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BigQuery Explained: Google's Serverless Data Warehouse
What “serverless” means in BigQuery, why its bill depends on the bytes a query reads, and the three habits that keep that number small: select only needed columns, partition and cluster tables, and check a query’s cost before running it.
Materialized Views in BigQuery Explained
A materialized view stores the result of a query and keeps it current as the base table changes. This guide shows how BigQuery combines stored results with new rows, models it on 2.9 million real trips, and covers refresh settings, max_staleness, smart tuning, unsupported SQL and when a scheduled table is better.
Loading Data into BigQuery: Batch and Streaming
BigQuery can be loaded in batches from files or fed continuously through the Storage Write API. This guide compares the methods by latency, cost, limits and what a retry does, with documented commands, a model of at-least-once against exactly-once delivery, and a rule for choosing.
BigQuery Window Functions and Analytic SQL
A window function computes a value for each row from a set of related rows, without collapsing them. This guide works through running totals, moving averages, period change, share of total and top-per-group on 2.9 million real trips, then shows the two frame mistakes that return wrong numbers without an error.
Migrating to BigQuery: A Practical Playbook
Moving a warehouse to BigQuery is less a copy job than a proof: that the SQL still means the same thing and the numbers still match. This playbook follows Google's iterative method, then reproduces two steps locally: a translator that gets one statement wrong, and a copy that passes row counts while 23 rows differ.
Scheduling Queries in BigQuery
A scheduled query is SQL that BigQuery runs for you on a timetable. This guide covers the run parameters, destination table templates checked against Google's own examples, backfills, credentials and notifications, and shows on real data why an appending statement doubles on a rerun while a MERGE does not.