Trino is a distributed MPP SQL engine that queries data where it lives — across object storage, relational databases, and streaming systems — without loading it into a central warehouse first. This guide covers the architecture, connectors, optimizer, failure model, and honest operational realities.
Analytics
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Trino: One Query Engine Over Everything -
ClickHouse for Analytics Workloads ClickHouse columnar storage, the MergeTree engine family, primary key vs sorting key, materialized views for pre-aggregation, query profiling with EXPLAIN, replication with ClickHouse Keeper, distributed tables for sharding, and when to choose ClickHouse over PostgreSQL.
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Apache Spark Fundamentals: RDDs, DataFrames, Catalyst, and When Spark Still Wins How Apache Spark actually works under the hood — RDDs, DataFrames, the Catalyst optimizer, and the execution model — and an honest look at when Spark still wins versus DuckDB, Trino, Polars, and Flink.
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Building Internal Data Platforms: The Modern Data Stack in Practice A practical guide to building an internal data platform — combining dbt, Airflow, data warehouses, data contracts, and data quality checks into a coherent system your engineers and analysts actually want to use.
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dbt Core in Practice: Data Modeling That Doesn't Rot A complete hands-on guide to dbt Core — project structure, sources and staging layers, testing data quality, documentation, incremental models, and running dbt in production with Airflow.
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DuckDB for Analytics Engineers: In-Process OLAP That Replaces Your Spark Cluster A comprehensive guide to DuckDB—covering its columnar vectorized execution engine, querying Parquet and S3 directly, replacing heavy Spark jobs for medium data workloads, Python and R integration, performance tuning, and when to use DuckDB vs alternatives.