Month: December 2021

Article originally posted on Data Science Central. Visit Data Science Central

Semantic data consultant @EmekaOkoye made a couple of great points on Twitter back in November and early December 2021:

1. “I am amused how some of us are pretending that #SmartAgent [tech] is not going to be a thing.” 
2. “With a #KnowledgeGraph of provenance data, lying to a blockchain will be harder.”

Agents: An expanding, but still underused, umbrella category

Semantic data model benefits can often be both cumulative and synergistic. That’s particularly the case when logic (in the form of “code”) becomes more capable when stored and managed as knowledge instead–as machine-readable, shareable, contextualized subsets of data that harness predicate logic (e.g., set theory)) in the form of knowledge graphs. 

The convergence of blockchains (distributed ledgers) and decentralized knowledge graphs allow the dynamic enrichment of logic that ends up in a graph. That logic can make up the declarative part of code in agents, and it can logically disambiguate or abstract the kinds of statements agents use in smart contracts.

Agents (code that takes action anonymously on your behalf), for example, are hardly a new thing. Ecommerce bots such as WeChat and Shopify Messenger that automate parts of the online search process for eCommerce consumers have been commonplace for years now, not to mention smart bots for many other use cases. 

But agents gained new capabilities when smart contracts appeared. The concept of smart contracts blends the power of software agents and agreements or contracts. Software agent techniques enabled contracts to become self-executing. 

Smart contracts should be double the power of simpler agents (such as the eCommerce bots), but to date have often been more than double the trouble, which is why most smart contracts are still unilateral (i.e., customer has to accept the fixed terms of the provider) and still in their infancy. 

To harness those new capabilities at scale in decentralized, resource-sharing environments such as blockchains and peer-to-peer data networks needed for supply chain and overall compliance visibility, smart contracts need to be much more trustworthy. Otherwise, whole supply chains could be compromised.

Years ago, with the help of an illustrator, I put together an infographic that reviews the different levels of smart contracts by example: 

I realized once we put this infographic together that the vast majority of the smart contracts feasible then had to be unilateral, limited use and dead simple–otherwise, the risk of using them would be too great. Blockchains themselves might be tamperproof, but as Emeka points out, the code and data they store can often be questionable, because blockchains do suffer from the “garbage in, garbage out” problem in computing. Blockchains do not have the capability to verify data not generated on-chain.

Smart contract exploits in the Web3 era

Smart contracts have been known since they were proposed in the 1990s for the vulnerabilities they can open the doors to. Attackers who exploit these vulnerabilities can steal millions of dollars in digital coin. Even more havoc can result from security deficiencies in the tooling used to create smart contracts.

In a December 2021 example reported in Ars Technica, “An accounting error built into the company’s software [which is used to draft smart contracts] let an attacker inflate the price of the MONO token and to then use it to cash out all the other deposited tokens…” resulting in the theft of all the deposits held by the exchange in question.

In other cases, “agreement” code shared via a blockchain can masquerade as one kind of smart contract, but turn out in practice to be another. Smart contracts designed as honeypots attract users who are deceived into believing, for instance, that they can get even more funds back if they transfer a specified amount of funds via the smart contract. Instead, what happens is that the person being deceived can’t withdraw any funds at all, and the attacker keeps the whole pot, according to Cointelegraph in December 2021.

Smarter contracts by design

Some providers now claim the smart contracts they offer have been audited, but what does that really mean? The issues smart contract developers and users confront go well beyond the ability of one company and its designated third-party auditor to address. 

How could you reduce these kinds of vulnerabilities and make the vulnerabilities that are still lurking  harder to exploit? A few of different ways that come to mind include these:

Adhere to best practices and principles already developed by the Ethereum community. Consensys.io’s Best Practices site on Github, for example, provides helpful guidance that echoes the lessons learned over years of smart contract use.

Harness a broader open source security coalition by using the Linux Foundation’s Open Source Security Foundation and the principles and practices embodied in the Advanced Encryption Standard as a model.

Add the ability to disambiguate, logically abstract or otherwise tighten up the legal language of smart contracts by using semantic standards such as SKOS, OWL, and RDF (Resource Description Language) from bodies such as the W3C. 

Build a data and logic governance knowledge graph designed to connect and work with blockchains to validate and verify Web3 provenance and best practices. Consultancies such as Enterprise Knowledge and Semantic Arts and providers including data.world, Fluree, Franz, OpenLink Software, Semantic Web Company and TopQuadrant are all helping enterprise clients catalog and model their data assets.

Why aren’t many smart contracts harnessing open source-style governance including knowledge graphs yet? Lack of awareness or willingness to explore new tech. It’s the pervasive “If all you have is a hammer, everything looks like a nail” problem.

Convergence, trivergence, quadvergence….?

Enterprise tech prognosticator Don Tapscott declared at the end of 2021 (just as he did in December 2020) that we’ll be experiencing a “trivergence” of blockchain, AI, and IoT over the coming decade. I’d only add that the coming convergence will be more all-encompassing than just what’s suggested by those three vague, overused terms. 

Competitive advantage will hinge on all the technologies that contribute to data-centric architecture–turning current application-first architecture on its head so we can knit the data we need to share together, and harness the logic and contextualizing power of relationship data in the process. The long-term goal is to work together online in real time at scale with all the resources articulated at our fingertips, but without the complexity of application-centric architectures that create more and more data siloing and trapped code. 

What’s the solution to bigger challenges than just smart contracts? Get all the tools you may need out on the worktable and learn enough about them to know if they’ll be helpful or not within the frame of data-centric or knowledge graph-based architecture. And then be diligent enough to find other tools that may help that you didn’t know about before. Doing so may seem overwhelming at first, but keep in mind that your most serious competition will be quite diligent.

What we could call “A-to-Z design” doesn’t just focus on one class of emerging data tech–it explores the synergies of numerous emerging technologies in use together. System-level design is another word for this approach–though these days, the focus is more on intersystem design.

Article originally posted on InfoQ. Visit InfoQ

The Cloud Native Computing Foundation (CNCF) released Kubernetes 1.23 recently. The release has new features such as the events subcommand for kubectl, gRPC probes, and expression language validation for custom resources, generally available or stable features such as generic ephemeral volumes, Horizontal Pod Autoscaling, and IPv4/IPv6 dual-stack networking, beta features such as PodSecurity, and deprecated features such as FlexVolume.

In the new release kubectl, the command-line tool to run commands against clusters, includes an events subcommand (kubectl events) to better troubleshoot problems. The new subcommand addresses issues with kubectl get events including sorting and watching events.

gRPC probes have been introduced to enable developers to improve the resiliency of applications by adding liveness or readiness probes not only for the HTTP or TCP protocols but for gRPC. A high performance framework that’s commonly used for distributed applications and services that allows a client application to seamlessly call a method on a remote server.

In version 1.23 there is a validation mechanism for Custom Resource Definitions (CRDs). This will make it possible to have everything related to CRDs in one place instead of writing validation webhooks. The validation rules use the Common Expression Language (CEL).

Generic ephemeral volumes moved to GA in version 1.23. Such volumes provide storage for pods for temporary data and are usually empty after provisioning. They can work with any storage driver that supports dynamic provisioning.

In addition, Version 2 of the HorizontalPodAutoscaler (HPA), a Kubernetes component that automatically updates workloads to cope with demand, graduated to GA. The API autoscaling/v2beta2 is now replaced with autoscaling/v2.

Also, IPv4/IPv6 dual-stack mode networking support went GA. This enables developers to use both IPv4 and IPv6 addresses for pods and operate clusters in a dual-stack mode in production.

PodSecurity moved to beta in version 1.23 replacing the deprecated PodSecurityPolicy admission controller. The new admission controller can enforce pod security standards, a set of policies that range from highly-permissive to highly-restrive, on a namespace based on a specific namespace label.

Also, In 1.23 Container Runtime Interface (CRI) v1 API graduated to beta and became the project-wide default. CRI is a plugin interface that enables the kubelet to use many container runtimes. For example, Docker might be the container runtime for one cluster and containerd for another.

FlexVolume, a plugin interface to deal with storage drivers, is deprecated in 1.23. Users of FlexVolume drivers are encouraged to move their workloads to Container Storage Interface (CSI) drivers.

Kubernetes is open-source software that automates the deployment, scaling, and management of application containers.

According to the release notes, Kubernetes version 1.23 has 47 enhancements including 11 becoming generally available or stable, 17 graduating to beta, and 19 entering alpha. In addition, one feature has been deprecated. Version 1.23 is the last release for 2021.

A CNCF webinar is scheduled on January 4, 2022, to go over the major features of the new release.

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Growth forecast on “ Cloud-based Database Market Size Share | Industry Segment by Applications (Large Enterprises andSmall and Medium Business), by Type (NoSQL Database andSQL Database), Regional Outlook, Market Demand, Latest Trends, Cloud-based Database Industry Share & Revenue by Manufacturers, Company Profiles, Growth Forecasts – 2027, Analyzes the current market size and growth of this industry.

The report on the Cloud-based Database market provides a bird’s eye view of the current proceeding within the Cloud-based Database market. Further, the report also takes into account the impact of the novel COVID-19 pandemic on the Cloud-based Database market and offers a clear assessment of the projected market fluctuations during the forecast period.

Cloud-based Database Market 2020-2027 research report is an in-depth analysis of the latest trends, market size, status, upcoming technologies, industry drivers, challenges opportunity with key company profiles and strategies of players. The Cloud-based Database Market report also provides the market impact and new opportunities created due to the COVID19 catastrophe.

Request Sample Copy of this Report @ https://www.business-newsupdate.com/request-sample/241477

Major points covered in this research are:

• IBM
• Amazon Web Services
• Couchbase
• Rackspace Hosting
• Salesforce
• Google
• Oracle
• SAP
• Teradata
• Tencent
• Cassandra
• MongoDB
• Microsoft andAlibaba

The Cloud-based Database Market research report offers an in-depth analysis of the market, providing relevant information for the new market entrants or well-established players. Some of the key strategies employed by leading key players operating in the market and their impact analysis have been included in this research report.

Market Analysis by Types:

• NoSQL Database andSQL Database

Market Analysis by Applications: Large Enterprises andSmall and Medium Business

The research provides answers to the following key questions:

• What is the estimated growth rate of the market for the forecast period 2020–2027? What will be the market size during the estimated period?
• What are the key driving forces responsible for shaping the fate of the Cloud-based Database market during the forecast period?
• Who are the major market vendors and what are the winning strategies that have helped them occupy a strong foothold in the Cloud-based Database market?
• What are the prominent market trends influencing the development of the Cloud-based Database market across different regions?
• What are the major threats and challenges likely to act as a barrier in the growth of the Cloud-based Database market?
• What are the major opportunities the market leaders can rely on to gain success and profitability?

Important Information that can be extracted from the Report:

• Country-wise evaluation of the Cloud-based Database market Growth in different geographies
• Market entry strategies adopted by emerging market players
• Pricing and marketing strategies adopted by prominent market players
• Assessment of the different factors likely to impact the growth of the Cloud-based Database market Size.
• Year-on-Year growth of each market segment over the forecast period

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Article originally posted on InfoQ. Visit InfoQ

AWS Local Zones are an infrastructure deployment that places compute, storage, database, and other select AWS services close to a large population and industrial centers. And recently, AWS announced the launch of over 30 new AWS Local Zones in significant cities worldwide.

The company launched the first Local Zone in Los Angeles back in 2019, followed by three more in 2020 and an additional 12 in 2021. In 2022 the company will increase the number of Local Zones by 30 plus in over 21 countries to further enhance their global infrastructure. During the recent re:Invent conference, AWS CTO Werner Vogels said:

I’m very excited to announce that we will be expanding the local zones internationally across the world in Europe, South America, Africa, Asia, and Australia. And of course, we will have one in my home city of Amsterdam.

Source: https://aws.amazon.com/about-aws/global-infrastructure/localzones/locations/

AWS introduced the Local Zones as a core of services available close to enterprises with low latency demands. The core set of services includes EC2 instance types, EBS volume types, and S3 storage classes, which can be provisioned quickly. Each Local Zone is a ‘child’ of a particular parent region and is managed by the control plane in that region. Moreover, the Local Zone is accessible from API Endpoint and the Console of their parent region. By enabling an AWS Local Zone using an AWS Account, enterprises can subsequently deploy resources.

Source: https://aws.amazon.com/about-aws/global-infrastructure/localzones/

Note that Local Zones are not the same as Availability Zones, which provide a full array of AWS services, have their own power, cooling, and network connectivity. However, both are suitable for hosting high available applications. The Local Zone is basically more an extension of an Availability Zone for applications requiring low latency. In a recent Reddit thread on Local Zones, a respondent stated:

Think of local zones as “mini-regions.”

In addition, Hernan Garcia, a Solution Architect at AWS, tweeted:

If you are required to maintain a hybrid architecture, planning to incrementally migrate complex legacy on-premises systems, creating media content, or serving ML inference with ultra- low latency, Local Zones may fit the scenario.

Like AWS, Google Cloud Platform and Microsoft Azure have global data centers (regions), including ones with availability zones. Furthermore, Azure offers private multi-access edge compute (MEC) for enterprises with low-latency requirements similar to Local Zones. While Google provides colocation facilities for low latency scenarios.

Lastly, more details of AWS Local Zones are available in the FAQs.

Article originally posted on InfoQ. Visit InfoQ

Google added support for Kotlin to its open source Protocol Buffers project, which is able to translate a proto definition into an idiomatic Domain Specific Language (DSL) leveraging Kotlin advanced syntax features.

Thanks to Kotlin’s interop with Java, Kotlin programs could already work with Protocol Buffers by simply using the classes that the Protocol Buffers compiler is able to generate for the Java language. Direct support for Kotlin in the proto compiler, thus, does not introduce any previously unavailable capabilities. Yet, it is highly interesting thanks to the way it leverages Kotlin advanced syntax to generate idiomatic Kotlin bindings using a set of factory methods that constitute a nice DSL.

The Kotlin version uses Kotlin type-safe builders, which makes it concise and removes the need to explicitly call a build method. Note that this works with both the proto compiler’s standard and “proto lite” modes, the latter generating smaller, higher performance classes which are more suitable for Android.

The benefit of using a DSL resides in the generated code using almost no sugaring code and thus being more expressive, with a declarative look to it. Google has shown a simple example of a protocol buffers message representing a series of dice rolls to make that obvious:

/* Protobuf definition: */
message DiceSeries {
  message DiceRoll {
    int32 value = 1;      // value of this roll, e.g. 2..12
    string nickname = 2;  // string nickname, e.g. "snake eyes"
  }

  repeated DiceRoll rolls = 1;
}

// Kotlin bindings usage:
val series = diceSeries {
  rolls = listOf(
    diceRoll { value = 5 },
    diceRoll {
      value = 20
      nickname = "critical hit"
    }
  )
}

The Java bindings for the same message definition would create instead a couple of classes that you would need to glue together using the flexible but verbose builder pattern to the same aim as the code shown above.

In its announcement, Google also illustrates how Kotlin protocol buffers can be easily integrated with Kotlin gRPC, which was open sourced last year, and provided some basic client/server sample projects.

While Kotlin bindings for Protocol Buffers are a nice addition, they are still relatively new and not exempt from potential issues. In particular, if your program uses a complex proto model, you might want to make sure it does not hit into a known Kotlin compiler bug, or that you can tackle it by increasing the memory reserved to it.

As an alternative to using Google’s own proto bindings for Kotlin, you could look into Square’s Wire, an open-source, independent Protocol Buffers implementation from Square that specifically targets the Java/Android platform and also provides idiomatic Kotlin bindings as well as integration with gRPC.

Article originally posted on Data Science Central. Visit Data Science Central

What is a digital worker?

Digital workers are your software-based co-workers. Think Siri, Alexa, or Cortana, not exactly like them but to some extent. Yes, we are calling them your co-workers or you can refer to them as your virtual assistants if you like. They will help you in doing all the repetitive and boring tasks that you hate doing, that take the most amount of time but require the least amount of thinking.

Digital worker vs Software bot

To emphasize, a digital worker is not a software bot. Even though both automate repetitive tasks, the former is smarter than the latter. A bot uses simple rule-based conditional programming to complete tasks. While a digital worker uses machine learning, natural language processing, and artificial intelligence to complete a task. A digital worker has also more capabilities than a software bot. A bot can only do one task. A digital worker can automate an entire end-to-end business process.

For example, take your interaction with a simple chatbot. First, they prompt you to provide your name, and given that they ask for your email address. You say anything else you are not supposed to and they simply can’t process it because it hasn’t been defined so in their programming.

A digital worker on the other hand has the capacity to learn. Limited than a human but still present. Depending on your past interactions with it, it will analyze your patterns and adjust its responses accordingly. Think about your interactions with digital assistants like Siri or Alexa, they run on similar technology.

Digital worker in a business context

Digital workers are the future. They will be designed by humans, for the humans to automate repetitive, boring tasks so that humans can focus on creative work rather than manual work. This means that everybody can design their own virtual assistant.

To give a very simplified example, in a business every role has some tasks that are repeating and manual. The employee could potentially specify the sequence of steps that need to be taken to complete the task and assign it to their assistant. The digital worker would initially make mistakes but with each progression of doing would analyze and learn to do the task better.

Benefits of automation in business processes

1. Productivity is Increased

The most obvious benefit of intelligent automation is increasing in productivity. Automating manual tasks frees humans to pursue more challenging, creative, and intelligent tasks. This increases the productivity of the employee and leads to business growth.

2. Improvement in Accuracy

Humans are prone to making mistakes. Learning algorithms like virtual assistants make them too but still less than humans. Especially if there are vast amounts of data to be processed. It is easier for humans to make corrections on data already processed and corrected by a machine. It takes lesser time and the number of errors gets reduced by a lot.

3. Increase in business growth and flexibility

Digital workers will allow businesses to operate at all hours of the day without being inhuman to their employees. Businesses can give their employees holidays without worrying about their business growth stagnating. Virtual workers would have been especially helpful during lockdown if they had been integrated by businesses.

4. Better employee and customer experience

Digital workers will free employees to work on more skill-based, critical and strategic tasks. Employees will be able to use their time and energy in more satisfying ways rather than killing their efficiency with manual tasks. This would increase the standard of work satisfaction in employees as they would feel challenged by their job instead of being bored by it.

Digital workers also help create a better customer experience. Sometimes an employee is able to answer customer inquiries on time. Digital workers can step in and deal with the query or comply as appropriate.

Wrapping Up

Intelligent automation is making a huge impact on all industries. For example, in banking digital workers can automate processes like data migration, credit decisions, customer service, regulatory compliance, new application processing. In healthcare tasks like operational analytics, patient preauthorization claims processing, medical record, and data management. Technology can automate business processes like modern and legacy applications, repetitive front office tasks, labor-intensive back-office processes. The public sector can automate its human resources, mission processes, procurement, financial processes, etc.

Digital co-workers are going to be a reality soon. But this fact is not meant to be feared. While your digital workers can do a lot of boring tasks, they cannot replace humans anytime soon. They can only assist humans to do their job better and perhaps free to do the one thing that humans do best- innovation!

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New Jersey, United States,- The latest report published by Verified Market Research shows that the Database As A Service Providers Market is likely to garner a great pace in the coming years. Analysts examined market drivers, confinements, risks and openings in the world market. The Database As A Service Providers report shows the likely direction of the market in the coming years as well as its estimates. A close study aims to understand the market price. By analyzing the competitive landscape, the report’s authors have made a brilliant effort to help readers understand the key business tactics that large corporations use to keep the market sustainable.

The report includes company profiling of almost all important players of the Database As A Service Providers market. The company profiling section offers valuable analysis on strengths and weaknesses, business developments, recent advancements, mergers and acquisitions, expansion plans, global footprint, market presence, and product portfolios of leading market players. This information can be used by players and other market participants to maximize their profitability and streamline their business strategies. Our competitive analysis also includes key information to help new entrants to identify market entry barriers and measure the level of competitiveness in the Database As A Service Providers market.

Get Full PDF Sample Copy of Report: (Including Full TOC, List of Tables & Figures, Chart) @ https://www.verifiedmarketresearch.com/download-sample/?rid=160513

Key Players Mentioned in the Database As A Service Providers Market Research Report:

IBM, Beats, AWS, Ninox, Aiven, MongoDB Atlas, Zoho Creator, Azure, Kintone, Oracle, Google Cloud Bigtable 

Database As A Service Providers Market Segmentation:  

Database As A Service Providers Market, By Verticles

• Banking, Financial, Services, and Insurance (BFSI)
• Government and Defense
• Healthcare and Pharmaceuticals
• IT and Enterprises
• Telecommunication
• Retail
• Others (Incl. Energy, manufacturing, gaming and gambling, media and education)

Database As A Service Providers Market, By Database Type

• Structured Query Language (SQL)
• Not only Structured Query Language (NoSQL)

The global market for Database As A Service Providers is segmented on the basis of product, type, services, and technology. All of these segments have been studied individually. The detailed investigation allows assessment of the factors influencing the Database As A Service Providers Market. Experts have analyzed the nature of development, investments in research and development, changing consumption patterns, and growing number of applications. In addition, analysts have also evaluated the changing economics around the Database As A Service Providers Market that are likely affect its course.

The regional analysis section of the report allows players to concentrate on high-growth regions and countries that could help them to expand their presence in the Database As A Service Providers market. Apart from extending their footprint in the Database As A Service Providers market, the regional analysis helps players to increase their sales while having a better understanding of customer behavior in specific regions and countries. The report provides CAGR, revenue, production, consumption, and other important statistics and figures related to the global as well as regional markets. It shows how different type, application, and regional segments are progressing in the Database As A Service Providers market in terms of growth.

Get Discount On The Purchase Of This Report @ https://www.verifiedmarketresearch.com/ask-for-discount/?rid=160513

Database As A Service Providers Market Report Scope 

Geographic Segment Covered in the Report:

The Database As A Service Providers report provides information about the market area, which is further subdivided into sub-regions and countries/regions. In addition to the market share in each country and sub-region, this chapter of this report also contains information on profit opportunities. This chapter of the report mentions the market share and growth rate of each region, country and sub-region during the estimated period.  

 • North America (USA and Canada)
 • Europe (UK, Germany, France and the rest of Europe)
 • Asia Pacific (China, Japan, India, and the rest of the Asia Pacific region)
 • Latin America (Brazil, Mexico, and the rest of Latin America)
 • Middle East and Africa (GCC and rest of the Middle East and Africa) 

Key questions answered in the report: 

1. Which are the five top players of the Database As A Service Providers market?

2. How will the Database As A Service Providers market change in the next five years?

3. Which product and application will take a lion’s share of the Database As A Service Providers market?

4. What are the drivers and restraints of the Database As A Service Providers market?

5. Which regional market will show the highest growth?

6. What will be the CAGR and size of the Database As A Service Providers market throughout the forecast period?

For More Information or Query or Customization Before Buying, Visit @ https://www.verifiedmarketresearch.com/product/database-as-a-service-providers-market/ 

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About Us: Verified Market Research® 

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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New Jersey, United States,- The latest report published by Verified Market Research shows that the NoSQL Software Market is likely to garner a great pace in the coming years. Analysts examined market drivers, confinements, risks and openings in the world market. The NoSQL Software report shows the likely direction of the market in the coming years as well as its estimates. A close study aims to understand the market price. By analyzing the competitive landscape, the report’s authors have made a brilliant effort to help readers understand the key business tactics that large corporations use to keep the market sustainable.

The report includes company profiling of almost all important players of the NoSQL Software market. The company profiling section offers valuable analysis on strengths and weaknesses, business developments, recent advancements, mergers and acquisitions, expansion plans, global footprint, market presence, and product portfolios of leading market players. This information can be used by players and other market participants to maximize their profitability and streamline their business strategies. Our competitive analysis also includes key information to help new entrants to identify market entry barriers and measure the level of competitiveness in the NoSQL Software market.

Get Full PDF Sample Copy of Report: (Including Full TOC, List of Tables & Figures, Chart) @ https://www.verifiedmarketresearch.com/download-sample/?rid=153255

Key Players Mentioned in the NoSQL Software Market Research Report:

Amazon, Couchbase, MongoDB Inc., Microsoft, Marklogic, OrientDB, ArangoDB, Redis, CouchDB, DataStax.  

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

The global market for NoSQL Software is segmented on the basis of product, type, services, and technology. All of these segments have been studied individually. The detailed investigation allows assessment of the factors influencing the NoSQL Software Market. Experts have analyzed the nature of development, investments in research and development, changing consumption patterns, and growing number of applications. In addition, analysts have also evaluated the changing economics around the NoSQL Software Market that are likely affect its course.

The regional analysis section of the report allows players to concentrate on high-growth regions and countries that could help them to expand their presence in the NoSQL Software market. Apart from extending their footprint in the NoSQL Software market, the regional analysis helps players to increase their sales while having a better understanding of customer behavior in specific regions and countries. The report provides CAGR, revenue, production, consumption, and other important statistics and figures related to the global as well as regional markets. It shows how different type, application, and regional segments are progressing in the NoSQL Software market in terms of growth.

Get Discount On The Purchase Of This Report @ https://www.verifiedmarketresearch.com/ask-for-discount/?rid=153255

NoSQL Software Market Report Scope 

Geographic Segment Covered in the Report:

The NoSQL Software report provides information about the market area, which is further subdivided into sub-regions and countries/regions. In addition to the market share in each country and sub-region, this chapter of this report also contains information on profit opportunities. This chapter of the report mentions the market share and growth rate of each region, country and sub-region during the estimated period.  

 • North America (USA and Canada)
 • Europe (UK, Germany, France and the rest of Europe)
 • Asia Pacific (China, Japan, India, and the rest of the Asia Pacific region)
 • Latin America (Brazil, Mexico, and the rest of Latin America)
 • Middle East and Africa (GCC and rest of the Middle East and Africa) 

Key questions answered in the report: 

1. Which are the five top players of the NoSQL Software market?

2. How will the NoSQL Software market change in the next five years?

3. Which product and application will take a lion’s share of the NoSQL Software market?

4. What are the drivers and restraints of the NoSQL Software market?

5. Which regional market will show the highest growth?

6. What will be the CAGR and size of the NoSQL Software market throughout the forecast period?

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