Everything you need to know about Extract, Transform and Load (ETL) tools

As a central repository that holds all the bid data from various different sources, a data lake is the vast, richest pool of raw data, the purpose of which is undefined. Data from this lake is then put to use for analytics and machine learning by organizations.

A data lake is used for storing both relational and non-relational data from devices, apps, and users. Different types of analytics, such as machine learning, big data analytics, textual search, SQL queries, and real-time analytics can be derived from a data lake. It has both curated and non-curated data. An SAP data lake is easy to set up and can ingest data from on-premise or from a cloud warehouse.

According to a survey, 90% of businesses believe that big data initiatives help them determine future success. A data lake allows data engineers, data scientists, and business analysts to access information using the tools and applications of their choice.

There’s a lot of value in a data lake for businesses, the ability to harness data from multiple sources leads to numerous advantages, such as:

1.    Improved Customer Interactions

2.    Faster Decision Making

3.    Increased Operational Efficiency

A modern data lake has three main features, namely - a landing zone for raw data, a staging zone where data transformation takes place, and a data exploration zone where transformed data is utilized for analytics and other business applications. 

The integration of SAP with a data lake SAP data lake enables data processing inside an SAP environment. This is beneficial as the SAP Hana cloud enables low-cost storage options for businesses while providing fast processing and compression. 

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