Monday, June 18, 2018

What is Data Engineering

ata engineering ensuring all right data (internal/external, structured/unstructured) are identified, sourced, cleaned, analyzed, modelled, and decisions implemented — without losing on granularity and value as the data travels this path.
Data Engineering has to help businesses by building robust capabilities to deal with the volume, velocity, reliability, and variety of data and makes this data available for business users to consume — both as traditional marts and warehouses, and new-age big data ecosystems.
Data engineering is dealing with data — data lakes, clouds, pipelines, and platforms. Data Warehouse is the base of BI (Business Intelligence) project, and ETL (Extract, Transform and Load) is the base of Data Warehouse.

Data Approaches: There are many data engineering approaches which are very helpful to understand different techniques as given below-
1. Implement Data Lakes/ Data Warehouses/ Data Marts: Help lay or enlarge the enterprise data foundation so a range of analytics solutions can be built on top
2. Develop Data Pipelines: Facilitate production grade end-to-end pipeline of data-to-value that takes data solutions from sandbox environments, and rolls them out to end users

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