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 26 of 114
  1. 01Lakehouse Context Layers with Atlan and Iceberg v3Lakehouse context layers bridge the gap between raw Iceberg tables and AI agents that need business meaning. Atlan and Snowflake Horizon each take different approaches to the same problem.
  2. 02Real-Time Agentic Analytics with ClickHouseClickHouse has become the leading real-time analytics engine for AI agent workloads, with event-loop architectures that let agents query and act at sub-second speeds.
  3. 03Composable Analytics Beats Metric CatalogsMetric catalogs define what terms mean. Composable analytics defines how terms combine, transform, and relate. For AI agents, composability is what turns definitions into reasoning.
  4. 04Goal-Directed Analytics Agents on Apache IcebergHow goal-directed analytics agents decompose business questions into sub-tasks, execute action loops over Apache Iceberg tables, and use the lakehouse as both a data source and a state store for agent action logs.
  5. 05Iceberg Remote Signing for Regulated DatasetsIceberg REST catalog remote signing provides per-file pre-signed URL access for regulated datasets. How it differs from credential vending, audit trail capabilities, and Snowflake implementation for PII/compliance workloads.
  6. 06Apache Iceberg v3 Deletion Vectors on SnowflakeApache Iceberg v3 deletion vectors replace positional delete files with binary bitmaps in Puffin files, delivering up to 10x faster DML on Snowflake. Deep dive into architecture, benchmarks, and migration.