What are common use cases for Data Lake Platform

What are common use cases for Data Lake Platform


Example use cases for Data Lake Platform

Provides a managed service using Kubernetes for delivering a set of integrated solutions (Data Flow, Data Transformation, Data as a Service) that ingests data from multiple data sources into a Data Lake. Offers a data workflow to orchestrate data transformation and data curation processes for analytics for business and operations.
Stores data as-is without first structuring the data. Run different types of analytics—from dashboards and visualizations to big data processing, real-time analytics, and machine learning to guide better decisions. Provides Metadata (Catalog) Management to make data visible and easily accessible to consumers.
  • BI Reporting - Process of gathering data to extract relevant insights
  • Ingestion of semi-structured and unstructured data sources - Also known as Big Data, such as equipment readings, telemetry data, logs, streaming data, and so forth for near real-time analysis, as well as structured data (i.e., extracted from a relational data source).
  • Machine Learning - Process of teaching a computer system how to make accurate predictions when fed data
  • Data Quality Control - Process of controlling the usage of data for an application or a process
  • Data Catalog (Metadata) - Informs customers about available data sets and metadata around a topic and assists users in locating it quickly.
  • Data Management - Active and ongoing management of data through its life cycle of interest and usefulness
  • Ad hoc analysis - Discover the value or purpose of data by analyzing it before it's fully defined. Agility is essential for every business, and a Data Lake can play an important role in the "proof of value" type of situation.
  • Archival and historical data storage - Sometimes, data is used infrequently but does need to be available for analysis. A Data Lake strategy can be valuable to support an active archive strategy.
  • Support for Lambda architecture - Includes a speed layer, batch layer, and serving layer.
  • Preparation for data warehousing - Using a Data Lake as a staging area of a Data Warehouse is one way to utilize the Data Lake
  • Augment a data warehouse - A Data Lake may contain data that isn't easily stored in a Data Warehouse or frequently queried. Access the Data Lake via federated queries making its separation from the Data Warehouse transparent to end-users via a data virtualization layer.

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