Most Crucial Benefits of Consolidating Data Analytics Pipelines with Data Lakes

CIOReviewIndia Team | Wednesday, 30 October 2019, 13:31 IST

CIOReviewIndia TeamTo stay relevant in today’s Data-driven world, data engineering teams are using a wide range of data analytics techniques, ranging from streaming analytics, machine learning to deep learning. With such a surge in the current data-flow, traditional data analytics pipelines are failing to meet the desired requirements. Even, most of the engineering team will agree that building and managing data analytics pipelines and sandbox environments takes up a significant amount of time. Hence, consolidating the data analytics pipeline through use of a consolidated data lake simplifies the design and management of these complex analytical systems. As, integral part of any analytics pipeline is the data lake, Azure’ Data Lake Storage Gen2 provides secure, cost effective, and scalable storage for the structured, semi-structured, and unstructured data arriving from diverse sources, which means, this can now generate events that can be consumed by Event Grid and can also be routed to subscribers with webhooks, Azure Event Hubs, Azure Functions, and Logic Apps as endpoints.

Some of the benefits of Data Lakes:

Data Point Elimination – Data Lake can efficiently manage intermediate representations of ingested data; the data can stay in the data lake, or can instantaneously be available for a broad variety of data analytics tools, ideally as a network file system (NFS) or server messaging protocol (SMB) mount-point or as an object store with RESTful application program interfaces (APIs). Moreover, to provide consolidated views of the data, visualization tools are no longer needed to connect multiple data sources.

Simplifying Data Analytic Pipelines - Regardless of where it is in the analytic pipeline, it consolidates the data subject to analytics, all at one place. Thereby, streamlining and simplifying the management of data security, data resiliency, audit, lineage and metadata.

Flexible Analytics and AI Platforms – Without having to move data around, consolidating the data in a data lake will enable data engineering teams to quickly introduce new analytics and AI tools. 

Analytics Sandbox Productivity – Data Lakes will considerably improve the quality and accuracy of the models, as opposed to synthetic data. It will effectively aid data engineering teams to build and maintain analytic models using real-world production data sets.

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