5 Suitable Reasons To Consider A Data Lake

5 Suitable Reasons To Consider A Data Lake

1. Increase the Time-to-Value and Time-to-Insights

By supplying an immutable coating of data ingested, we create the information available to all customers immediately after receiving that info. By giving raw information, you’re enabling exploratory evaluation that would be hard to achieve when different data groups can use the exact same dataset in a really different manner. Frequently different data users may require unique transformations based on identical raw information. A data lake permits you to dive everywhere into a variety of flavors of information and decide on your own what may be practical for you to create insights.

2. Costs

With the increasing volume of information from social networking, detectors, logs, and web analytics, it can get expensive over time to put away all your information in a data warehouse. Many traditional data warehouses connect processing and storage closely together, which makes scaling of every hard.

3. Future Proof

Such information sources are ingested, cleaned, and kept”in case” they may be required afterward. It follows that info engineers are investing a great deal of effort and time in creating and maintaining something which might not even have a clear business requirement.

4. Building a Staging Area for Your Data Warehouse

A data lake does not have to be the conclusion destination to your information. Data is continuously moving and changing its shape and form. A contemporary data platform must facilitate the ease of intake and discoverability, while at precisely the exact same time allowing for an intensive and rigorous arrangement for coverage demands. A typical emerging pattern is a data lake functions as an immutable coating for your information intake. All raw data ingested into your information platform is discovered at an information lake.

5. A Single Data Platform for Real-Time and Batch Analytics

Ingesting real-time information to a data warehouse remains a challenging issue. Though there are tools available on the marketplace which attempt to tackle it, this issue could be solved much simpler when employing an info lake within an immutable coating for eating all your data. For example, many options like Kinesis Data Streams or even Apache Kafka permit you to define an S3 place for a sink to your own data.



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