Flink – A Serious Alternative to Spark
By Chris Ruegger | 1 Min Read | 70 Min Video
Apache Flink is an Apache Software Foundation open-source framework for distributed stream and batch processing; it’s more mature and superior to Spark.
Flink features include “Streaming-first” processing. This is not micro-batching, but true event-time streaming that was built into it from the start. Streams are treated as infinite, unbounded data sets in Flink. Batch processing is more than a subset of Flinks’ capabilities; Batch data is treated as “finite streams” or bounded data sets. It provides a flexible API to handle late and out-of-order data and stateful/fault-tolerant streaming with exactly-once processing semantics. Along with its programming API, Flink has powerful SQL and R interfaces for event time data analysis.
Flink performs uniformly at large scale, running on thousands of nodes. It can run in single JVM, standalone cluster, YARN or cloud. This is why companies like Alibaba, Bouygues Telecom, Capital One, Comcast, eBay, Ericsson, Huawei, King.com, Lyft, Netflix, Portugal Telecom, ResearchGate, Styx, Telefonica, Uber and Zalando all trust Flink in mission critical production.
Follow along with expert Chris Ruegger to learn more about Flink!
Overview – 1:56
Components and Programming Model – 12:30
Batch Processing – 16:14
Streaming – 26:33
SQL – 51:15
Fault Tolerance – 55:05
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