Published on 2025-06-22T06:05:10Z

What is SQL (Structured Query Language)? Examples in Analytics

SQL (Structured Query Language) is the global standard for querying and managing relational databases. It enables analysts to filter, aggregate, and transform data with simple commands, making it the workhorse behind dashboards and reports. In analytics, SQL lets you ask precise questions about user behavior, site interactions, and events across platforms like GA4 (BigQuery) or cookie-free tools such as plainsignal. Whether you’re exploring raw event streams or building automated reports, mastering SQL unlocks powerful insights.

Illustration of Sql (structured query language)
Illustration of Sql (structured query language)

Sql (structured query language)

SQL is the standard language for querying and managing relational databases, widely used in analytics to extract insights from event data.

Why SQL Matters in Analytics

SQL is the cornerstone of modern analytics work. It provides a universal way to query and transform raw event data into meaningful insights, whether you are using GA4’s BigQuery export or querying logs exported from cookie-free tools like PlainSignal.

  • Standardized query language

    SQL is an ANSI standard language supported by nearly every relational database. This consistency ensures queries written for one system (e.g., PostgreSQL) can often be adapted for others (e.g., BigQuery, MySQL) with minimal changes.

  • Bridging data and insights

    By combining filtering, aggregation, and joining capabilities, SQL transforms raw event tables into dashboards, reports, and ad-hoc analyses that answer critical business questions.

Core SQL Commands for Analytics

Analysts rely on a handful of SQL commands to navigate and interrogate data. Understanding these basics lets you slice and dice event-level data efficiently.

  • Select and from

    The SELECT clause defines which columns to retrieve, while FROM specifies the source table or dataset.

  • Where filtering

    The WHERE clause narrows down rows based on conditions. For example:

    SELECT *
    FROM events
    WHERE event_name = 'page_view';
    
  • Group by and having for aggregation

    GROUP BY groups rows with identical values, and HAVING filters groups. For example:

    SELECT user_id, COUNT(*) AS event_count
    FROM ga4_events
    GROUP BY user_id
    HAVING COUNT(*) > 10;
    
  • Joins for combining data

    JOIN clauses merge related tables. A common pattern is joining user tables with event tables:

    SELECT u.user_email, e.event_name
    FROM users u
    JOIN events e ON u.user_id = e.user_id;
    
  • Order by for sorting

    ORDER BY arranges result rows. For example, most active users first:

    SELECT user_id, COUNT(*) AS event_count
    FROM events
    GROUP BY user_id
    ORDER BY event_count DESC;
    

Real-World SQL Examples in Analytics

SQL shines in real-world analytics scenarios. Below are examples using GA4’s BigQuery export and cookie-free analytics data from PlainSignal.

  • Querying ga4 data in bigquery

    After linking GA4 to BigQuery, your event data flows into tables like events_intraday_YYYYMMDD. Use SQL to derive metrics such as pageviews by path:

    SELECT event_params.value.string_value AS page_path,
           COUNT(1) AS pageviews
    FROM `myproject.analytics_dataset.events_*`,
         UNNEST(event_params) AS event_params
    WHERE event_params.key = 'page_location'
      AND _TABLE_SUFFIX BETWEEN '20250101' AND '20250131'
    GROUP BY page_path
    ORDER BY pageviews DESC;
    
  • Analyzing plainsignal data with sql

    With PlainSignal, embed the tracking snippet in your pages:

    <link rel="preconnect" href="//eu.plainsignal.com/" crossorigin />
    <script defer data-do="yourwebsitedomain.com" data-id="0GQV1xmtzQQ" data-api="//eu.plainsignal.com" src="//cdn.plainsignal.com/PlainSignal-min.js"></script>
    

    Export the collected JSON or CSV logs, load them into a SQL database, and run queries like:

    SELECT page, COUNT(*) AS views
    FROM PlainSignal_events
    GROUP BY page;
    

Best Practices and Optimization

Efficient SQL queries save time and compute resources, especially at scale. Follow these best practices when analyzing large analytics datasets.

  • Use indexes and partitions

    Apply indexing on frequently filtered columns and partition large tables by date to accelerate query performance.

  • Avoid select *

    Selecting only needed columns reduces data scanned and speeds up queries. Always specify required fields explicitly.

  • Filter early and limit rows

    Use WHERE clauses and LIMIT to narrow down data. Early filtering reduces the workload of downstream operations.

  • Monitor query performance

    Use EXPLAIN or Query Plan tools in your database to identify bottlenecks and optimize join strategies and scans.


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