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Do you really need a data lake – 4 best practices to build a data lake

by Sachin Rane, on Jan 10, 2020 5:19:09 PM

Estimated reading time: 2 mins

Big Data is presenting challenges as well as opportunities. Data lakes are forward-looking data storage solutions to the traditional dilemma of storing and using high volume, multi-dimensional data and information for enterprise-wide collaboration.

Do you really need a data lake – 4 best practices to build a data lake (1)-1

Enterprises usually invest in data lake solutions when they have operational complexity, high operational costs, and multi-protocol analytics. If not, then you are good to go with a traditional data warehouse or a data mart.

What is the difference between data mart, data warehouse, and data lake. Sachin Rane, Executive Vice President & Head - Software Solutions, elucidates this difference. Watch now>

Do you really need a data lake – 4 best practices to build a data lake



Benefits offered by a data lake

A data lake solution helps you build a massive collaborative space around multi-structured data. It thereby offers multiple business benefits:

  • Data ingestion: Captures the data in its “as-is” form, be it structured, semi-structured, or un-structured, and does not need to create a schema prior to data capture.
  • Data democratization: Allows users to analyze the data as and when required without having to overly rely on the data science team.
  • Data discovery: Enables storage of years of data, which is usually discarded on use, and allows exploring, testing, refining as well as extrapolating of data points by engaging predictive and prescriptive analytics.
  • Data analytics: Allows exploration of old and new data sources and zero down on key variables that hint at better business performance.
  • Collaboration: Breaks down the silos and offers a mammoth distributed architecture, where data resides and can be used whenever it is required.
  • Pattern identification: Allows to explore data relationships and identify new patterns in the data points harbored within the colossal data set thus unlocking value in near real-time.

Best practices for data lake implementation

4 best practices to build a Data Lake:

  1. Culture shift
  2. Metadata
  3. Training
  4. Gate keeping


Enterprise-wide collaboration is the basis of building a data lake . The following key points enable you to establish a strong foundation towards a functional data lake: 

  1. Culture shift: Inculcate organizational discipline and employ a conscious culture shift towards self-service analytics, insights-driven thinking and building enterprise collaboration. In absence of which a data lake is just a “white elephant”.
  2. Metadata: Build your data lake on a foundation of metadata if you want to turn your data into action. Otherwise, you are at the risk of turning the data lake in to a data swamp, which is of no use.
  3. Training: Invest in data skills and trainings to develop the right skill-set and organizational culture. You have to lead by example. Failing which you are risking the return to the era of dependency on programmers.
  4. Gate keeping: Institute agile governance. You can delete the data sets which seem to be not utilized for more than two years.

Data lakes offer obvious benefits over traditional data warehouses. 

Gaurav Gandhi elucidates how data lakes built using Apache Hadoop systematically extend a contemporary data warehouse. Read now>

Build modern data platform with Apache Hadoop (data lakes)-1



In summary

You can manage without a data lake if you are still working in a low complexity business environment and can do with two-dimensional analytics. With the global market orienting itself towards increasingly complex transactions and enterprises generating multi-structured data, you need to expand your scope of data management and analytics. Having said that, the enterprise data management can be a winning ball game only if you evolve the data lake deployment around an insights-driven culture, metadata, training investments, and agile governance. Otherwise, you risk the mistake of creating a massive data swamp.

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Topics:Data Lakes


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