Five Effective Ways to Build a Robust Data Lake Architecture

Five Effective Ways to Build a Robust Data Lake Architecture
Five Effective Ways to Build a Robust Data Lake Architecture

Enterprises going through a digital transformation should know the authenticity and accuracy of the data source to make the most out of the high volume of data generated.

Businesses today can scale at an exponential rate if they make effective use of their data. But only gathering data won’t help any organization scale; they need to also analyze it thoroughly to get valuable SWOT insights into the business process.

With massive amounts of data generated daily, enterprises require a robust data infrastructure to manage and analyze information efficiently. According to IDC’s research published in the white paper titled “data age 2025,” sponsored by Seagate, predicts that there will be approximately 175 zettabyte of data would be created by 2025, Out of which almost 80% would be unstructured data.

To become a data-driven enterprise, the CIO should consider designing, developing, and maintaining robust data lake architecture. Here are a few ways that can help to streamline the process.

Determine the business data goals

Before CIOs embark on a journey to create an advanced data infrastructure for their organization, it is crucial to understand the enterprise’s short-term and long-term data-related goals. Understanding the business goals is imperative to get more clarity on the outcomes and develop a strategic action plan to accomplish them. A strategic data-driven action plan will ensure only relevant data is gathered and analyzed to help businesses scale exponentially.

Select the right data repository to gather and store information

Gone are the days when data warehouses were enough to manage the entire data. With industry 4.0 and big data on the scene, enterprises generate a massive amount of data, requiring an advanced data repository to manage structured and unstructured data.

The CIOs today have a choice to either develop a data repository with a structured database to streamline data analysis or select a data lake with robust and easy architecture. However, the CIOs also have a choice to go for a hybrid approach, wherein according to business requirements, they can make necessary decisions. But for a hybrid approach, one crucial aspect which needs to be considered is that both the repositories bank on different technologies. Data lake banks on NoSQL and Data warehouses bank on SQL; hence it is one primary contradiction to resolve before enterprises plan to develop robust data lake architecture.

Also Read: How Enterprises Can Leverage Data Lakehouse Architecture to Get the Most Value from Their Data

Develop a data governance strategy

It is essential to design and implement a stringent data governance policy to ensure the workforce has seamless access to the required data sets. Since data governance and metadata management play a crucial role in the efficient maintenance of data infrastructure, robust data lake architecture will have clean, trustworthy, secure, and easily accessible information for business users to access at any time.

Integrate AI and Automation

Data pipeline automation, right from injecting and updating data to creating analytics-ready datasets for visualization, is a perfect way to improve the data lake performance. The CIOs should consider leveraging AI and automation applications to segregate, analyze and learn efficiently from the diverse datasets generated faster.

Integration of automation in the data lake infrastructure will assist enterprises in minimizing data swamps, deploying ready-reference architectures, get a better data governance, and creating a data-driven work culture.

Also Read: Implementing an Effective DataOps Strategy for Maximizing Business Value

Integrate DataOps

It is the responsibility of the DataOps teams to develop and maintain  robust data lake architecture. Hence, it is essential to set the right guidelines to understand where and how the data is gathered. The CIOs should consider the guidelines to determine if the data collected is reliable or not.

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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.