In today’s competitive retail and e-commerce world, the ability to turn raw data into insights can make or break your business. If you’re new to business intelligence (BI) and thinking of building your own on-premise data platform, this guide is for you.

Whether you’re moving beyond spreadsheets or avoiding high recurring cloud costs, on-prem setups can offer control, flexibility, and long-term cost-efficiency — if done right.

Let’s walk through the key components and considerations.

🔌 Section 1: Connecting to Your Data Sources (From OLTP Systems & APIs)

Your first step is to bring data into the system from your current operational systems (also known as OLTP — Online Transaction Processing systems). These can include relational databases and external APIs like marketing platforms.

🗃️ From RDBMS (e.g., MySQL, PostgreSQL, SQL Server)
You’ll need:

📣 From Marketing APIs (e.g., Facebook Ads)
You’ll need:

🏢 Section 2: Choosing Your Data Warehouse & Cost Optimization

Recommendation: ClickHouse

Fast columnar storage, excellent for aggregation-heavy queries, and open-source.
ClickHouse has very detail of recommendations about sizing and hardware system: https://clickhouse.com/docs/guides/sizing-and-hardware-recommendations

🛠️ Environments Setup

Split your environment:
Development: For data modeling, testing new pipelines.
Production: Clean, validated data for dashboards.

💡 Cost Tip: Optimize by running dev environments only during work hours or using a small instance.

🔁 Replica Setup

Why: Backups, disaster recovery, scaling read loads.

Types: You can use asynchronous replicas or real-time syncs.

How many: Depends on your SLA and load

If your dashboards are queried by 10+ users every hour with filters by product, region, and channel — queries can get heavy. In that case, consider:

1 replica for BI/reporting only
1 replica for backup or failover

📦 Handling Semi-Structured or NoSQL Data

Recommendations:
Compress data before storing
Use MongoDB for JSON-style data.
Use HDFS for larger unstructured datasets.
If volume is low, consider cloud storage for easier management.

🧰 Section 3: Data Modeling with dbt

dbt (Data Build Tool) is a powerful open-source tool for transforming raw warehouse data into clean models.

SQL-based: Easy for analysts.

Version control: Git-native workflows.

CI/CD-friendly: Automate your pipeline with tests and staging.

Documentation: Auto-generates lineage and model descriptions.

🔄 Section 4: Orchestration with Apache Airflow

Once you have ingestion and modeling in place, you need to automate it.

⚙️ Why Airflow?
Widely adopted: Huge community and plugin support.

Flexible: Supports Python, SQL, Bash, and more.

On-prem and cloud compatible

Custom operators: Build anything you need.

Example: You can schedule ETL jobs to pull sales data every night at 2AM, transform it with dbt at 3AM, and have dashboards refreshed by 6AM.

📊 Section 5: Data Visualization Tools

🆓 Open-Source Options: Metabase, Apache Superset

Pros: Free to use, lightweight
Cons: Requires technical setup and some maintenance

💼 Enterprise / Subscription Tools: Power BI, Tableau, Looker

Pros: Rich UI, better UX, advanced analytics, support
Cons: Licensing cost (starts around $10/user/month for Power BI)

💬 Final Thoughts
Setting up an on-premise data platform isn’t just about tools — it’s about choosing the right foundation for your business size, data volume, and team skills.

For retail and e-commerce businesses, this means balancing performance, security, and cost — while ensuring your team can actually use the data to make decisions.

If you’re just getting started, don’t overbuild. Start small:

One reliable data source
One clean warehouse
One useful dashboard

If you’re building your first data platform and want some help evaluating tools, cost, or architecture — feel free to reach out. We’re happy to share what we’ve learned from real-world experience.

Website: www.diligence-intelligence.com
Email: info@diligence-intelligence.com