Month: June 2022
MMS • Sergio De Simone
Article originally posted on InfoQ. Visit InfoQ
Uncovered by a team at MIT CSAIL, PACMAN is a new vulnerability affecting a defense mechanism available in Apple Silicon processors and known as pointer authentication code (PAC). While Apple downplayed the severity of this finding, the researchers hint at the fact PACMAN brings an entire new class of attacks.
Pointer authentication code is a mechanism available on ARM-based processors, including Apple’s, which aims to enable detecting and guarding against unexpected changes to pointers in memory.
Pointer authentication works by offering a special CPU instruction to add a cryptographic signature — or PAC — to unused high-order bits of a pointer before storing the pointer. Another instruction removes and authenticates the signature after reading the pointer back from memory.
This mechanism makes it possible to detect when a value has been tampered with between the write and the read operation by checking the signature. If the signature is invalid, the CPU will treat that value as corrupted and will cause a program crash.
According to a paper by Ravichandran et al., a new attack methodology makes it possible to speculatively leak PAC verification results via microarchitectural side channels without causing any crashes. Furthermore the attack works across privilege levels, thus allowing an unprivileged user to gain access to the OS kernel space.
For PACMAN to pose a real threat, though, it must count on an existing software-level memory-corruption bug. In other words, since PAC is a line of defense against memory tampering, if you have a bug that makes it possible to access a memory location and modify its content after a write, then you could use PACMAN to prevent the CPU from detecting it and crashing.
As mentioned Apple somewhat downplayed the relevance of this vulnerability. In a statement to Tom’s Hardware, an Apple spokesperson said “this issue does not pose an immediate risk to our users and is insufficient to bypass operating system security protections on its own.” Still, as the researchers point out, PACMAN hints at a new category of vulnerabilities:
We believe that this attack has important implications for designers looking to implement future processors featuring Pointer Authentication, and has broad implications for the security of future control-flow integrity primitives.
PACMAN is not the first vulnerability found in Apple Silicon processors. Recently, researchers at the University of Illinois Urbana-Champaign, the University of Washington, and the Tel Aviv University described Augury, an attack that leaks data at rest the A14 and the M1 family of processors. Augury targets another novel optimization mechanism available in modern processors, data memory-dependent prefetchers (DMP) by making it possible to access out-of-bounds memory locations when prefetching data belonging to an array.
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(VIANEWS) – Shares of MongoDB (NASDAQ: MDB) slid 8.41% to $273.74 at 14:43 EST on Tuesday, following the last session’s downward trend. NASDAQ is falling 2.41% to $11,247.29, following the last session’s downward trend. This seems, so far, an all-around negative trend exchanging session today.
MongoDB’s last close was $298.89, 49.34% under its 52-week high of $590.00.
Volume
Today’s last reported volume for MongoDB is 1254490 which is 23.81% below its average volume of 1646630.
The company’s growth estimates for the present quarter and the next is 40% and 41.7%, respectively.
MongoDB’s Revenue
Year-on-year quarterly revenue growth grew by 55.8%, now sitting on 873.78M for the twelve trailing months.
Volatility
MongoDB’s last week, last month’s, and last quarter’s current intraday variation average was 3.01%, 1.52%, and 5.12%, respectively.
MongoDB’s highest amplitude of average volatility was 3.92% (last week), 6.31% (last month), and 5.12% (last quarter), respectively.
Stock Price Classification
According to the stochastic oscillator, a useful indicator of overbought and oversold conditions,
MongoDB’s stock is considered to be oversold (<=20).
MongoDB’s Stock Yearly Top and Bottom Value
MongoDB’s stock is valued at $273.74 at 14:43 EST, way under its 52-week high of $590.00 and way higher than its 52-week low of $213.39.
MongoDB’s Moving Average
MongoDB’s worth is below its 50-day moving average of $296.59 and way below its 200-day moving average of $415.71.
More news about MongoDB (MDB).
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The summer has barely started, but MongoDB World and Snowflake Summit are now past tense, even as the paint is still drying on all the announcements made at each event. With its Data + AI Summit kicking off as a hybrid virtual/in-person event in San Francisco today, Databricks is wasting no time responding, with a huge manifest of its own announcements.
Databricks’ cofounder and chief technologist (and Apache Spark creator) Matei Zaharia briefed VentureBeat on all the announcements. They fall into two buckets: enhancements to open-source technologies underlying the Databricks platform — like Apache Spark — on the one hand, and enhancements, previews and general availability (GA) releases pertaining to the proprietary Databricks platform on the other.
In this post, I’ll cover the full range of announcements. There’s a lot here, so feel free to use the subheads as a kind of random access interface to read the bits you might care about most, then come back and read the rest if you have time.
Spark Streaming goes Lightspeed
Because Spark and its companion open-source projects have become de facto industry standards at this point, I’d like to start with the announcements in that sphere. First, to Spark itself, Databricks is making two roadmap announcements, covering streaming data processing as well as connectivity for Spark client applications. Spark Streaming has been a subproject of Spark for many years, and its last major enhancement — a technology called Spark Structured Streaming — GA’d five years ago. Essentially, this has meant that the tech around processing of streaming data on Spark has languished, a fact advocates of competing platforms had started to leverage.
In Zaharia’s words, “We didn’t have a very large streaming team, you know, after we built the Spark streaming APIs in the first three or four years of the company.” Matei added, “We were just kind of maintaining that and we found it was one of the fastest growing workloads on our platform; it’s growing faster than the rest.”
This realization that Spark Streaming needed some love has resulted in an umbrella effort that Databricks is calling Project Lightspeed, to create a next-gen implementation of Spark Streaming. Databricks says Lightspeed will bring performance and latency improvements to processing streaming data; add new functionality, like advanced windowing and pattern matching; and make streaming operations easier in general.
Databricks has formed a new streaming team to drive Lightspeed and has named recent hire Karthik Ramasamy, formerly of Twitter and co-creator of Apache Pulsar, to lead it. Databricks also recently recruited Alex Balikov from Google Cloud, and has appointed him senior tech lead on the streaming team. Now let’s wait and see if processing streaming data on Spark can become relatively manageable for the average developer.
RESTful access
Speaking of developers, another Spark roadmap announcement involves something called Spark Connect, which will essentially implement a REST API for Spark, both for operational tasks (like submitting jobs and retrieving results) and managerial ones (like sizing and load balancing Spark clusters or scheduling jobs). This will remove the hard requirement for using programming language- and version-specific client libraries and allow application developers to take a more loosely coupled approach to working with Spark, using just HTTP.
Delta Lake opens up
Sticking with open-source announcements but moving beyond Apache Spark proper brings us to two related projects, both domiciled at the Linux Foundation: Delta Lake and MLflow. Delta Lake is one of three popular technologies for bringing data warehouse-like functionality to data lakes stored in open storage formats like Apache Parquet. Delta Lake has seemingly been in the lead, but rival format Apache Iceberg has recently lurched ahead, seeing adoption at companies like Dremio, Cloudera and Snowflake. One of the chief criticisms of Delta Lake has been that Databricks has maintained overly-tight control of it and has co-mingled the open-source file format with Databricks-proprietary technology like time travel (which allows previous states of a dataset to be examined).
Perhaps in reaction to that criticism, Databricks is today announcing Delta Lake 2.0. The new version brings both performance enhancements and greater openness. Specifically, Databricks says it is contributing all of Delta Lake to the Linux Foundation open-source project, so that all adopters of the format can work with the same codebase and have access to all features.
MLflow, part deux
Open-source project MLflow forms the backbone of Databricks’ MLOps capabilities. Although proprietary components, including the Databricks feature store, exist, the MLflow-based functionality includes machine learning experiments execution and management, as well as a model repository with versioning. Today, Databricks is announcing MLflow 2.0, which will add a major new feature, called Pipelines. Pipelines are templates for setting up ML applications, so everything’s ready for productionalization, monitoring, testing and deployment. The templates — based on code files and Git-based version control — are customizable and allow monitoring hooks to be inserted. Although based on source code files, developers can interact with Pipelines from notebooks, providing a good deal of flexibility. Adding Pipelines should be a boon to the industry, as numerous companies, including all three major cloud providers, have either adopted MLflow as a standard or documented how to use it with their platforms.
Databricks SQL matures
There’s a lot going on, on the proprietary side as well. To begin with, Databricks SQL‘s Photon engine, which brings query optimization and other data warehouse-like features to the Databricks platform, will be released to GA in July. Photon has recently picked up important enhancements, including support for nested data types and accelerated sorting capabilities.
Along with that, Databricks is releasing several open source connectors to Databricks SQL, for languages including Node.js, Python and Go. Databricks SQL is also getting its own command line interface (CLI), too, and will now sport a query federation feature, allowing it to join tables/data sets between different sources in the same query. The latter feature leverages Spark’s own ability to query multiple data sources.
One interesting thing about Databricks SQL is that it supports different cluster types than are made available for other Databricks workloads. The special clusters, called SQL warehouses (and formerly called SQL endpoints), are “T-shirt-sized” and feature cloud server instances that are optimized for business intelligence-style queries. However, now a new option, Databricks SQL Serverless, which will allow customers to query their data via Databricks SQL without creating a cluster at all, is launching in preview on AWS.
Delta Live Tables
Want more? Delta Live Tables, the Databricks platform’s SQL-based declarative facility for ETL and data pipelines, is getting several enhancements, including new performance optimization, Enhanced Autoscaling and change data capture (CDC), to make the platform compatible with slowly changing dimensions, and allowing them to be updated incrementally, rather than from scratch, when dimensional hierarchies change.
The last of these is important — it allows analytical queries to run undisrupted when, for example, a certain branch office is reclassified as being in a different regional division. Queries covering a timespan when it was in its original division will attribute sales at that office to that division; queries covering a later time span will attribute sales to the new division, and queries spanning both will allocate the correct sales amounts to each of the respective divisions.
Catalog, Cleanrooms and Marketplace
Dataricks Unity Catalog will be released to GA later this summer, complete with new lineage capabilities that were just recently added. A new “Data Cleanrooms” feature will allow queries that span data from two different parties to be performed in the cloud without either party needing to send its data to the other. Instead, each party’s data will be put into a kind of digital escrow and, provided both parties grant approval, jobs using both their data will be executed in Databricks’ cloud, from which the data will subsequently be deleted.
Finally, Databricks is starting up its own marketplace, but with a couple of differences from typical data marketplace offerings. To begin with, Databricks Marketplace offerings can consist of whole solutions, including applications and examples, rather than datasets alone. And because the product is based on Delta Sharing, Databricks says it can be used by clients that are not actually using the Databricks platform itself.
Where this leads us
As the data and analytics space consolidates and the new generation of leaders emerges, the competition is getting fierce. The customer benefits as major players start to play in each other’s territory, all looking to service analytical, operational, streaming, data engineering and machine learning workloads in a multicloud fashion. Databricks has doubled down on investments in certain of these areas and has expanded investments to others. What’s especially nice about that is the cascading effect it has on several open source projects, including Spark, Delta Lake and MLflow.
Will Databricks eventually allow single clusters to span multiple clouds, or even turn its focus to on-premises environments? Will Delta Lake or Apache Iceberg emerge as the standard lakehouse storage technology? Will the Databricks feature store component get open sourced to round out MLflow’s appeal versus commercial MLOps platforms? Will Databricks SQL Serverless slay Amazon Athena‘s business franchise? Watch this data space. Customers will place their bets in the next couple of years, as the lakehouse standard bearers build their momentum and map out their territory.
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Global Enterprise Data Management Market Insights Forecast 2029| Intel, MongoDB, Mule …
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The detailed analysis of the global Enterprise Data Management market delivers key insights on the changing industry dynamics, value chain analysis, leading investment pockets, competitive scenarios, regional landscape, and crucial segments. It also offers an extensive inspection related to the driving and restraining ingredients for the global Enterprise Data Management market. Additionally, explains the superior data about the working strategies and growth prospects of the global Enterprise Data Management market. This will help industry players, policymakers, stakeholders, investors, and new aspirants to grab innovative opportunities, uncover important strategies, and also accomplish a competitive edge in the global Enterprise Data Management Industry.
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Accenture
Adaptive
Cambridge Semantics
GoldenSource
IBM
Informatica
Innovative
Intel
MongoDB
Mule Soft
Oracle
Phasic Systems
Radiare Software Solutions
SAP
SAS Institute
Snowflake Computing
Solix Technologies
Symantec Corporation
Teradata Corporation
Trusted Data Solutions
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On-Premise
Cloud-based
Key Applications included in the Enterprise Data Management market:
Large Enterprise
Small and Medium Enterprises
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North America Market(United States, Canada, North American country and Mexico),
Europe Market (Germany, Enterprise Data Management France, UK, Russia and Italy),
Asia-Pacific Market (China, Enterprise Data Management Japan and Korea, Asian nation, India and Southeast Asia),
South America Market (Brazil, Argentina, Republic of Colombia etc.),
Middle East & Africa Market (Saudi Arabian Peninsula, UAE, Egypt, Nigeria and South Africa)
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MongoDB Inc. (NASDAQ:MDB) has a beta value of 0.83 and has seen 1.01 million shares traded in the last trading session. The company, currently valued at $19.52B, closed the last trade at $298.89 per share which meant it lost -$4.2 on the day or -1.39% during that session. The MDB stock price is -97.4% off its 52-week high price of $590.00 and 28.61% above the 52-week low of $213.39. The 3-month trading volume is 1.63 million shares.
Sporting -1.39% in the red in last session, the stock has traded in the green over the last five days, with the highest price hit on Monday, 06/27/22 when the MDB stock price touched $298.89 or saw a rise of 4.71%. Year-to-date, MongoDB Inc. shares have moved -43.54%, while the 5-day performance has seen it change 19.68%. Over the past 30 days, the shares of MongoDB Inc. (NASDAQ:MDB) have changed 29.51%.
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Wall Street analysts have a consensus price target for the stock at $373.60, which means that the shares’ value could jump 20.0% from current levels. The projected low price target is $270.00 while the price target rests at a high of $500.00. In that case, then, we find that the current price level is -67.29% off the targeted high while a plunge would see the stock lose 9.67% from current levels.
MongoDB Inc. (MDB) estimates and forecasts
Figures show that MongoDB Inc. shares have outperformed across the wider relevant industry. The company’s shares have lost -45.77% over the past 6 months, with this year growth rate of 54.24%, compared to 3.70% for the industry. Other than that, the company has, however, increased its growth outlook for the 2022 fiscal year revenue. Growth estimates for the current quarter are 40.00% and 41.70% for the next quarter. Revenue growth from the last financial year stood is estimated to be 35.20%.
14 analysts offering their estimates for the company have set an average revenue estimate of $266.45 million for the current quarter. 14 have an estimated revenue figure of $277.85 million for the next quarter concluding in Jul 2022.
If we evaluate the company’s growth over the last 5-year and for the next 5-year period, we find that annual earnings growth was 3.70% over the past 5 years.
MDB Dividends
MongoDB Inc. is expected to release its next earnings report between March 07 and March 11 this year, and investors are excited at the prospect of better dividends despite the company’s debt issue.
MongoDB Inc. (NASDAQ:MDB)’s Major holders
Insiders own 5.59% of the company shares, while shares held by institutions stand at 89.29% with a share float percentage of 94.58%. Investors are also buoyed by the number of investors in a company, with MongoDB Inc. having a total of 793 institutions that hold shares in the company. The top two institutional holders are Price (T.Rowe) Associates Inc with over 7.16 million shares worth more than $3.79 billion. As of Dec 30, 2021, Price (T.Rowe) Associates Inc held 10.60% of shares outstanding.
The other major institutional holder is Capital World Investors, with the holding of over 6.53 million shares as of Dec 30, 2021. The firm’s total holdings are worth over $3.46 billion and represent 9.67% of shares outstanding.
Also the top two Mutual Funds that are holding company’s shares are Growth Fund Of America Inc and Vanguard Total Stock Market Index Fund. As of Feb 27, 2022, the former fund manager holds about 7.06% shares in the company for having 4.77 million shares of worth $1.82 billion while later fund manager owns 1.79 million shares of worth $945.81 million as of Dec 30, 2021, which makes it owner of about 2.64% of company’s outstanding stock.
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Cloud-based Database Market – New Business Opportunities and Investment Research Report 2030
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The Latest research study released by DLF “Global Cloud-based Database Market” with 100+ pages of analysis on business Strategy taken up by key and emerging industry players and delivers know-how of the current market development, landscape, technologies, drivers, opportunities, market viewpoint, and status. Understanding the segments helps in identifying the importance of different factors that aid the market growth. Some of the Major Companies covered in this Research are Google, Amazon Web Services, IBM, Microsoft, Oracle, Rackspace Hosting, Salesforce, Cassandra, Couchbase, MongoDB, SAP, Teradata, Alibaba, Tencent
The global Cloud-based Database market was valued at 20070 million in 2021 and is projected to reach US$ 364270 million by 2028, at a CAGR of 51.3% during the forecast period.
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A cloud-based database is a database, as the name suggests, resides on a cloud computing platform. Databases are used to store and manage various forms of data generated by a company. Database services can be provided on-premises or on-demand. On-demand services are known as cloud-based databases, which is gaining increasing acceptance among several organizations. A cloud-based database is suitable for organizations that require immediate access to database services, easy scalability options, low cost, and low maintenance. Service providers offer end-to-end solutions, which help organizations focus on their core business areas.
The cloud database solutions have empowered organizations to focus on their core business competencies by relying on the cloud resources completely. Various cloud databases are becoming relevant as they improve the overall efficiency of the client offerings and reduce the support and maintenance considerably, giving almost total control of operations over the data which is stored.
At last, all parts of the Global Cloud-based Database Market are quantitatively also subjectively valued to think about the Global just as regional market equally. This market study presents basic data and true figures about the market giving a deep analysis of this market based on market trends, market drivers, constraints, and future prospects. The report supplies the worldwide monetary challenge with the help of Porter’s Five Forces Analysis and SWOT Analysis.
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On the basis of the report- titled segments and sub-segment of the market are highlighted below:
Global Cloud-based Database Market By Application/End-User:
Small and Medium Business, Large Enterprises
Market By Type:
SQL Database, NoSQL Database
Global Cloud-based Database Market by Key Players:
Google, Amazon Web Services, IBM, Microsoft, Oracle, Rackspace Hosting, Salesforce, Cassandra, Couchbase, MongoDB, SAP, Teradata, Alibaba, Tencent
Geographically, this report is segmented into some key Regions, with manufacture, depletion, revenue (million USD), and market share and growth rate of Cloud-based Database in these regions, from 2017 to 2030 (forecast), covering China, USA, Europe, Japan, Korea, India, Southeast Asia & South America and its Share (%) and CAGR for the forecasted period 2022 to 2030
Cloud-based Database Market
Informational Takeaways from the Market Study: The report Cloud-based Database matches the completely examined and evaluated data of the noticeable companies and their situation in the market considering the impact of Coronavirus. The measuring tools including SWOT analysis, Porter’s Five Forces analysis, and assumption return debt were utilized while separating the improvement of the key players performing in the market.
Key Development’s in the Market: This segment of the Cloud-based Database report fuses the major developments of the market that contains confirmations, composed endeavors, R&D, new thing dispatch, joint endeavors, and relationships of driving members working in the market.
Some of the important questions for stakeholders and business professionals for expanding their position in the Global Cloud-based Database Market:
Q 1. Which Region offers the most rewarding open doors for the market ahead of 2022?
Q 2. What are the business threats and Impacts of the latest scenario on the market Growth and Estimation?
Q 3. What are probably the most encouraging, high-development scenarios for the Cloud-based Database movement showcased by applications, types, and regions?
Q 4. What segments grab the most noteworthy attention in Cloud-based Database Market in 2022 and beyond?
Q 5. Who are the significant players challenging and developing in Cloud-based Database Market?
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Key poles of the TOC:
Chapter 1 Global Cloud-based Database Market Business Overview
Chapter 2 Major Breakdown by Type [SQL Database, NoSQL Database]
Chapter 3 Major Application Wise Breakdown (Revenue & Volume)
Chapter 4 Manufacture Market Breakdown
Chapter 5 Sales & Estimates Market Study
Chapter 6 Key Manufacturers Production and Sales Market Comparison Breakdown
Chapter 8 Manufacturers, Deals, and Closings Market Evaluation & Aggressiveness
Chapter 9 Key Companies Breakdown by Overall Market Size & Revenue by Type
Chapter 11 Business / Industry Chain (Value & Supply Chain Analysis)
Chapter 12 Conclusions & Appendix
Thanks for reading this article; you can also get individual chapter-wise sections or region-wise report versions like North America, LATAM, Europe, or Southeast Asia.
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YugabyteDB, the self-styled double-decker distributed relational database, has introduced a read-committed isolation level, allowing for more flexibility for devs and bringing it into step with its more established RDBMS rivals.
With the general availability of YugabyteDB 2.15, the company behind the PostgreSQL-compatible database, said to be inspired by Google Spanner, claims to be adding performance enhancements and dynamic workload optimization.
Speaking to The Register, CTO Karthik Ranganathan, a former Facebook technical lead, said Yugabyte was the first and only distributed SQL vendor to offer all three PostgreSQL isolation levels, making it that much easier to shift existing applications to a distributed database with advanced support for the default PostgreSQL isolation level.
DB newbie? This is what we mean by isolation levels
Database isolation levels give users a guarantee of what data a transaction can see when it is running in parallel with other transactions. PostreSQL offers four levels of isolation. From strongest to weakest, they are:
- serializable,
- read repeatable,
- read committed,
- and read uncommitted.
YugabyteDB already offers the highest two levels of isolation, but developers wanted greater flexibility, Ranganathan said.
“We supported serializable and repeatable read, and all of our [distributed SQL] competitors only serializable. But the market said the default for every traditional relational database including Oracle, SQL Server, PostgreSQL, MySQL, and so on is read committed. We argued they’re not cloud-native databases: they’re older; we’re modern. We don’t want to do stuff that’s unnecessary. They didn’t relent at all.
“We had big customers telling us they could not move to 300 applications and retrain our developers to think in a new isolation model. They were saying they knew how to make applications safe using the isolation model, which gives them some level of guarantees and some level of flexibility and they didn’t want to give that up. So finally, we capitulated and we built that.
“They want to have to support for the third – and a slightly weaker level of isolation – because that’s the default in PostgreSQL. That’s what most people use and what makes application building without handling restarts and failure cases much easier, even if it’s inefficient.”
According to last week’s StackOverflow survey, PostgreSQL was the most popular database among professional developers, although MySQL was almost on par.
YugabyteDB is available on the Apache 2.0 license after the company “went full open source mid-2019,” Ranganathan said.
The 2.15 update also introduces dynamic workloads, which the company said would help organizations cover the widest set of existing and new transactional workloads with a single, unified database. It includes features such as dynamic app-aware database sharding, enhanced quality of service (QoS), and support for multi-tenancy.
Also new to the database is a built-in performance advisor that offers performance analytics and tailored recommendations, which the company said offered 20x faster data loading performance.
Filling in the gaps
Merv Adrian, Gartner research VP, data and analytics, said that YugabyteDB had secured nearly $250 million in funding over its last two rounds, and the tech announcements were designed to “fill a lot of the gaps that they and many of the other [distributed] vendors have in terms of enhanced Postgres.”
“It’s very important they target the developers. They care about those isolation levels. Read committed has been in PostgreSQL for a long time. But they’ve also got things like Change Data Capture, as well as the Cassandra API, which is a big deal,” he said.
Not only would YugabyteDB have to compete with other database companies but also the cloud platform providers, each of which has PostgreSQL-like databases.
“If you don’t have a pretty robust, maturing product, and a reasonable set of high-profile customers who are relying on you, it’s going to be very difficult to compete with those platforms. YugabyteDB is getting there on all of these fronts. It’s a horse race, they’re on the track, and they’ve got a chance,” Adrian said. ®
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This Key-Value Stores market report provides details of recent new developments, trade regulations, import-export analysis, production analysis, value chain optimization, market share, impact of national and localized market players, analyzes opportunities in terms of emerging revenue pockets, changes in market regulations, strategic market growth analysis, market size, category market growth, application and domain niches, product approvals , product launches, geographic expansions, technological innovations in the market. For more information on Data Bridge Market Research Key-Value Stores market, please contact us for analyst briefing,
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The analysis and estimates made through exceptional Key-Value Stores report help to get an idea about product launches, future products, joint ventures, marketing strategy, developments, mergers and acquisitions and the effect thereof on sales, marketing, promotions, revenue values, import, export and CAGR. With the latest and up-to-date market insights mentioned in the report, companies can reflect on how to improve their marketing, promotion and sales strategies. The business report helps determine and optimize each stage of the industrial process lifecycle that includes engagement, acquisition, retention, and monetization. The Key-Value Stores Market Research Report
Leading Key-Value Stores Market Players are as followed:
- Redis
- Azure Redis Cache
- ArangoDB
- Hbase
- Google Cloud Datastore
- Aerospike
- BoltDB
- Couchbase
- Memcached
- Oracle
- DataStax
- Infinispan
Global Key-Value Stores Market segmentation :
Key-Value Stores Market Segment by Type :
- Cloud Based
- Web Based
Key-Value Stores Market Segment by Application :
- Large Enterprises
- SMEs
Regional market analysis Key-Value Stores can be represented as follows:
This part of the report assesses key regional and country-level markets on the basis of market size by type and application, key players, and market forecast.
The base of geography, the world market of, Key-Value Stores has segmented as follows:
-
- North America includes the United States, Canada, and Mexico
-
- Europe includes Germany, France, UK, Italy, Spain
-
- South America includes Colombia, Argentina, Nigeria, and Chile
-
- The Asia Pacific includes Japan, China, Korea, India, Saudi Arabia, and Southeast Asia
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Key-Value Stores Market Report Scope
| ATTRIBUTES | DETAILS |
|---|---|
| ESTIMATED YEAR | 2022 |
| BASE YEAR | 2021 |
| FORECAST YEAR | 2029 |
| HISTORICAL YEAR | 2020 |
| UNIT | Value (USD Million/Billion) |
| SEGMENTS COVERED | Types, Applications, End-Users, and more. |
| REPORT COVERAGE | Revenue Forecast, Company Ranking, Competitive Landscape, Growth Factors, and Trends |
| BY REGION | North America, Europe, Asia Pacific, Latin America, Middle East and Africa |
| CUSTOMIZATION SCOPE | Free report customization (equivalent up to 4 analysts working days) with purchase. Addition or alteration to country, regional & segment scope. |
Visualize Anesthesia Delivery Systems Market using Verified Market Intelligence:-
Verified Market Intelligence is our BI-enabled platform for narrative storytelling of this market. VMI offers in-depth forecasted trends and accurate Insights on over 20,000+ emerging & niche markets, helping you make critical revenue-impacting decisions for a brilliant future.
VMI provides a holistic overview and global competitive landscape with respect to Region, Country, and Segment, and Key players of your market. Present your Market Report & findings with an inbuilt presentation feature saving over 70% of your time and resources for Investor, Sales & Marketing, R&D, and Product Development pitches. VMI enables data delivery In Excel and Interactive PDF formats with over 15+ Key Market Indicators for your market.
Visualize Anesthesia Delivery Systems Market using VMI @ https://www.verifiedmarketresearch.com/vmintelligence/
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About Us: Verified Market Reports
Verified Market Reports is a leading Global Research and Consulting firm servicing over 5000+ global clients. We provide advanced analytical research solutions while offering information-enriched research studies.
We also offer insights into strategic and growth analyses and data necessary to achieve corporate goals and critical revenue decisions.
Our 250 Analysts and SME’s offer a high level of expertise in data collection and governance using industrial techniques to collect and analyze data on more than 25,000 high-impact and niche markets. Our analysts are trained to combine modern data collection techniques, superior research methodology, expertise, and years of collective experience to produce informative and accurate research.
Our research spans over a multitude of industries including Energy, Technology, Manufacturing and Construction, Chemicals and Materials, Food and Beverages etc. Having serviced many Fortune 2000 organizations, we bring a rich and reliable experience that covers all kinds of research needs.
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MMS • Daniel Dominguez
Article originally posted on InfoQ. Visit InfoQ
AWS in a joint effort with Microsoft have established PyWhy as a fresh GitHub organization to integrate AWS algorithms into DoWhy, a casual ML library from Microsoft, which has moved to PyWhy.
The mission of PyWhy is to build an open-source ecosystem for causal machine learning that advances the state of the art and makes it available to practitioners and researchers. PyWhy, will build and host interoperable libraries, tools, and other resources spanning a variety of causal tasks and applications, connected through a common API on foundational causal operations and a focus on the end-to-end analysis process.
The majority of real-world systems, whether they be industrial procedures, supply chain systems, or distributed computer systems, may be characterized using variables that may or may not have a causal relationship with one another.
The evaluation of causal machine learning models and the formalization and integration of domain knowledge into machine learning pipelines present significant research problems. Finding the best identification technique, creating an estimator, and performing robustness checks are all phases that are often completed entirely from scratch as part of the normal procedure. The assumptions were difficult to comprehend and validate, though.
DoWhy is one of the existing causality libraries that focuses on several methods of effect estimation, with the overall objective of determining the impact of interventions on a target variable.
By utilizing the strength of graphical causal models, AWS work enhances DoWhy’s current feature set GCMs. Judea Pearl, who won the Turing Award, created the formal framework known as GCMs to model the causal links between variables in a system. The causal diagrams, which visually depict the cause-effect linkages among the observed variables with an arrow from a cause to its effect, are a crucial component of GCMs.
DoWhy already integrates possible outcomes and graphical causal models, two of the most well-liked scientific frameworks for causal inference, into a single library for effect estimates. AWS contribution seeks to strengthen the relationship between the frameworks and the communities of researchers who are committed to them.
NoSQL Databases Software Market Outlook 2022 And Growth By Top KeyPlayers – Designer Women
MMS • RSS
Posted on mongodb google news. Visit mongodb google news
NoSQL Databases Software Market study by “Verified Market Reports” provides details about the market dynamics affecting the NoSQL Databases Software market, Market scope, Market segmentation and overlays shadow upon the leading market players highlighting the favorable competitive landscape and trends prevailing over the years.
This NoSQL Databases Software market report provides details of recent new developments, trade regulations, import-export analysis, production analysis, value chain optimization, market share, impact of national and localized market players, analyzes opportunities in terms of emerging revenue pockets, changes in market regulations, strategic market growth analysis, market size, category market growth, application and domain niches, product approvals , product launches, geographic expansions, technological innovations in the market. For more information on Data Bridge Market Research NoSQL Databases Software market, please contact us for analyst briefing,
Get Sample Copy (Including FULL TOC, Graphs And Tables) Of This Report @ https://www.verifiedmarketreports.com/download-sample/?rid=87212
The analysis and estimates made through exceptional NoSQL Databases Software report help to get an idea about product launches, future products, joint ventures, marketing strategy, developments, mergers and acquisitions and the effect thereof on sales, marketing, promotions, revenue values, import, export and CAGR. With the latest and up-to-date market insights mentioned in the report, companies can reflect on how to improve their marketing, promotion and sales strategies. The business report helps determine and optimize each stage of the industrial process lifecycle that includes engagement, acquisition, retention, and monetization. The NoSQL Databases Software Market Research Report
Leading NoSQL Databases Software Market Players are as followed:
- MongoDB
- Amazon
- ArangoDB
- Azure Cosmos DB
- Couchbase
- MarkLogic
- RethinkDB
- CouchDB
- SQL-RD
- OrientDB
- RavenDB
- Redis
Global NoSQL Databases Software Market segmentation :
NoSQL Databases Software Market Segment by Type :
- Cloud Based
- Web Based
NoSQL Databases Software Market Segment by Application :
- Large Enterprises
- SMEs
Regional market analysis NoSQL Databases Software can be represented as follows:
This part of the report assesses key regional and country-level markets on the basis of market size by type and application, key players, and market forecast.
The base of geography, the world market of, NoSQL Databases Software has segmented as follows:
-
- North America includes the United States, Canada, and Mexico
-
- Europe includes Germany, France, UK, Italy, Spain
-
- South America includes Colombia, Argentina, Nigeria, and Chile
-
- The Asia Pacific includes Japan, China, Korea, India, Saudi Arabia, and Southeast Asia
Get Exclusive Discount on this Premium Report @ https://www.verifiedmarketreports.com/ask-for-discount/?rid=87212
NoSQL Databases Software Market Report Scope
| ATTRIBUTES | DETAILS |
|---|---|
| ESTIMATED YEAR | 2022 |
| BASE YEAR | 2021 |
| FORECAST YEAR | 2029 |
| HISTORICAL YEAR | 2020 |
| UNIT | Value (USD Million/Billion) |
| SEGMENTS COVERED | Types, Applications, End-Users, and more. |
| REPORT COVERAGE | Revenue Forecast, Company Ranking, Competitive Landscape, Growth Factors, and Trends |
| BY REGION | North America, Europe, Asia Pacific, Latin America, Middle East and Africa |
| CUSTOMIZATION SCOPE | Free report customization (equivalent up to 4 analysts working days) with purchase. Addition or alteration to country, regional & segment scope. |
Visualize Anesthesia Delivery Systems Market using Verified Market Intelligence:-
Verified Market Intelligence is our BI-enabled platform for narrative storytelling of this market. VMI offers in-depth forecasted trends and accurate Insights on over 20,000+ emerging & niche markets, helping you make critical revenue-impacting decisions for a brilliant future.
VMI provides a holistic overview and global competitive landscape with respect to Region, Country, and Segment, and Key players of your market. Present your Market Report & findings with an inbuilt presentation feature saving over 70% of your time and resources for Investor, Sales & Marketing, R&D, and Product Development pitches. VMI enables data delivery In Excel and Interactive PDF formats with over 15+ Key Market Indicators for your market.
Visualize Anesthesia Delivery Systems Market using VMI @ https://www.verifiedmarketresearch.com/vmintelligence/
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Global Learning Management System (LMS) Software Market Size And Forecast
Global Student Information Systems SIS Software Market Size And Forecast
Global Higher Education Student Information Systems Software Market Size And Forecast
Global Language Learning Software Market Size And Forecast
About Us: Verified Market Reports
Verified Market Reports is a leading Global Research and Consulting firm servicing over 5000+ global clients. We provide advanced analytical research solutions while offering information-enriched research studies.
We also offer insights into strategic and growth analyses and data necessary to achieve corporate goals and critical revenue decisions.
Our 250 Analysts and SME’s offer a high level of expertise in data collection and governance using industrial techniques to collect and analyze data on more than 25,000 high-impact and niche markets. Our analysts are trained to combine modern data collection techniques, superior research methodology, expertise, and years of collective experience to produce informative and accurate research.
Our research spans over a multitude of industries including Energy, Technology, Manufacturing and Construction, Chemicals and Materials, Food and Beverages etc. Having serviced many Fortune 2000 organizations, we bring a rich and reliable experience that covers all kinds of research needs.
Contact us:
Mr. Edwyne Fernandes
US: +1 (650)-781-4080
UK: +44 (753)-715-0008
APAC: +61 (488)-85-9400
US Toll-Free: +1 (800)-782-1768
Email: sales@verifiedmarketreports.com
Website: – https://www.verifiedmarketreports.com/
To Gain More Insights into the Market Analysis, Browse Summary of the Research Report @ https://www.verifiedmarketreports.com/product/global-nosql-databases-software-market-growth-status-and-outlook-2019-2024/
Article originally posted on mongodb google news. Visit mongodb google news