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Wade Waldron

Wade Waldron

Staff Software Practice Lead

Closing Remarks

Congratulations on reaching the end of our course. Let's review what we covered, next steps, and how to keep in touch.

What We Covered

  • Overview
  • Datastream Programming
  • Setup your Flink environment (Exercise)
  • The Flink Job Lifecycle
  • Running a Flink Job (Exercise)
  • Anatomy of a Stream
  • Flink Data Sources
  • Creating a Flink Data Source (Exercise)
  • Serializers & Deserializers
  • Deserializing Messages in Flink (Exercise)
  • Transforming Data in Flink
  • Flink Data Transformations (Exercise)
  • Flink Data Sinks
  • Creating a Flink Data Sink (Exercise)
  • Closing Remarks

Next Steps

Now that you have finished the course, here are some next steps you might want to consider:

  • Continue learning about more advanced Flink operations such as joins, aggregations etc.
  • Research the Flink Table API and Flink SQL API for alternate ways of interacting with Flink.
  • Try integrating with endpoints other than Kafka (eg. SQL, Cassandra, etc).
  • Start using Flink in your own personal, or production projects.
  • And check out our Flink 101 course if you haven't already.

Keep In Touch

You may have reached the end of the course, but we'd love to hear more from you. Reach out to us through our community and social media channels and let us know how you are doing in your learning journey:

Watch Future Content

And keep your eyes open. We'll be expanding on this course and other courses in the near future. Stay tuned for future updates.

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Closing Remarks

Hi, I'm Wade from Confluent. Let's review what we learned in the Building Apache Flink Applications in Java course. If you've made it this far, congratulations, you've finished the course. You've learned a lot of new ideas and you'll want to start making use of them in your own projects. Recap Let's take a look at what we covered. We started by learning some of the basics of Flink, including how to run a Flink job. We saw how to consume data from a Kafka topic as we started to build out a data stream. We learned about the different types of serialization in Flink, and saw how to convert to and from plain old java objects. We transformed data in a Flink data stream and applied filters to eliminate unwanted messages. We took data from our data stream and pushed messages to a new Kafka topic. We added a second stream to our pipeline and saw various ways that streams could be combined or separated to create branching workflows. We learned about the importance of windowing and watermarks, especially in an infinite datastream. And finally, we introduced stateful operations so that we could apply aggregations that persisted across multiple windows and messages. Next Steps So, what comes next? Now that you have a solid foundation with Flink, you can take that knowledge and use it in your own production systems. However, the learning doesn't have to end here. You can continue to experiment with the data streams we have built. Maybe you want to add some new jobs of your own. Or you might try adding new operations into the existing jobs. If you haven't already done so, you should consider deepening your knowledge by taking our Flink 101 course on Confluent Developer. While you are there, you might want to have a look at some of the other courses available to you. And keep an eye out for more Flink content in the future. Whatever route you choose, please stay in touch. Reach out to us through our community forums and community Slack channel to keep the conversation going. Or, tag us on social media and let us know what you thought of the course. Thank you for joining me on this journey, and make sure to keep learning. If you aren't already on Confluent Developer, head there now using the link in the video description to access other courses, hands-on exercises, and many other resources for continuing your learning journey.