Previous: Stream Processing with Apache Flink: True Real-Time Analytics
In the last post, we looked at Flink’s DataStream API. Today, we’re tackling the big questions: How does Flink handle the messy reality of the real world? How does it talk to other systems? And how does it deal with data that shows up late?
Previous: Flink DataSet API: Transformations, Joins, and Aggregations
We’ve talked about how Spark handles streaming using micro-batches. It’s a great approach, but some people argue it’s not “true” streaming. If you need the absolute lowest latency possible, you want Apache Flink.
Previous: Batch Analytics with Apache Flink: The New Challenger
In the last post, we got Flink up and running. Now, let’s actually do something useful with it. Chapter 8 of Sridhar Alla’s book focuses on the DataSet API, which is what you’ll use for all your batch processing needs.
Previous: Structured Streaming: The Modern Way to Handle Data Streams
We’ve spent a lot of time on Spark, and for good reason - it’s amazing. But if you’re serious about big data, you need to know about Apache Flink. In Chapter 8, Sridhar Alla introduces us to the technology that many experts consider the “true” successor to MapReduce for real-time processing.