Best Practices To Build A Data Lake

Hashedin

Vaishnavi Agarwal

31 Mar 2021

A data lake1 is a pool of data from multiple sources. It is different from a data warehouse as it can store both structured and unstructured data, which can be processed and analyzed later. This eliminates a significant part of the overhead associated with traditional database architectures, which would commonly include lengthy ETL and data modeling when ingesting the data (to impose schema-on-write).

 

With ever-growing massive amounts of data collected and the need to leverage the data to build solutions, and strategies, organizations are facing a major challenge maintaining these massive pools of data and extracting valuable business insights from it. However, if the data in a data lake is not well-curated, it may flood it with random information which is difficult to manage and consume, leading to a data swamp. Therefore before going forward with a data lake it’s important to be aware of what are the best practices when designing, implementing, and operationalizing your data lake.
 
Let’s look at the best practices that help build an efficient data lake.

 

 

Following the above best practices will help create and maintain a sustainable and healthy data lake. By devising the right strategy of collecting and storing data in the right way, one can reduce the cost of the storage, make data access efficient and cost-effective, and ensure data security.

 

References:

  1. https://aws.amazon.com/big-data/datalakes-and-analytics/what-is-a-data-lake/
  2. https://www.stitchdata.com/resources/data-ingestion/
  3. https://parquet.apache.org/
  4. https://orc.apache.org/
  5. https://azure.microsoft.com/en-in/blog/optimize-cost-and-performance-with-query-acceleration-for-azure-data-lake-storage/
  6. https://docs.snowflake.com/en/user-guide/views-materialized.html
  7. https://www.talend.com/resources/what-is-data-governance/

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