How do you integrate stream processing with other data sources and systems in your architecture?

1 Stream processing frameworks

There are many frameworks and tools that can help you build and run stream processing applications, such as Apache Kafka, Apache Spark, Apache Flink, or Apache Beam. These frameworks provide different features and trade-offs, such as scalability, fault tolerance, latency, throughput, state management, and windowing. Depending on your use case and requirements, you may need to choose one or more frameworks that suit your needs and preferences.

3 Data processing and analysis

To process and analyze the data streams, you need to apply various operations and functions, such as aggregation, filtering, joining, grouping, or windowing. You may also need to use complex event processing (CEP) or machine learning (ML) techniques to detect patterns, anomalies, or trends in the data streams. You may need to use stateful or stateless processing, depending on whether you need to store or update the intermediate results of the operations.

4 Data output and delivery

To output and deliver the data streams, you need to send the processed data streams to various destinations, such as databases, files, APIs, or message brokers. You also need to ensure the reliability, consistency, and quality of the data streams. You may need to use acknowledgements, checkpoints, or retries to handle failures or errors. You may also need to use schemas, formats, or standards to ensure the compatibility and interoperability of the data streams.

5 Stream processing patterns and architectures

To design and implement stream processing solutions, you need to follow some common patterns and architectures, such as lambda architecture, kappa architecture, or microservices architecture. These patterns and architectures can help you balance the trade-offs between latency, accuracy, complexity, and scalability. They can also help you decouple the components and layers of your stream processing solution, such as ingestion, processing, output, and query.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?