682 tutorials · free to read

A working handbook for data and software engineering.

Hands-on tutorials by Alex Merced on Apache Iceberg, the data lakehouse, pipelines, agentic AI, and the languages and tools that hold it all together.

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Page 35 of 114
  1. 01What Iceberg V3 Advances Mean for CDC PipelinesApache Iceberg V3 brings deletion vectors and row lineage that reshape CDC pipeline design. Learn what these features mean for your streaming data architecture.
  2. 02Kafka 4.0 Changes Streaming Platform OperationsKafka 4.0 removes ZooKeeper and ships KRaft and KIP-848 by default. Learn what those changes mean for platform operations, upgrades, and client configurations.
  3. 03Lance and Iceberg for Multimodal AI DataLanceDB and Apache Iceberg serve complementary roles in a multimodal AI lakehouse. Learn when to use Lance for embeddings and random access, and Iceberg for structured metadata and SQL analytics.
  4. 04Bringing MLflow and Data Pipelines Closer TogetherMLflow 3 extends observability from classic ML experiments to GenAI tracing and data pipeline lineage. Learn how to connect data quality monitoring with model performance tracking.
  5. 05Modern Feature Stores Beyond Batch PipelinesFeature stores like Feast now support streaming feature views from Kafka and Kinesis alongside batch pipelines. Learn how to build real-time features that maintain training-serving consistency.
  6. 06OpenLineage as the Spine of Data ObservabilityOpenLineage provides a standard API for collecting pipeline lineage across Airflow, Spark, Flink, and dbt. Learn how it powers blast radius analysis and incident triage.