---
title: "Using the Query History Log in BigQuery: A Guide"
canonical: "https://www.owox.com/blog/articles/bigquery-query-history-log"
updated: "2025-08-14"
---

# BigQuery query history: how to find, query and audit past jobs

[Google BigQuery](/blog/topics/bigquery) · Updated August 14, 2025 · [Ievgen Krasovytskyi](/team/ievgen-krasovytskyi) · 14 min read

![BigQuery query history: how to find, query and audit past jobs](https://cdn.owox.ai/www/webflow/696a743a2dcae047b39467ff_HOW-TO-USE-QUERY-HISTORY-LOG.png/public)

Ever wished you could go back and see exactly what queries you or your team ran in BigQuery? **Querying history logs** makes it easy to look back and see exactly what happened. They let you revisit past queries, figure out what went wrong, and build on what worked, whether you’re debugging an issue or just checking data access.

![Banner reading “How to use query history log”, with the BigQuery logo.](https://cdn.owox.ai/www/webflow/68ac73a7825d680dde8accb4_Frame-23137749-min.jpg/public)

In this article, we’ll explain what query history logs are, how to access them, and why they matter. You’ll learn how to **view past queries**, use audit logs for compliance, and analyze query performance. We’ll also cover advanced tips, common issues, and tools to simplify your workflow.

## **Introduction to Query History Logs in BigQuery**

Query history in [BigQuery](https://www.owox.com/blog/articles/bigquery-everything-you-need-to-know/) helps you keep **track of all the queries** you’ve run automatically. This built-in feature saves every query, making it easy to revisit and learn from your past work. Whether you’re fixing an issue or building on previous analysis, having this history speeds up your workflow. It acts like a timeline of your data exploration.

Looking at past queries also helps you understand how your analysis has changed over time. You can **spot trends**, **improve future queries**, and make better decisions using this information. In short, query history is a simple but powerful way to manage and improve your work in BigQuery.

## **Benefits of Query History Logs in BigQuery** 

Query history logs in BigQuery offer more than just a record of past queries; they help you work faster, smarter, and more accurately. In the sections below, we’re covering **key benefits** like reuse, performance tuning, accuracy, and team collaboration.

### **Improve Efficiency by Reusing Past Queries**

Query history in BigQuery helps you save time by letting you reuse queries you’ve already written. Instead of starting from scratch every time, you can **copy and adjust past queries to fit new tasks**. This is especially useful for recurring reports or analyses. Over time, it saves you effort and helps you get more done by cutting out repetitive manual tasks.

### **Ensure Accuracy with Reference to Successful Queries**

Using past successful queries as a reference helps maintain accuracy in your analysis. When you’re unsure how to approach a task, looking at queries that **previously delivered correct results** gives you a strong starting point. This practice supports consistency across projects and ensures that you’re applying trusted logic to new data problems.

### **Optimize Performance Through Historical Query Analysis**

Your query history is a great way to find slow or inefficient queries. By reviewing execution time, slot usage, and other metrics, you can **spot areas that need improvement**. Tweaking complex logic or removing unnecessary joins can make a big difference. These small fixes help speed up your queries and reduce BigQuery costs over time.

### **Support Collaboration and Knowledge Sharing**

Teams work better when they can see what others have done. BigQuery’s query history lets users review each other’s queries, learn from past work, and build on successful examples. This makes handovers easier and encourages better teamwork. Sharing queries saves time and disseminates useful knowledge across your team without requiring additional meetings. 

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## **Key Features of BigQuery Query History Logs** 

BigQuery query history logs offer detailed records of every executed query, including timing, status, and resource usage. In this section, we’re covering the key details these logs provide and how they help with performance tracking and analysis.

### **Information Provided in Query History Logs**

BigQuery’s query history section gives you a full list of the queries you’ve run, along with important details like **execution time, query ID, and resource usage**. This view helps you understand when each query ran and how much data it processed. You can also use filters to narrow results by date, user, or query content.

Having access to this information makes it easier to track your work and see how your queries have evolved. It’s especially useful when you’re trying to debug something or want to reuse a previous query, keeping everything organized and easy to find.

### **BigQuery Query History for Performance Analysis**

When analyzing query performance, your history log gives access to key **metrics like duration, bytes processed, and slots used**. These figures help you spot slow or costly queries that may need optimization. High resource use often points to complex joins or unfiltered data scans that could be improved.

BigQuery also shows query plans and error messages within the history view. These tools help you **find performance bottlenecks** and troubleshoot problems. Over time, reviewing this data helps you write faster, cleaner queries and manage your BigQuery resources more effectively.

## **Audit Logs vs. Query History: Compliance and Security Insights**

While query history focuses on executed queries and their performance, audit logs track a much broader range of user activities. These include who accessed what data, when it happened, and what they did with it. That’s why audit logs are so important for security checks and keeping things accountable in BigQuery.

Audit logs help **detect unusual behavior**, such as unauthorized access or sudden spikes in data activity. They’re also key for meeting [compliance](https://www.owox.com/glossary/data-compliance) needs, since they record detailed system events. Unlike query history, audit logs **capture everything from job creation to permission changes**, making them critical for audits and regulatory checks.

## **How to Query History Logs in BigQuery?** 

Querying history logs in BigQuery helps you retrieve detailed records of past jobs, including queries, loads, and exports. In this section, we’ll show you how to access these logs using both the [BigQuery interface](https://www.owox.com/blog/articles/bigquery-user-interface/) and SQL-based system views.

### **Navigate to the Job History Section in the BigQuery Console**

To review query history activity in BigQuery, navigate to the **Job History** section. 

![Navigating to the “Job History” section in the BigQuery console to view past queries along with helpful details.](https://cdn.owox.ai/www/webflow/689e26b4a2241b76d7e0065c_AD_4nXcORI7l_yc6CBjBVt7VB0JE3xkX_QOJMhbiOJs2_gdGGGqKTRtyFTQtitOAxeAWLOc0RmaCqTTr-NqFNRFPEgt2ZTSDRWOpztcFrzuh_M38HIubalAgxZIO22kAeMoeDu0Vqn8s.png/public)

Once you click on the arrow beside the “Refresh” option, a window of history will pop up. 

![Clicking on the arrow beside “Refresh” for expanding the “Job History” section in BigQuery.](https://cdn.owox.ai/www/webflow/689e26b4a2241b76d7e00659_AD_4nXcmaOw1bq8Rt34LJgKmEb_r5nqcIAlvFK_FIK6Q3QehuxGSPoxAcFPgql5RZnvXXRrNLSmM9jPQ408AN3MizkH2VScvh4qAOlDWDMRkYOKzriNB0Uc5gHpJILsNDS-PSl5wuP8l.png/public)

You can click on each one and see the query used.

![Expanded view of “Job History” section in the BigQuery showing the query used for the job.](https://cdn.owox.ai/www/webflow/689e26b4a2241b76d7e00656_AD_4nXfK3OryKwtc3Y6YHrzXKvG2rFq3Y0me1aUU48tauxXmXykZ23edgZWvqpAjvMMwPR3qYOtqj0jemp5qs3O9ag0CW8SHTEMSGTecoqwqwS3MAQBoq-wnEGGsEgWpADrIlnt5cf8jbg.png/public)

### **View Query History with INFORMATION\_SCHEMA.JOBS** 

You can also access query history using [SQL](https://www.owox.com/blog/use-cases/google-bigquery-functions-overview/) by querying the **INFORMATION\_SCHEMA.JOBS** view. It shows metadata for all jobs in your project, including queries, loads, and exports.

#### **Using INFORMATION\_SCHEMA.JOBS**

The **INFORMATION\_SCHEMA.JOBS** view gives you details about every job that’s run in your BigQuery project, whether it’s a query, a data load, or an export. It’s a handy way to track what’s been done and how well it performed.

**Example:**

Suppose you want to review all completed query jobs across the project to see who ran them and how much data they processed. The query below lists all successful jobs, ordered by when they were created.

```
SELECT
  job_id,
  user_email,
  state,
  total_bytes_processed,
  creation_time
FROM
  `region-us`.INFORMATION_SCHEMA.JOBS
WHERE
  job_type = 'QUERY'
  AND state = 'DONE'
ORDER BY
  creation_time DESC;
```

![Retrieving completed query jobs using INFORMATION\_SCHEMA.JOBS in BigQuery to monitor job status, resource usage, and execution time.](https://cdn.owox.ai/www/webflow/689e26b4a2241b76d7e00653_AD_4nXe0j_arMaMYEPdal7cFHXSXAAXwTQN0u95Lw1_Fa4JgBeOPecNiaW9ymeSSz_0qKf5Nj29LLDAmVp1zmOAVP2GE10uyMD_FytvXEWiQzUQX1oTiCsO99uqY_iTHxqmNrDWvDmjb.png/public)

Here\*\*:\*\*

*   **job\_id, user email, state**: Identifies the job and its completion status.
*   **Total bytes\_processed**: Shows how much data the query scanned.
*   **creation\_time**: Used for sorting queries by when they were run.
*   **Filters**: Focuses on QUERY jobs that were successfully completed.

This query helps you monitor completed queries, track resource usage, and analyze when each query was run. It’s a simple way to review performance and spot trends across your BigQuery jobs.

#### **Using INFORMATION\_SCHEMA.JOBS\_BY\_USER**

The **INFORMATION\_SCHEMA.JOBS\_BY\_USER** view shows job metadata for only the currently logged-in user. It’s helpful when you want to review your query activity without seeing jobs run by others in the project.

**Example:**

Suppose you want to review your recent successful queries and see how much data each one processed, without viewing jobs from other users. The query below lists only your completed query jobs, sorted by the most recent.

```
SELECT
  job_id,
  state,
  total_bytes_processed,
  creation_time
FROM
  `region-us`.INFORMATION_SCHEMA.JOBS_BY_USER
WHERE
  job_type = 'QUERY'
  AND state = 'DONE'
ORDER BY
  creation_time DESC;
```

![Viewing completed query jobs for the current user with INFORMATION\_SCHEMA.JOBS\_BY\_USER in BigQuery to analyze query performance and resource usage.](https://cdn.owox.ai/www/webflow/689e26b4a2241b76d7e0065f_AD_4nXe7JjReOqvA0FAi-FBxBSndVdmsL5iIzmyICDAu9pWKjZyaDye8HLWNwj9cMqLE8VQUfns8r1mW_XXotd8lHRl0cSpvjpNYPlI-Btm3lSz6e5meeB9t-ZddHIfQOPf7GxCl3bu0DQ.png/public)

**Here:**

*   **job\_id, state**: Identifies each job and confirms it completed successfully.
*   **total\_bytes\_processed**: Displays the volume of data scanned per query.
*   **creation\_time**: Orders the jobs from most recent to oldest.
*   **View scope**: Automatically filters results to only show your own jobs, no need to specify your email. 

This provides a clear view for individual users who want to review, debug, or optimize their own BigQuery queries. It removes noise from shared project history, helping you stay focused on your activity.

## **Advanced Techniques for Query History Analysis** 

BigQuery offers more than just viewing past queries. You can use tools to track query performance and even automate the process. Below are two simple ways to do this effectively.

### **Integrating Query History with Monitoring Tools**

You can connect BigQuery query history to tools like [Looker Studio](https://www.owox.com/blog/articles/looker-studio-tutorial/) or Cloud Monitoring to [track performance visually](https://www.owox.com/blog/articles/data-analysis-tools/). Using labels in INFORMATION\_SCHEMA.JOBS, you can identify Looker Studio-triggered jobs, monitor usage, and troubleshoot reports more efficiently.

**Example:**

Suppose you want to identify which BigQuery jobs in the last 7 days were triggered by Looker Studio reports, along with the related report and data source URLs. The query below extracts those details using job labels.

```
-- Define Looker Studio label keys and values
DECLARE requestor_key STRING DEFAULT 'requestor';
DECLARE requestor_value STRING DEFAULT 'looker_studio';

CREATE TEMP FUNCTION GetLabel(labels ARRAY<STRUCT<key STRING, value STRING>>, label_key STRING)
AS (
  (SELECT l.value FROM UNNEST(labels) l WHERE l.key = label_key)
);

CREATE TEMP FUNCTION GetDatasourceUrl(labels ARRAY<STRUCT<key STRING, value STRING>>)
AS (
  CONCAT("https://lookerstudio.google.com/datasources/", GetLabel(labels, 'looker_studio_datasource_id'))
);

CREATE TEMP FUNCTION GetReportUrl(labels ARRAY<STRUCT<key STRING, value STRING>>)
AS (
  CONCAT("https://lookerstudio.google.com/reporting/", GetLabel(labels, 'looker_studio_report_id'))
);

SELECT
  job_id,
  GetDatasourceUrl(labels) AS datasource_url,
  GetReportUrl(labels) AS report_url
FROM
  `region-us`.INFORMATION_SCHEMA.JOBS
WHERE
  creation_time > TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 7 DAY)
  AND GetLabel(labels, requestor_key) = requestor_value
LIMIT 100;
```

![Tracking Looker Studio-triggered BigQuery jobs using labels in INFORMATION\_SCHEMA.JOBS to monitor dashboard activity and query performance.](https://cdn.owox.ai/www/webflow/689e26b4a2241b76d7e00662_AD_4nXfbBUs6RReeo8EUJIrvNP2h-0Jcx-g_L8CidhsBzSA8VYxNbwEqMuIOsrPbbqtB5yFDAxI-T06PwDbN49zNIB45jZJiGu1rxpyJq5gw-7mnndNFhCqyzaqbMAF8sfNp08_ghiV8ZQ.png/public)

**Here:**

*   **Label extraction functions**: These pull specific values from the job labels array.
*   **URL constructors**: Builds direct links to Looker Studio reports and data sources.
*   **Time filter**: Limits the result to the last 7 days of activity.
*   **Label filter**: Includes only jobs triggered by [Looker Studio](https://www.owox.com/blog/articles/looker-and-looker-studio/).

This query helps you monitor which reports and dashboards are actively querying BigQuery. It’s useful for debugging, optimizing performance, or understanding Looker Studio usage patterns in your data workflows.

### **Automating Query History Analysis**

You can save time by setting up scheduled queries to automatically analyze your query history and automate the analysis of your query history by setting up scheduled queries that run at regular intervals. This helps you track performance, spot issues early, and reduce manual effort.

**Example:**

Suppose you want to monitor all queries that scanned over 5 MB of data in the last 7 days. The scheduled query below will help you keep track of heavy queries automatically.

```
SELECT
  job_id,
  user_email,
  total_bytes_processed,
  creation_time
FROM
  `region-us`.INFORMATION_SCHEMA.JOBS
WHERE
  job_type = 'QUERY'
  AND state = 'DONE'
  AND total_bytes_processed > 5242880  -- 5 MB
  AND creation_time >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 7 DAY)
ORDER BY
  creation_time DESC;
```

![Filtering and scheduling BigQuery jobs to automatically track high-volume queries over the past 7 days using INFORMATION\_SCHEMA.JOBS for ongoing performance analysis.](https://cdn.owox.ai/www/webflow/689e26b4a2241b76d7e00650_AD_4nXd6gdjiqfg7OvyQ1UIGOKfKIA8imvulgWzraGEb6zfJZm0abK340lEg9H-DzUZpk__xU9kVqv-UnKQF7E-8WwY72lhhioLiSQnnCZvAXhXOVoYPt55v6qgkRjVCcBEzosC8xgZfoA.png/public)

**Here:** 

*   **total\_bytes\_processed > 5242880**: Filters queries that used more than 5 MB of data.
*   **TIMESTAMP\_SUB**: Limits results to the last 7 days.
*   **ORDER BY creation\_time**: Sorts results from newest to oldest.

This query can be saved and scheduled in BigQuery to run daily or weekly. It provides you with ongoing visibility into resource-intensive queries, enabling you to monitor usage and enhance query performance over time.

## **Common Errors and Troubleshooting Tips for Query History Logs** 

Even though BigQuery makes it easy to track queries, you might run into issues while accessing or analyzing query history logs. In the next sections, we’ll cover common errors and simple troubleshooting tips to help you resolve them quickly.

### **Verify Permissions for Query History Access**

⚠️ **Common issue:** You can’t see your query history or session logs in BigQuery, even after running queries. 

✅ **Solution:** Make sure your user account has roles like **bigquery.admin** or **bigquery.jobUser**. These roles allow access to job and session logs. Check [IAM permissions](https://www.owox.com/blog/articles/bigquery-iam-roles-and-permissions/) in the Google Cloud Console to update or request the correct access.

### **Fix Incomplete Logs by Checking Logging Configurations**

⚠️ **Common issue:** Some queries are missing from your history, or logs appear incomplete in Cloud Logging.

✅ **Solution:** Make sure your audit logs are turned on in Cloud Logging settings. Check for [BigQuery Data Access](https://www.owox.com/blog/articles/bigquery-public-datasets/) logs, whether they are correctly set or not, and review your retention settings so you don’t accidentally lose important log data.

## **Effectively Managing Query History in BigQuery** 

Managing your query history in BigQuery helps you stay organized, improve performance, and ensure data accuracy. In the sections below, we’ll cover practical ways to monitor queries, filter history, connect with audit pipelines, and handle exporting or deleting logs.

### **Monitoring Query Execution to Optimize Workloads**

Query history in BigQuery is more than just a list of past queries, it’s a tool for improving performance. By **reviewing queries** that run slowly, consume high resources, or often fail, you can **pinpoint areas to fix**. Optimizing these queries helps speed up workloads and reduce costs. Look out for patterns in execution time and resource use. Small changes can make a big difference.

### **Filtering INFORMATION\_SCHEMA Queries by Specific Parameters**

BigQuery’s **INFORMATION\_SCHEMA** views let you **filter query history by time, user, project, or job status**. This helps you focus only on what matters, whether you’re troubleshooting or reviewing past workloads. If you use filter, you can keep your analysis clean and relevant, especially when working with large volumes of query data or specific time ranges.

### **Integrating Query History Data with Auditing Pipelines**

You can connect your query history with audit log pipelines to **get a full picture of data usage**. This is useful for tracking user activity, identifying unusual patterns, or ensuring compliance. Integrating with tools like Cloud Logging or BigQuery itself helps centralize your monitoring efforts and supports automated auditing and reporting.

### **Deleting and Restoring Query History**

BigQuery allows you to delete individual queries from your history to keep things clean. However, **once deleted, queries can’t be recovered unless they were exported earlier**. If supported in your environment, the restore feature may let you recover recently deleted queries. Always export important queries before deleting them permanently.

### **Exporting Your Query History Data**

BigQuery **lets you export query history** for backup or deeper analysis. This is useful when you want to track trends over time or use the data outside the platform. You can use built-in export tools, data transfer services, or APIs to move the data wherever you need it. It’s a simple way to keep more control over your query records and analyze them outside BigQuery when needed.

## **Make Your Work Easier with BigQuery Functions**

BigQuery provides a comprehensive set of built-in functions that simplify the transformation, analysis, and management of large datasets. These functions help reduce complexity in your queries, improve performance, and support faster, more efficient data processing across all types of workloads. 

*   [**Date Functions**](https://www.owox.com/blog/articles/bigquery-date-functions/)**:** Shift dates, calculate differences, or extract day, month, and year. Useful for filtering and time-based analysis.
*   [**Timestamp Functions**](https://www.owox.com/blog/articles/bigquery-timestamp-functions/)**:** Format, extract, and compare timestamp values using functions like TIMESTAMP\_DIFF and FORMAT\_TIMESTAMP. Ideal for analyzing time-based events and session data.
*   [**String Functions**](https://www.owox.com/blog/articles/string-functions-bigquery/)**:** Modify and extract text using functions like CONCAT and REGEXP\_EXTRACT. Ideal for cleaning and formatting data.
*   [**Statistical Aggregate Functions**](https://www.owox.com/blog/articles/bigquery-statistical-aggregate-functions/)**:** Calculate metrics like average, standard deviation, and correlation for data analysis.
*   [**Conversion Functions**](https://www.owox.com/blog/articles/bigquery-conversion-functions/)**:** Change data types with CAST, SAFE\_CAST, or TO\_JSON\_STRING. These are useful when cleaning data or transforming it for analysis and reporting.
*   [**DML Functions**](https://www.owox.com/blog/articles/bigquery-data-manipulation-language/)**:** Modify table data directly using INSERT, UPDATE, DELETE, and MERGE. Great for managing records without relying on external ETL tools.

## **Enhance Your Data Analysis with OWOX Reports**

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## FAQ

## Frequently Asked Questions

Can I use SQL to retrieve past queries in BigQuery?

Yes. INFORMATION\_SCHEMA.JOBS\_BY\_USER returns the jobs you submitted and INFORMATION\_SCHEMA.JOBS returns every job in the project. Both need a region qualifier such as \`region-us\`, and both should be filtered on creation\_time, because a query over these views is billed like any other.

Can I delete a query from BigQuery job history?

Not from the console. You can delete the metadata of a finished job with \`bq rm -j\` and the job ID, or with the Python client. Google documents this as a way to remove sensitive information from a query statement. A deleted job cannot be restored.

What is the difference between job history and saved queries in BigQuery?

Job history is the automatic log of every query, load, export and copy job, kept for six months. Saved queries are files you chose to keep: they are under Files in the left pane and have their own version history. A query you ran and never saved is only in job history.

Why can't I see a query in my BigQuery history?

Usually one of five reasons: someone else ran it, so it is in Project history and not Personal history; you have bigquery.jobs.list but not bigquery.jobs.listAll, so other users' jobs are redacted; you are querying the wrong region; the job belongs to another project; or it is older than six months.

How long does BigQuery store query history?

Six months. BigQuery saves a six-month job history for all the jobs of a project, and the INFORMATION\_SCHEMA.JOBS views cover the same window as 180 days. To keep a longer record, append the view to your own table with a daily scheduled query.

Where is query history in BigQuery?

In the Google Cloud console, open BigQuery, click Explorer in the left pane, then Job history. Personal history lists the jobs you ran and Project history lists the jobs of everyone in the project. Click a job to see its SQL, user, duration and bytes billed.

## Who wrote this

![Ievgen Krasovytskyi](https://cdn.owox.ai/www/webflow/68404586b341508a789a4aa5_.png/public)

[Ievgen Krasovytskyi](/team/ievgen-krasovytskyi) · Head of Marketing

Ievgen Krasovytskyi is the Head of Marketing at OWOX, leading strategy across content, SEO, product marketing, and AI-powered automation. With deep expertise in analytics infrastructure, data warehouses, and marketing technology, he builds systems that connect marketing performance to business outcomes. Ievgen writes about SaaS growth, analytics workflows, and the future of AI in marketing operations.

[LinkedIn](https://www.linkedin.com/in/ievgen-krasovytskyi-a38a1253/) · [All articles](/team/ievgen-krasovytskyi)

[Google BigQuery](/blog/topics/bigquery)

## Related articles

[Google BigQuery · BigQuery Partitioned Tables: Complete Guide for 2025 · September 16, 2024](/blog/articles/bigquery-partitioned-tables)

[Google BigQuery · BigQuery Aggregates: Boost Your Data Analysis in 2025 · June 10, 2024](/blog/articles/bigquery-statistical-aggregate-functions)

[Google BigQuery · BigQuery DML Commands: A Complete Guide for Data Analysts · March 14, 2024](/blog/articles/bigquery-data-manipulation-language)

[See all articles →](/blog/articles)

## Links

Pages this page links to, on this site and on docs.owox.com. Where the page has a Markdown twin, its address follows the link.

- [Google BigQuery](https://www.owox.com/blog/topics/bigquery) — /blog/topics/bigquery.md
- [Ievgen Krasovytskyi](https://www.owox.com/team/ievgen-krasovytskyi) — /team/ievgen-krasovytskyi.md
- [BigQuery](https://www.owox.com/blog/articles/bigquery-everything-you-need-to-know) — /blog/articles/bigquery-everything-you-need-to-know.md
- [Book a Demo](https://www.owox.com/book-a-call) — /book-a-call.md
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- [SQL](https://www.owox.com/blog/use-cases/google-bigquery-functions-overview)
- [Looker Studio](https://www.owox.com/blog/articles/looker-studio-tutorial) — /blog/articles/looker-studio-tutorial.md
- [track performance visually](https://www.owox.com/blog/articles/data-analysis-tools) — /blog/articles/data-analysis-tools.md
- [Looker Studio](https://www.owox.com/blog/articles/looker-and-looker-studio) — /blog/articles/looker-and-looker-studio.md
- [IAM permissions](https://www.owox.com/blog/articles/bigquery-iam-roles-and-permissions) — /blog/articles/bigquery-iam-roles-and-permissions.md
- [BigQuery Data Access](https://www.owox.com/blog/articles/bigquery-public-datasets) — /blog/articles/bigquery-public-datasets.md
- [Date Functions](https://www.owox.com/blog/articles/bigquery-date-functions) — /blog/articles/bigquery-date-functions.md
- [Timestamp Functions](https://www.owox.com/blog/articles/bigquery-timestamp-functions) — /blog/articles/bigquery-timestamp-functions.md
- [String Functions](https://www.owox.com/blog/articles/string-functions-bigquery) — /blog/articles/string-functions-bigquery.md
- [Statistical Aggregate Functions](https://www.owox.com/blog/articles/bigquery-statistical-aggregate-functions) — /blog/articles/bigquery-statistical-aggregate-functions.md
- [Conversion Functions](https://www.owox.com/blog/articles/bigquery-conversion-functions) — /blog/articles/bigquery-conversion-functions.md
- [DML Functions](https://www.owox.com/blog/articles/bigquery-data-manipulation-language) — /blog/articles/bigquery-data-manipulation-language.md
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