer insights into strategic and growth analyses, Data necessary to achieve corporate goals and help make critical revenue decisions.
Our research studies help our clients make superior data-driven decisions, understand market forecast, capitalize on future opportunities and optimize efficiency by working as their partner to deliver accurate and valuable information. The industries we cover span over a large spectrum including Technology, Chemicals, Manufacturing, Energy, Food and Beverages, Automotive, Robotics, Packaging, Construction, Mining & Gas. Etc.
We, at Verified Market Research, assist in understanding holistic market indicating factors and most current and future market trends. Our analysts, with their high expertise in data gathering and governance, utilize industry techniques to collate and examine data at all stages. They are trained to combine modern data collection techniques, superior research methodology, subject expertise and years of collective experience to produce informative and accurate research.
Having serviced over 5000+ clients, we have provided reliable market research services to more than 100 Global Fortune 500 companies such as Amazon, Dell, IBM, Shell, Exxon Mobil, General Electric, Siemens, Microsoft, Sony and Hitachi. We have co-consulted with some of the world’s leading consulting firms like McKinsey & Company, Boston Consulting Group, Bain and Company for custom research and consulting projects for businesses worldwide.
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MMS • Tomas Neubauer
Article originally posted on InfoQ. Visit InfoQ
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Transcript
Introduction [00:44]
Roland Meertens: Welcome everyone to the InfoQ podcast. My name is Roland Meertens, your host today, and I will be interviewing Tomas Neubauer. He is the CTO and founder of Quix. We are talking to each other in person at the QCon London conference where he gave the presentation simplifying realtime ML pipelines with Quix Streams and Opensource Python library from ML engineers. Make sure to watch his presentation as it delivers tremendous insights and to review time ML pipelines and how to get started with Quix Streams yourself.
During today’s interview, we will dive deeper into the topic of real-time ML. I hope you enjoy it and I hope you can learn something from it.
Tomas. Welcome to the InfoQ podcast.
Tomáš Neubauer:
Thank you for having me.
Roland Meertens: You are giving your talk tomorrow here at QCon London. Can you maybe give a short summary of your talk?
About Quix Streams [01:37]
Tomáš Neubauer: Sure, yeah. So I’m talking about the open-source library Quix Streams. It’s a Python stream processing library for data and workloads on top of Kafka. And I’m talking about how to use this library in projects that involve realtime machine learning. And I’ll talk about the landscape, different architecture designs to solve this problem, pros and cons of each. And then I put this against a real use case, which I, at the end of the presentation, develop on stage from scratch. And in this case, it’s detecting a cyclist crash. So imagine a fitness app running on your handlebars and you crashed and you want to inform your relatives or emergency services.
Roland Meertens: So then you are programming this demo live on stage. Which programming language are you using for this?
Tomáš Neubauer: Yes, I’m using the Opensource library Quix Stream. So I’m using Python and yeah, I’m basically starting with having just data from the app, telemetry data like g-force sensor, GPS-based location, speed, et cetera. And I use a machine learning model that has been trained on history data to detect that the cyclists crashed.
Roland Meertens: And what kind of machine learning model is this?
Tomáš Neubauer: It’s a sensor flow model and we basically train it beforehand, so that’s not done on the stage and we label data correctly and train it in Google Colab. And I’m going to talk about how to get that model from that Colab to production.
What is Real Time ML? [02:40]
Roland Meertens: And so if you’re talking about real-time machine learning, what do you mean with real time? How fast is real time? When can you really say this is real time ML?
Tomáš Neubauer: Well, real time in this case will be five times per second. We will receive telemetry data points from the cyclist. So all of these parameters that I mentioned will be five times per second stream to the cloud. And we will with, I would say 50 milliseconds, delay. Inform either the services or a consuming application that there was a crash. There’s no one hour, one day, one minute delay.
Roland Meertens: Okay. So you get this data from your smart device and you are cutting this up into chunks which are then sent in to your API or to your application?
Tomáš Neubauer: So we’re streaming this data directly through the pipeline without batching anything. So basically it’s coming piece by piece and we are not waiting for anything either. So every 200 milliseconds we do this detection and either say this is not a crash or this is a crash. And in the end of the presentation, I will have a simple front end application with a map and alert because obvious I’m not going to crash a bike on the stage. I’m going to have a similar model that will detect shaking with the phone and I’m going to show everyone that the shake is detected.
Data for Real Time ML [04:19]
Roland Meertens: And where does this come from? How did you get started with this?
Tomáš Neubauer: The roots of this idea, for this Opensource library is coming from my previous job where I was working in McLaren and I was leading a team that was connecting F1 cars to the cloud and therefore to the factory. So people don’t have to travel every second weekend around the world to build real-time decision insight. What I mean by that is basically deciding in a split second that the car needs different tires, different settings for the wing, et cetera. And it was a challenging use case, lots of data around 30 million numbers per minute from each car. And so we couldn’t use any database technology that I’m going to talk about in a presentation and we had to adapt streaming technology. But the biggest problem we faced actually was to get this technology to the hands of our functional team, which were made of mechanical engineers, ML engineers, data scientists. They all use Python and really struggled to use this new tech that we gave them.
Roland Meertens: And how should I see this? So you have this car going over circuits, generating a lot of data, this sends it back to some kind of ground station and then do you have humans making decisions real time or is this also ML models which are making decisions?
Tomáš Neubauer: The way how it works is that in a car, there are sensors that’s collecting data. Some of them are even more than kilohertz or more than a thousand numbers per second, that is streamed over the radio to the garage where there’s a direct connection to the cloud. And then through the cloud infrastructure, it’s being consumed in a factory where people during the week, building new models. And then in a race day there is plenty of screens in the garage where there are dashboards and different waveforms which basically visualizing the result of these models. So the people in the garage can immediately decide that car need something else.
Roland Meertens: And so this is all part of the race strategy where people need to make decisions in split seconds and this needs the data to be available and the models to run in split seconds?
Tomáš Neubauer: Yes, exactly. And basically during my time in Mclaren, we took that know-how from racing and actually applied outside. So at the end we end up doing the same thing for high-speed railway in Singapore where basically we were using machine learning to detect break and suspension deterioration based on the history of data. So there are certain vibration signatures that will lead to a deterioration of the object.
Programming languages for Real Time ML [06:45]
Roland Meertens: And you were talking about different programming languages like either Java or Python. How does this integrate with what you’re working on?
Tomáš Neubauer: Basically, the whole streaming world is traditionally Java-based. Most of the brokers are built in Java or Scala. And as a result, most of the tools around it and most of the libraries and frameworks are built in Java. Although there are some ports and some libraries that let you use these libraries for Python, although there are just a few of them. It’s quite painful because this connection doesn’t really work well and therefore it’s quite difficult for patent people, especially people from data teams to leverage this stack. And as a result, most of the projects really doesn’t work that way. And most of the people work in Jupyter Notebooks and silos and then software engineering taking these models into production.
Roland Meertens: So what do you do to improve this?
Tomáš Neubauer: What I believe is that unless data team work directly on product, it’s never going to work really well because people don’t see the result of their work immediately and they are dependent on other teams. And every time that one team is dependent on another, it just kills innovation and efficiency. So the idea of this is that a data team directly contribute to a product and can test and develop straightaway. So the code doesn’t run in Jupyter Notebook or stays there but actually goes to realtime pipelines and so people can immediately see a result of their work on a physical thing.
Roland Meertens: And you mentioned that there’s different ways people can orchestrate something like this. There’s different ML architectures you could use or you could use for such an approach. Which ones are there?
Tomáš Neubauer: So there’s many options to choose, from all different dimensions that you look at the architecture of building such a system. But one of them is obviously if you’re going to go for batch or streaming. So are you going to use technology like Spark and reactive data in batches or you need a real time system where you need to use something like Kafka or Pulsar or other streaming technologies. And the second thing is how you actually going to use your ML models?
So you can deploy them behind the API or you can actually embed them to a streaming transformation and discuss both pros and cons of each solution.
Roland Meertens: And what do you mean with a streaming transformation?
Tomáš Neubauer: This is a fundamental major concept of what I’m going to talk about, which is a pub and sub service. So basically we are going to subscribe in our model to a topic where we are going to get input data from the phone and we going to output the results. Therefore, is there a crash or no? And this is the major architectural cornerstone of this approach.
The tools needed [09:22]
Roland Meertens: Okay. And you mentioned for example, Kafka and you mentioned some other tools. How does your work then relate to this?
Tomáš Neubauer: Well, what we found out is that Kafka, although it’s powerful, it’s quite difficult to use. So we have built a level abstraction on top of it. Then we found that that’s not enough actually because streaming on itself introduce complexities and different approaches to common problems. I have a nice example of that tomorrow. So we are building abstraction on top of streaming concept as well, which means that you would operate and you would develop your code in Python as it would be in Jupyter Notebook. So what you are used to when you working with a static data would apply to building a streaming transformation.
Roland Meertens: And how do you do this? How can people test this with a pre-recorded stream which they then replay and can you still use a Jupyter Notebook or do you as a machine learning or as a data scientist, do you then use and lose part of your tooling?
Tomáš Neubauer: So the Quix Stream is Opensource library that you can just download from paper and use and connect to your broker. If you don’t have a broker, you can set it up. It’s Opensource software as well. If you don’t want to, you can use our manage broker as well, doesn’t matter, it works the same. And then we have some Opensource simulators of data that you can use if you don’t have your own. So for example, we have F1 simulator which will give you higher solution data, so that’s quite cool. You can also, for example, subscribe to Reddit and get messages on Reddit or you can use the app I’m going to show you tomorrow. It’s also Opensource, so you can install it from up store or possibly you can even clone it and change it to suit your need and deploy by yourself.
Different modalities [11:06]
Roland Meertens: So then Quix handles both text messages but also audio or what kind of data do you handle?
Tomáš Neubauer: Yeah, so we handle time series data, which involves a numerical and string values. Then we handle pioneer data, which is audio and video and geospatial, et cetera. Where we allow developers to just attach this and the column and then we have a metadata. So you don’t have to repeat for example that this bike has a VMware 1.5. You just send it once and the stateful pipeline will persist that information. And then at the end you also can send events. So for example, crash is a good example of event, it doesn’t have any continuous information.
Roland Meertens: Okay. So can you also connect these pipelines such that one pipeline for example gets all the information from your sensor and then sends events to another pipeline? Is this something which is sustainable?
Tomáš Neubauer: Yes. So the whole idea of building systems with this approach is to building pipelines. So each Node in your architecture is a container that connects to one or more input topics and output results to one or more output topics. You create a pipeline that has multiple branches, sometimes they join back together, sometimes they end and when they end they usually either go to database or back to the product. And same is with the stats, they could be from your product or could be CDC from database. So you have multiple sources, multiple destinations, and in the middle you have one or more transformations.
Roland Meertens: And is there some kind of limit to the amount of input or the amount of consumers you have for a pipeline?
Tomáš Neubauer: There isn’t really limit to number of transformations or sources. One thing is that Kafka is designed to be one to one or one to a small number of consumers and producers. So if you have a use case like we going to do today with the phones where you can possibly have thousands or millions users, you need to put some gateway between your devices and Kafka, which we’ll do. And in our case it’ll be a web socket gateway collecting data and then funneling it to topic.
Roland Meertens: Okay. So do you still have some queue in between?
Tomáš Neubauer: There’s really any queue in between, but there’s a queue obviously in Kafka. So as the data flowing to the gateway, they’re being put to the queue in topic and then the services listening to it will just collect, consume and process this data from that queue.
Use cases for Real Time ML [13:33]
Roland Meertens: You already have some consumers who are using this in some creative or interesting ways? What’s the most interesting use cases you’ve seen?
Tomáš Neubauer: Yes, so one really cool use case is from healthcare where there’s sensors on your lung and listening to your breathing and then being sent to the cloud. And machine learning is used to detect different illnesses that you have and that’s all going to the company app. So it’s quite similar to what we are going to do here. Then second quite interesting use cases in a public transport are wifi sensors detecting the occupation of the underground stations and automatically closing opening doors and sending people to a less occupied part of the stations.
Roland Meertens: Oh, interesting. So then you have some signal which tells you how many people are in certain parts of the station?
Tomáš Neubauer: Yes, correct. So you have the realtors all around the station, and then in real time you know that in the north part of the station there is more people than in the south and therefore it will be better if people come from the south and you can do this in a split second.
The implementation [14:33]
Roland Meertens: Oh, interesting. And then in terms of this implementation, if we, for example, want to have some machine learning model act on it, are there specific limitations or specific frameworks you have to use?
Tomáš Neubauer: Basically the beauty of this approach is, and I think that’s why it’s so suited to machine learning, is that it’s just a patent at the end where all the magic happening. So you read data from Kafka into Python, and then in that code you are free to do whatever you want. So that could be using any PIP package out there, you can use the library like open CV for image processing and really anything that is possible in Python, it’s possible with this approach. And then you just output it again with the Python interface. So there’s no black box operation, there’s no domain specific language that you will find in Flink.
Roland Meertens: Do I basically just say, “Whenever you have a new piece of data, call this Python function with these augments?”
Tomáš Neubauer: Correct. And even more than python functions, you can build python classes with all the structure that you are using in Python. You can also try in Jupyter Notebook, so the library will work in a cell in Jupyter Notebook. So again, there’s basically a freedom of deployment in running this code anywhere, it’s just a python.
Roland Meertens: If people are listening and they’re beginners in realtime machine learning, how would you get started? What would you recommend to people?
Tomáš Neubauer: Well, first of all, what I’m doing here today, it’s available as a tutorial, all the codes is Opensource, so you can basically try it by yourself. There are other tutorials that we have published that going to are different use cases and going step by step from literally installing Python, installing Kafka, things like that to get this going from the start. So I recommend to people to go to docs that we have for the library. There are tutorials and there are some concepts described. What is the detail of this? So yeah, that would be the best place to start.
Roland Meertens: Are there specific concepts which are difficult to grasp or is it relatively straightforward?
Tomáš Neubauer: What is really complicated is stateful processing that we are trying to solve and abstract from. But if you are interested to learn more about stateful processing, we have it in the docs explained. That’s a very interesting concept and it will open the intricacy of the stream processing. But I think the goal of the library really is to make it simpler. Obviously, it’s a journey, but I’m confident that we already have done a great job in making it a bit easier than it was.
Roland Meertens: Thank you very much. Thank you for joining the podcast and good luck with your talk tomorrow and hopefully people can watch the recording online.
Tomáš Neubauer: Tthank you for having me
.
From this page you also have access to our recorded show notes. They all have clickable links that will take you directly to that part of the audio.
MMS • RSS
Posted on mongodb google news. Visit mongodb google news
As of April 24, 2023, MongoDB‘s shares are experiencing a dip in the market, following a broader decline in tech stocks. However, this decline is not attributed to the company’s Q4 results or weak outlook. Instead, it is due to a wider-than-expected Q3 loss guidance and tough comparisons for their subscription-based Atlas offerings in the second half of the year. This has resulted in a pre-market session drop of over 17% in MongoDB’s stock price.
The year has been a challenging one for tech stocks, particularly for high price-to-sales software stocks. MongoDB shares have taken a significant hit, now down 52% year to date. Despite the wider-than-expected loss projection, Wall Street analysts remain optimistic about MongoDB’s future prospects. The company has received 16 Buy, three Hold, and one Sell recommendations, resulting in a Strong Buy rating consensus.
Furthermore, analysts have an average price target of $397.93, indicating that the stock has a potential upside of 70.5% from its current price. This suggests that despite the current dip in the market, MongoDB’s long-term outlook remains positive.
MDB Stock Update: Impressive Growth in 2023 with Positive Earnings and Revenue Growth Projections
On April 24, 2023, MDB stock opened at 233.64, which was slightly higher than the previous day’s close of 232.76. The day’s range was between 222.63 and 235.49, with a volume of 63,723 shares traded. MDB’s earnings growth for the last year was -5.89%, but this year it has shown an impressive growth of +26.99%. The projected earnings growth for the next five years is +8.00%. The revenue growth for the last year was +46.95%. MDB operates in the technology services sector and the packaged software industry. The next reporting date for MDB is June 1, 2023, and the EPS forecast for this quarter is $0.19.
MongoDB Incs Median Target Price Forecasted to Increase by 11.38%: Analysts Recommend Buying Stock
On April 24, 2023, MongoDB Inc (MDB) had a median target price of $250.00, according to 22 analysts offering 12-month price forecasts. The high estimate was $290.00, while the low estimate was $180.00. This median estimate represented an 11.38% increase from the last price of $224.45. The consensus among 27 polled investment analysts was to buy stock in MongoDB Inc, and this rating had held steady since April. In terms of financial performance, MongoDB Inc had reported earnings per share of $0.19 and sales of $348.0M for the current quarter.
Article originally posted on mongodb google news. Visit mongodb google news
MMS • RSS
Posted on mongodb google news. Visit mongodb google news
Sundry Photography/iStock Editorial via Getty Images
Snowflake (NYSE:SNOW), Datadog (NASDAQ:DDOG), MongoDB (NASDAQ:MDB) and other cloud data stocks could see “fresh headline risks” when the large cloud computing companies report their quarterly results, investment firm Baird said.
Analyst William Power, who has an outperform rating on the three aforementioned companies,noted that Amazon (AMZN) Web Services is likely to see slowing growth in the second-quarter, which suggests further cuts to consensus. As such, worsening trends for Amazon, as well as Microsoft (MSFT) and Google (GOOG) (GOOGL) could further “pressure sentiment and results,” Power explained.
“The Azure and AWS results and outlooks are likely to set the tone for upcoming cloud software earnings results, particularly consumption-based models, which include SNOW, DDOG and MDB,” Power wrote in an investor note, adding the big question is how much of a potential slowdown is already baked into the stocks.
Microsoft (MSFT) and Google (GOOG) (GOOGL) are set to report fiscal quarterly results on April 25 while Amazon (AMZN) is slated to report on April 27.
Power added that estimates could come down “modestly” for other cloud software stocks such as the aforementioned trio, but Baird is still positive, citing “longer-term secular drivers.”
More on Snowflake
Article originally posted on mongodb google news. Visit mongodb google news
MMS • Sergio De Simone
Article originally posted on InfoQ. Visit InfoQ
Now available to developers, the first beta of Android 14 focuses on privacy, security, performance, developer productivity, and user customization. In addition, it improves user experience with large-screen devices on tablets and foldables.
To better protect sensitive data, Android 14 introduces the new accessibilityDataSensitive attribute. This attribute can be used by apps to enable access to specific data and views only to Google’s and third-party services meant to help users with disabilities.
If an app uses this new attributes, its visibility will be in fact limited to apps that declare the isAccessibilityTool attribute. Play Protect is the mechanism responsible to scan apps when they are downloaded from the Play Store and make sure they use the isAccessibilityTool attribute only if they are actually meant to help people with disabilities.
Google says that there are two main use cases where apps can benefit from this new feature: protecting user data from third-party access and preventing critical actions being carried through, such as authorizing a payment using a credit card. The importance of this feature cannot be underestimated, since it brings fully under developer control which data an app considers sensitive and thus protected from general external access.
Additionally, Android 14 beta improves a number of system UI elements, including a new, more prominent back arrow and a customizable share sheet.
Apps can add custom actions to the system share sheet creating a ChooserAction which will be shown to the user when Intent.EXTRA_CHOOSER_CUSTOM_ACTIONS is invoked. This will have the effect of displaying a separate row of app-specific actions on top of the cross-system action row.
The new share sheet makes it also easier to go back to the invoking app and add new items to those being shared. Finally, the UI has been improved by allowing you to scroll, in case you are sharing a large number of images, and to mix text and images.
Android 14 beta 2 will become available during Google I/O next month, and beta 3 in June. Android beta 4, coming in July, will be the final beta before the official release.
For a full list of all changes in Android 14 beta, do not miss this Twitter thread by Mishal Rahman, co-host of the All About Android show,
MMS • Robert Krzaczynski
Article originally posted on InfoQ. Visit InfoQ
Recently Microsoft released .NET 8 Preview 3. This new release contains many new improvements to ASP.NET Core such as support for native AOT, server-side rendering with Blazor, rendering Razor components outside of ASP.NET Core, sections support in Blazor or monitoring Blazor Server circuit activity.
In .NET 8 Preview 3, native AOT support for ASP.NET Core was added. Thanks to that it is possible to publish an ASP.NET Core application with native AOT, creating a standalone application that is compiled ahead of time (AOT) into native code. Publishing and deploying a native AOT application can reduce the following things such as disk size, memory demand and startup time.
Microsoft developers launched a simple ASP.NET Core API application to compare the differences in application size, memory usage, runtime and CPU load published with and without native AOT. Publishing the application as a native AOT improves start-up time and application size. In the experiment, start-up time was reduced by 80% and application size by 87%. These and other metrics are available on Microsoft’s public benchmarking dashboard.
However, not all features and libraries in ASP.NET Core are compatible with native AOT. The .NET 8 platform represents the beginning of work to include native AOT in ASP.NET Core, with an initial focus on including application support using Minimal APIs or gRPC, and deployed in cloud-native environments. A table showing the compatibility of ASP.NET Core features with the native AOT is attached in the article with the announcement of ASP.NET Core updates in .NET 8 Preview 3.
This preview version also adds initial support for server-side rendering using Blazor components. This is the start of the Blazor unification work to enable the usage of Blazor components for all web UI needs, client-side and server-side. Blazor components are available for server-side rendering without the need for any .cshtml files. The framework will discover Blazor components with routing support and configure them as endpoints. There are no WebAssembly or WebSocket connections and no necessity to load any JavaScript. Each request is handled separately by the Blazor component for the corresponding endpoint.
The work to enable server-side rendering with Blazor components resulted that it is now possible to render Blazor components outside the context of an HTTP request. Razor components can be rendered as HTML directly into a string or stream regardless of the ASP.NET Core hosting environment. This is helpful in scenarios where you want to generate HTML fragments.
Another point related to Blazor is the addition of the SectionOutlet and SectionContent components. They provide support for identifying outlets for content to be filled in later. Sections are often used to define placeholders in layouts that are then populated by specific pages. Sections are referenced by either a unique name or a unique object identifier.
Moreover, it is now an option to monitor inbound circuit activity in Blazor Server applications using the new CreateInboundActivityHandler method in CircuitHandler. Inbound circuit activity is any activity sent from the browser to the server, such as user interface events or JavaScript-to-.NET inter-operational calls.
The improvements added to ASP.NET Core received positive feedback from the community. .NET developers left many comments under the release announcement. They appreciated especially the focus on performance and AOT compilation. There was also a question from Ömer Kaya about the availability of Blazor United in .NET 8. Daniel Roth, a principal program manager at Microsoft, answered:
The Blazor United effort is really a collection of features we’re adding to Blazor so that you can get the best of server & client-based web development. These features include: Server-side rendering, streaming rendering, enhanced navigations & form handling, adding client interactivity per page or component, and determining the client render mode at runtime. We’ve started delivering server-side rendering support for Blazor with .NET 8 Preview 3, which is now available to try out. We plan to deliver the remaining features in upcoming previews. We hope to deliver them all for .NET 8, but we’ll see how far we get.
MMS • Almir Vuk
Article originally posted on InfoQ. Visit InfoQ
Microsoft has published a detailed release post that announces three new features that will be part of the upcoming release of C# 12. While still in the preview version, the C# 12 version introduces the features like primary constructors (for non-record classes and structs), using aliases for any type, and default values for lambda expression parameters.
The first highlight is the ability to use primary constructors which allows adding parameters to the class declaration and use them in the class body. This feature was previously only available for record type in C# 9 as part of the positional syntax for records, but it will now be extended to all classes and structs. With primary constructors, developers now can use the parameters to initialize properties or to include them in the code of methods and property accessors.
This feature is designed to simplify the process of creating and initializing objects in C#, allowing for more concise and readable code. The following code example shows how a primary constructor can be used in C# 12:
public class Student(int id, string name, IEnumerable grades)
{
public Student(int id, string name) : this(id, name, Enumerable.Empty()) { }
public int Id => id;
public string Name { get; set; } = name.Trim();
public decimal GPA => grades.Any() ? grades.Average() : 4.0m;
}
(Code sample provided by Microsoft devblogs.microsoft.com)
In addition to primary constructors, the default values for lambda expression parameters are another preview version feature. With this feature, developers can now specify and define default values for lambda expression parameters using the same syntax as for other default parameters. The default value will be emitted in metadata and is available via reflection as the DefaultValue of the ParameterInfo of the lambda’s Method property.
Before this preview release, if a developer wanted to provide default values for lambda expression parameters, they had to use local functions or a method called DefaultParameterValue from a specific namespace called System.Runtime.InteropServices.
Kathleen Dollard, Principal Program Manager, .NET, the author of the original blog post states the following:
These approaches still work but are harder to read and are inconsistent with default values on methods. With the new default values on lambdas you’ll have a consistent look for default parameter values on methods, constructors and lambda expressions.
Moving on to, the third highlight of C# 12 preview is a feature that enables the way to provide an alias to any type. Starting from this version, developers will be able to use using directives to abstract actual types and provide friendly names for “confusing or long generic names”. By having these aliases, developers can improve the readability of their code and make it easier to understand.
This feature allows developers to give alias names to almost any type, including nullable value types and tuples, like in the following code sample:
using Measurement = (string Units, int Distance);
using PathOfPoints = int[];
using DatabaseInt = int?;
public void F(Measurement x){ ... }
(Code sample provided by Microsoft devblogs.microsoft.com)
In addition to this, a user named Muhammad Miftah wrote an interesting comment regarding the usage of aliases. On April 11, 2023, the user wrote the following:
using type aliases seem to only work within the file, unless you define it as a global using, but then the global scope is polluted.
I think you should introduce a namespace-wide equivalent, maybe introduce a type keyword for that? Also it would be extra cool to also have the option to reify the type alias into something that exists and is reflection-queryable at runtime. Also usable in generics and generic type constraints. TypeScript already has this!
Furthermore, the comments on the post indicate a significant level of interest in the C# 12 version and upcoming releases. Some commenters have requested further details and clarifications on the features, while others have provided suggestions for additional ones. However, there are also users who expressed their lack of interest and critics who express concern about the rapid development of this programming language.
Thus, it is recommended for readers to take a look in the comments section since it is very insightful and it brought a lot of discussion between the users and author, including a lot of code samples and information regarding the future evolution of this programming language.
Also, to test these out users need to download the latest Visual Studio 17.6 preview or the latest .NET 8 preview. Microsoft is calling the community members to provide their feedback about mentioned features: primary constructors, using aliases for any type, and default values for lambda expression parameters.
Lastly, interested users can track the implementation progress of C# 12 through the Roslyn Feature Status page and follow the design process of this programming language through the CSharpLang GitHub repository page.
MMS • Peter Miscovich
Article originally posted on InfoQ. Visit InfoQ
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Transcript:
Hey folks, QCon New York is returning to Brooklyn this June 13 to 15. Learn from over 80 senior software leaders at early adopter companies as they share their firsthand experiences implementing emerging trends and best practices. From practical techniques to pitfalls to avoid, you’ll gain valuable insights to help you make better decisions and ensure you adopt the right organizational patterns and practices. Whether you’re a senior software developer, architect, or team lead, QCon New York features over 75 technical talks across 15 tracks covering ML ops, software architectures, resilient security, staff plus engineering, and more to help you level up on what’s next. Learn more at qconnewyork.com. We hope to see you there.
Good day, folks. This is Shane Hastie for the InfoQ Engineering Culture podcast. Today I’m sitting down with Peter Miscovich. Peter is one of the authors of a new book, The Workplace You Need Now: Shaping Spaces for the Future of Work. Peter, welcome. Thanks for taking the time to talk to us today.
Peter Miscovich: Shane, it’s a pleasure to be with you today.
Shane Hastie: My first question to my guests is almost always, who’s Peter?
Introductions [01:25]
Peter Miscovich: Well, thank you. I’ve been involved in hybrid workplace transformation for over 25 years. Began my early career in architecture and design, have an engineering background. I was former Andersen Consulting, Accenture partner and then PwC advisory partner, and then came to JLL 15 years ago to lead our global workplace transformation and strategy innovation practice. So I’ve helped to transform 1.5 billion square feet of corporate real estate. I’ve worked with almost every major financial institution and technology firm, and I’ll add life sciences to that mix and telecommunications and media. I’ve also served on a number of boards. I serve on the Accenture technology vision board under Accenture’s Chief Technology Officer Paul Daugherty and I serve on the series sustainability NGO board focused upon climate change risk and investments to mitigate climate change risk.
So over the past 25, 30 years, I’ve been focused upon workplace transformation at scale. I pioneered early programs in the 1990s with AT&T, IBM at the time, Accenture, Citigroup, PwC in really examining how technology can enable new levels of human performance and how the physical and digital workplace evolution can help enterprise performance and business performance. So I’m a bit of a hybrid myself, Shane, in having an engineering background, but yet focused on technology, focused on behavior, focused on human performance and then physical workplace design and the physical digital, if you will, blur that we’ve all been experiencing certainly in the post pandemic world.
The shift to hybrid and remote working has been going on for a long time [03:13]
Shane Hastie: Talking about that physical digital blur, is that has been so much of what has happened in the last few years where there was the radical shift for most technologists, not everyone, and certainly not every role in every company, but the vast majority of our audience will have experienced that radical shift from working predominantly or if not entirely in person to suddenly remote. And now we’re starting to see the hybrid workspace come back. What are the big challenges and the current state of the workplace when we look at that today?
Peter Miscovich: Yes, I’ll be glad to share maybe a bit of history, Shane, if that’s helpful,
Shane Hastie: Please do.
Peter Miscovich: Relative to the evolution. So if we go back to the 1990s and the early 2000s, the majority of the work, and this is from our work with Accenture and Gartner and Forrester and McKinsey and Brookings leading think tanks, but certainly from a historical perspective, work was office centric and location centric actually for the last 90 to 100 years. And it began shifting in that 1990s, 2000s timeframe as mobile technologies, the advent of wireless technologies. I had an early white paper Shane, where I predicted that wireless would come into the office environment, and I published that white paper in 1999 and four CIOs challenged me in stating that wireless will never come to the corporate workplace due to security issues and we’ll never see wifi or wireless in the workplace.
So even in those early days, 1990s, 2000s, there was a lot of resistance to the transformation that was already underway. In the 2010 to 2015 and as we approached 2020, we saw the advent of the cloud. I’ll use 2007 and 2010 as pivotal years, iPhone, iPad, mobile technologies, 4G now moving to 5G wireless. And so work was leaving the office 30 years ago when I began my journey in terms of hybrid and remote work. And I’ve been working in a hybrid or remote manner myself for over 25 years. What happened in March 2020, which is really fascinating at scale, is we had three billion people over a period of three to four weeks all go remote. And I think we had many clients, many of my clients for instance, that had frontline workers who never really went remote. It had to be on the front lines with our consumer products clients or other critical workforce cohorts.
The chasm in workplace design represents the societal shift that is happening as we move to more human centric working 05:53]
But then a majority of the office-based workers, knowledge workers in particular all went remote. And the chasm that that occurred from 2020 March to I’ll say even 2023 March, is that there’s a deepening divide between the historical office centric design that was based upon consistent work experiences. It enabled in place physical serendipitous collaboration, it required visibility based management. That 90 year ecosystem of office based work was disrupted at scale. And we’re moving now in 23 through 25 through 2030, certainly into a human centric model. And we’re leaving the location-based, office base work design model, but we have many leaders, many managers, many folks even in the tech sector that are still very much tied to that office centric work model. And so as the new model is emerging, providing flexible work experiences, intentional digital collaboration, empathy based management, human centricity, regenerative workplace, which we’ve written a lot about, it’s a huge shift.
It’s a societal shift. And many folks in leadership, many of the C-suite folks that I work with, senior leaders are very uncomfortable with the shift that’s occurring. And so there’s expectations today from management. I have multiple clients, I can’t name them, but you would know all of them. And the CEOs are mandating three days back, two days back, four days back in the office. And what will most likely evolve over time is that there’ll be hybrid centricity, human centricity as the go forward model and moving away from a place-based approach to work to probably a time-based and then I’ll add probably a virtual metaverse based hybrid approach to work.
And that transformation could take three years, could take seven years, could take 15 years. But clearly the transformation that started 30 years ago is now accelerating. The pandemic was an accelerant to the transformation of work. And those organizations that are embracing these human-centric hybrid and flexible work behaviors and management styles and cultural norms will be the big winners in the future in our view. But the tension and the conflicts and the continuous experimentation we see continuing for the foreseeable future, certainly over the next two to three, perhaps five, seven years.
Shane Hastie: What are some of the experiments that our listeners could consider trying?
Experiments and rapid learning to find what works best for your ecosystem [08:37]
Peter Miscovich: The experimental landscape is quite vast, and I will share several. So for example, I have an R&D technology group, even during the pandemic and post pandemic that’s been experimenting with a 8:00 AM to 12 noon, 1:00 PM three days a week, sometimes two days a week, hybrid patterning whereby they get together in their scrum teams or innovation teams or R&D teams for a period of time, usually in the mornings eight, 9:00 AM to 12, one, sometimes they’ll have lunch together and then they disperse in the afternoon to do their other work either at home or co-working sites or wherever. And that’s an example of looking at a time-based solution to hybrid versus a place-based solution. And they’ve been highly effective. It’s very intentional. They look forward to their time in the office together. And that time together is orchestrated in a very intentional and successful synergistic way.
And it’s built upon itself. It’s actually proven very successful for this firm, both in terms of their innovation cycles, their product development cycles and the like. I have another client organization that’s highly remote digital first. They have built a large retreat center. They have 80,000 employees, primarily tech and engineering, and they’re bringing those folks through that retreat center at a pretty strong episodic cadence. And groups of cohorts of R&D, engineering, software development will get together. There’s opportunities for brainstorming. You can go for a hike and have a meeting. It’s community building, but it’s not office space. It’s strictly innovation slash retreat, regenerative, resort if you will, almost environments. And so that’s another example of where innovation and new ways of working can be orchestrated outside of the traditional or typical corporate office environment.
Experimenting with the metaverse [10:43]
And then I’ll use a third experimentation which has been published from our work, for example, with Accenture, 120,000 folks. We’ve exercised for the last two years, metaverse training, metaverse socialization, lots of experiments in the virtual mixed reality world at Accenture and with Accenture clients that we’re partnering with in using the Metaverse, not as a sole means of hybrid workplace experimentation, but an opportunity for immersion in some cases for socialization, in some cases for onboarding, training and engaging employees perhaps in a new and unique way relative to collaborative, socialization, behavioral shifts, upskilling. And we do think again, by 2026, 27, we’ll start seeing more and more of the extended reality, mixed reality Metaverse, virtual reality environments. We’ll all be in the Metaverse probably one to two hours a day by 2027, and it may be bumpy and a ways until we get there relative to the technologies. But those are three examples of both time-based, if you will, hybrid experimentation as well as place-based and technology-based experimentation, pilot programs that are scaling well and showing good promise for future success.
Shane Hastie: So three distinctly different but very interesting experiments.
Becoming learning organisations [12:09]
Peter Miscovich: Yeah. And I think Shane, I’ve been in the trenches of hybrid work for many years and I’ve always been a big believer in experimentation. And what’s fascinating, it’s great to hear, for instance, we have Microsoft as a client. Satya Nadella, CEO of Microsoft, one of his pillars of Microsoft’s vision and mission is having this learning mindset and a mindset for experimentation. We were doing a lot of this hybrid piloting 15, 20 years ago. The technology hadn’t quite matured then. But what’s fascinating today, Shane, is that our insurance clients, our consumer products clients, our financial clients, our governmental clients, our educational clients, certainly our technology clients are all experimenting. And we’re hoping that the experimentation continues.
As the economic headwinds develop and uncertainty continues as we go into 2023, there are those who believe that we’ll retrench back to old ways of working, but our sense is that this level of experimentation will continue. The talent war, if you will, especially for tech talent and digital talent will continue to be a challenge for many organizations. And the only way to engage talent and to innovate and to innovate both business model product innovation is through experimentation. And as workplace and hybrid work, experimentation becomes part of the DNA of any leading organization, those that experiment more and embrace experimentation and that learning mindset per Satya Nadella will be the leaders and will take market share. They’ll be the success stories of 2026, 2027, 2030 and beyond.
Shane Hastie: Looking to those leaders, looking to that future, what are the challenges and the opportunities for organizations? So if they want to become one of these leaders, if they want to attract great talent, and if we’re talking to our listeners on this podcast who are typically the technical influencers, technical leaders, what can they do?
Leaders need to embrace the changes that are underway [14:17]
Well, the one thing that we share with our executive leadership teams consistently is that they need to embrace the present and embrace the changes that are underway. And that requires sometimes intentional forgetting and intentional letting go of historical work behaviors and work norms. And then I would add to really start listening to your people. And we will soon have potentially five if not six generations in the workplace. And Google just announced this morning a very interesting hybrid workplace pilot, and they’re piloting the program slightly differently for their cloud engineers and some of their other engineering groups. But what I found fascinating about this Google pilot, and I would recommend again, commend Google for their ability to really listen to their employees and understand what do their people want and to take an empathy based listening approach for every group, for every cohort in your organization.
Work-life balance doesn’t really exist today – the need is for effective work-life integration [15:22]
And the question and the challenge will be, and it is for many of our clients right now, technology clients and our clients in general, is that you may not be able to provide a human-centric individualistic program and cultural norm for everyone, but you’ve got to be able to meet people at least halfway and show with good intent, with good transparency, with mission-driven authenticity, that you’re really listening to your people. You want to give them the tools and capabilities and practices and policies that will make them successful and to meet their life-work balance or life-work integration, I should say. And for years, people have, many organizations, many HR, think tanks and the like have professed the need for work-life balance, which doesn’t really exist today, especially in our whole accelerated post pandemic world of ambiguity, complexity, disruption, acceleration. But the majority of employees today want life-work integration.
And the majority that I know want to work for organizations that will enable that in a way where they can have a healthy balance between their lifestyle, their work style, our research in the regenerative workplace to have the balance between mental wellbeing, social wellbeing, and physical wellbeing. And if you’re a leader out there of a large tech team, if you’re listening to those employees and to your people and you have empathy for them, and as individual managers, you show enough caring and enough ability to flex and meet them at least halfway, I think you’re going to have committed employees who are going to be exceptional performers. And I think that’s a major societal shift in organizational shift, Shane, that I think is a very positive outcome from the pandemic.
The challenge with it is that most leaders and many CEOs and executive leadership teams find that level of engagement either to be threatening or they’re not comfortable with that level of transparency, openness, and empathy. And so here in lies a challenge that we have from a management and behavioral and policy perspective that will probably continue over the next two to three years or longer. And it might require the next generation of managers who will have all of these great values and capabilities, who will probably be the next leaders to really actualize what we’re describing.
Shane Hastie: So that sounds to me like a generational shift. Are we teaching this next generation of leaders to be more humanistic or are they just following in the footsteps?
The generational shift in leadership attitudes [18:03]
Peter Miscovich: Well, it’s a great question. I have nine godchildren and they’re all pretty much Gen Z. They range in age from let’s say 15 years old to 32 years old. So maybe young Gen Y, mid-gen Y. I think they have a level of digital literacy and awareness of the world and awareness of the globalized interconnected world that I think surpasses any previous generation. So I think their mindsets and their values are already quite advanced and prepared for what is coming. I think where we need to do a better job, those of us that are more senior to Gen Y or Gen Z or Gen Alpha that will soon be following Gen Z is that we’ve got to mentor and enable and share with them some of the good humanistic lessons that we’ve learned. And this is where time spent in personalized intentional mentoring and engagement is really important.
And it’s in all the surveys, Gallup surveys, all the HR global workforce surveys show that Gen Z, they very much appreciate in-person engagement. It’s really interesting. There’s also a survey for Gen Alpha and Gen Z, which is fascinating, that they find that in-person engagement is so unique that it’s sort of like for those of us we were raised where going on a vacation was something unique and kind of experiential. They feel like they’re so connected and so accustomed to their digital literacy and their digital lifestyles that’s sort of in-person, human to human in real life, IRL engagement is considered sort of an interesting novelty to them and they really treasure it.
And so I think we have to think about how we make those in-person moments that matter meaningful, both from a learning, mentoring and perhaps even reverse mentoring perspective. And I think the challenge today at a generational level with social media, I have my issues about social media and its negative impact at a societal level and how we need to perhaps engage differently, especially with our younger generations as they manage through all the complexities and all of the mental and emotional and psychological challenges that social media presents to them as an entire generation. So I think there’s a lot of great generational exchange and partnership that will help that next generation. And we do need to make the time and the effort and that intentionality and again, strong empathy to understand what they’re experiencing and what they’re going through and how we can help them better navigate.
So I know that’s a rather lengthy response, but I think there’s a lot of complexity to the generational evolution that’s occurring. And if we have five or six generations in the workforce, virtual, digital, physical, digital, whatever, combination thereof, it’s going to require a lot more good communication, authenticity, transparency, empathy to make that multi-generational workforce high performing. So I think the effort definitely needs to be made. And I think there’s a bit of fatigue Shane, post pandemic for all of us. And I think the ability to refresh and regenerate right now is really important as we move into 23 and 2024 and beyond, as we still need to heal our psyches from the post-traumatic stress of the pandemic at a societal level. And I don’t know if everyone agrees with that, but I think there’s a lot of post pandemic, post-traumatic stress from the pandemic that we’ve never really recognized at a societal level that we do need to recognize and heal from.
Shane Hastie: How do we start? How do we start the healing? How do we start the changes towards the human-centric workplace?
Start with quiet [22:03]
Peter Miscovich: Well, I always start with quiet. And I’ve known Cal Newport who many in your audience may know around deep work and deep thinking, and I’ve been a practitioner of meditation for 24 years, 25 years. But we have to start with quiet, and we have to start with the quietness in terms of listening to ourselves. And I think that healing and that regenerative process begins with the quietness of our own consciousness and tuning into that quietness and getting grounded and healed, if you will, in terms of our own sense of self and sense of wellbeing and sense of wellness. And then I think the next step in the process is the listening, the listening and deep listening and listening without judgment, listening unconditionally if you will, and listening that will hopefully lead to understanding. And then with the understanding, I think we can begin to co-create, co-partner at a generational level or cross-generational level, some of the potential solutioning that is required.
And what I just described, I think will heal political divides. I think it will heal racial divides. I think it will heal cultural divides. I mean, if we think of the war in Ukraine to the politics of the US, to the rise of dictatorships and authoritarian regimes, a lot of it’s around the fact of sort of a non-humanistic, non-listening, non-quiet, non-empathy based approach to life. And so I think the pandemic in a very, I mean, as horrific as the pandemic was, it was also a wake-up call to tune into our deeper selves, and to find that quiet within ourselves, and to listen to ourselves and to be guided to listen deeply to others, and hopefully then gain that empathy and understanding to co-create and partner together collectively at a societal level to the healing and I think the path forward that we’re all seeking as we move ahead.
Shane Hastie: Shifting context slightly to just talk about the book, there are three key themes in there, the personalized workplace, the responsible workplace, and the experiential workplace. What do you mean by personalized workplace?
The book: The workplace you need now [24:26]
Peter Miscovich: We wrote the book, our CEO and our head of research and myself in early 2021 in three months during the middle of the pandemic. And at the time we divided the book up into three sections. And as you name them, the personalized section, the responsible section, and then the experiential section. And the personalized section of the book is really about the individual and it relates to some of what I just shared relative to how to make the workplace and the hybrid work environment work for the individual at a personalized level. And so if we think about personalization today, whether it’s retail or vacationing or personalized learning, the ability to have a personalized work experience is one of the key tenants in the book. And it all centers around what we call this win-win win theme. The future of work needs to be a win for the individual in terms of personalized work.
It needs to be a win for society in terms of responsible and sustainable workplace and real estate practices. And then it needs to be a win for the organization in terms of experiential outcomes and experience. So the personalized section begins sort of that individualistic journey. How do we make the hybrid workplace inclusive of technology enablement work for the individual? And then how do we make the hybrid workplace ecosystem in the second section work for society in terms of responsible workplace practices, responsible technology, real estate practices? And then finally the third section is focused on the win for the organization in terms of delivering these experiential outcomes. And the experiential outcomes are enabled both by technology, by physical space, by services, by human interaction, all of the things that we experience every day. How do we make those meaningful moments that matter every day for our people?
So the book was a bit prescient, if that’s the right word. And we were not sure in January 2021, for instance, whether hybrid work would stick. I was a strong believer that it would stick, having practiced hybrid work for many years and seeing that over time as any exponential growth curve can relate to that slow growth, slow growth, slow growth, and then exponentially, it really begins to take off. And I don’t know if hybrid work follows an exponential growth pattern, but as we look at personalized work, responsible work, experiential work, all of that certainly seems to be moving forward in 2023 post the book being published in late 2021. And we believe that all of the trends we predicted will probably continue as we go to 2027 and beyond.
Shane Hastie: So what is a regenerative workplace?
What is a regenerative workplace? [27:23]
Peter Miscovich: I spoke at several conferences in the fall on this topic, and the challenge at a societal level right now at an organizational level is that anxiety, depression, loneliness, fear, insecurity, I mean adolescent suicides, all of those mental health crises are at an all time high. And as we look at how we orchestrate work and workforce policies and workplace transformation, the regenerative workplace begins to address in again three very specific areas, how do we enable and help with mental health and mental health interventions both in terms of work, work practice, management styles, engagement levels? Again, going back to empathy and listening. So the first element of the regenerative workplace is really the mental wellness and wellbeing element. And then the second element is the social wellbeing and socialization, having a sense of community, feeling that you’re part of not only an organization, but you’re part of your community, part of your network of community members, whether that’s a virtual community, could be an in-person community, but socialization and social wellbeing is the second pillar of the regenerative workplace.
And then the third pillar is physical wellbeing, and that involves everything from not being on Zoom or Microsoft Teams calls 15 hours a day. The ability to work in a manner where your physical health, both from things like ergonomics, from sleep patterns to eye strain, to the ability to exercise and engage and take care of your body as best we can in again, this very accelerated period of disruption and accelerated change. What’s fascinating is we have several clients now that have global wellness executives within their organizations, often reporting up to the CEO or chief human resource officers. And we’re finding at the enterprise level, companies are beginning to understand that if they take care of their workers and really provide this regenerative workplace approach, mental, social, physical, that the rewards from a commitment perspective, a performance perspective, a wellbeing perspective, a reduction in healthcare cost perspective are going to be considerable.
I’ve been in the trenches, Shane, on sustainability for 25 years and in the trenches of integrative wellness for 20 years, and some of these things just take a long time. I was hybrid working for 25 plus years. So we believe in the next 10 years, regenerative workplace practice will become the practice, but it’s going to take perhaps again, another generational shift to fully embrace it, to endorse it, and to practice it at scale. But the pandemic was a big wake-up call, especially as it relates to mental wellness and mental wellbeing. So we think it has a future and we strongly believe in it, and we’re helping advise our clients to embrace the regenerative workplace leading practices.
Shane Hastie: Peter, some really, really interesting and deep thoughts there. If people want to continue the conversation, where do they find you?
Peter Miscovich: Well, they’re welcome to reach out to me via LinkedIn or I’m glad to share as a follow-up from your podcast, my email address, it’s Peter, P-E-T-E-R dot Miscovich, M-I-S-C-O-V-I-C-H@jll.com. And I welcome technologists and the technology sector very openly. I think the human centricity of the technology sector is really key and critical to our future. Shane, I’ve been working with ChatGPT and had about 10 years of immersion, and I have a couple of articles focused on artificial intelligence and work automation, and I do think the human centricity and the humanistic values of the technologists who are listening to this today, they are critical to the future of all of us in terms of how we navigate and how we move forward with all of the incredible technological change that is just beginning. So I embrace your audience in a big way and would be glad to be a resource to you or to your audience in any way that I can be helpful.
Shane Hastie: Thank you so much.
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