Custom BI Dashboard Development (Business Intelligence)

Custom BI Dashboard (Business Intelligence)

Companies often face the problem: data is scattered across CRM, ERP, marketplaces, and reports are prepared manually in Excel. This leads to delays in decision-making, errors due to human factors, and inefficient use of resources. A BI dashboard solves all these problems by unifying data into a single repository and allowing business users to build reports in real time independently. Our experience — over 20 completed projects, 7 years on the market. Certified specialists in ClickHouse and PostgreSQL. Quality guarantee at all development stages.

Why Data Warehouse Is the Foundation of Any BI Solution?

Data Warehouse is a centralized storage optimized for analytical queries, not for transactions. Unlike simple connections to sources, a DWH avoids N+1 queries and ensures data consistency. The standard approach is the dimensional model (star schema):

 dim_customers | dim_products — fact_orders — dim_dates | dim_locations 

fact_orders — facts (transactions), contains numeric metrics (amount, quantity) and keys to dimensions. dim_* — dimensions (descriptive data). Queries like "sales by region for Q3" work quickly thanks to this structure. A proper data model can save up to 40% of analysts' time.

How ClickHouse Accelerates Analytical Queries?

ClickHouse is a columnar DBMS with enormous aggregation speed. In practice: a query COUNT(*) + SUM(revenue) on a table with 1 billion rows executes in 0.5 seconds, while on PostgreSQL it takes 15 minutes. ClickHouse is 10–100 times faster than PostgreSQL for typical BI queries.

-- ClickHouse: sales by category for the last 30 days SELECT category, sum(revenue) AS total_revenue, uniqExact(customer_id) AS unique_customers, count() AS orders FROM orders_mv WHERE toDate(created_at) >= today() - 30 GROUP BY category ORDER BY total_revenue DESC; 

ETL from PostgreSQL to ClickHouse: via clickhouse-local + scheduled job or Apache Airflow. Materialized views can also be used for precomputed aggregates.

How We Build an ETL Pipeline for BI?

1. Data source audit — identify all relevant systems (CRM, ERP, marketplaces), assess volumes and update frequency.
2. DWH design — develop a star schema considering future reports.
3. ETL development — use Apache Airflow for orchestration, dbt for transformations. Example DAG: source_extract → load_to_staging → transform_to_dwh → load_to_clickhouse.
4. ClickHouse optimization — configure partitioning, primary key, materialized views.
5. Dashboard creation — 10–20 typical reports with filters, drill-down, parameterized queries.

What Is Self-Service BI and How to Embed It?

Self-service means an analyst or manager can build the required report themselves. Components:

• Query builder UI — drag & drop fields, select aggregations, filters without SQL.
• Dashboard builder — add widget, choose chart type, configure axes.
• Parameterized reports — template with variables, user input values.

Comparison of tools for embedding BI:

We help choose the optimal option for your task. Contact us for a consultation to discuss details.

OLAP and Slice & Dice

OLAP allows "slicing" data across multiple dimensions:

• Drill-down: year → quarter → month → day
• Slice: only one region from all
• Dice: region × category × period
• Pivot: rows and columns swap places

In a BI dashboard, this is implemented through hierarchical filters and pivot tables.

Cohort Analysis

Cohort analysis groups users by the period of first action and tracks metrics over time:

SQL for cohort retention:

WITH cohorts AS ( SELECT user_id, DATE_TRUNC('month', created_at) AS cohort_month FROM users ), activity AS ( SELECT user_id, DATE_TRUNC('month', event_at) AS activity_month FROM user_events WHERE event_type = 'purchase' ) SELECT cohort_month, EXTRACT(MONTH FROM AGE(activity_month, cohort_month)) AS period, COUNT(DISTINCT a.user_id)::FLOAT / COUNT(DISTINCT c.user_id) AS retention_rate FROM cohorts c LEFT JOIN activity a USING (user_id) GROUP BY 1, 2; 

Access and Security

• Row-level security: each manager sees only their own clients/region.
• Policies at the dataset level: who can create reports, who can only view.
• Query audit: who viewed which reports and when.

What Is Included in BI Dashboard Development

• Data source audit and modeling.
• ETL pipeline development (ClickHouse, Airflow).
• Dashboard creation (10–20 reports).
• Architecture documentation.
• Access rights and security setup.
• Training for the analytics team.
• Technical support after launch.

Timeline

MVP BI dashboard (ClickHouse/PostgreSQL, 10–15 reports, basic filters, user roles): 3–4 months. Full BI platform with self-service builder, cohorts, ETL, and embedded SDK: 5–9 months. Order BI dashboard development and get an engineer consultation. Contact us to discuss the details of your project.