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How BigQuery’s scores are calculated

The full audit trail, recomputed from the verdict data at build time through the same code that produced the leaderboard: verdict × quality × story weight per cell, cells sum to dimension scores, dimensions blend into the PA Score. Every number on the product page is reproducible from this page alone; for why the formula looks like this, see the methodology.

verdict factors: full ×1.0 · partial ×0.6 · disputed ×0.3 · none ×0.0 · n/a excluded from both sides · cell points = weight × quality × factor · cell max = weight × 10

PA Score41/100

Agent-ready 41.3 × 0.30 = 12.39

API quality 27.1 × 0.20 = 5.42

Openness 34.8 × 0.20 = 6.96

Built-in AI 57.1 × 0.15 = 8.56

Automation 47.8 × 0.15 = 7.17

(12.39 + 5.42 + 6.96 + 8.56 + 7.17) ÷ (0.30 + 0.20 + 0.20 + 0.15 + 0.15) = 40.51 ÷ 1.00 = 40.5

Scores are stored to 1 decimal; the product page’s pills round to whole numbers for display. Each dimension below shows the stories, verdicts, and cited evidence behind its number.

Agent-ready41.3/100×0.30 of the PA blend

Outside-in: can YOUR agent reach and drive this product — API, MCP, CLI, headless runs, agent docs.

Point an agent at llms.txt or agent-oriented docsweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

  • [probe] https://cloud.google.com/llms.txt“PROBE llms.txt: HTTP 404 at https://cloud.google.com/llms.txt”

Run the product headlessly / in CI for automationweight 2

2 (weight) × 8 (quality) × 1.0 (full) = 16.0 of 20 max

  • [claimed-docs] https://cloud.google.com/bigquery/docs/bq-command-line-tool“vai aprender a usar o `bq`, a ferramenta de interface de linha de comandos (CLI) baseada em Python para o BigQuery, para criar um conjunto de dados, carregar dados de amostra e consultar tabelas.”
  • [claimed-docs] https://cloud.google.com/bigquery/docs/bq-command-line-tool“learn how to use bq, the Python-based command-line interface (CLI) tool for BigQuery to create a dataset, load sample data, and query tables”
  • [probe] https://cloud.google.com/bigquery/docs/bq-command-line-tool“official CLI documented at https://cloud.google.com/bigquery/docs/bq-command-line-tool”
  • [claimed-docs] https://cloud.google.com/bigquery/docs/loading-data“you can schedule load jobs. You can schedule one-time or batch data transfers at regular intervals”
  • [claimed-docs] https://cloud.google.com/bigquery/docs/reference/odbc-jdbc-drivers“The Simba Open Database Connectivity (ODBC) and Java Database Connectivity (JDBC) drivers for BigQuery connect your applications to BigQuery, letting you use BigQuery features with your preferred tooling and infrastructure.”
  • [probe] https://cloud.google.com/bigquery/docs/reference/rest“PROBE runtime (recorded 2026-09-06): the BigQuery v2 REST discovery document downloaded keylessly from bigquery.googleapis.com and parsed cleanly — id bigquery:v2, basePath /bigquery/v2/, resources datasets/jobs/models/projects/routines/rowAccessPolicies/tabledata/tables — a machine-readable, self-describing API surface.”

Plug MCP servers into this product so it can use their toolsweight 3

3 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 30 max

  • [probe] https://github.com/googleapis/mcp-toolbox“official MCP server documented at https://github.com/googleapis/mcp-toolbox”
  • [probe] https://github.com/googleapis/mcp-toolbox“PROBE runtime (recorded 2026-09-06): Google's official MCP Toolbox for Databases ran from npm — `npx -y @toolbox-sdk/server --version` printed toolbox version 1.10.0 keylessly; starting the prebuilt BigQuery toolset requires ADC credentials and a BIGQUERY_PROJECT (documented), so the live server handshake is credential-gated.”
  • [claimed-docs] https://cloud.google.com/gemini/docs/bigquery/overview“대화형 분석을 사용하면 자연어로 데이터와 대화할 수 있습니다.”
  • [claimed-docs] https://cloud.google.com/gemini/docs/bigquery/overview“BigQuery의 Gemini로 자연어를 사용하여 테이블 애셋을 찾고 조인하고 쿼리하고 결과를 시각화하며 전체 프로세스에서 다른 사용자와 원활하게 공동작업할 수 있습니다.”

Connect an agent via an official MCP serverweight 3

3 (weight) × 6 (quality) × 0.6 (partial) = 10.8 of 30 max

  • [probe] https://github.com/googleapis/mcp-toolbox“official MCP server documented at https://github.com/googleapis/mcp-toolbox”
  • [probe] https://github.com/googleapis/mcp-toolbox“PROBE runtime (recorded 2026-09-06): Google's official MCP Toolbox for Databases ran from npm — `npx -y @toolbox-sdk/server --version` printed toolbox version 1.10.0 keylessly; starting the prebuilt BigQuery toolset requires ADC credentials and a BIGQUERY_PROJECT (documented), so the live server handshake is credential-gated.”

Use an official CLIweight 2

2 (weight) × 8 (quality) × 1.0 (full) = 16.0 of 20 max

  • [claimed-docs] https://cloud.google.com/bigquery/docs/bq-command-line-tool“vai aprender a usar o `bq`, a ferramenta de interface de linha de comandos (CLI) baseada em Python para o BigQuery, para criar um conjunto de dados, carregar dados de amostra e consultar tabelas.”
  • [claimed-docs] https://cloud.google.com/bigquery/docs/bq-command-line-tool“learn how to use bq, the Python-based command-line interface (CLI) tool for BigQuery to create a dataset, load sample data, and query tables”
  • [claimed-docs] https://cloud.google.com/bigquery/docs/bq-command-line-tool“you learn how to use `bq`, the Python-based command-line interface (CLI) tool for BigQuery to create a dataset, load sample data, and query tables”
  • [probe] https://cloud.google.com/bigquery/docs/bq-command-line-tool“official CLI documented at https://cloud.google.com/bigquery/docs/bq-command-line-tool”

Drive the product through a documented public APIweight 3

3 (weight) × 8 (quality) × 1.0 (full) = 24.0 of 30 max

  • [probe] https://cloud.google.com/bigquery/docs/reference/rest“PROBE runtime (recorded 2026-09-06): the BigQuery v2 REST discovery document downloaded keylessly from bigquery.googleapis.com and parsed cleanly — id bigquery:v2, basePath /bigquery/v2/, resources datasets/jobs/models/projects/routines/rowAccessPolicies/tabledata/tables — a machine-readable, self-describing API surface.”
  • [claimed-docs] https://cloud.google.com/bigquery/docs/write-api“The Storage Write API (gRPC) has lower pricing and more robust features, including exactly-once delivery semantics.”
  • [claimed-docs] https://cloud.google.com/bigquery/docs/write-api“we recommend using the Storage Write API (gRPC) instead of the Storage Write API (REST). The Storage Write API (gRPC) has lower pricing and more robust features, including exactly-once delivery semantics.”
  • [claimed-docs] https://cloud.google.com/bigquery/docs/reference/odbc-jdbc-drivers“The Simba Open Database Connectivity (ODBC) and Java Database Connectivity (JDBC) drivers for BigQuery connect your applications to BigQuery, letting you use BigQuery features with your preferred tooling and infrastructure.”
  • [probe] https://cloud.google.com/bigquery/docs/bq-command-line-tool“official CLI documented at https://cloud.google.com/bigquery/docs/bq-command-line-tool”
  • [probe] https://cloud.google.com/llms.txt“PROBE llms.txt: HTTP 404 at https://cloud.google.com/llms.txt”
  • [probe] https://cloud.google.com/openapi.json“PROBE openapi: all candidate paths 404 (https://cloud.google.com/openapi.json, https://cloud.google.com/swagger.json, https://cloud.google.com/api/openapi.json, https://cloud.google.com/.well-known/openapi.json)”
  • [probe] https://github.com/googleapis/mcp-toolbox“PROBE runtime (recorded 2026-09-06): Google's official MCP Toolbox for Databases ran from npm — `npx -y @toolbox-sdk/server --version` printed toolbox version 1.10.0 keylessly; starting the prebuilt BigQuery toolset requires ADC credentials and a BIGQUERY_PROJECT (documented), so the live server handshake is credential-gated.”

Issue scoped/least-privilege API credentials for an agentweight 2

2 (weight) × 5 (quality) × 0.6 (partial) = 6.0 of 20 max

  • [claimed-docs] https://cloud.google.com/bigquery/docs/access-control“In diesem Dokument finden Sie eine Liste der vordefinierten IAM-Rollen (Identity and Access Management) und Berechtigungen für BigQuery.”
  • [claimed-docs] https://cloud.google.com/bigquery/docs/access-control“BigQuery: Roles and permissions that apply to BigQuery resources such as datasets, tables, views, and routines.”
  • [claimed-docs] https://cloud.google.com/bigquery/docs/custom-quotas“複数の BigQuery プロジェクトとユーザーが存在している場合は、カスタム割り当てを要求することで費用を管理できます。この割り当てでは、1 日に処理されるデータ量の上限を指定します。”

Build against official SDKsweight 2

2 (weight) × 7 (quality) × 1.0 (full) = 14.0 of 20 max

  • [claimed-docs] https://cloud.google.com/bigquery/docs/release-notes“The Rust SDK for BigQuery is now in Preview.”
  • [claimed-docs] https://cloud.google.com/bigquery/docs/release-notes“The Rust SDK for BigQuery is now in Preview”
  • [claimed-docs] https://cloud.google.com/bigquery/docs/write-api“we recommend using the Storage Write API (gRPC) instead of the Storage Write API (REST). The Storage Write API (gRPC) has lower pricing and more robust features, including exactly-once delivery semantics.”
  • [claimed-docs] https://cloud.google.com/bigquery/docs/reference/odbc-jdbc-drivers“The Simba Open Database Connectivity (ODBC) and Java Database Connectivity (JDBC) drivers for BigQuery connect your applications to BigQuery, letting you use BigQuery features with your preferred tooling and infrastructure.”
  • [probe] https://cloud.google.com/bigquery/docs/bq-command-line-tool“official CLI documented at https://cloud.google.com/bigquery/docs/bq-command-line-tool”
  • [probe] https://cloud.google.com/bigquery/docs/reference/rest“PROBE runtime (recorded 2026-09-06): the BigQuery v2 REST discovery document downloaded keylessly from bigquery.googleapis.com and parsed cleanly — id bigquery:v2, basePath /bigquery/v2/, resources datasets/jobs/models/projects/routines/rowAccessPolicies/tabledata/tables — a machine-readable, self-describing API surface.”

Subscribe to events via webhooksweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

  • [claimed-docs] https://cloud.google.com/bigquery/docs/loading-data“To stream data into BigQuery, you can use a BigQuery subscription in Pub/Sub. Pub/Sub can handle high throughput of data loads into BigQuery.”
  • [claimed-docs] https://cloud.google.com/bigquery/docs/loading-data“To stream data into BigQuery, you can use a BigQuery subscription in Pub/Sub.”
  • [claimed-docs] https://cloud.google.com/bigquery/docs/loading-data“Pub/Sub can handle high throughput of data loads into BigQuery. It supports real-time data streaming, loading data as it's generated.”
  • [claimed-docs] https://cloud.google.com/bigquery/docs/continuous-queries-introduction“BigQuery continuous queries are SQL statements that run continuously. Continuous queries let you analyze incoming data in BigQuery in real time.”

Agent-ready = 86.8 ÷ 210 × 100 = 41.3

API quality27.1/100×0.20 of the PA blend

The programmable surface once an agent is there — machine-readable spec, interactive docs, sandbox, versioning discipline.

Explore an interactive API reference with runnable examplesweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

  • [probe] https://cloud.google.com/llms.txt“PROBE llms.txt: HTTP 404 at https://cloud.google.com/llms.txt”
  • [probe] https://cloud.google.com/openapi.json“PROBE openapi: all candidate paths 404 (https://cloud.google.com/openapi.json, https://cloud.google.com/swagger.json, https://cloud.google.com/api/openapi.json, https://cloud.google.com/.well-known/openapi.json)”
  • [probe] https://cloud.google.com/bigquery/docs/reference/rest“PROBE runtime (recorded 2026-09-06): the BigQuery v2 REST discovery document downloaded keylessly from bigquery.googleapis.com and parsed cleanly — id bigquery:v2, basePath /bigquery/v2/, resources datasets/jobs/models/projects/routines/rowAccessPolicies/tabledata/tables — a machine-readable, self-describing API surface.”

Download a machine-readable API spec (OpenAPI or equivalent)weight 2

2 (weight) × 6 (quality) × 0.6 (partial) = 7.2 of 20 max

  • [probe] https://cloud.google.com/openapi.json“PROBE openapi: all candidate paths 404 (https://cloud.google.com/openapi.json, https://cloud.google.com/swagger.json, https://cloud.google.com/api/openapi.json, https://cloud.google.com/.well-known/openapi.json)”
  • [probe] https://cloud.google.com/bigquery/docs/reference/rest“PROBE runtime (recorded 2026-09-06): the BigQuery v2 REST discovery document downloaded keylessly from bigquery.googleapis.com and parsed cleanly — id bigquery:v2, basePath /bigquery/v2/, resources datasets/jobs/models/projects/routines/rowAccessPolicies/tabledata/tables — a machine-readable, self-describing API surface.”

Test against a sandbox environment without touching production dataweight 1

1 (weight) × 7 (quality) × 1.0 (full) = 7.0 of 10 max

  • [claimed-docs] https://cloud.google.com/bigquery/docs/sandbox“Mit der BigQuery-Sandbox können Sie BigQuery nutzen, ohne eine Kreditkarte anzugeben oder ein Rechnungskonto für Ihr Projekt zu erstellen.”
  • [claimed-docs] https://cloud.google.com/bigquery/docs/sandbox“The BigQuery sandbox lets you explore limited BigQuery capabilities at no cost to confirm whether BigQuery fits your needs.”
  • [claimed-docs] https://cloud.google.com/bigquery/docs/sandbox“The BigQuery sandbox lets you experience BigQuery without providing a credit card or creating a billing account for your project.”

Rely on versioned APIs with a documented deprecation policyweight 2

2 (weight) × 4 (quality) × 0.6 (partial) = 4.8 of 20 max

  • [probe] https://cloud.google.com/bigquery/docs/reference/rest“PROBE runtime (recorded 2026-09-06): the BigQuery v2 REST discovery document downloaded keylessly from bigquery.googleapis.com and parsed cleanly — id bigquery:v2, basePath /bigquery/v2/, resources datasets/jobs/models/projects/routines/rowAccessPolicies/tabledata/tables — a machine-readable, self-describing API surface.”
  • [claimed-docs] https://cloud.google.com/bigquery/docs/release-notes“The Rust SDK for BigQuery is now in Preview.”

API quality = 19.0 ÷ 70 × 100 = 27.1

Openness34.8/100×0.20 of the PA blend

Can you leave, inspect, or self-host — data export, open source, portability.

Do everything through the API that I can do in the UIweight 2

2 (weight) × 7 (quality) × 0.6 (partial) = 8.4 of 20 max

  • [probe] https://cloud.google.com/bigquery/docs/reference/rest“PROBE runtime (recorded 2026-09-06): the BigQuery v2 REST discovery document downloaded keylessly from bigquery.googleapis.com and parsed cleanly — id bigquery:v2, basePath /bigquery/v2/, resources datasets/jobs/models/projects/routines/rowAccessPolicies/tabledata/tables — a machine-readable, self-describing API surface.”
  • [probe] https://cloud.google.com/bigquery/docs/bq-command-line-tool“official CLI documented at https://cloud.google.com/bigquery/docs/bq-command-line-tool”
  • [claimed-docs] https://cloud.google.com/bigquery/docs/bq-command-line-tool“vai aprender a usar o `bq`, a ferramenta de interface de linha de comandos (CLI) baseada em Python para o BigQuery, para criar um conjunto de dados, carregar dados de amostra e consultar tabelas.”
  • [claimed-docs] https://cloud.google.com/bigquery/docs/introduction-sql“Data Manipulation Language (DML) statements enable you to update, insert, and delete data from your BigQuery tables.”
  • [claimed-docs] https://cloud.google.com/bigquery/docs/access-control“In diesem Dokument finden Sie eine Liste der vordefinierten IAM-Rollen (Identity and Access Management) und Berechtigungen für BigQuery.”
  • [claimed-docs] https://cloud.google.com/gemini/docs/bigquery/overview“BigQuery 데이터 캔버스로 데이터 탐색, 변환, 쿼리, 시각화”
  • [claimed-docs] https://cloud.google.com/gemini/docs/bigquery/overview“대화형 분석을 사용하면 자연어로 데이터와 대화할 수 있습니다.”
  • [claimed-docs] https://cloud.google.com/bigquery/docs/notebooks-introduction“notebooks let you combine SQL queries with Python code, rich text, and visualizations to tell a comprehensive story with your data.”

Export all of my data in open formats and leaveweight 3

3 (weight) × 5 (quality) × 0.6 (partial) = 9.0 of 30 max

  • [claimed-docs] https://cloud.google.com/bigquery/docs/iceberg-tables“Iceberg managed tables offer the same fully managed experience as standard BigQuery tables, but store data in customer-owned storage buckets.”
  • [claimed-docs] https://cloud.google.com/bigquery/docs/iceberg-tables“Iceberg managed tables support the open Iceberg table format for better interoperability with open-source and third-party compute engines”
  • [claimed-docs] https://cloud.google.com/bigquery/docs/iceberg-tables“Iceberg managed tables support the open Iceberg table format for better interoperability with open-source and third-party compute engines on a single copy of data.”
  • [claimed-docs] https://cloud.google.com/bigquery/docs/introduction“BigQuery provides a uniform way to work with both structured and unstructured data and supports open table formats like Apache Iceberg, Delta, and Apache Hudi.”

Read the product's source under an open licenseweight 2

n/a — not applicable to this product: excluded from numerator and denominator

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Self-host the core productweight 3

n/a — not applicable to this product: excluded from numerator and denominator

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Openness = 17.4 ÷ 50 × 100 = 34.8

Built-in AI57.1/100×0.15 of the PA blend

Inside-out: how agentic the product itself is for its users — built-in assistants, autonomous features.

Get AI-generated insights and suggestions from my data inside the productweight 2

2 (weight) × 8 (quality) × 1.0 (full) = 16.0 of 20 max

  • [claimed-docs] https://cloud.google.com/gemini/docs/bigquery/overview“대화형 분석을 사용하면 자연어로 데이터와 대화할 수 있습니다.”
  • [claimed-docs] https://cloud.google.com/gemini/docs/bigquery/overview“BigQuery 데이터 캔버스로 데이터 탐색, 변환, 쿼리, 시각화”
  • [claimed-docs] https://cloud.google.com/gemini/docs/bigquery/overview“BigQuery의 Gemini를 사용하여 SQL 또는 Python에서 코드를 생성하거나 제안하고 기존 SQL 쿼리를 설명할 수 있습니다.”
  • [claimed-docs] https://cloud.google.com/bigquery/docs/release-notes“Conversational analytics now supports questions about market basket analysis.”
  • [claimed-docs] https://cloud.google.com/bigquery“Train, evaluate, and deploy predictive analytics models directly within BigQuery using SQL.”
  • [claimed-docs] https://cloud.google.com/bigquery“Build sophisticated context-retrieval and RAG applications with embeddings and vector, text, or hybrid search to find information based on meaning, not just keywords.”
  • [claimed-docs] https://cloud.google.com/gemini/docs/bigquery/overview“BigQuery의 Gemini로 자연어를 사용하여 테이블 애셋을 찾고 조인하고 쿼리하고 결과를 시각화하며 전체 프로세스에서 다른 사용자와 원활하게 공동작업할 수 있습니다.”
  • [claimed-docs] https://cloud.google.com/bigquery“Use generative AI in your workflows with AI functions for text summarization, sentiment analysis, and data enrichment.”
  • [claimed-docs] https://cloud.google.com/gemini/docs/bigquery/overview“您还可以使用自然语言查询来开始数据分析。如需了解如何生成、补全和总结代码”

Set up automations that run autonomously in the backgroundweight 2

2 (weight) × 5 (quality) × 0.6 (partial) = 6.0 of 20 max

  • [claimed-docs] https://cloud.google.com/bigquery/docs/continuous-queries-introduction“BigQuery の継続的クエリは、継続的に実行される SQL ステートメントです。継続的クエリを使用すると、BigQuery で受信データをリアルタイムで分析できます。”
  • [claimed-docs] https://cloud.google.com/bigquery/docs/continuous-queries-introduction“BigQuery continuous queries are SQL statements that run continuously. Continuous queries let you analyze incoming data in BigQuery in real time.”
  • [claimed-docs] https://cloud.google.com/bigquery/docs/loading-data“you can schedule load jobs. You can schedule one-time or batch data transfers at regular intervals”
  • [claimed-docs] https://cloud.google.com/dataform/docs/overview“Dataform is a service for data analysts to develop, test, control versions, and schedule complex workflows for data transformation in BigQuery.”
  • [claimed-docs] https://cloud.google.com/dataform/docs/overview“Dataform lets you manage data transformation in the Extraction, Loading, and Transformation (ELT) process for data integration.”
  • [claimed-docs] https://cloud.google.com/dataform/docs/overview“View a visualization of the dependency tree of your workflow.”
  • [claimed-docs] https://cloud.google.com/dataform/docs/overview“Collaborate with team members on workflow development through Git.”

Delegate tasks to a built-in AI assistant inside the productweight 3

3 (weight) × 7 (quality) × 1.0 (full) = 21.0 of 30 max

  • [claimed-docs] https://cloud.google.com/gemini/docs/bigquery/overview“대화형 분석을 사용하면 자연어로 데이터와 대화할 수 있습니다.”
  • [claimed-docs] https://cloud.google.com/gemini/docs/bigquery/overview“BigQuery 데이터 캔버스로 데이터 탐색, 변환, 쿼리, 시각화”
  • [claimed-docs] https://cloud.google.com/gemini/docs/bigquery/overview“BigQuery의 Gemini를 사용하여 SQL 또는 Python에서 코드를 생성하거나 제안하고 기존 SQL 쿼리를 설명할 수 있습니다.”
  • [claimed-docs] https://cloud.google.com/bigquery/docs/release-notes“Conversational analytics now supports questions about market basket analysis.”
  • [claimed-docs] https://cloud.google.com/gemini/docs/bigquery/overview“BigQuery의 Gemini로 자연어를 사용하여 테이블 애셋을 찾고 조인하고 쿼리하고 결과를 시각화하며 전체 프로세스에서 다른 사용자와 원활하게 공동작업할 수 있습니다.”
  • [claimed-docs] https://cloud.google.com/gemini/docs/bigquery/overview“您还可以使用自然语言查询来开始数据分析。如需了解如何生成、补全和总结代码”

Operate the product with natural-language commandsweight 2

2 (weight) × 7 (quality) × 0.6 (partial) = 8.4 of 20 max

  • [claimed-docs] https://cloud.google.com/gemini/docs/bigquery/overview“대화형 분석을 사용하면 자연어로 데이터와 대화할 수 있습니다.”
  • [claimed-docs] https://cloud.google.com/gemini/docs/bigquery/overview“BigQuery 데이터 캔버스로 데이터 탐색, 변환, 쿼리, 시각화”
  • [claimed-docs] https://cloud.google.com/gemini/docs/bigquery/overview“BigQuery의 Gemini를 사용하여 SQL 또는 Python에서 코드를 생성하거나 제안하고 기존 SQL 쿼리를 설명할 수 있습니다.”
  • [claimed-docs] https://cloud.google.com/bigquery/docs/release-notes“Conversational analytics now supports questions about market basket analysis.”
  • [claimed-docs] https://cloud.google.com/gemini/docs/bigquery/overview“BigQuery의 Gemini로 자연어를 사용하여 테이블 애셋을 찾고 조인하고 쿼리하고 결과를 시각화하며 전체 프로세스에서 다른 사용자와 원활하게 공동작업할 수 있습니다.”
  • [claimed-docs] https://cloud.google.com/gemini/docs/bigquery/overview“您还可以使用自然语言查询来开始数据分析。如需了解如何生成、补全和总结代码”

Built-in AI = 51.4 ÷ 90 × 100 = 57.1

Automation47.8/100×0.15 of the PA blend

Depth of automation primitives — rules, scheduling, bulk operations, webhooks.

Perform bulk operations across many items at onceweight 2

2 (weight) × 7 (quality) × 1.0 (full) = 14.0 of 20 max

  • [claimed-docs] https://cloud.google.com/bigquery/docs/introduction-sql“Data Manipulation Language (DML) statements enable you to update, insert, and delete data from your BigQuery tables.”
  • [claimed-docs] https://cloud.google.com/bigquery/docs/write-api“we recommend using the Storage Write API (gRPC) instead of the Storage Write API (REST). The Storage Write API (gRPC) has lower pricing and more robust features, including exactly-once delivery semantics.”
  • [claimed-docs] https://cloud.google.com/bigquery/docs/write-api“For new projects, we recommend using the Storage Write API (gRPC) instead of the Storage Write API (REST). The Storage Write API (gRPC) has lower pricing and more robust features, including exactly-once delivery semantics.”
  • [claimed-docs] https://cloud.google.com/bigquery/docs/loading-data“you can schedule load jobs. You can schedule one-time or batch data transfers at regular intervals”
  • [claimed-docs] https://cloud.google.com/bigquery/docs/bq-command-line-tool“learn how to use bq, the Python-based command-line interface (CLI) tool for BigQuery to create a dataset, load sample data, and query tables”
  • [community] https://hn.algolia.com/api/v1/items/39446789“I've worked with much larger datasets on BQ (petabyte scale) and managed to not spend more than $1000 in an hour; BQ tells you how much data will be processed BEFORE running the query.”
  • [claimed-docs] https://cloud.google.com/bigquery/docs/introduction“the distributed and scalable analysis engine of BigQuery allows querying terabytes in seconds and petabytes in minutes”

Define rules that trigger actions automatically on eventsweight 3

3 (weight) × 4 (quality) × 0.6 (partial) = 7.2 of 30 max

  • [claimed-docs] https://cloud.google.com/bigquery/docs/continuous-queries-introduction“BigQuery の継続的クエリは、継続的に実行される SQL ステートメントです。継続的クエリを使用すると、BigQuery で受信データをリアルタイムで分析できます。”
  • [claimed-docs] https://cloud.google.com/bigquery/docs/continuous-queries-introduction“BigQuery continuous queries are SQL statements that run continuously. Continuous queries let you analyze incoming data in BigQuery in real time.”

Schedule recurring jobs or workflowsweight 2

2 (weight) × 7 (quality) × 1.0 (full) = 14.0 of 20 max

  • [claimed-docs] https://cloud.google.com/bigquery/docs/loading-data“you can schedule load jobs. You can schedule one-time or batch data transfers at regular intervals”
  • [claimed-docs] https://cloud.google.com/dataform/docs/overview“Dataform is a service for data analysts to develop, test, control versions, and schedule complex workflows for data transformation in BigQuery.”
  • [claimed-docs] https://cloud.google.com/dataform/docs/overview“Dataform lets you manage data transformation in the Extraction, Loading, and Transformation (ELT) process for data integration.”
  • [claimed-docs] https://cloud.google.com/bigquery/docs/continuous-queries-introduction“BigQuery の継続的クエリは、継続的に実行される SQL ステートメントです。継続的クエリを使用すると、BigQuery で受信データをリアルタイムで分析できます。”
  • [claimed-docs] https://cloud.google.com/bigquery/docs/continuous-queries-introduction“BigQuery continuous queries are SQL statements that run continuously. Continuous queries let you analyze incoming data in BigQuery in real time.”
  • [claimed-docs] https://cloud.google.com/dataform/docs/overview“View a visualization of the dependency tree of your workflow.”
  • [claimed-docs] https://cloud.google.com/dataform/docs/overview“Collaborate with team members on workflow development through Git.”

Version, review, and roll back my automationsweight 1

1 (weight) × 5 (quality) × 0.6 (partial) = 3.0 of 10 max

  • [claimed-docs] https://cloud.google.com/dataform/docs/overview“Dataform is a service for data analysts to develop, test, control versions, and schedule complex workflows for data transformation in BigQuery.”
  • [claimed-docs] https://cloud.google.com/dataform/docs/overview“View a visualization of the dependency tree of your workflow.”
  • [claimed-docs] https://cloud.google.com/dataform/docs/overview“Collaborate with team members on workflow development through Git.”
  • [claimed-docs] https://cloud.google.com/bigquery/docs/release-notes“You can now create, store, and manage pipelines in Git folders.”
  • [claimed-docs] https://cloud.google.com/dataform/docs/overview“Dataform lets you manage data transformation in the Extraction, Loading, and Transformation (ELT) process for data integration.”

Automation = 38.2 ÷ 80 × 100 = 47.8