How Is Hadoop-based Data Lake Beneficial


Data lake refers to a repository that captures, stores, and manages raw, detailed data in its original schema or format. Detailed source data is the focus of Data Lake, which can be repurposed to meet new requirements as advanced analytics emerge and evolve. In a way, data lake future proofs analytics by provisioning ample source data for a wide range of analytics. As data volumes today are exploding, it becomes important for a data lake to scale to hundreds of terabytes or petabytes. For enabling massive scalability at an economical cost, Hadoop has emerged as the most preferred data platform for data lakes. Besides Hadoop, other similar data-driven patterns, such as enterprise data hubs or data vaults, may be deployed on RDBMSs (relational database management systems).

How Data Lakes and Data Warehouses Can Work Together?

A Hadoop data lake can extend the capabilities and life of a data warehouse to a considerable extent. In the present hybrid environment, the core warehouse remains the preferred platform for dimensional data, reporting, and data requiring extensive accuracy or improvement. Storing and processing advanced forms of analytics on other platforms of DWE to take the load off the core warehouse, so that it can focus on data requires mature relational functionality. It also involves taking raw, detailed data to platforms that are best suited for advanced forms of analytics at a reasonable cost.

With many organizations modernizing their DWEs, Hadoop-based data lake is emerging to be a natural fit for large volumes of data for advanced analytics that is being relocated. Hadoop-based data lake is preferred as an archive as well as an ingestion platform for the DWE. And although sandboxing and set-based analytics are majorly done on RDBMS, the same can be carried on Hadoop.

Architectures are Still Evolving

As organizations let go of older paradigms, the demand for tightly integrated purpose-built platforms that are tightly is on the rise. Hadoop data lake fits the trend quite well. The need for better fact-based decisions, optimization of organizational performance, and competing on analytics are the real drivers here.

Owing to its capability to capture huge volumes of big data and other sources that an enterprise might want to leverage via analytics, the Hadoop data lake is gaining prominence. It does this at a reasonable cost and adds value to the data warehouse without slashing and replacing mature investments. One can turn to big data consulting services to make the best use of their enterprise data and bring efficiency in operations.

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