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    What is a Cloud Data Lake?

    A data lake is a platform for processing data and analytics that is beyond standard SQL data warehouses and supports a wide variety of data types and analytics. The development of on-premises data lakes has been a major enterprise investment for over a decade. Recent years, however, have seen the emergence of a new trend, the Cloud Data Lake.

    Next-generation Data Lake that delivers more attractive price/performance, a variety of analytical engines, and first-class tooling, all on virtually unlimited cloud storage is the Cloud Data Lake. There is no difference between a cloud data lake and any other data lake, except that it is stored in the cloud. Cloud storage platforms, such as Amazon S3, Azure Blob storage, Google Cloud Storage, and other lower-cost options, are ideal for storing large amounts of data because data lakes generally store huge amounts of information. Using cloud storage doesn't require you to plan ahead, since the services are elastic in nature. As well, you only have to pay for the services you use.

    How do they work?

    The purpose of a data lake is to collect and store data in its original format, in a system or repository capable of handling different schemas and structures, until the data is needed by downstream processes.

    An organization can use a data lake to store raw data, prepared data, and third-party data assets in one place. The tools are used to power various operations such as data transformations, reporting, interactive analytics, and machine learning. It is also necessary to organize, govern, and service the data when managing a production data lake.

    How do you build a data lake?

    Almost all cloud data lakes follow a similar process, but there are some key steps that they all take. Let's look at each of them and their challenges.

    Step 1: Understand the business

  • Decide which data should be included in the data lake.
  • Reduce the amount of data stored.

    Step 2: Storing and ingesting data

  • Make sure your data ingestion system is fast.
  • Analyze the cost-effectiveness of storing the data lake.

  • Step 3: Preparation of data

  • The preparation of data for processing.
  • Data storage costs can be reduced.

  • Step 4: Analyzing the data lake

  • Analyzing the data to find information.

  • Step 5: Machine Learning

  • Trains the ML with high-performance storage.
  • Efficiently storing local copies.

  • READ MORE: How The Adoption Of Data Lake As A Service Is Transforming The Businesses