Raghav Raghavendra Pratap Singh
Raghavendra is the Assistant Marketing Manager at Sigmoid. He specializes in content marketing domains, digital and social media marketing.
Raghavendra Singh
He is the Assistant Marketing Manager at Sigmoid.
Why Open Source is the Future of Big Data Analytics

Today we generate so much data that around 2.7 zettabyte of information is available over the internet. This number is rapidly increasing as more and more people get connected via multiple platforms. Access and insights from this data has changed the way people interact with technology, shifting the focus to data-driven decisions and outcomes. For many businesses today, Big data is an evolving practice and profitable space where there is continuous research, technology development and models to improve the existing systems.

Big data analytics helps an organization use its data to unlock new opportunities and insights that were never possible before. It not only helps to achieve better and faster decision making but has a huge bearing on cost-saving. Companies use several big data technologies to get the most out of their data. But these platforms and technologies are an enigma to people in the industry. To get the most out of the data, companies are investing in open source. And in 2019, we can clearly see the future of Big data is in Open Source technologies.

Mathias Gelbmann, Co-Founder of Solid IT talks about open source in his blog stating that- “We often see, that once Redis is installed for caching, and people experience its speed and reliability, they start moving more and more functionality there.” Redis is an open-source in-memory data structure project implementing a distributed, in-memory key-value database with optional durability.

To state briefly, open-source big data analytics or open source technologies in big data analytics refer to the utilization of open source tools and softwares such as Apache Spark and Hadoop for analysis of data to gather valuable insights. Organizations can then use these insights to make decisions and reach their business goals in the most effective manner possible. Companies can either use a complete open source technology stack or certain essential open source tools for specific tasks in their data analytics framework.

Gideon Mann, Bloomberg’s head of data science, said – “Open source has increasingly been a very important part of technology at Bloomberg”. “It’s just not possible to stay competitive without doing open source these days, so there’s a lot of open source that we leverage,” he added.

From a software design & developement perspective, open source is a very desirable option which has added benefits of feasibility, solution desirability, and attainability. And the best part is that it can deliver accurate real-time analysis on a huge volume of data with reduced data processing and storage cost. One main reason for the popularity of open source is that it allows multiple users globally to effectively collaborate and continuously enhance the technology. When companies share their knowledge in the community and allow others to contribute to it, the end result gives rise to a whole new advancement in technology that is better, faster, secure for all. 

Another reason for companies to adopt open source systems is the unavailability of data scientists & engineers. According to a study by McKinsey, there is a shortage of talent in the data science. Companies want to expand their data science capabilities and its applications but lack of talent is a big hurdle. Now open sourcing their data and technology help them keep up with the constantly evolving market. These open source software don’t need expensive hardware as compared to leading legacy platforms that come with high maintenace and scaling costs.

Mike Tuchen, a former Microsoft executive who is now CEO of big-data vendor Talend, told Inforworld in an interview, “ We expect the entire next-generation data platform will be open source.” “It’s the new normal,” he added. Talend, a big-data vendor claims big names such as GE, Citi, Lufthansa, Orange and Virgin Mobile as their customers.

Open source provides “try before you buy” concept, which is not only good for SMBs who don’t want to invest in a complete suite of paid big data applications but also for larger corporations that wish to have a hands on experience with minimum risk. The Apache family (i.e. Spark, Kafka, Hadoop, HBase, Tomcat, Flume, Flink and cassandra etc) is one of the biggest names in Open Source which can be easily downloaded from the internet.

With shortage of data scientists in the market, open source provides easy access to up-to-date solutions with minimum cost. On the top of that, open source technology is growing and evolving rapidly as compared to other proprietary software technology. Hence, its safe to say that the future of big data is open source technology and it will be the greatest strength in data analytics.

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