Custom Analytics and Reporting Dashboard Development
We had a project: an e-commerce platform with 50 million order rows. Management wanted "one screen showing all KPIs." Queries against PostgreSQL were taking 20 seconds and bringing the database to its knees. After migrating to ClickHouse with materialized views, the dashboard loaded in 1.2 seconds. We've been tackling such tasks for over five years and know how to avoid common pitfalls.
An analytics dashboard is an interface for visualizing business metrics in real time. Developing one requires integrating multiple sources and fast aggregation. Key requirements: load speed, flexible filtering, and correct aggregation. Users shouldn't wait 30 seconds for a page to open. Below are the typical bottlenecks and our solutions.
Why analytics dashboards are slow
The main cause is heavy queries against an OLTP database. For example, SELECT * FROM orders JOIN users ... without indexes. Result: timeout or load average 200. We solve this with three approaches: materialized views, a separate analytical database, and Redis caching.
ClickHouse is a columnar DBMS for real-time analytical data processing (ClickHouse Documentation).
Materialized views
CREATE MATERIALIZED VIEW daily_revenue AS SELECT DATE_TRUNC('day', created_at) AS date, SUM(amount) AS revenue, COUNT(*) AS orders FROM orders WHERE status = 'completed' GROUP BY 1; CREATE UNIQUE INDEX ON daily_revenue (date); -- Refresh on schedule REFRESH MATERIALIZED VIEW CONCURRENTLY daily_revenue; Separate analytical DB: replicate from OLTP into ClickHouse or a read-replica PostgreSQL. All heavy queries go there.
Redis cache: results of expensive queries are cached with a TTL of 5–60 minutes. On data update, the cache is invalidated. This architecture reduces production load by a factor of 10.
How Redis caching works
We set TTL per query based on data update frequency. For critical metrics, 5 minutes; for long-lived ones, 60 minutes. When data changes in the source, a cache invalidation signal is sent via pub/sub.How to choose a visualization library
The choice depends on chart complexity. For the React stack, we use Recharts—well documented, customizable via props. For heatmaps or treemaps, we use Apache ECharts (more powerful but heavier). For rapid prototyping, Tremor fits—ready-made components with Tailwind. In our tests, ECharts renders 10,000 points in 200 ms, Recharts in 150 ms on the same dataset. Tremor lags in performance with large volumes (over 5,000 points). Typical widgets:
| Widget | Purpose | Example |
|---|---|---|
| KPI card | Single number with trend | Revenue yesterday vs week |
| Line/Area chart | Time trends | DAU over a month |
| Bar chart | Category comparison | Sales by region |
| Funnel | Conversion funnel | Visits → Cart → Purchase |
| Table | Paginated details | Order list for a period |
How to set up ETL for the dashboard
The ETL process includes several steps, each affecting speed and data accuracy:
- Identify data sources. We list all necessary tables, APIs, and files (CSV, Excel).
- Set up replication. Use Debezium or pglogical for continuous sync from OLTP to the analytical DB.
- Build materialized views. Aggregate data by time hierarchies (day, week, month).
- Configure caching. Save results of slow queries in Redis with TTL 5–15 minutes.
- Monitor and alert. Track replication lag and resource consumption.
Comparison of ClickHouse and PostgreSQL for analytical workloads:
| Characteristic | ClickHouse | PostgreSQL |
|---|---|---|
| Aggregation speed (GROUP BY) on 10M rows | 0.2 sec | 8 sec |
| Data compression | 5-10x | 2-3x |
| Materialized view support | Yes (CONCURRENTLY) | Yes (REFRESH) |
| Indexing for analytics | Partitioning + primary key | B-tree, indexes |
ClickHouse is on average 40x faster on analytical queries, as confirmed by our projects handling up to 1000 requests per minute.
What's included in the work
We deliver not just code, but a fully documented solution:
- Architecture diagram with data sources and ETL processes
- Dashboard source code (React/Next.js or Vue/Nuxt) with comments
- Docker configuration for local development and deployment
- Access to repository and CI/CD pipeline
- Client team training (2–3 workshops)
- 6 months warranty support
Our experience: over 10 dashboard projects for retail, fintech, and logistics. We guarantee stable performance under loads up to 1000 requests per minute.
Filters and drill-down
Global filters (period, region, category) must apply to all widgets simultaneously. Drill-down: click a data point on a chart → detailed table for that slice.
The URL should reflect filter state (?period=last30d®ion=Moscow) for saving and sharing a specific dashboard view.
Export and sharing
- Export to Excel/CSV – table data download
- Export to PDF – dashboard snapshot (puppeteer or print CSS) – generation takes 5 seconds for 50 widgets
- Scheduled reports – automatic email delivery on a schedule
- Public URL – read-only dashboard access via link (no login)
Timelines
MVP (5–10 widgets, global filters, main data sources): 6–8 weeks. Full-featured dashboard with widget builder, drill-down, export, and scheduled reports: 3–4 months.
If you need a dashboard that won't break under load and will be understandable to management, contact us. We'll evaluate your project in 2 days and provide a realistic plan and cost. Request a preliminary consultation to determine the optimal architecture.