Building an analytics stack in 2026 means choosing the right tools at each stage of the pipeline: collection, storage, transformation and visualization. This guide compares the main options and proposes a recommended stack.
Overview of the available tools
The market has organized itself around specialized building blocks. Here’s a comparison of the most widely used solutions:
| Tool | Type | Monthly cost | Strengths |
|---|---|---|---|
| GA4 | Collection | Free | Google integration, flexible events |
| Segment | Collection | ~$120/mo | Multi-destination, unified protocol |
| BigQuery | Storage | ~$50-200 | Native SQL, automatic scaling, GA4 export |
| Snowflake | Storage | ~$200-500 | Performance, cross-team data sharing |
| dbt | Transformation | Free (core) | SQL versioning, built-in tests, documentation |
| Looker Studio | Visualization | Free | Native BigQuery connector, easy sharing |
The simplest stack is often the best. Before adding a tool, ask yourself whether you can solve the problem with what you already have. A well-configured GA4 paired with BigQuery covers 80% of the analytics needs of a mid-sized company.
Collection: GA4 vs the alternatives
For most websites, GA4 remains the default choice. Its native export to BigQuery makes it a formidable tool when you want to go beyond the standard reports. If you’re just getting started, check out the GA4 setup guide to start on solid ground.
Alternatives like Segment or RudderStack make sense when you need to send data to several destinations at once (CRM, data warehouse, marketing tools).
How to choose a collection tool
The questions to ask before deciding:
- Do I need to send data to more than 2 destinations?
- Does my team have the skills to maintain a complex tagging plan?
- Does my event volume exceed GA4’s free limits?
- Do I have compliance constraints that require first-party hosting?
Storage and transformation
BigQuery has become the standard for storing raw analytics data. The GA4 BigQuery export takes a few clicks to set up and gives you access to every event.
Once the data is in BigQuery, the first step is to create materialized tables for recurring queries. Here’s a sample sessions table:
-- Scheduled query: sessions aggregated by day
CREATE OR REPLACE TABLE `my-project.analytics.sessions_daily` AS
SELECT
event_date,
COUNT(DISTINCT CONCAT(user_pseudo_id, '.',
(SELECT value.int_value FROM UNNEST(event_params) WHERE key = 'ga_session_id')
)) AS sessions,
COUNT(DISTINCT user_pseudo_id) AS users,
COUNTIF(event_name = 'purchase') AS conversions,
SUM(ecommerce.purchase_revenue) AS revenue
FROM
`my-project.analytics_123456789.events_*`
WHERE
_TABLE_SUFFIX BETWEEN
FORMAT_DATE('%Y%m%d', DATE_SUB(CURRENT_DATE(), INTERVAL 1 DAY))
AND FORMAT_DATE('%Y%m%d', DATE_SUB(CURRENT_DATE(), INTERVAL 1 DAY))
GROUP BY
event_date
ORDER BY
event_date DESC;
The event_name field is the cornerstone of the GA4 model: every user interaction is an event with its own parameters in the event_params array.
Recommendations by team size
Depending on your organization’s data maturity, the recommended stack varies:
- Solo / early-stage startup: free GA4 + Looker Studio. Zero cost, covers the basics.
- SMB with one analyst: GA4 + BigQuery + dbt core + Looker Studio. The cost/power sweet spot.
- Scale-up with a data team: GA4 + Segment + BigQuery + dbt cloud + Looker/Tableau. A robust, scalable pipeline.
- Large enterprise: a custom solution with Snowflake, Airflow orchestration, and centralized governance.
Don’t underestimate the human cost. A free tool that needs 3 days of setup per month costs more than a $200/mo SaaS that just runs on its own.
The most important thing is to document your choices and reassess your stack every 6 months. Tools evolve fast, and what was the right call in January isn’t necessarily the right one in July.