Scope and Requirements:
The scope and requirements for building a Data Warehouse (DWH) involve many factors. Let’s look at them one by one. First comes the volume of data, understanding that the size and complexity of DWH depend on the amount of data stored and processed, Choosing the DWH on-premise or cloud, based on the storage, control, performance, and accessibility. Coming to the architecture we have to keep in mind the relational model, or hybrid approach based on the user requirements. Along with the above, we have the end-users who access the DWH, We need to keep in mind the complexity of the analytics and reporting done using DWH matches their requirements, the most important thing is the data ingestion i.e the method of ingestion like Truncate and load, CDC, Scheduled incremental load.
Choose your Architecture and Platform:
Choosing an architecture and platform for a solution, there are multiple critical factors to be considered, which involves technical approach like deciding right tools and services and data pipelines.
Tools:
This involves identifying them based on data ingestion, transformation and storage. Then we will go with either open-source tools like Apache airflow or PostgreSQL or cloud-based tools like AWS DataLake or Google Dataflow.
Data Pipeline:
Data pipeline involves Data ingestion from various sources, Processing is applying transformation and cleaning, Storage is processing data in right format, Access and Analytics is like making data available for decision-making. The complexity and scalability depends on chosen tools.
Hybrid vs CSP (Cloud Service Provider) services:
A combination of both on-premise infrastructure and cloud services is hybrid. This is ideal for organisations which maintain sensitive data on-premises but need scalability and flexibility on cloud.
Coming to CSP is fully managed cloud services like AWS, GCP, Azure provides pre-built, fully managed services reducing operational overhead. Major points to be considered are scalability by managing resources based on demand, Cost-effective by paying for what we use.
Choosing semi-automated tools vs Brute-force approach of building from scratch:
Semi-automated tools are platform-as-a-service (PaaS) or software-as-a-service (SaaS) that automate much of the operational overhead such as AWS redshift or snowflake. These helps in reducing time and complexity.
Brute-Force approach: Building all from scratch gives full control over customization but requires more time, resource and experience
Design your data model and ETL process:
First we should have a clear understanding of business requirements, data sources, target data warehouse schema, then design the extraction, transformation and loading steps, ensuring data checks and validation.
- Choose an ETL tool based on data sources and requirements. Tools like Talend, Informatica.
- Data sources may be heterogeneous and homogeneous, this tool should support both regardless of type.
- Implementing data auditing to check data is complete, accurate and consistent by tracking data during ETL process.
- Ensure data flow meets resources may be heterogeneous and homogeneous, this tool should support both regardless of type.
- Ensure that data loaded to DWH is still intact, that it is not corrupted or changed during the ETL process.
- COming to performance optimization, especially with large datasets by implementing parallel processing to ensure efficient data handling and quicker load times.
Maintain and Optimise:
Once DWH is up and running, we need to focus on maintaining and optimising its long term usage.
- DWH should be integrated with BI and ML tools like Helical insights or Tableau to enable processing, real-time reporting and advanced analytics.
- We can choose options like columnar storage for analytics and read-focused queries, object storage for unstructured data, HTAP for real-time analytics on operational data, DSS storage is suitable for complex queries involving large datasets.
- Automate data cleansing and validation to ensure accuracy and consistency throughout the ETL pipeline by using Talend or Informatica.
- Optimise performance through indexing, partitioning and caching, and also implementing parallel processing to speedup ETL and query execution.
- We can also use tools like AWS Cloudwatch or Datadog to monitor query performance, resource utilisation, and data ingestion rates.
Conclusion:
Building a data warehouse is no small feat—it requires careful planning, the right tools, and a deep understanding of data needs. From selecting the right architecture to defining ETL pipelines and ensuring data governance, each step plays a crucial role in creating a system that delivers meaningful insights. As businesses become more data-driven, mastering the fundamentals of building a robust data warehouse is essential to stay ahead in today’s competitive landscape.
Whether you’re just starting out or refining an existing system, always remember that a well-constructed data warehouse isn’t just a repository—it’s the foundation for better decision-making and scalable growth. The journey may be complex, but the payoff—empowering data-backed strategies—is worth the effort.