Designing and Implementing a Data Warehouse: Understand Key Processes & Aspects
“You can have data without information, but you cannot have information without data.” – Daniel Keys Moran
Data is the new, modern normal. All businesses, be it any size or segment, revolve around data to get the best information for their further decision-making. And one popular technology that makes the most out of data is data warehousing.
Data warehousing offers an architectural model for the data flow from operational methods to decision support systems. It enhances the speed and efficacy of reaching out to disparate data sets, making it easy for businesspeople to derive insightful information for their futuristic actions.
But the crux lies in designing and implementing a data warehouse. If done with perfection and a laid down process, it can lead to a successful output else could lead to hurdles.
Key Benefits of a Data Warehouse
- Enhances data quality and business analytical capabilities
- Increases data security and data consistency
- Saves on time, effort, and costs
- Maximized RoI and efficiency levels
- Enables historical insight
- Interoperates with On-premises and cloud
This article focuses on the key processes that are involved in designing and creating a data warehouse, leading to its successful implementation. Let us have a look at each of these processes, with their relevant details.
An Overall Look at the Phases Involved in Data Warehousing
Detailing It Further and Focussing on the Data Warehouse Design
Phase 1: Analysis and Business Requirement Gathering
This phase involves understanding the client’s current environment and analyzing the problems that are occurring in the current data warehouse if any. It focuses on analyzing the business requirements that the clients have and preparing relevant documents for the same.
- Business Requirements Sessions
Since data warehousing relates to all areas of business, all departments must get involved for better goal alignment and effective results. Detailed business requirement sessions must be conducted which cover the following objectives:
- Understanding the client’s current environment, database, and infrastructure
- Determining the scope of the project in relation to business objectives
- Discovering future needs and current needs by diving deep into the data sources and optimizing them
- Creating a disaster recovery plan in the case of system failure
- Thinking about each layer of security
- Anticipating compliance needs and mitigating regulatory risks
- Analyzing problems in the client’s existing data warehouse staging and production databases
- Document details of the current environment and problems, along with data sources and dataflow in the data warehouse
Migration of Data
Data migration is one important activity that aims toward cost reduction and lessening the operational burden of executing licensed OLAP database systems on-premises.
Phase 2: Data Model Design
Designing an apt data model is crucial to a successful data warehouse. Data modeling is a process of visualizing data distribution in a warehouse. A data model is created with proper naming conventions, relationships are created between data sets and relevant security processes are worked upon, that align with the primary IT goals.
The steps involved in creating a data model design are:
- Analyze new data warehouse services (Snowflake) and create data models according to the new data warehouse
- Create a Logical Data Model
- Define Subject Areas, Entities, and Attributes in Logical Data Model.
- Understand Tables and relations between tables and create ER Diagrams
- Create Physical Data Model
- Add storage tables and views as needed.
- Generate a DDL script for the physical data model
- Metadata Management
- Metadata contains all the data about the data warehouse
- Metadata will also contain all the information about the ETL processing which will help us to track the ETL easily and monitor them without any trouble. There will be monitoring tables that keep track of each file move.
Data Design Considerations to be Kept in Mind:
Clients should strive to be future-proof. Design choices based exclusively on immediate needs may cause problems later.
Data warehouse design will be a collaborative process that will include all key stakeholders.
Data quality will be a priority. Strong data governance practices ensure clean data and encourage adherence to rules and regulations
Subject matter experts will lead the data modeling process. This guidance ensures that the data pipeline will be robust, consistently organized, and documented.
Businesses will design for optimized query performance, pulling only relevant data, using efficient data structures, and tuning systems often.
Phase 3: Integration
Integration of all the key elements is important for a successful migration, this will decide the path for successful data movement.
- Identify the migration candidates from current sources
- Transformation design mapping for all relevant candidates
- Identify candidates for a rebuild and redevelopment
Phase 4: Design and Development
Once the client data warehouse design is ready, we will work towards the mapping of the source and data warehouse and document them to develop the pipeline for migrating the retrospective and prospective data.
All the data sources, data from the existing data warehouse and staging and production databases, and other sources will be moved to the new data warehouse staging layer (S3 Bucket) and then to the Snowflake data warehouse.
Phase 5: Migration and Deployment
The migration process will take care of the smooth transition of all components from the development environment to UAT and production. It could be classified into rapid migration, reinstalling, and re-platform.
Deployment could be done through a managed private cloud or shared cloud.
Phase 6: Validate Migration
Testing should start from the moment we begin manipulating data and continue throughout all further stages. When the new system starts working, it is important to validate the project results and monitor the system’s performance in the long run.
As We Wrap Up
A robust data warehouse proves to be a unified data repository where records from disparate data sources are integrated for online business analytical processing (OLAP). Creating, designing, and implementing a data warehouse is one of the prime important aspects of any successful project.
Taking help from experienced Data service experts can lead to wonderful business results, someone who holds expertise in the different facets of data warehousing, transformation, and related aspects.