775 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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  1. 01Bringing 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.
  2. 02Modern 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.
  3. 03OpenLineage 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.
  4. 04When Paimon Beats Iceberg for Mutable StreamsApache Paimon uses LSM-Tree storage for native CDC upserts without restart. Learn when Paimon outperforms Iceberg for high-churn mutable streaming workloads.
  5. 05Policy as Code for Lakehouse GovernanceOPA, ABAC, row filters, and column masks make lakehouse governance programmable and scalable. Learn how Databricks, Snowflake Horizon, and BigQuery implement policy-as-code.
  6. 06Why Semantic Layers Make Enterprise Text-to-SQL SaferText-to-SQL accuracy jumps from 40% to 85-95% when grounded in a semantic layer. Learn how Dremio, Snowflake Cortex Analyst, and dbt Semantic Layer improve AI analytics reliability.