The two positions are not interchangeable—and misperceptions of their roles can hurt teams and compromise productivity.
Multi-layer architecture, scalability, multitenancy, and durability are just some of the reasons companies have been using Pulsar.
Or, why science and engineering are still different disciplines.
Getting DataOps right is crucial to your late-stage big data projects.
Poll results reveal where and why organizations choose to use containers, cloud platforms, and data pipelines.
Learn some of the benefits of using real-time processing of data for some use cases.
Learn to identify problems that may indicate data team dysfunction.
Merging the gaps between data science and engineering, and what each side can learn from the other.
Using Apache Beam to become data-driven, even before you have big data.
A business leader’s guide to beginning the big data journey.
Jesse Anderson will show you how to recognize the opportunities, avoid the problems, and get the most value from your data.