Favourite 5 Self-Hostable Analytics Tools Researchers Use to Store Raw Events Locally and Run Custom Cohort Queries

In the age of data-driven research, the ability to collect, store, and analyze granular behavioral data at scale is crucial. Researchers — whether in academia, healthcare, digital humanities, or behavioral science — often deal with large sets of raw events and need sophisticated tools to process and gain insight from this information. For many, cloud-based SaaS analytics tools pose concerns over privacy, cost, customization, and long-term control over data. For these reasons, self-hostable analytics platforms have become an increasingly popular solution.

TLDR (Too long, didn’t read)

Self-hostable analytics tools give researchers full control over their data, privacy, and infrastructure. This article highlights five favorite open-source tools that allow event tracking, local data storage, and flexible cohort analysis. Whether for reproducible academic research or secure institutional insights, each tool balances usability and analytical power. From lightweight dashboards to event stream warehouses, there’s a solution for every research need.

Why Self-Host Analytics?

Before diving into the tools, it’s important to understand why many researchers prefer self-hostable solutions:

• Privacy and Compliance: Especially in fields like healthcare or education, ensuring data stays within institutional boundaries is mandatory (HIPAA, FERPA, etc.).
• Reproducibility and Control: Academic research mandates reproducible results. Full control over the analytics stack ensures repeatable experiments and version control of data transformations.
• Cost Management: Many commercial platforms charge per event tracked or per seat; this can become prohibitive at scale. Open-source self-hosted tools offer predictable infrastructure costs.
• Query Flexibility: Custom SQL or cohort queries often fall outside the scope of hosted tools, or are limited by the vendor’s UI. Self-hosted backends allow deeper, more custom analysis.

Top 5 Self-Hostable Analytics Tools Researchers Prefer

We’ve compiled a curated list of top self-hostable tools based on features like local raw event storage, analytical backend complexity, community support, and the ability to run flexible cohort queries.

1. PostHog

PostHog has quickly become a go-to platform for developers and researchers who need product analytics that is private, flexible, and feature-rich. It’s written in Python and designed for scalability.

• Core Features: Behavioral tracking, user journeys, session recording, A/B testing, and dashboards
• Data Backend: Stores raw events in ClickHouse, enabling real-time and historical queries
• Custom Queries: Offers both graphical UI builders and SQL-like event filters for holistic cohort analysis
• Installation: One-click Docker-based deployment or Helm charts for Kubernetes
• Best For: Behavioral researchers, UI/UX studies, psychologists tracking stimulus-response

PostHog bridges the line between marketing analytics and empirical research tools, giving full SQL access and even exporting your raw data for offline processing.

2. Matomo (formerly Piwik)

For over a decade, Matomo has been one of the most respected self-hosted analytics platforms that prioritizes privacy and GDPR compliance. It is widely used by universities and public institutions for secure web analytics.

• Core Features: Website tracking, custom dimensions, user profiles, goal tracking
• Custom Cohorts: While limited compared to dedicated event stores, plugins allow segmentation and cohort exploration
• Integrations: A variety of CMS and LMS packages like WordPress and Moodle
• Deployment: Available via standalone PHP app on any Apache/Nginx server with MySQL
• Best For: Web behavior analysis, educational platforms, institutional research dashboards

Matomo’s strength lies in its flexibility and ability to capture fine-grained browsing events across large academic audiences.

3. Plausible Analytics

If simplicity, data privacy, and speed are your top priorities, Plausible packs a surprising punch for such a light analytics solution. Its modern dashboard coupled with raw event storage makes it ideal for quick iteration and analysis.

• Designed For: Simple usage metrics and ethical analytics with 100% data ownership
• Data Access: Stores event data in PostgreSQL for easy extraction by researchers
• Custom Analytics: No native cohort analysis UI, but accessible via direct SQL or Python-based tooling
• Deployment: Lightweight Docker image with minimal system overhead
• Best For: Researchers with low compute needs and higher emphasis on privacy and reproducibility

Plausible is often used in surveys, online experiments, and journal media sites where gathering lightweight usage insights is sufficient.

4. Redash (with Event Store)

Though not an analytics tracker itself, Redash is a powerful data visualization and cohort analysis tool that’s often used on top of raw event stores like PostgreSQL, BigQuery, or ClickHouse. It empowers researchers with highly flexible querying.

• Function: Build SQL queries and visualize data across multiple datasources
• Use Case: Integrate with event stores like Segment, Snowplow, or Apache Kafka
• Visualizations: Cohort tables, time-series, retention curves, and funnel flows
• Authentication: LDAP, SSO, and API-based access management for security-conscious institutions
• Best For: Collaborative research labs, data scientists, and quantitative market studies

Redash is incredibly versatile and can be fed from virtually any data warehouse storing raw granular events.

5. Snowplow

Snowplow is a full-featured open-source platform focused on event collection, enrichment, and modeling. Designed for flexibility and scalability, it is the heavy-lifter in academic and commercial research contexts alike.

• Components: Collectors, enrichers, data modelers, and optional stream processing
• Storage: Supports data lakes, Redshift, BigQuery, and Postgres for local setup
• Cohort Modeling: Researchers often pair Snowplow with dbt for event-to-cohort transformation
• Extensibility: Schema-driven tracking and complete version history of event data
• Best For: Healthcare analytics, behavioral research at scale, governmental surveys

Although complex to deploy compared to others, Snowplow is beloved by teams that require deep customization and rigorous schema validation of every tracked event.

Best Practices for Setting Up Your Research Analytics Stack

Choosing the right tool is only the first step. To maximize insights and ensure reproducibility, researchers should follow these practices:

• Define Clear Events: Identify what interactions are meaningful (clicks, sessions, behaviors) ahead of time
• Store Raw Events: Even if not immediately useful, raw logs allow reprocessing under future hypotheses
• Use Versioned Schemas: Especially with tools like Snowplow, define strict data contracts per event
• Automate ETL Pipelines: Use tools like dbt or Airflow to process raw data into analysis-ready formats
• Built-in Governance: Document your metrics, queries, and dashboards for review and re-use

Final Thoughts

For researchers seeking independence, compliance, and rich analytical capabilities, self-hosted analytics platforms are indispensable. Each tool in this list provides differing advantages: some are great for simplicity (Plausible), others for depth (Snowplow), and some strike a balance in between (PostHog). Your ideal solution depends on your organization’s size, compliance needs, query flexibility, and comfort with infrastructure management.

As the demand for actionable, reproducible data grows, building a customizable, locally hosted analytics stack might just be one of the best