Published: 17 July 2024
Consumer demand for usable data has increased the need for data engineers. D&A leaders can use this research to improve data engineering practices by fostering collaboration, proving business value early, automating release processes, managing data as a product and eliminating operations overhead.
Successful data engineering teams are cross-functional and adopt DataOps practices.
Organizations that focus on business value are more efficient in prioritizing data delivery demands.
Testing and release processes are heavily manual tasks for new/emerging data engineering teams. Established teams have begun to automate these processes.
Many organizations are operating monolithic data systems and processes that drastically slow their data delivery time.
While many organizations have enabled business users to explore data through self-service data preparation, very few have established gatekeeping processes to deliver these workloads to production.
Data and analytics (D&A) leaders seeking to enhance their data engineering practice must embrace the following
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Analysts:
Robert Thanaraj, Ehtisham Zaidi, Mark Beyer, Roxane Edjlali, Michael Simone
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