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

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

• 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

• 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

• 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

• 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.

Related terms