Month: May 2021

Article originally posted on InfoQ. Visit InfoQ

Facebook open sourced Zstandard almost six years ago with the aim to outperform Zlib in both speed and efficiency. Zstandard 1.5 improves compression speed at intermediate compression levels, compression ratio at higher levels, and brings faster decompression speed.

Zstandard supports compression levels up to 22. Thanks to a new default match finder, Zstardard 1.5 reaches higher compression speed for levels between 5 and 12 and inputs larger than 256K. According to Facebook benchmarks, improvements range from +25% to +140% without significant loss in terms of compression ratio. Facebook claims even better results on heavily loaded machines under significant cache contention.

The biggest gains are achieved on files typically larger than 128KB. On files smaller than 16KB, by default we revert back to the legacy match finder which becomes the faster one. This default policy can be overriden manually: the new match finder can be forcibly enabled with the advanced parameter ZSTD_c_useRowMatchFinder, or through the CLI option --[no-]row-match-finder.

At the opposite end of the compression level spectrum, Zstandard uses block splitting by default to improve compression ratio, although not by such an impressive margin as in the previously described case.

The amount of benefit varies depending on the workload. Compressing archives comprised of heavily differing files will see more improvement than compression of single files that don’t vary much entropically (like text files/enwik).

In particular, Facebook benchmarks show that Zstandard 1.5 may get up to 0.71% better compression ratio at level 22, the maximum allowed.

Zstandard 1.5 also significantly improves decompression speed, with improvements up to 21% in the best case. Actual results, though, depend largely on compiler version, payload, and compression level, says Facebook.

In general, a majority of scenarios see benefits ranging from +1 to +9%. There are also a few outliers here and there, from -4% to +13%. The average gain across all these scenarios stands at ~+4%.

Besides improving compression performance, Zstandard 1.5 builds by default with multithreaded support, standardizes a few new APIs, and deprecates a number of older ones. You can find the full detail in the official release notes.

Zstandard is based on work by Facebook engineers Yann Collet and Chip Turner. In particular, it leverages previous work by Collet based on Asymmetric numeral system (ANS).

Zstandard is integrated both within the Linux kernel, where it is used for the btrfs and squashfs filesystems, and FreeBSD, where it is used to compress core dumps. Additionally, it is used in Arch Linux and Fedora. If you want to try out Zstandard, the open-source file archiver 7zip includes support for it, along with many other codecs.

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This week’s roundup features news from OpenJDK JEPs targeted for JDK 17, GA releases of Jakarta EE 9.1 and Microsoft Build of OpenJDK, milestone and candidate releases for Micronaut and Hibernate Reactive, respectively, Spring releasing a vulnerability report to address a local privilege escalation attack with Spring WebFlux, and birthday celebrations for Hibernate and Java.

OpenJDK and JDK 17

It was another busy week at OpenJDK with updates to some JEPs and the JDK 17 early access builds. Labeled as Build 24, there are numerous changes from Build 23 that include fixes to various issues.

Two JEPs, JEP 403: Strongly Encapsulate JDK Internals and JEP 407: Remove RMI Activation were promoted from Proposed to Target to Targeted or Integrated status yielding this updated list of JEPs targeted for the JDK 17 GA release in September:

Still under review, there are proposed JEPs for JDK 17 that were recently promoted from Candidate to Proposed to Target status:

More details may be found in the release notes and developers are encouraged to report bugs via the Java Bug Database.

Jakarta EE 9.1 and Compatible Implementations

Five months after the release of Jakarta EE 9, the Jakarta EE Working Group has announced the release of the Platform and Web Profile specifications of Jakarta EE 9.1 and related TCKs. Since its debut in 2018, this is the first incremental release of Jakarta EE featuring multiple updates for the Java community to develop and deploy Jakarta EE 9.1 applications on JDK 11, the current long-term support release of Java SE, as well as JDK 8.

At this time, there are five compatible implementations of Jakarta EE 9.1 including IBM and Tomitribe having announced this past week that Open Liberty and Apache TomEE, respectively, have passed the TCKs.

InfoQ will follow-up with a more detailed news story on the release of Jakarta EE 9.1.

Microsoft Build of OpenJDK

Less than two months after Microsoft introduced the preview release of Microsoft Build of OpenJDK, the GA release has made available to the Java community to include: Docker images and corresponding Dockerfiles; and the latest Minecraft Java Edition snapshot version 21W19A, which has been updated to a JDK 16 runtime based on Microsoft Build of OpenJDK.

The Docker images, designed to be used by any Java application for deployment, may be accessed for a specific tag by invoking the Docker command:

    
$ docker pull mcr.microsoft.com/openjdk/jdk:<tag>
    

More details may be found in the container images guide. InfoQ will also follow-up with a more detailed news story.

The Road to Micronaut 3.0

Object Computing, Inc. released Micronaut 3.0.0-M1 containing minor breaking changes. Future milestones releases will include a migration to Jakarta Annotations for dependency injection and a switch from RxJava 2 to Project Reactor.

Hibernate

Hibernate Reactive 1.0.0.CR5 was made available to the Java community featuring bug fixes and dependency upgrades to Hibernate ORM 5.4.32.Final and SmallRye Mutiny 0.17.0. Developers are encouraged to upgrade to Hibernate ORM 5.4.32.Final as recent internal changes will require Hibernate Reactive to use this latest version.

Spring Framework

It was a quiet week over at Spring after a very active previous two weeks. A vulnerability report was released to address CVE-2021-22118: Local Privilege Escalation within Spring Webflux Multipart Request Handling. This fixes an issue with Spring WebFlux applications being vulnerable to a privilege escalation, a network attack used to obtain unauthorized access within the security perimeter of an organization. It was noted that Spring MVC applications and applications that do not handle multipart file requests are not affected by privilege escalation attacks.

On the Lighter Side

This past week marked birthday celebrations for Hibernate and Java.

Vlad Mihalcea, CEO at Hypersistence and former Hibernate developer advocate at Red Hat, announced via Twitter that Hibernate has turned 20 and described his personal journey with Java persistence and Hibernate.

It has already been a year since the 25th birthday celebration of Java as the language has turned 26. The Kansas City Java Users Group celebrated with a 26-hour livestream event hosted by Billy Korando, Nicolai Parlog, Sebastien Blanc and Ted Young. Special guests included: Brian Goetz, Maurizio Cimadamore, Ron Pressler, Pratik Patel, and Josh Long as they discussed all things Java, its past, present, and future.

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LOS ANGELES, United States: QY Research offers an overarching research and analysis-based study on, “Global Non-relational SQL Market Report, History and Forecast 2016-2027, Breakdown Data by Companies, Key Regions, Types and Application“. This report offers an insightful take on the drivers and restraints present in the market. Non-relational SQL data reports also provide a 5 year pre-historic and forecast for the sector and include data on socio-economic data of global. Key stakeholders can consider statistics, tables & figures mentioned in this report for strategic planning which lead to success of the organization. It sheds light on strategic production, revenue, and consumption trends for players to improve sales and growth in the global Non-relational SQL Market. Here, it focuses on the recent developments, sales, market value, production, gross margin, and other significant factors of the business of the major players operating in the global Non-relational SQL Market. Players can use the accurate market facts and figures and statistical studies provided in the report to understand the current and future growth of the global Non-relational SQL market.

This report includes assessment of various drivers, government policies, technological innovations, upcoming technologies, opportunities, market risks, restrains, market barriers, challenges, trends, competitive landscape, and segments which gives an exact picture of the growth of the global Non-relational SQL market.

Competitive Landscape

Competitor analysis is one of the best sections of the report that compares the progress of leading players based on crucial parameters, including market share, new developments, global reach, local competition, price, and production. From the nature of competition to future changes in the vendor landscape, the report provides in-depth analysis of the competition in the global Non-relational SQL market.

Key questions answered in the report:

• What is the growth potential of the Non-relational SQL market?
• Which product segment will grab a lion’s share?
• Which regional market will emerge as a frontrunner in the coming years?
• Which application segment will grow at a robust rate?
• What are the growth opportunities that may emerge in the Interactive Patient Engagement Systems industry in the years to come?
• What are the key challenges that the global Non-relational SQL market may face in the future?
• Which are the leading companies in the global Non-relational SQL market?
• Which are the key trends positively impacting the market growth?
• Which are the growth strategies considered by the players to sustain hold in the global Non-relational SQL market

Table of Contents

1 Market Overview of Non-relational SQL
1.1 Non-relational SQL Market Overview
1.1.1 Non-relational SQL Product Scope
1.1.2 Non-relational SQL Market Status and Outlook
1.2 Global Non-relational SQL Market Size Overview by Region 2016 VS 2021VS 2027
1.3 Global Non-relational SQL Market Size by Region (2016-2027)
1.4 Global Non-relational SQL Historic Market Size by Region (2016-2021)
1.5 Global Non-relational SQL Market Size Forecast by Region (2022-2027)
1.6 Key Regions, Non-relational SQL Market Size (2016-2027)
1.6.1 North America Non-relational SQL Market Size (2016-2027)
1.6.2 Europe Non-relational SQL Market Size (2016-2027)
1.6.3 Asia-Pacific Non-relational SQL Market Size (2016-2027)
1.6.4 Latin America Non-relational SQL Market Size (2016-2027)
1.6.5 Middle East & Africa Non-relational SQL Market Size (2016-2027) 2 Non-relational SQL Market Overview by Type
2.1 Global Non-relational SQL Market Size by Type: 2016 VS 2021 VS 2027
2.2 Global Non-relational SQL Historic Market Size by Type (2016-2021)
2.3 Global Non-relational SQL Forecasted Market Size by Type (2022-2027)
2.4 Key-Value Store
2.5 Document Databases
2.6 Column Based Stores
2.7 Graph Database 3 Non-relational SQL Market Overview by Application
3.1 Global Non-relational SQL Market Size by Application: 2016 VS 2021 VS 2027
3.2 Global Non-relational SQL Historic Market Size by Application (2016-2021)
3.3 Global Non-relational SQL Forecasted Market Size by Application (2022-2027)
3.4 Data Storage
3.5 Metadata Store
3.6 Cache Memory
3.7 Distributed Data Depository
3.8 e-Commerce
3.9 Mobile Apps
3.10 Web Applications
3.11 Data Analytics
3.12 Social Networking 4 Non-relational SQL Competition Analysis by Players
4.1 Global Non-relational SQL Market Size by Players (2016-2021)
4.2 Global Top Players by Company Type (Tier 1, Tier 2 and Tier 3) & (based on the Revenue in Non-relational SQL as of 2020)
4.3 Date of Key Players Enter into Non-relational SQL Market
4.4 Global Top Players Non-relational SQL Headquarters and Area Served
4.5 Key Players Non-relational SQL Product Solution and Service
4.6 Competitive Status
4.6.1 Non-relational SQL Market Concentration Rate
4.6.2 Mergers & Acquisitions, Expansion Plans 5 Company (Top Players) Profiles and Key Data
5.1 Microsoft SQL Server
5.1.1 Microsoft SQL Server Profile
5.1.2 Microsoft SQL Server Main Business
5.1.3 Microsoft SQL Server Non-relational SQL Products, Services and Solutions
5.1.4 Microsoft SQL Server Non-relational SQL Revenue (US$ Million) & (2016-2021)
5.1.5 Microsoft SQL Server Recent Developments
5.2 MySQL
5.2.1 MySQL Profile
5.2.2 MySQL Main Business
5.2.3 MySQL Non-relational SQL Products, Services and Solutions
5.2.4 MySQL Non-relational SQL Revenue (US$ Million) & (2016-2021)
5.2.5 MySQL Recent Developments
5.3 MongoDB
5.3.1 MongoDB Profile
5.3.2 MongoDB Main Business
5.3.3 MongoDB Non-relational SQL Products, Services and Solutions
5.3.4 MongoDB Non-relational SQL Revenue (US$ Million) & (2016-2021)
5.3.5 PostgreSQL Recent Developments
5.4 PostgreSQL
5.4.1 PostgreSQL Profile
5.4.2 PostgreSQL Main Business
5.4.3 PostgreSQL Non-relational SQL Products, Services and Solutions
5.4.4 PostgreSQL Non-relational SQL Revenue (US$ Million) & (2016-2021)
5.4.5 PostgreSQL Recent Developments
5.5 Oracle Database
5.5.1 Oracle Database Profile
5.5.2 Oracle Database Main Business
5.5.3 Oracle Database Non-relational SQL Products, Services and Solutions
5.5.4 Oracle Database Non-relational SQL Revenue (US$ Million) & (2016-2021)
5.5.5 Oracle Database Recent Developments
5.6 MongoLab
5.6.1 MongoLab Profile
5.6.2 MongoLab Main Business
5.6.3 MongoLab Non-relational SQL Products, Services and Solutions
5.6.4 MongoLab Non-relational SQL Revenue (US$ Million) & (2016-2021)
5.6.5 MongoLab Recent Developments
5.7 MarkLogic
5.7.1 MarkLogic Profile
5.7.2 MarkLogic Main Business
5.7.3 MarkLogic Non-relational SQL Products, Services and Solutions
5.7.4 MarkLogic Non-relational SQL Revenue (US$ Million) & (2016-2021)
5.7.5 MarkLogic Recent Developments
5.8 Couchbase
5.8.1 Couchbase Profile
5.8.2 Couchbase Main Business
5.8.3 Couchbase Non-relational SQL Products, Services and Solutions
5.8.4 Couchbase Non-relational SQL Revenue (US$ Million) & (2016-2021)
5.8.5 Couchbase Recent Developments
5.9 CloudDB
5.9.1 CloudDB Profile
5.9.2 CloudDB Main Business
5.9.3 CloudDB Non-relational SQL Products, Services and Solutions
5.9.4 CloudDB Non-relational SQL Revenue (US$ Million) & (2016-2021)
5.9.5 CloudDB Recent Developments
5.10 DynamoDB
5.10.1 DynamoDB Profile
5.10.2 DynamoDB Main Business
5.10.3 DynamoDB Non-relational SQL Products, Services and Solutions
5.10.4 DynamoDB Non-relational SQL Revenue (US$ Million) & (2016-2021)
5.10.5 DynamoDB Recent Developments
5.11 Basho Technologies
5.11.1 Basho Technologies Profile
5.11.2 Basho Technologies Main Business
5.11.3 Basho Technologies Non-relational SQL Products, Services and Solutions
5.11.4 Basho Technologies Non-relational SQL Revenue (US$ Million) & (2016-2021)
5.11.5 Basho Technologies Recent Developments
5.12 Aerospike
5.12.1 Aerospike Profile
5.12.2 Aerospike Main Business
5.12.3 Aerospike Non-relational SQL Products, Services and Solutions
5.12.4 Aerospike Non-relational SQL Revenue (US$ Million) & (2016-2021)
5.12.5 Aerospike Recent Developments
5.13 IBM
5.13.1 IBM Profile
5.13.2 IBM Main Business
5.13.3 IBM Non-relational SQL Products, Services and Solutions
5.13.4 IBM Non-relational SQL Revenue (US$ Million) & (2016-2021)
5.13.5 IBM Recent Developments
5.14 Neo
5.14.1 Neo Profile
5.14.2 Neo Main Business
5.14.3 Neo Non-relational SQL Products, Services and Solutions
5.14.4 Neo Non-relational SQL Revenue (US$ Million) & (2016-2021)
5.14.5 Neo Recent Developments
5.15 Hypertable
5.15.1 Hypertable Profile
5.15.2 Hypertable Main Business
5.15.3 Hypertable Non-relational SQL Products, Services and Solutions
5.15.4 Hypertable Non-relational SQL Revenue (US$ Million) & (2016-2021)
5.15.5 Hypertable Recent Developments
5.16 Cisco
5.16.1 Cisco Profile
5.16.2 Cisco Main Business
5.16.3 Cisco Non-relational SQL Products, Services and Solutions
5.16.4 Cisco Non-relational SQL Revenue (US$ Million) & (2016-2021)
5.16.5 Cisco Recent Developments
5.17 Objectivity
5.17.1 Objectivity Profile
5.17.2 Objectivity Main Business
5.17.3 Objectivity Non-relational SQL Products, Services and Solutions
5.17.4 Objectivity Non-relational SQL Revenue (US$ Million) & (2016-2021)
5.17.5 Objectivity Recent Developments 6 North America
6.1 North America Non-relational SQL Market Size by Country (2016-2027)
6.2 United States
6.3 Canada 7 Europe
7.1 Europe Non-relational SQL Market Size by Country (2016-2027)
7.2 Germany
7.3 France
7.4 U.K.
7.5 Italy
7.6 Russia
7.7 Nordic
7.8 Rest of Europe 8 Asia-Pacific
8.1 Asia-Pacific Non-relational SQL Market Size by Region (2016-2027)
8.2 China
8.3 Japan
8.4 South Korea
8.5 Southeast Asia
8.6 India
8.7 Australia
8.8 Rest of Asia-Pacific 9 Latin America
9.1 Latin America Non-relational SQL Market Size by Country (2016-2027)
9.2 Mexico
9.3 Brazil
9.4 Rest of Latin America 10 Middle East & Africa
10.1 Middle East & Africa Non-relational SQL Market Size by Country (2016-2027)
10.2 Turkey
10.3 Saudi Arabia
10.4 UAE
10.5 Rest of Middle East & Africa 11 Non-relational SQL Market Dynamics
11.1 Non-relational SQL Industry Trends
11.2 Non-relational SQL Market Drivers
11.3 Non-relational SQL Market Challenges
11.4 Non-relational SQL Market Restraints 12 Research Finding /Conclusion 13 Methodology and Data Source 13.1 Methodology/Research Approach
13.1.1 Research Programs/Design
13.1.2 Market Size Estimation
13.1.3 Market Breakdown and Data Triangulation
13.2 Data Source
13.2.1 Secondary Sources
13.2.2 Primary Sources
13.3 Disclaimer
13.4 Author List

About Us:

QYResearch always pursuits high product quality with the belief that quality is the soul of business. Through years of effort and supports from huge number of customer supports, QYResearch consulting group has accumulated creative design methods on many high-quality markets investigation and research team with rich experience. Today, QYResearch has become the brand of quality assurance in consulting industry.

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I’m starting to see the big consultancies and advisory services coming out with their lists of “what’s hot” from a data and analytics perspective.  While I may not have the wide purview of these organizations, I certainly do work with some interesting organizations who are at various points in their data and analytics journey.

With that in mind, I’d like to share my perspective as to what I think will be big in the area of data and analytics over the next 18 months.

• Contextual Knowledge Center. A contextual directory, on AI steroids, that facilitates the identification, location, reuse, and refinement (including version control) of the organization’s data and analytic assets (such as workflows, data pipelines, data transformation and enrichment algorithms, critical data elements, composite metrics, propensity scores, entity or asset models, ML models, standard operating procedures, governance policies, reports, dashboard widgets, and design templates).  It upon an organization’s data catalog by integrating contextual search, Natural Language Processing (NLP), asset scoring, graph analytics, and a decisioning (recommendation) engine to recommendation data and analytic assets based upon the context of the user’s request.
• Autonomous Assets. These are composable, reusable, continuously-learning and adapting data and analytic assets (think intelligent data pipelines and ML models) that appreciate, not depreciate, in value the more that they are used. These autonomous assets produce pre-defined business and operational outcomes and are constantly being updated and refined based upon changes in the data and outcomes effectiveness, with minimal human intervention.  This could apply to almost any digital asset including data pipelines, data transformation and enrichment algorithms, process workflows, AI / ML analytic models (like Tesla’s Fully Self Driving or FSD module), and standard operating procedures and policies.  Yea, this is probably one of my top 3 topics.

• Entity Behavioral Models: These Analytic Profiles capture, codify, share, re-use, and continuously-refine the predicted propensities, patterns, trends and relationships for the organization’s key human and device (things) assets…at the level of the individual asset. This is the heart of nanoeconomics, which is the economics of individual human or device predicted propensities.  It is Entity Behavioral Models or Analytic Profiles that drive the optimization of the organization’s key business and operational use cases.

• AIOps / MLOps. This is an emerging IT field where organizations are utilizing big data and ML to continuously enhance IT operations (such as operational task automation, performance monitoring, load balancing, asset utilization optimization, predictive maintenance, and event detection, correlation, and resolution) with proactive and dynamic insights.
• DataOps. An automated, process-oriented methodology to improve model quality and effectiveness while reducing the cycle time in the training, testing and operationalizing data analytics. DataOps is an integrated suite of data management capabilities including best practices, automated workflows, data pipelines, data transformations and enrichments, and architectural design patterns.
• Data Apps / Data Products. Data apps or data products are a category of domain-centric, AI-powered apps designed to help non-technical users manage data-intensive operations to achieve specific business and operational outcomes.  Data apps use AI to mine a diverse set of customer, product, and operational data, identify patterns, trends, and relationships buried in the data, make timely predictions and recommendations with respect to next best action, and track the effectiveness of those recommendations to continuously refine AI model effectiveness.
• Software 2.0. An emerging category of software that learns through advanced deep learning and neural networks versus being specifically programmed. Instead of programming the specific steps that you want the software program to execute to produce a desired output, Software 2.0 uses neural networks to analyze and learn how to produce that final output without defining the processing steps and with minimal human intervention. For example, Software 2.0 using neural networks can learn to differentiate a dog from a cat versus trying to program the characteristics and differences between a dog and a cat (good luck doing that!).
• AI Innovation Office. The AI Innovation Office is responsible for the testing and validation of new ML frameworks, career development of the organization’s data engineering and data science personnel, and “engineering” of ML models into composable, reusable, continuously refining digital assets that can be re-used to accelerate time-to-value and de-risk use case implementation. The AI Innovation Office supports a “Hub and Spoke” data science organizational structure where the centralized “hub” data scientists collaborate with the business unit “spoke” data scientists to engineer (think co-create) the data and analytic assets. The AI Innovation Office supports a data scientist rotation program where data scientists cycle between the hub and the spoke to provide new learning and development opportunities.
• Data Literacy, Critical Thinking, and AI Ethics. AI will impact every part of your organization not to mention nearly every part of society.  Consequently, there is a need to train everyone on data literacy (understanding the realm of what’s possible with data), critical thinking (to overcome the natural human decision-making biases), and ethical AI (to ensure that the AI models are treating everyone equally and without gender, race, religious, or age biases). Everyone must be prepared to think critically about the application of AI across a range of business, environmental, and societal issues, and the potential ethical ramifications of AI model false positives and false negatives.  Organizations must apply a humanistic lens from which to ensure that AI will be developed and used to the highest ethical standards. 

Well, that’s it for this 2021.  And if we can avoid another pandemic or some other major catastrophe, I’m sure that next year will be even more amazing!

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Study report on the global  NoSQL  market elements offers the current market status and future estimations backed by verifiable data. Understanding of the market variables both intrinsic and extrinsic to the  NoSQL  market is determined by a thorough quantitative and qualitative analysis. It also offers the  NoSQL  market size and volume globally studied offering a multi-dimensional study report. Along with this, the basic factors major influencing the  NoSQL  market growth segmented as drivers and restrains are key to the market report.
The latest research report on  NoSQL  market consists of a detailed examination of the major trends influencing the growth of this industry dynamics with respect to the competitive landscape and regional aspects. Moreover, the study emphases on key growth catalysts and lucrative prospects that will amplify the market remuneration scope over the forecast duration. It also specifies restraints and bottlenecks to the industry growth along with strategies to subdue their impacts.
Proceeding further, the business intelligence report encompasses a detailed evaluation of the various segments to help investors and shareholders identify the key areas that have high revenue potential. Additionally, it compiles latest updates on Covid-19 pandemic to arrive at a clearer interpretation of the changing landscape.
Request Sample Copy of this Report @ https://www.express-journal.com/request-sample/415126
Key highlights from COVID-19 impact analysis: 
Consequences of Covid-19 on society and economy
Implications of the pandemic on the business development
Disruptions in the supply and demand chain
Outline of the regional landscape:
The regional  NoSQL  market spans across North America, Europe, Asia-Pacific, Middle East and Africa, South America.
Contribution of each region to the worldwide market growth is assessed in the report.
Crucial aspects like accounts of sales, returns, and growth rate forecasts for each region are included.
Other highlights from the  NoSQL  market report: 
The product landscape of the  NoSQL  market is categorized into Key-Value Store,Document Databases,Column Based Stores andGraph Database.
Estimations for revenue and growth rate of each product type are over the forecast duration are statistically confirmed in the report.
Details relating to market share, growth rate, and production patterns of each product type are elucidated in the report.
The application scope of the  NoSQL  market is classified into Retail,Online Game Development,IT,Social Network Development,Web Applications Management andOthers.
The report mentions the market share held by each application together with their respective growth rate over the stipulated timeframe.
Established firms in the  NoSQL  market is MarkLogic Corporation,PostgreSQL,MariaDB,Basho Technologies,IBM Corporation,Neo technology,MongoDB,Couchbase,Aerospike Inc,Hibernate andOracle Database.
Important details of manufactured products and services, production patterns as well as the net market remuneration of the key contenders are hosted in the report.
Detailed analysis of the supply chain, comprising of the traders, suppliers, and customers, is encompassed in the report.
Porter’s five forces analysis and SWOT assessment tools are utilized to form a conclusive study on the investment feasibility of a new project.
The key questions answered in this report:
What will be the Market Size and Growth Rate in the forecast year?
What are the Key Factors driving  NoSQL  Market?
What are the Risks and Challenges in front of the market?
Who are the Key Vendors in  NoSQL  Market?
What are the Trending Factors influencing the market shares?
What are the Key Outcomes of Porter’s five forces model?
Which are the Global Opportunities for Expanding the  NoSQL  Market?
Reasons to Purchase the  NoSQL  report:
The  NoSQL  market report provides widespread impact of COVID-19 pandemic on all the regions and segments of the market.
Current technological and other trends and future opportunities of the  NoSQL  market is also explained in the report.
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The research report also provides regional level analysis and its current and forecast trends of the  NoSQL  market
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The entire market segmentation along with relevant information
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Global Operational Database Management System Market 2021 – COVID 19 Outbreak, SWOT analysis and Forecast 2021-2026 By Key Players – The major players covered in Operational Database Management System are: , Oracle , MarkLogic , IBM , Microsoft , Fujitsu , SAP SE , MongoDB , Google , Amazon Web Services , InterSystems , Aerospike and Datastax

According to reliable estimates, the Operational Database Management System market is anticipated to record a considerable growth over 2021-2026, registering a CAGR of XX throughout.

The Operational Database Management System market research report delivers a thorough analysis of this business sphere with expert insights on the past and present growth matrix. Factors such as driving forces, opportunities, and obstacles that will shape the industry dynamics are explained in detail. Besides, the study meticulously defines the size and shares of the market and its segments, uncovering the key growth prospects in the process.

Request Sample Copy of this Report @ https://www.business-newsupdate.com/request-sample/127071

Proceeding further, the study scrutinizes COVID-19 footprint on the industry, highlighting the impediments faced by companies, such as disruptions in supply-demand and complications in cost-management. In this context, the research document helps in building actions plans that ensure profitability and continuity of businesses in the long-term.

Crucial pointers from the Operational Database Management System market report:

• COVID-19 effect on the remuneration scale of the industry.
• Predicted growth rate of the market.
• Key trends in the market.
• Opportunities with strong profit potential.
• Merits and demerits of indirect and direct sales channels.
• Leading distributors, traders, and dealers.

Operational Database Management System market segments covered in the report:

Regional bifurcation: North America, Europe, Asia-Pacific, South America, Middle East and Africa

• Country-level assessment of each regional market.
• Net profit accrued by each geography.
• Market share accounted by each region.
• Projected growth rate and revenue generated by each regional market over the forecast duration.

Product types: Software-as-a-Service and On-premise

• Market share, revenue, and sales of each product type.
• Pricing pattern of each product category.

Application scope: BFSI , IT & Telecommunications , Government & Defense , Transportation , Manufacturing , Healthcare , Retail , Energy & Utilities and Others

• Sales and revenue amassed by each application segment.
• Pricing of the concerned products in terms of their application reach.

Competitive dashboard: The major players covered in Operational Database Management System are: , Oracle , MarkLogic , IBM , Microsoft , Fujitsu , SAP SE , MongoDB , Google , Amazon Web Services , InterSystems , Aerospike and Datastax

• Product and services offered by major players.
• Manufacturing facilities of major contenders across the serviced areas.
• Emerging and new contenders in the marketplace.
• Evaluation of market share, gross margins, overall sales, pricing patterns, and total revenue of listed companies.
• SWOT analysis of the mentioned firms.
• Assessment of the popular business tactics, commercialization rate, and market concentration ratio.

What to Expect From This Report:

• Focused study on global Operational Database Management System market development & penetration Scenario
• Analysis of M&As, Partnership & JVs in Industry & Other Emerging Geographies
• Top companies in global Operational Database Management System market share analysis.
• Gain strategic insights on competitor information to formulate effective R&D moves
• Identify emerging players and create effective counter-strategies to outpace competitive edge
• Identify important and diverse product types/services offering carried by major players for market development

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NoSQL Database Software Market Growth, Trends, Forecasts, and COVID-19 Impact (2021)

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Database Management Software Market ( 2021 Updated )

The Global Database Management Software Market report provides information about the Global industry, including valuable facts and figures. This research study explores the Global Market in detail such as industry chain structures, raw material suppliers, with manufacturing The Database Management Software Sales market examines the primary segments of the scale of the market. This intelligent study provides historical data from 2015 alongside a forecast from 2021 to 2030.

Results of the recent scientific undertakings towards the development of new Database Management Software products have been studied. Nevertheless, the factors affecting the leading industry players to adopt synthetic sourcing of the market products have also been studied in this statistical surveying report. The conclusions provided in this report are of great value for the leading industry players. Every organization partaking in the global production of the Database Management Software market products have been mentioned in this report, in order to study the insights on cost-effective manufacturing methods, competitive landscape, and new avenues for applications.

This report contains a thorough analysis of the pre and post pandemic market scenarios. This report covers all the recent development and changes recorded during the COVID-19 outbreak.

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Top Key Players of the Market:

Embarcadero Technologies, Couchbase Server, EnterpriseDB Software Solution, MongoDB, Teradata, Informatica Corporation, CA Technologies, HP (Vertica System), Oracle Inc., InterSystems, MetaMatrix, IBM Inc., Actian Corporation, iWay Software, BMC Software

Types covered in this report are:

Cloud-based
On-premises

Applications covered in this report are:

Banking & financial
Government
Hospitality
Healthcare and life sciences
Education, media & entertainment
Professional service
Telecom& IT

With the present market standards revealed, the Database Management Software market research report has also illustrated the latest strategic developments and patterns of the market players in an unbiased manner. The report serves as a presumptive business document that can help the purchasers in the global market plan their next courses towards the position of the market’s future.

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Regional Analysis For Database Management Software Market

North America (the United States, Canada, and Mexico)

Europe (Germany, France, UK, Russia, and Italy)

Asia-Pacific (China, Japan, Korea, India, and Southeast Asia)

South America (Brazil, Argentina, Colombia, etc.)

The Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria, and South Africa)

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• Get a clear understanding of the Database Management Software market, how it operates, and the various stages of the value chain.
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• Understand your competitors’ business structures, strategies, and prospects, and respond accordingly.
• Make more informed business decisions with the help of insightful primary and secondary research sources.

This report provides:

1. An in-depth overview of the global market for Database Management Software.
2. Assessment of the global industry trends, historical data from 2011, projections for the coming years, and anticipation of compound annual growth rates (CAGRs) by the end of the forecast period.
3. Discoveries of new market prospects and targeted marketing methodologies for Global Database Management Software
4. Discussion of R&D, and the demand for new products launches and applications.
5. Wide-ranging company profiles of leading participants in the industry.
6. The composition of the market, in terms of dynamic molecule types and targets, underlining the major industry resources and players.
7. The growth in patient epidemiology and market revenue for the market globally and across the key players and market segments.
8. Study the market in terms of generic and premium product revenue.
9. Determine commercial opportunities in the market sales scenario by analyzing trends in authorizing and co-development deals.

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In the end, the Database Management Software Market report includes investment come analysis and development trend analysis. The present and future opportunities of the fastest growing international industry segments are coated throughout this report. This report additionally presents product specification, manufacturing method, and product cost structure, and price structure.

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Article originally posted on InfoQ. Visit InfoQ

Google has been working on its capability-based OS Fuchsia for at least six years. A few days ago, Fuchsia started rolling out to Nest Hub devices, thus marking its official release.

Google is not making a big announcement of Fuchsia launch, but confirmed the news to 9to5Google:

Google has told us that as of today, an update is beginning to roll out to owners of the first-generation Nest Hub, first released in 2018. For all intents and purposes, this update will not change any of the functionality of the Nest Hub, but under the hood, the smart display will be running Fuchsia OS instead of the Linux-based “Cast OS” it used before.

Google Nest Hub is device provided with a touchscreen display that can be used to control smart home devices. Previous to the update to Fuchsia, the device used a Linux-based OS running the Google Cast protocol. Fuchsia, on the contrary, is not Linux-based and includes a number of specific components.

Fuchsia Kernel is called Zircon and is written in C++. Zircon departs from Unix-like OSes by not supporting Unix-style signals and replacing fork and exec through the launchpad library.

Other components that makes up Fuchsia are Garnet, which provides services common to all OSes for software installation, administration, communication with remote systems and so on; Peridot is a framework for composed, intelligent, and distributed user experiences; Topaz augments system functionality by implementing interfaces defined by underlying layers and exposing them as modules, agents, shells, and runners.

Fuchsia is not tied to a specific language and supports a variety of languages and runtimes, including C++, Web, Rust, Go, Flutter, and Dart. Dart and Flutter enjoy a special status, though, since the Nest Hub display experience was based on them and are being leveraged by the Fuchsia update.

InfoQ has covered Fuchsia since its public repo was first spotted in 2016 and the OS was still surrounded by an aura of mystery and believed to be a replacement for Android. Four years later, Google opened the project to external contributions while retaining control over its evolution. At some later point, Google removed all UI components from the repo.