Akanksha Sri
by on March 29, 2021
As the name suggests, the data lake stores an enormous amount of data. The data in the data lake is in a raw and detailed format. Strategies in any content are essential to keep the source healthy, make it sustainable for the long-run. Data lake strategies also do the same. It keeps your data lake flexible, adaptable so that your business aims can be achieved. Thus, a data lake strategy can be defined as the practices that effectively manage the voluminous and varied reservoir of data so that the desired business goals can be achieved. Without a cohesive and well-planned strategy, organizations may suffer data integration and management issues. The traditional ways of data management like ETL and ESB are not meant to manage the large volume of data stored in a data lake. A data lake needs dedicated and powerful data management strategies. We have discovered some of the tips that will help in enhancing the data lake strategies:

Grip on Data Governance

Data governance is the key strategy enhancement practice that needs to be implemented for effective data lake management. It can keep the data lake flexible and unstructured. The policy-based data governance effectively controls the data from being hard-to-navigate and data swamp. Data governance helps to enforce a metadata requirement. As the data lake contains voluminous data, a user can retrieve data and execute complex queries effectively. This is possible only when the data stored is governed and controlled by automated metadata creation.

Create data governance zones

As data lake contains data that is separated by physical or logical partitions, some specific data lake strategies need to be designed so that the environment can be organized. Many approaches can be implemented depending upon the business requirement to build up the governance zones within the data.

Assess more data integration methods

We know that traditional data integration methods like ETL, ESB are not meant for voluminous data stored in the data lake. As data lake stores the unstructured, real-time data that is imported and exported, these traditional data loading tools cannot work this technology. For effective perception and migration of real-time data, organizations must evaluate modern data integration methods. These methods come with in-built connectors that ease the work of citizen developers.

Locate efficient staff

A vehicle may be equipped with modern facilities, which is not useful if it doesn’t have a proper driver. Similarly, a data lake is powerful and possesses several functionalities, but it is of no use if professionals do not handle it. There is a scarcity of expert professionals that is capable of handling the data lake. Organizations must find out the real talent that can handle and design appropriate data lake strategies. There are some general data lake strategy pitfalls that you must avoid while developing effective strategies: - Loading data - Poor Pre-planning - uncatalogued data - data duplication - Inelastic architecture If you avoid these pitfalls, you can set up the right data lake strategies that will provide better returns. And the data lake of your organization also will not become the data swamp. With the data lake strategies mentioned above, one can organize and manage the enormous data reservoir effectively.
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