Sunday, February 5, 2023

Strategies to Build a Robust Big Data Tech Stack

By Nikhil Sonawane - December 02, 2022 4 Mins Read

Many global organizations have successfully embraced big data in their operations to improve their business efficiency and effectiveness.

Enterprises of all sizes, industries, and types need to have agility in their operations to stay competitive in the constantly evolving business landscape. Businesses need to have effective strategies in place to gather, store, and process data from all channels to embrace a culture that is data-driven to scale their operations. It is crucial for modern enterprises to have a robust big data analytics tech stack integrated into their IT infrastructure to make the most out of the data gathered. However, it can be a daunting task for IT decision-makers to explore, evaluate and select the right tools for their tech stack to process data efficiently.

Following are a few strategies that CDOs can consider to develop a robust data analytics tech stack:

Develop a Tech stack for the business processes

Organizations that need big data to focus on offering analytics for the next day operations should have tools that enable them to stream load data in real time. As the streaming infrastructure is highly volatile and challenging to manage, it will need a substantial amount of resources and infrastructure to execute efficiently. Hence, business leaders need to understand their business needs to make strategic decisions.

CDOs should consider evaluating their end-to-end data management processes to develop a big data analytics stack based on the business processes. 

Also Read: Busting Three Key Fallacies About Multi-Cloud Computing

Integrate tools that can interact with each other

Organizations might have their data spread across various datasets and repositories. Enterprises should have tools that communicate with distributed data sources without having to migrate them. CDOs should consider integrating tools that enable a real-time connection to multiple data sources on a single cluster to streamline their operations. Enterprises that have data spread through multiple vendors and third parties should select tools that help them to create an abstraction layer between the tools and the storage.

Separate storage & compute

CDOs should consider that their data should not be tied to any particular vendor and data set. Modern enterprises should embrace data democratization and flexibility as an enabler to integrate big data analytics tools in their tech stack. It is one of the most effective ways to eliminate silos from the same data store; while integrating data analytics tools, it is crucial to separate the storage and analytics tool to maintain the security of data in use.

Explore, evaluate and implement the best big data analytics tools in the tech stack

Business leaders need to explore the entire data analytics technology marketplace to evaluate the best tools that adapt to their organizations’ goals. It is essential to filter out the best vendors that are reliable and scalable and offer customized solutions that cater to business needs. Moreover, enterprises need to look out for a data analytics solution provider that is secure because relying on them exposes sensitive data to a third party which can have devastating impacts on business.

Also Read: Cloud Computing Trends That Will Dominate in 2023

Strengthen the Security

CDOs should encrypt their big data that is in transit and at rest to ensure security throughout the data management process. It is crucial to integrate data analytics tools that offer restricted access to users based on their job. Security should be one of the primary considerations that businesses need to focus on while developing their data analytics tech stack.

CDOs can consider the above-mentioned strategies to build and implement an effective big data tech stack to process data effectively throughout their enterprise.

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AUTHOR

Nikhil Sonawane

Nikhil S is a Tech Journalist with OnDot Media. He is a media professional with eclectic experience in communications for various technology media brands. He brings his eye for editorial detail and keen sense of language skills to every article he writes.

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