The Top Data Engineering Challenges and How to Solve Them


From building infrastructure and running jobs to fielding ad-hoc queries, data engineering is tough work. Most data engineers wear many hats, and their role in today's organizations is growing rapidly. Ultimately, the goal is to produce fast, reliable, quality data that can be easily and securely shared across the organization to enable data science and analytics. There are plenty of challenges that come with this job—integration and governance, scaling and optimizing data pipelines, and reliability and observability are all hot-button issues.

At the same time, the landscape of tools and technologies is constantly evolving in areas spanning data ingestion, storage, processing, integration, workflow management, monitoring, and more. Next-generation data architecture patterns are gaining a stronger foothold at organizations. The rise of DataOps is closing the gap between data consumers and data producers. And automation and orchestration are helping data teams build more efficient, agile, and resilient data systems.

To help IT leaders and data professionals navigate the top data engineering challenges and key technologies and strategies for solving them today, DBTA is hosting a special roundtable webinar on April 27th. Reserve your seat today.

Don't miss this live event on Thursday, April 27th, 11:00 AM PT / 2:00 PM ET.

Register Now to attend the webinar The Top Data Engineering Challenges and How to Solve Them.

headshot image image image
Aric Bandy
EVP, Corporate Development & CloudOps
Francisco Alberini
Product Manager
Monte Carlo
John de Saint Phalle
Senior Product Manager
Stephen Faig
Research Director
Unisphere Research and DBTA