What is the difference between on-demand and flat-rate pricing in BigQuery?

On-demand charges $5/TB of processed data, ideal for irregular queries. Flat-rate (slots) offers reserved capacity at fixed price, beneficial at >100TB monthly.

What is BigQuery ML and which models does it support?

BigQuery ML enables training ML models directly in SQL. Supports linear regression, logistic regression, k-means clustering, time series forecasting, deep neural networks, and TensorFlow model import.

How does BigQuery BI Engine work?

BI Engine is an in-memory cache layer for sub-second query response. Automatically caches data for Looker and Data Studio dashboards without query changes.

What is BigQuery Omni and when to use it?

BigQuery Omni enables analyzing data in AWS S3 or Azure Blob directly from BigQuery without copying data. Ideal for multi-cloud architectures.

How to set up partitioning and clustering for optimal performance?

Partition by timestamp or integer range columns, cluster by columns in WHERE and JOIN. Partitioning limits scanned data, clustering improves I/O efficiency.

What is BigLake and how does it differ from external tables?

BigLake adds fine-grained access control (row/column level security) to external data in Cloud Storage. Unlike external tables, supports Apache Iceberg and Delta Lake formats.

How to implement row-level security in BigQuery?

Using policy tags and Data Catalog, or dynamic views with SESSION_USER(). For BigLake use fine-grained reader permissions in IAM.

What are best practices for streaming insert vs batch load?

Streaming insert for real-time data (<1s latency), batch load for historical data (cheaper). Streaming has 90-day buffer, batch load is free.

How to optimize BigQuery costs?

Partition pruning, clustering, materialized views, BI Engine cache, proper slot sizing, and regular INFORMATION_SCHEMA.JOBS_BY_* analysis to identify expensive queries.

What are materialized views and when to use them?

Materialized views are pre-computed aggregations that BigQuery automatically refreshes. Ideal for repeated aggregation queries - reduce both cost and latency.

How to integrate BigQuery with Looker?

Native integration via Looker connection. BI Engine automatically accelerates Looker dashboards. LookML modeling defines semantic layer over BigQuery.

BigQuery

BigQuery Data Warehouse

Q: How to optimize BigQuery costs?

Partition pruning, clustering, materialized views, BI Engine cache, proper slot sizing, and regular INFORMATION_SCHEMA.JOBS_BY_* analysis to identify expensive queries.

Q: What are materialized views and when to use them?

Materialized views are pre-computed aggregations that BigQuery automatically refreshes. Ideal for repeated aggregation queries - reduce both cost and latency.

Serverless, petabyte-scale data warehouse s integrated machine learning

Serverless Data Warehouse

Plně managed warehouse bez nutnosti infrastruktury s automatickým škálováním

BigQuery ML

Machine learning modely přímo v SQL - lineární regrese, clustering, deep learning

BigQuery BI Engine

In-memory analysis pro sub-second query response v Looker a Data Studio

BigQuery Omni

Multi-cloud analytics - query data v AWS S3 a Azure Blob bez kopírování

BigLake

Unified storage pro strukturovaná i nestrukturovaná data s fine-grained access control

Real-time Analytics

Streaming inserts a BigQuery Streaming pro real-time dashboards

BigQuery — klíčová témata

Google BigQuery — serverless DWH, BigQuery ML a BigLake

Hluboký přehled BigQuery: slot-based pricing, partitioning a clustering, BigQuery ML, BI Engine, BigLake a Omni pro multi-cloud analytiku.

Serverless architektura a sloty

On-demand vs. capacity (Editions: Standard/Enterprise/Plus), slot reservations, autoscaling, idle slots sharing, baseline + max capacity.

Partitioning a clustering

Time-unit/integer-range/ingestion-time partitioning, až 4 clustering keys, partition pruning, require_partition_filter pro cost control.

BigQuery ML (BQML)

Trénink modelů SQL příkazem (linear/logistic regression, boosted trees, DNN, ARIMA+, k-means), import TensorFlow/ONNX, Vertex AI integrace.

BI Engine a Materialized Views

In-memory cache pro sub-second dashboardy v Looker Studio/Looker, materialized views s automatickým refresh, smart tuning.

BigLake a externí tabulky

Unified governance pro data v Cloud Storage, S3, Azure (Omni), Iceberg/Hudi/Delta Lake formáty, row/column-level security napříč storage.

BigQuery Omni (multi-cloud)

Query data v AWS S3 a Azure Blob bez přesunu, cross-cloud transfers, jednotný billing v GCP, Anthos-based architektura.

Streaming inserts a Storage Write API

Legacy streaming (tabledata.insertAll), Storage Write API (exactly-once, vyšší throughput, nižší cena), Pub/Sub → BigQuery subscriptions.

Data Transfer Service

Konektory pro Google Ads, YouTube, GA4, S3, Redshift, Teradata, scheduled queries, backfills, error notifications.

Column-level a row-level security

Policy tags (Data Catalog) pro column masking, row-access policies, dynamic data masking, authorized views/datasets pro sharing.

INFORMATION_SCHEMA a query insights

JOBS_BY_PROJECT, query plan/timeline, slot utilization, hot spotting detection, query optimizer hints, EXPLAIN/dry-run.

Cost control a billing

On-demand ($6.25/TB scanned) vs. Editions (autoscaling slots), custom quotas, billing labels, FinOps best practices, query cost preview.

Geospatial a JSON funkce

GIS funkce (ST_*), GeoViz integrace, native JSON type, JSON_VALUE/JSON_QUERY, semi-structured data analytics bez schema-on-write.

Implementační proces

Strukturovaný přístup k implementaci BigQuery

Fáze 1: Assessment a design

2-3 týdny

Sběr business požadavků
Inventarizace datových zdrojů
Návrh schématu (Star, Snowflake, OBT)
Strategie partitioning a clustering
Odhad nákladů (on-demand vs slots)
Návrh security a governance

Fáze 2: Nastavení platformy

2-4 týdny

Vytvoření BigQuery datasetů
IAM a row-level security
Nastavení Data Transfer Service
Plánované dotazy
Materializované pohledy
Monitoring a alerting

Fáze 3: Datové pipeline

4-8 týdnů

Dataflow jobs pro ETL
Orchestrace Cloud Composer
Streaming pipelines (Pub/Sub)
Kontroly kvality dat
Testování a validace
Optimalizace výkonu

Fáze 4: Analytika a ML

2-4 týdny

Integrace Looker/Data Studio
Trénování BigQuery ML modelů
Optimalizace BI Engine
Dokumentace
Školení a předání
Spuštění do produkce

Technology Stack

BigQuery ekosystém a integrace

BigQuery Features

BigQuery MLBI EngineBigLakeBigQuery OmniStreamingScheduled Queries

Data Integration

DataflowData Transfer ServicePub/SubCloud ComposerDataproc

Analytics & BI

LookerLooker StudioConnected SheetsNotebooksData Canvas

Governance

Data CatalogDataplexIAMColumn-level SecurityData Masking

Často kladené otázky o BigQuery

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