A technical tutorial demonstrating how to access and query nested JSON arrays in ksqlDB using array indexing and the EXPLODE function.
A tutorial on building a Telegram bot using Apache Kafka and ksqlDB to process, query, and respond to real-time data streams.
A technical exploration of ksqlDB window start times, including creating streams, inserting test data, and querying windowed aggregations.
A technical guide on integrating RabbitMQ with Kafka using Kafka Connect, including setup, schema handling, and use cases.
A technical guide on using ksqlDB and Kafka Connect to ingest and analyze Syslog data for detecting SSH attacks.
A workaround technique for exploding arrays in ksqlDB using manual indexing, simulating EXPLODE/UNNEST functionality.
A technical tutorial on using CASE statements in ksqlDB to pivot and aggregate event stream data, similar to SQL PIVOT operations.
A technical guide exploring stream-stream joins in KSQL using orders and shipments data, covering setup, aggregations, and join implementation.
Explains how ksqlDB handles case sensitivity for topics, statements, and object names, with examples of quoting rules.
A quick reference guide for using the KSQL REST API, including curl examples for queries, statements, and runtime statistics.
A technical tutorial on using Apache Kafka, KSQL, and Elasticsearch to analyze network event data, detect anomalies, and set up automated alerts.
Explains how to resolve KSQL's topic conformity error when creating streams/tables, detailing partition and replica mismatch solutions.
A list of 19 Apache Kafka-related technical sessions at Oracle OpenWorld, JavaOne, and Oak Table World 2017 conferences.
Author's 2017 speaking schedule for tech conferences and meetups, focusing on Apache Kafka and real-time data streaming.
An overview of Kafka Streams, a new library for building stream processing applications using Apache Kafka, with key concepts and a code example.
A technical guide on processing millions of small text files using GNU Parallel and stream processing, without needing Hadoop or a database.
Explains Lambda Architecture for Big Data, combining batch processing (Hadoop) and real-time stream processing (Spark, Storm) to handle large datasets.
