Modern streaming architectures
Putting the pieces together to create real-time value
This course has been updated from Modern streaming architectures to Real-time data foundations: Time Series Architectures and is the final part in the series. Ted Malaska demonstrates how to put all the pieces together to produce value for your business. You'll explore different architectures for streaming and stream processing and dig into use cases like real-time metrics, aggregation, customer and entity 360, and reacting to real-time triggers.
What you'll learn-and how you can apply it
By the end of this live, online course, you’ll understand:
- Core use cases and architecture for value creation from data at speed
- The weaknesses of a streaming system
- Available storage systems that can add streaming solutions
And you’ll be able to:
- Evaluate the success of a real-time system
- Make and evaluate actionable decisions using streaming data
This training course is for you because...
- You're a data engineer who wants to create value out of data.
- You're a product manager who is trying to figure out what use cases and functionality are provided by the IoT and stream processing.
- A basic understanding of working with data
- Familiarity with Java or Scala (useful but not required)
Materials or downloads needed in advance:
- A machine with Docker and the IDE of your choice installed
- A GitHub account
About your instructor
Ted Malaska is the director of engineering for data streaming and persistence at Capital One. Previously, he was on the Battle.net team at Blizzard Entertainment, he was also a principal solutions architect at Cloudera, where he helped clients succeed with Hadoop and the Hadoop ecosystem, and a lead architect at the Financial Industry Regulatory Authority (FINRA). He has contributed code to Apache Flume, Apache Avro, Apache Yarn, Apache HDFS, Apache Spark, Apache Sqoop, and many more. Ted is the coauthor of Hadoop Application Architectures, a frequent conference speaker, and a blogger on data architectures.
The timeframes are only estimates and may vary according to how the class is progressing
- Kafka and streaming engine overview (15 minutes)
- Common architectures: Alerting, firing machine learning models at speed, sessionization, and windowing (30 minutes)
- Break (10 minutes)
- Weaknesses in a streaming architecture (10 minutes)
- Things you want to make "easy to do" in the world of the IoT: Deployment, routing, registration, and auditing (15 minutes)
- How to watch and validate streaming systems (10 minutes)