BigQuery SQL Explained: Legacy SQL vs Standard SQL Made Simple

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QL and Standard SQL isn’t just about syntax it’s the key to unlocking the platform’s full potential. Whether you’re writing simple SELECT statements or designing complex analytics pipelines, the SQL dialect you choose can dramatically impact performance, readability, and maintainability. BigQuery supports both a modern, ANSI-compliant Standard SQL and its older, proprietary Legacy SQL, each with its own rules and capabilities. In this guide, we’ll break down the core differences between the two, explain when and why to use each, and provide examples that make switching and migrating straightforward. Whether you’re a data analyst, developer, or engineer, understanding these two modes ensures you’re writing queries that are not only correct, but optimized for the future.

Introduction to Standard SQL and Legacy SQL in BigQuery

Google BigQuery i is a powerful cloud-based data warehouse that supports two distinct SQL dialects: Standard SQL and Legacy SQL. Understanding the differences between these dialects is essential for writing efficient and reliable queries. Standard SQL is modern, ANSI-compliant, and recommended for all new projects, while Legacy SQL is BigQuery’s original dialect with unique syntax and limited functionality. Although both can process queries, they vary significantly in terms of functions, error handling, and compatibility. Choosing the right SQL mode affects not only query performance but also maintainability and future scalability. In this article, we’ll explore the key differences between Standard SQL and Legacy SQL in BigQuery and guide you on when and how to use each one effectively.

What Is Standard SQL in BigQuery?

Standard SQL in BigQuery is a modern, ANSI-compliant dialect based on the SQL 2011 standard. It aligns closely with common SQL syntax used in databases like PostgreSQL and MySQL. This makes it easier to write portable, maintainable code. It supports advanced functions, nested queries, arrays, window functions, and more. Since 2016, Google recommends using Standard SQL as the default dialect in BigQuery.

Standard SQL Example:

SELECT
  c.customer_id,
  COUNT(o.order_id) AS total_orders
FROM
  `project.dataset.customers` AS c
JOIN
  `project.dataset.orders` AS o
ON
  c.customer_id = o.customer_id
GROUP BY
  c.customer_id;

Legacy SQL Example:

SELECT
  c.customer_id,
  COUNT(o.order_id) AS total_orders
FROM
  [dataset.customers] c
JOIN
  [dataset.orders] o
ON
  c.customer_id = o.customer_id
GROUP BY
  c.customer_id;

How to Choose the Right SQL Dialect in BigQuery

• Use Standard SQL if you’re starting a new project or need modern features.
• Use Legacy SQL only if you’re maintaining older reports or dashboards.
• To switch dialects: Use the UI toggle in the BigQuery console or set useLegacySql flag in the API.

Migrating from Legacy SQL to Standard SQL

• Step-by-step Rewrite: Convert joins, functions, and table references to Standard SQL syntax.
• Use Tools: BigQuery UI flags and formatters can help during conversion.
• Validate Results: Compare outputs between both dialects.
• Test Incrementally: Migrate queries in smaller, manageable blocks.

Why Do We Need Standard SQL and Legacy SQL in BigQuery?

BigQuery supports both Standard SQL and Legacy SQL to balance modern features with backward compatibility. While Standard SQL offers advanced capabilities and ANSI compliance, Legacy SQL exists to support older scripts and legacy systems. Understanding the need for both helps in choosing the right dialect for your project.

1. Backward Compatibility with Older Systems (Legacy SQL)

Many BigQuery users built pipelines, dashboards, and scripts using Legacy SQL when it was the original default. Removing support would break these existing systems and force large-scale rewrites. Keeping Legacy SQL allows businesses to maintain operations without disruption. It ensures older tools and applications continue to function as expected. This backward compatibility provides a smoother migration path. For long-standing systems, Legacy SQL is a practical bridge.

2. Advanced Features and Modern Syntax (Standard SQL)

Standard SQL is ANSI-compliant and offers modern features like WITH clauses, ARRAY, STRUCT, window functions, and better error handling. These capabilities allow users to build more scalable, maintainable, and powerful queries. Its syntax is consistent with other modern SQL engines, making it easier for new users to adapt. Standard SQL also supports data governance features like column-level security. This makes it the preferred choice for future-proof analytics. It’s built for today’s and tomorrow’s data needs.

3. Smooth Transition for Existing Users (Dual Support)

By supporting both SQL dialects, BigQuery provides a smooth transition for teams shifting from Legacy to Standard SQL. Users can gradually migrate their queries, test them in Standard SQL, and update them over time. This dual support prevents rushed rewrites and minimizes production risks. It enables hybrid environments where legacy and modern queries can coexist. Developers can refactor legacy scripts without impacting business continuity. It’s a flexible approach that supports diverse organizational needs.

4. Support for Rapid Prototyping and Simplicity (Legacy SQL)

Legacy SQL offers a simpler syntax for quick, flat queries—ideal for fast prototyping and ad hoc data exploration. It allows users to run queries without full table paths or complex structuring. This lightweight behavior appeals to analysts or users running exploratory queries. While not suitable for complex operations, it delivers speed and simplicity in basic use cases. It’s also easier to learn for non-technical users. In some cases, this quick-start ability is still valuable.

5. Migration Flexibility Without Disruption

Not all organizations can migrate hundreds of queries instantly. Supporting both dialects gives teams the time they need to rewrite and validate each query. Legacy SQL can still run older jobs while Standard SQL is used for new development. This avoids breaking business-critical workflows. Google even provides auto-conversion tools to assist in this migration. It ensures that development and operations can progress independently. Such flexibility minimizes risk and maximizes adoption success.

6. User Preference and Query Context

Some users may prefer Legacy SQL for its minimal syntax, while others need Standard SQL for complex data logic. Depending on the query’s purpose, one dialect may be more efficient than the other. For example, quick summaries may be easier in Legacy SQL, but data transformations are better in Standard SQL. Supporting both enables teams to choose based on context. It provides freedom without locking users into one approach. This flexibility enhances user productivity across roles.

7. Support for Legacy Dashboards and BI Tools

Many business intelligence dashboards and reporting tools were originally built using Legacy SQL queries. These tools often include embedded queries that would require time-consuming updates if Legacy SQL were removed. Maintaining support ensures that dashboards continue to deliver insights without interruption. This is critical for enterprises with large user bases and complex reporting systems. Until all tools are updated to support Standard SQL, Legacy SQL ensures business continuity. It’s essential for protecting historical investments.

8. Encouraging Gradual Adoption of Modern Practices

By supporting both dialects, Google encourages organizations to gradually adopt Standard SQL rather than forcing immediate migration. This lets teams learn modern SQL features at their own pace while keeping current systems running. It reduces the learning curve for analysts unfamiliar with advanced SQL features. Over time, teams can refactor and optimize their queries without disrupting operations. This dual support model fosters long-term growth and modernization. It balances innovation with stability across data projects.

Example of Standard SQL and Legacy SQL in BigQuery

BigQuery supports two SQL dialects: Standard SQL and Legacy SQL, each with its own syntax and features. Understanding their differences becomes easier when seen through practical query examples. This guide provides side-by-side examples to help you learn how both dialects work in real scenarios.

1. SELECT with Filtering

Standard SQL

SELECT name, age
FROM `my_dataset.my_table`
WHERE age > 25;

Legacy SQL

SELECT name, age
FROM [my_dataset.my_table]
WHERE age > 25;

Both queries retrieve name and age for people older than 25.
Standard SQL uses backticks and dot notation (dataset.table), while Legacy SQL uses square brackets and colon notation.

2. COUNT Aggregation with GROUP BY

Standard SQL

SELECT department, COUNT(*) AS total_employees
FROM `company.employees`
GROUP BY department;

Legacy SQL:

SELECT department, COUNT(*) AS total_employees
FROM [company.employees]
GROUP BY department;

This query counts employees per department. The syntax is mostly the same, but Standard SQL uses backticks and supports more advanced grouping operations.

3. Using ARRAY Functions

Standard SQL:

SELECT name
FROM UNNEST(["Alice", "Bob", "Charlie"]) AS name
WHERE name LIKE "A%";

Legacy SQL:

SELECT name
FROM (
  SELECT "Alice" AS name
  UNION ALL
  SELECT "Bob"
  UNION ALL
  SELECT "Charlie"
)
WHERE name CONTAINS "A";

Standard SQL supports native arrays with UNNEST() and array literals.
Legacy SQL does not support arrays, so we use UNION ALL to simulate the same list.

4. Subquery in the FROM Clause

Standard SQL:

SELECT name, avg_salary
FROM (
  SELECT name, AVG(salary) AS avg_salary
  FROM `company.payroll`
  GROUP BY name
);

Legacy SQL:

SELECT name, avg_salary
FROM (
  SELECT name, AVG(salary) AS avg_salary
  FROM [company.payroll]
  GROUP BY name
);

Both queries calculate the average salary by name using a subquery.
Standard SQL supports this fully; Legacy SQL also does but lacks support for more complex nested queries and CTEs.

Advantages of Using Standard SQL and Legacy SQL in BigQuery

This are the Advantages of Using Standard SQL and Legacy SQL in BigQuery:

1. ANSI Compliance and Cross-Platform Compatibility (Standard SQL): Standard SQL in BigQuery is fully compliant with the ANSI SQL 2011 standard. This means your queries follow industry-standard syntax, making them portable across other SQL-based platforms like PostgreSQL, MySQL, and SQL Server. This is especially useful for developers or analysts working across multiple environments. The consistency of Standard SQL makes it easier to collaborate and maintain code over time. If you’re migrating queries between systems, Standard SQL offers a smoother transition. Its universal structure reduces learning curves for new team members.
2. Support for Advanced SQL Features (Standard SQL): Standard SQL enables advanced functionality such as Common Table Expressions (CTEs) using the WITH clause, window functions, and array operations. These features allow you to write modular, readable, and complex queries without sacrificing performance. The ability to chain queries logically helps in building robust data pipelines. Window functions, for example, let you perform calculations across rows without using subqueries. This enhances both clarity and performance. These modern tools are essential for complex analytical tasks and are unavailable in Legacy SQL.
3. Better Error Handling and Safe Functions (Standard SQL): Standard SQL includes advanced error handling through functions like SAFE_CAST, SAFE_DIVIDE, and descriptive error messages. These features help you catch and handle potential issues during query execution, reducing unexpected crashes or failed jobs. Unlike Legacy SQL, which may silently fail or throw ambiguous errors, Standard SQL clearly identifies the source of problems. This helps in debugging and maintaining complex queries. Safe functions also ensure better data integrity by preventing unintentional type mismatches or null operations.
4. Full JOIN and Subquery Support (Standard SQL): Standard SQL supports all types of JOINs—INNER, LEFT, RIGHT, and FULL OUTER—as well as subqueries in almost any clause, including SELECT, FROM, and WHERE. This flexibility allows you to model more complex relationships between tables and perform deep analytical operations. With Standard SQL, you can write more expressive queries that mirror real-world data structures. Subqueries make it easier to isolate logic and improve query readability. Legacy SQL has limited support for JOINs and subqueries, which can become a constraint in advanced use cases.
5. Legacy SQL Simplicity for Quick, Flat Queries (Legacy SQL): Legacy SQL is designed for simplicity, especially when working with flat, denormalized tables. It allows shorthand query styles, such as omitting the FROM clause, which can speed up quick exploratory analysis. This makes it useful for users who are less concerned with complex relationships and just need fast answers. Some users still prefer its minimal syntax when running basic aggregations or SELECTs. It also executes quickly in scenarios with simpler logic and smaller datasets. For fast prototyping, Legacy SQL can be a time-saver.
6. Compatibility with Older BigQuery Tools and Scripts (Legacy SQL): Many legacy workflows, dashboards, and scheduled jobs in BigQuery were built using Legacy SQL. Maintaining backward compatibility ensures these pipelines continue to run without errors. In cases where rewriting queries into Standard SQL would require major refactoring, Legacy SQL provides a practical fallback. Some tools and automation scripts developed before Standard SQL became default still depend on Legacy syntax. Until all systems are updated, Legacy SQL ensures continuity. It acts as a stable bridge during transitions to modern query standards.
7. Federated Query Support Across External Data Sources (Standard SQL): Standard SQL allows you to run federated queries across external data sources like Google Sheets, Cloud Storage (CSV/Parquet/JSON), and Cloud SQL. This feature enables seamless data analysis without needing to import the data into BigQuery first. You can join data from these sources with native BigQuery tables using SQL alone. This saves time, simplifies ETL workflows, and reduces storage costs. Legacy SQL does not support federated querying, making Standard SQL the clear choice for modern, integrated data environments. It’s especially beneficial for organizations working with hybrid or multi-source data architectures.
8. Better Data Type Handling and Casting (Standard SQL): Standard SQL offers robust support for data types, including STRUCT, ARRAY, DATE, DATETIME, and safe type conversion using CAST() and SAFE_CAST(). This allows for better precision, storage optimization, and compatibility across datasets. In contrast, Legacy SQL has limited data types and inconsistent casting rules. With Standard SQL, it’s easier to manipulate complex nested data and transform values safely. This reduces data errors during transformation or analysis. It’s also essential when working with semi-structured or time-sensitive data in analytics or reporting environments.
9. Migration and Maintenance Tools for Legacy to Standard SQL (Standard SQL): Google Cloud provides built-in tools in the BigQuery web UI to help convert Legacy SQL queries into Standard SQL. These tools simplify the migration process, especially for large codebases with many scripts or dashboards. As Standard SQL becomes the default, maintaining new projects using Legacy SQL adds unnecessary technical debt. Migrating ensures long-term compatibility and easier onboarding for new team members. Google also updates documentation and tutorials primarily for Standard SQL, making learning and support more accessible. These tools help you future-proof your data infrastructure.
10. Stronger Community and Documentation Support (Standard SQL): Standard SQL enjoys wider community adoption, richer documentation, and more educational resources. Most examples, Stack Overflow threads, tutorials, and Google’s own training materials are based on Standard SQL. This makes troubleshooting and learning more efficient. Community support means faster answers, more reusable solutions, and improved developer experience. As Legacy SQL becomes outdated, its community support shrinks over time. By adopting Standard SQL, you align your team with modern best practices and ensure continuous support from Google Cloud and the broader data community.

Disadvantages of Using Standard SQL and Legacy SQL in BigQuery

These are the Disadvantages of Using Standard SQL and Legacy SQL in BigQuery:

1. Steeper Learning Curve for New Users (Standard SQL): Standard SQL introduces advanced features like Common Table Expressions, window functions, and nested data types, which can be overwhelming for beginners. Users coming from simpler SQL environments may find the syntax complex at first. Understanding UNNEST() for arrays or STRUCT types requires additional training. For those unfamiliar with ANSI SQL standards, adoption takes time and documentation review. This complexity can slow down onboarding and reduce productivity in the early stages. However, it pays off once the learning curve is overcome.
2. Verbosity in Simple Queries (Standard SQL): Compared to Legacy SQL, Standard SQL is often more verbose, especially for basic exploratory queries. Simple operations that require just a field-level aggregation or quick filtering may need fully qualified table paths and exact syntax. This can feel excessive in use cases where speed and brevity matter more than structure. For analysts doing fast ad hoc querying, this verbosity can be frustrating. Even minor mistakes in field or dataset naming throw errors. While great for structure, it’s not always ideal for quick data exploration.
3. Potential Compatibility Issues with Older Scripts (Standard SQL): Some older BigQuery workflows and automation scripts were built with Legacy SQL. Migrating them to Standard SQL may require significant rewriting, especially if they use outdated functions or deprecated syntax. Compatibility issues may arise with tools or templates that were not updated to support Standard SQL. This can introduce bugs or unexpected behavior during migration. In large enterprise environments with legacy systems, this becomes a barrier. Maintaining backward compatibility might require supporting both dialects, adding complexity to deployment.
4. Limited Long-Term Support (Legacy SQL): Legacy SQL is no longer actively developed and is maintained only for backward compatibility. Google recommends using Standard SQL for all new queries and workflows. This means any new features or performance optimizations are exclusive to Standard SQL. Over time, support for Legacy SQL in newer tools and interfaces will diminish. Continuing to use it locks teams into an outdated model. It may also lead to technical debt and rework as systems evolve. Relying on Legacy SQL poses a long-term risk.
5. Lack of Modern Features (Legacy SQL): Legacy SQL does not support modern SQL capabilities like CTEs, window functions, ARRAYs, and federated queries. This limits what you can accomplish, especially in more advanced analytics and reporting use cases. Developers must work around these limitations using suboptimal methods or split logic into multiple queries. This increases complexity and reduces efficiency. As data workflows grow more sophisticated, Legacy SQL quickly becomes a bottleneck. Its limited feature set restricts growth and flexibility.
6. Inconsistent Function Naming and Behavior (Legacy SQL): Function names in Legacy SQL differ significantly from standard SQL practices, often causing confusion. For example, instead of using CAST(), Legacy SQL may use custom syntax for type conversion. Additionally, behavior may be inconsistent across datasets or between UI and API queries. This inconsistency makes debugging and cross-system collaboration more difficult. Developers with experience in other SQL dialects may struggle to adapt. It also reduces readability and makes knowledge sharing harder across teams.
7. No Automatic Fallback Between Dialects (Standard & Legacy SQL): BigQuery does not support automatic fallback between Standard SQL and Legacy SQL. This means if a query is written in the wrong dialect, it will simply fail rather than convert or suggest a fix. Developers must explicitly select the correct SQL mode, either in the UI or API. This can lead to confusion when reusing shared queries across different environments. Accidentally running a Legacy SQL query in Standard SQL mode (or vice versa) often results in errors. This strict separation adds an extra step in query configuration and testing.
8. Fragmentation of Codebase (Using Both Dialects): Using both Standard and Legacy SQL within the same organization or project can fragment the codebase. Queries written in different dialects become hard to maintain, test, and refactor consistently. It also introduces duplication, as similar logic must be re-implemented in both modes. Over time, this leads to higher maintenance effort and potential confusion among developers. Team members may struggle with debugging unfamiliar syntax from the other dialect. Best practice is to standardize on one dialect preferably Standard SQL to reduce complexity.
9. UI Confusion for New Users (Standard & Legacy SQL): In the BigQuery web UI, the option to toggle between Standard SQL and Legacy SQL can cause confusion for newcomers. They may not realize which mode they are working in, leading to syntax errors and unexpected behavior. Query snippets copied from tutorials or forums may not work if they’re written in the other dialect. This disrupts the learning process and adds frustration for beginners. Although the interface provides checkboxes and labels, it still creates a usability gap for first-time users or cross-functional teams.
10. Risk of Relying on Deprecated Features (Legacy SQL): Legacy SQL contains outdated features and syntax patterns that are either deprecated or scheduled for removal. Continuing to use these features in new queries creates long-term technical debt. It also prevents you from taking advantage of BigQuery’s latest capabilities, such as federated queries, scripting, and advanced analytics functions. Teams that rely heavily on Legacy SQL may find themselves forced into emergency migrations later. Avoiding deprecated features ensures a healthier, future-ready data platform. Moving to Standard SQL is the most sustainable solution.

Future Development and Enhancement of Using Standard SQL and Legacy SQL in BigQuery

Following are the Future Development and Enhancement of Using Standard SQL and Legacy SQL in BigQuery:

1. Continued Investment in Standard SQL Features: Google is actively enhancing Standard SQL by introducing new functions, improving query execution speed, and expanding data type support. This ensures developers have the tools needed for modern, high-performance analytics. Features like procedural scripting, ARRAY enhancements, and machine learning integrations continue to grow. Expect more performance optimizations and broader integration with Google Cloud tools. These advancements are exclusive to Standard SQL and signal its long-term relevance. Developers who adopt Standard SQL now will benefit from ongoing innovations.
2. Advanced AI and ML Integrations with Standard SQL: Google is aligning Standard SQL with BigQuery ML and AI offerings. Features like model training, evaluation, and prediction are directly available using Standard SQL syntax. As AI continues to shape the data industry, these built-in capabilities offer tremendous advantages. Future enhancements may include tighter integration with Vertex AI and generative AI models. This trend positions Standard SQL as a key language not just for querying, but for intelligent data analysis. Legacy SQL, lacking such capabilities, is excluded from this innovation path.
3. Enhanced Developer Experience and Tooling (Standard SQL): Future updates will focus on improving the developer experience with Standard SQL through better autocompletion, linting tools, syntax highlighting, and query validation. Google is also enhancing integration with IDEs like VS Code, and cloud-native editors. These improvements reduce errors, increase speed, and support enterprise-grade development workflows. Expect more developer-facing APIs and query optimization suggestions. These tools will make Standard SQL easier to adopt and master across teams. Legacy SQL will not benefit from these improvements.
4. Deprecation Warnings and Support Phase-Out (Legacy SQL): While Legacy SQL is still supported, Google is gradually phasing it out. Future updates may include deprecation warnings, limited UI visibility, and the removal of Legacy SQL from new features or beta programs. Organizations relying on Legacy SQL will need to plan for migration. Official documentation and tutorials already prioritize Standard SQL. Over time, Legacy SQL will become harder to maintain and risk compatibility issues with updated tools. Developers are encouraged to migrate early to avoid disruptions in future BigQuery environments.
5. Expansion of Procedural Capabilities in Standard SQL: Standard SQL is evolving beyond query capabilities into procedural logic, enabling scripts that include loops, variables, and conditional blocks. This makes BigQuery more like a full-featured programming environment for data pipelines. Features like DECLARE, IF, LOOP, and BEGIN...END offer new levels of control within SQL itself. These enhancements reduce the need for external orchestration tools in many use cases. Expect more scripting power in the coming releases. Legacy SQL does not support scripting and will not gain this functionality.
6. Integration with Real-Time and Streaming Data (Standard SQL): BigQuery is enhancing support for real-time analytics using Standard SQL, including integrations with Pub/Sub and BigQuery streaming inserts. As businesses demand faster decision-making, real-time capabilities will be essential. Standard SQL will support analytical queries on streaming data with minimal latency. Expect more user-friendly syntax and performance improvements for time-series and windowed queries. Legacy SQL is not compatible with these streaming advancements. Choosing Standard SQL prepares your architecture for real-time, event-driven data pipelines.
7. Community Contributions and Ecosystem Growth (Standard SQL): The ecosystem around Standard SQL continues to expand with community-contributed scripts, reusable functions, and open-source tools. Stack Overflow, GitHub, and Google Cloud’s sample repositories primarily focus on Standard SQL. Educational platforms, certifications, and training are all aligned with the Standard SQL syntax. This ensures stronger onboarding support and broader collaboration across teams. The community ecosystem makes Standard SQL more sustainable and accessible. Legacy SQL lacks similar momentum and is rarely covered in new learning resources.
8. Limited Maintenance for Legacy SQL: Legacy SQL will receive only critical patches or minimal maintenance in the future. Google has no plans to enhance or extend its capabilities. As tools and APIs evolve, compatibility with Legacy SQL will gradually decline. This means developers must eventually rewrite or refactor existing Legacy SQL queries. Relying on it today increases future migration efforts. The sooner teams move to Standard SQL, the more aligned they will be with Google’s roadmap. The limited future of Legacy SQL makes it a temporary bridge—not a long-term solution.
9. Seamless Integration with Other Google Cloud Services (Standard SQL): Standard SQL is being continuously optimized for tight integration with other Google Cloud services like BigLake, Looker, Cloud Functions, and Dataform. These integrations allow developers to build full-stack, data-driven applications directly on the cloud. Whether it’s embedding queries into visual dashboards or automating pipelines, Standard SQL is the preferred language. This ensures consistency and performance across tools. Legacy SQL lacks compatibility with many of these newer services. Going forward, full cloud-native workflows will rely entirely on Standard SQL.
10. Unified Governance and Security Enhancements (Standard SQL): Google is rolling out centralized governance, access controls, and auditability enhancements tied directly to Standard SQL usage. These improvements include column-level security, data masking, row-level policies, and support for identity-based access control. Standard SQL queries can be monitored and governed with fine-grained rules. These features help enterprises meet compliance and data privacy requirements. Legacy SQL does not support many of these advanced security frameworks. Future-ready organizations will need Standard SQL to ensure secure, compliant data environments.