Apache Flink
Course description
Apache Flink is a distributed system and computational engine for stateful big data streaming. That was a mouthful. In plain English, Flink is an library that allows you to process big data at scale, as it arrives, in almost real time. Flink gives you a variety of APIs that allow you to do plain functional programming on streaming data, and low-level APIs to give you ultimate control. Plus connectors to everything popular, including Kafka, JDBC, Cassandra, Pulsar, S3 and all sorts of data processors and storage systems. In this course, you'll learn how to be productive with Flink, and you'll grow as a data engineer.
Read more about the course
First of all, this course will give you everything you need to be productive with Flink:
- You'll deeply understand the Flink streaming engine and how it works
- You'll use functional programming on data streams
- You'll process any kind of data in real time, at scale
- You'll master complex transformations such as window functions
- You'll be able to run stateful computations, which is the main strength of Flink
- You'll know how to connect Flink to the most popular message buses, data streaming and data storage systems
- You'll be able to design your own connectors
- You'll be able to deploy Flink applications to a cluster
- You'll be able to troubleshoot and find relevant information in the Flink UI
After this course, you'll be able to process data in any way you need using Flink.
But most importantly, you'll develop timeless skills that you'll carry with you for your entire career, regardless of which data streaming tool you'll end up using:
- You'll deeply understand the practical benefits of streaming data in general
- You'll be able to work with event time and processing time
- You'll internalize the implications and tradeoffs of choosing latency vs throughput
- You'll understand the need for data consistency and persistence
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All Course Lessons (30)
| # | Lesson Title | Duration | Access |
|---|---|---|---|
| 1 | Welcome Demo | 11:59 | |
| 2 | Scala Recap | 31:05 | |
| 3 | Flink: First Principles | 16:42 | |
| 4 | DataStreams | 14:15 | |
| 5 | DataStreams: Exercise | 15:07 | |
| 6 | DataStreams: Explicit Transformations | 14:57 | |
| 7 | Handling Late Data: Watermarks | 05:22 | |
| 8 | Window Functions | 28:56 | |
| 9 | Window Functions, Part 2 | 26:33 | |
| 10 | Window Functions: Exercise | 12:48 | |
| 11 | Time Based Transformations | 33:15 | |
| 12 | Triggers | 16:38 | |
| 13 | Handling Multiple Streams | 33:36 | |
| 14 | Partitions | 14:33 | |
| 15 | State First Principles | 03:55 | |
| 16 | Rich Functions | 22:05 | |
| 17 | Keyed State | 24:10 | |
| 18 | Keyed State, Part 2 | 19:21 | |
| 19 | Checkpoints | 20:16 | |
| 20 | Broadcast State | 18:35 | |
| 21 | Integrations: Kafka | 25:07 | |
| 22 | Integrations: JDBC | 12:11 | |
| 23 | Integrations: Cassandra | 09:44 | |
| 24 | Custom Sources | 24:11 | |
| 25 | Custom Sinks | 16:47 | |
| 26 | Side Outputs | 08:07 | |
| 27 | How Flink Runs on a Cluster | 07:17 | |
| 28 | Managing and Submitting Applications | 13:48 | |
| 29 | Managing Applications in the Flink UI | 09:47 | |
| 30 | You Rock! | 00:31 |
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Read Book Apache Flink
| # | Title |
|---|---|
| 1 | Book 1 |
| 2 | Book 2 |
| 3 | Book 3 |
| 4 | Book 4 |
| 5 | Book 5 |
| 6 | Book 6 |
| 7 | Book 7 |
| 8 | Book 8 |
| 9 | Book 9 |
| 10 | Book 10 |
| 11 | Book 11 |
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