Architecture decisions, trade-offs, and the code behind them.
A practical Kafka EOS guide covering delivery semantics, idempotent producers, transactions, `read_committed`, and how to avoid data loss or duplicate processing.
Learn how Kafka Schema Registry handles schema evolution, schema IDs, compatibility rules, Avro or Protobuf serialization, and safer producer-consumer contracts.
A practical ksqlDB tutorial and Kafka Streams guide covering `CREATE STREAM`, windowed aggregations, joins, and real-time clickstream processing.
A practical Kafka Connect guide for ingesting data from databases, files, and APIs, with source connector examples, configuration patterns, and production best practices.
Learn Apache Kafka core concepts: events, topics, partitions, brokers, producers, consumers & KRaft. Essential guide with Python examples for beginners.
Kafka producer and consumer best practices for `acks`, idempotence, retries, offsets, commits, partitioning, and error handling in production streaming systems.
Learn Kafka topic and partition strategy for scalability, consumer parallelism, ordering guarantees, throughput planning, and long-term cluster design.
Five practical ways teams still use logistic regression, and why this classic model remains valuable even in a deep learning era.
A practical introduction to Apache Flink and where it fits in modern stream-processing and event-driven data systems.
A practical guide to sentiment analysis comparing Naive Bayes and LSTM, and how teams should think about modern sentiment-analysis pipelines today.
A practical machine learning mind map covering ML tasks, methods, model families, and application areas for a clearer overview of the field.
A practical guide to the most relevant Google AI tools for modern teams, including Gemini, Vertex AI, Agent Builder, search, vision, and MLOps workflows.