September 17, 2026 · Vistelio
A practical tutorial on building maps in Looker: the location, zipcode and region dimension types, distance measures, map_layer and custom TopoJSON layers, BigQuery GEOGRAPHY columns, and the performance and permission traps that make Looker maps slow or blank.
Read moreSeptember 14, 2026 · Vistelio
Most Looker models join dimensions as if attributes never change. They do. This tutorial shows how to model SCD Type 2 history in LookML, build valid-from/valid-to joins, expose both 'as-was' and 'as-is' views of the same dimension, and keep performance sane on BigQuery.
Read moreSeptember 14, 2026 · Vistelio
A practical tutorial on exposing semi-structured BigQuery data in Looker: when to UNNEST inside a view, how to model repeated records as their own view, how to handle the native JSON type with JSON_VALUE and JSON_QUERY, and how to keep fanout and query cost under control.
Read moreSeptember 14, 2026
BI Engine is the cheapest dashboard-latency win available to a Looker team on BigQuery, and most instances use it badly or not at all. This tutorial covers how BI Engine acceleration actually works, which Looker query patterns it accepts, how to size a reservation, how to prove hit rates from INFORMATION_SCHEMA and System Activity, and how to model LookML so your tiles stay in the accelerated path.
Read moreSeptember 9, 2026 · Vistelio
A practical guide to Looker caching: datagroups, sql_trigger vs max_cache_age, persist_with on models and explores, datagroup_trigger for PDTs, ETL-driven cache invalidation through the API, and how to prove a caching policy is working.
Read moreAugust 25, 2026
Flat-rate BigQuery pricing is gone. This refreshed guide covers Editions versus on-demand, slot baseline and max autoscaling, physical versus logical storage billing, partitioning and clustering from the Looker side, datagroup caching, PDT keys, aggregate awareness, and attributing spend to dashboards and users.
Read moreAugust 25, 2026 · Vistelio
Define rollups inside explores with aggregate_table and let Looker route matching queries to them automatically. Covers the query-matching rules, designing a small set of rollups, datagroup and incremental refresh, and measuring the win in System Activity.
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