Analyst Report Architecting and Automating Data Pipelines: A Guide to Efficient Data Engineering for BI and Data Science
Support data-informed decision making in your organization with these data architecture and automation recommendations from the Eckerson Group.
There just aren’t enough data engineers in the world to keep up with the demand for analytics-ready data. Unfortunately, that means that many data pipelines today aren’t so much engineered as they are MacGyvered together with duct tape and bubble gum.
This guide by the Eckerson Group’s Dave Wells demonstrates that it’s possible to produce reliable data pipelines at high speed and with minimum manual effort… if you have the right data pipeline architecture and automations in place.
Inside this guide to efficient data engineering, you’ll discover:
- The data pipeline lifecycle, and the roles and responsibilities it requires to stay healthy.
- How to overcome data pipeline and data engineering challenges through modern data architecture and holistic lifecycle automation.
- Why it’s important to design your data pipeline architecture to defined standards, patterns, and templates.
- How data pipeline automation technologies make it possible to produce reliable data pipelines at high speed and with minimum manual effort.
- How to leverage efficiencies of scale by automating and orchestrating workflows related to data architecture, data engineering, data operations (DataOps), and data governance, all while supporting business stakeholders in their data-informed decision making.