The hottest Stream Processing Substack posts right now

Watermarks in Stream Processing help handle event lateness and decide when to treat data as 'late data'.
In SQL Query execution, the order is FROM and JOIN, WHERE, GROUP BY, HAVING, SELECT, ORDER BY, LIMIT.
To optimize SQL Queries, reduce dataset sizes for joins and use subqueries for pre-filtering.

Bytewax v0.18 enables complex dataflows with multiple sources, joins, and branches.
Enhanced Kafka & Redpanda integration in Bytewax v0.18 offers advanced support and flexibility.
Autocomplete and type checking are now fully integrated in Bytewax v0.18, providing hints and error detection.

Google Cloud Dataflow is a service that helps process both streaming and batch data. It aims to ensure correct results quickly and cost-effectively, useful for businesses needing real-time insights.
The Dataflow model separates the logical data processing from the engine that runs it. This allows users to choose how they want to process their data while still using the same fundamental tools.
Windowing and triggers are important features in Dataflow. They help organize and manage how data is processed over time, allowing for better handling of events that come in at different times.

Stream processing has evolved significantly over the years, with frameworks like Samza and Flink leading the way in handling real-time data streams.
DoorDash developed its own search engine using Apache Lucene, achieving impressive performance improvements, like reduced latency and lower hardware costs.
Understanding metrics trees is essential for businesses as they visually represent how different inputs contribute to outputs, helping in decision-making.

One common use case for stream processing is transforming data into a format for different systems or needs.
Bytewax is a Python stream processing framework that allows real-time data processing and customization.
Bytewax enables creating custom connectors for data sources and sinks, making it versatile for various data processing tasks.

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Event-driven orchestrators are not suitable for stream processing because they cannot handle tasks with definite starts and ends.
Event-driven applications operate asynchronously by triggering tasks based on events like files appearing in a directory.
Unlike stream processors, orchestrators like Airflow and Dagster do not have the ability to hold state, distribute tasks for parallel execution, or shuffle data between tasks.

Memphis provides a better developer experience for stream processing.
Memphis is designed for quick setup, cost efficiency, and user-friendly monitoring.
Memphis is a platform of choice for companies looking to replace or enhance their streaming platforms.

Designing data systems requires resilience and scalability, which means they should handle growth and failures efficiently.
Data modeling is more than just making diagrams; it's about understanding the entire system and how data flows within it.
Using tools like DuckDB in the browser can open up new possibilities for data processing, making it more accessible and flexible.

The post discusses stateless operators in stream processing technology
It offers a technology-agnostic explanation of stateless operators
The focus is on understanding the basics of stateless operations in stream processing

The post introduces the basics of stream processing and the principles of Dataflow programming.
Stream processing is a key concept to grasp for those interested in working with data in real-time.
Understanding stream processing is fundamental for entry-level learners in the field of data processing.

Operational use cases with Kafka and Flink are crucial for business operations due to their message ordering, low latency, and exactly-once delivery guarantees.
Using polyglot persistency with different data stores for read and write purposes can help solve the mismatch between write and read paths in microservices data management.
Implementing a backend rate limiter using Flink as a Kafka consumer can help prevent exhausting an external system (e.g., a database) due to high message arrival rates from Kafka.