The new research report titles Global Hadoop Big Data Analytics market Report 2020 by Key Players, Types, Applications, Countries, Market Size, Forecast to 2025 that studies all the vital factors related to the Global Hadoop Big Data Analytics market that are crucial for the growth and development of businesses in the given market parameters. The report highlights the important elements related to the market such as the market size, share, company profiles, profitability, opportunities and threats, technological advancements, key market players, regional segmentation, and many more important elements related to the Global Hadoop Big Data Analytics market.

The recent Hadoop Big Data Analytics market report contains a detailed analysis of this business domain in accordance to the primary growth catalysts, opportunities, and limitations shaping the industry dynamics. An economy-wide database of the regional markets along with the leading organizations that occupy them is outlined in the document. Furthermore, it studies the impact of COVID-19 pandemic on the growth matrix of this vertical and draws attention to the popular tactics adopted by major players to adapt to the uncertainties in the industry.

Major highlights from COVID-19 impact analysis:

• Global footprint of the COVID-19 pandemic and its implications on the economy.
• Instabilities in the demand and supply channels.
• Predicted outlook of COVID-19 pandemic on the business development.

An overview of the regional analysis:

• From a regional point of view, the Hadoop Big Data Analytics market is bifurcated into North America, Europe, Asia-Pacific, Middle East and Africa and South America.
• A synopsis of each regional market, inclusive of their projected growth rate during the forecast duration is enclosed in the document.
• Figures reflecting sales and revenue netted by each geography are cited.

Additional highlights from the Hadoop Big Data Analytics market report:

• The product gamut of the Hadoop Big Data Analytics market is split into Managed Software,Application Software,Performance Management Software andOthers.
• Volume and revenue estimates of each product category are presented with supporting data.
• Estimations of the market share and annual growth rate for each product type over the forecast period are mentioned in the report.
• With regards to application terrain, the industry is fragmented into Risk & Fraud Analytics,Internet of Things,Customer Analytics,Security Intelligence,Distributed Coordination Service,Merchandising & Supply Chain Analytics,Operational Intelligence,Linguistic Analytics andOffloading Mainframe Application.
• Projections for the market share and yearly growth rate of each application type during the study duration are enumerated.
• Leading organizations that have an authoritative status in Hadoop Big Data Analytics market are IBM Corporation,Microsoft Corporation,Sap SE,Pentaho Corporation,Cloudera,Amazon Web Services (AWS),Hewlett-Packard Enterprise,Hortonworks,Memsql Inc,Marklogic Corporation,Pivotal Software,Mongodb,Datasift,Datameer,Qubole,MAPR Technologies andTableau Software.
• In-depth profile of the listed companies, along with their product offerings, production patterns, and industry remuneration are highlighted.
• Other important facets including pricing patterns, gross margins, and market share of each player are also included in the report.
• The study elucidates the competitive trends of the market and also provides a comprehensive assessment of the supply chain.
• Utilizing SWOT analysis and Porter’s five forces analysis, it explicates the feasibility of a new project.

Some of the key questions answered in this report:

What will the Hadoop Big Data Analytics market growth rate, growth momentum or acceleration market carries during the forecast period?

What was the size of the emerging Hadoop Big Data Analytics market by value in 2020?

What will be the size of the emerging Hadoop Big Data Analytics market in 2025?

Which are the key factors driving the Hadoop Big Data Analytics market?

Which region is expected to hold the highest market share in the Hadoop Big Data Analytics market?

What trends, challenges and barriers will impact the development and sizing of the Global Hadoop Big Data Analytics market?

What are the Hadoop Big Data Analytics market opportunities and threats faced by the vendors in the global Hadoop Big Data Analytics Industry?

What are sales volume, revenue, and price analysis of top manufacturers of Hadoop Big Data Analytics market?

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While Hadoop emerged as favorite for Big Data Technologies, it could not keep up with the hype!

Hadoop is one of the most popular open-source cloud platforms from Apache, used in big data community for data processing activities. Debuting in 2006, as Hadoop version 0.1.0, it was first developed by Doug Cutting and Mike Carafella, two software engineers that wanted to improve web indexing in 2002. It was built upon Google’s File System paper and was created as the Apache Nutch project. Since then, Hadoop has been used by Facebook, Yahoo, Google, Twitter, LinkedIn and many more.

With the rising importance of big data in industries, many business activities revolve around data.  Hadoop is great for MapReduce data analysis on huge amounts of data. Some of its specific use cases include data searching, data analysis, data reporting, large-scale indexing of files and other big data functions. It can also store and process any file data, be it large or small, plain text files or binary files like images, and even multiple data versions across different time periods. It basically stores the data using Hadoop distributed file system and processes it using the MapReduce programming model. Since it is based on cheap servers and requires less cost to store and process the data, Hadoop is a huge hit in business sector.

Hadoop has three components, viz.,

• Hadoop HDFS – Hadoop Distributed File System (HDFS) is the storage unit of Hadoop.

• Hadoop MapReduce – Hadoop MapReduce is the processing unit of Hadoop.

• Hadoop YARN – Hadoop YARN is a resource management unit of Hadoop.

Hadoop seemed highly promising prior to a decade. In 2008, Cloudera became the first dedicated Hadoop company, followed by MapR in 2009 and Hortonworks in 2011. It was a huge hit among Fortune 500 vendors who were fascinated by big data’s potential to generate a competitive advantage. However, as data analytics became mainstream, Hadoop faltered as it offered very little in the way of analytic capabilities. Further, as businesses migrated to the cloud, they soon found alternatives to the HDFS and the Hadoop processing engine.

Every cloud vendor offered their unique big data services capable of doing things that were previously only possible on Hadoop in a more efficient and hassle-free manner. Users were no longer bothered by the administration, security, and maintenance issues they faced with Hadoop. The security issues are mainly because, Hadoop is written in Java which is a widely used programming language.  Java has been heavily exploited by cybercriminals and as a result, a bull’s eye for numerous security breaches.

A 2015 study from Gartner found that 54% of companies had no plans to invest in Hadoop. The study also noticed that out of those who were not investing, 49% were still trying to figure out how to use it for value, while 57% said that the skills gap was the major reason. The latter is also another key reason behind the downfall of Hadoop. Most of the companies had jumped the bandwagon due to the hype surrounding it. Some of them did not have enough data to warrant a Hadoop rollout, or started leveraging big data technologies without estimating the amount of data they actually would need to process. While file-intensive MapReduce was a great piece of software for simple requests, it could not do much for iterative data. This is why it is a bad option for machine learning too. Machine learning functions on cyclic flow of data, in contrast Hadoop has data flowing in a chain of stages where output on one stage becomes the input of another stage. Therefore, machine learning is not possible in Hadoop unless tied with a 3rd party library.

It was also an inefficient solution for smaller datasets. In other words, while it is perfect for a small number of large files, however in case of an application dealing with a large number of small files, Hadoop fails again! This is because the large number of small files tends to overload the Namenode as it stores namespace for the system and makes it difficult for Hadoop to function. It is also not suitable for non-parallel data processing.

At the same time, Cloudera and Hortonworks were witnessing lesser adoption with every year, which led to the eventual merger of the two companies in 2019.

Lastly, another major reason behind downfall of Hadoop is the fact that it’s a batch processing engine. Batch processes are one that run in the background and do not have any kind of interaction with the user. The engines used for this are not efficient when it comes to stream processing. Also, they cannot produce output in real-time with low latency – which is a must for real time data analysis.

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Harbr, a London startup that helps organizations like Moody’s Analytics to create their own custom data exchanges, yesterday announced that it has completed a Series A round of financing, netting $38.5 million for the growing concern.

Gartner predicts that enterprise adoption of data exchanges will increase by 40% over the next two years. According to Harbr, that mirrors the type of growth that it’s seen since coming out of stealth back in May.

Harbr develops a platform that others can use to create their own private data exchanges, which in turn they can use to share data with their own customers and partners, and to foster collaboration. A core element to the process, the company says, is “convert[ing] data into products that are easy to find, use, share, and manage.”

The platform supports data in any format, including structured and unstructured data. It helps customers convert data, models, document and “even code” into “ready-to-use, monetizable products.” Customers retain control over who can access and consume the data products they share on Harbr, and the company also provides ways to help price the data products and charge for consumption.

Harbr’s data exchange supports “spaces” that come pre-loaded with support for common data science tools, such as Anaconda, Zeppelin, Spark, and Hadoop. A range of data endpoints for inputting data from S3, Google Cloud, and Azure Blob store, are also configured out of the box.

One early Harbr user is Moody’s Analytics, a subsidiary of the Moody’s ratings company that provides access to economic research tools. Michael Salk, managing director of content distribution at Moody’s Analytics, says Harbr “allows us to quickly and easily create and customize data products to streamline access and consumption for both customers and our internal users.”

While the Internet has removed limitations for data sharing, Harbr maintains there is immense potential to grow data exchanges. Currently, the cloud providers dominate the space, with last November’s launch of AWS Data Exchange, as well as Azure Data Share (which launched in July 2019), and Snowflake Marketplace (which launched a month earlier). Google so far has failed to impress with its limited data exchange, according to an Eckerson Group white paper on the topic.

Despite the entries of big tech firms, there is still room for smaller players to differentiate and offer value, according to Gary Butler, CEO and co-founder of Harbr.

“Despite significant investments in data-focused technologies and teams over the last decade, most enterprises are still unable to deliver targeted outcomes from data in a timely and scalable manner,” Butler says in a press release. “The secure data sharing and collaboration capabilities of Harbr’s enterprise data exchange platform fast-track those outcomes.”

The Series A investment was co-led by investors Dawn Capital and Tiger Global Management, which are new investors in Harbr. This brings the company’s total funding to $52 million.

According to Dawn Capital partner Evgenia Plotnikova, is Harbr is among “the vanguard” of companies helping to lead the way in data exchanges.

“For data to become truly powerful, we need more automation and collaboration. Today, human efforts are consumed by finding and preparing data, rather than focused on high-value activities that drive real productivity gains,” Plotnikova said in a press release. “Customers we’ve spoken to find Harbr’s enterprise data exchange transformative, and their engagement across Fortune 1000 companies substantiates this.”

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