Streamline Data Warehousing with 9 Essential Implementation Strategies 

The global data storage market is set to grow from $218.33 billion in 2024 to $774.00 billion by 2032, with a strong annual growth rate of 17.1%. Meanwhile, spending on public cloud services is expected to increase by 20.4%, reaching $675.4 billion in 2024, up from $561 billion in 2023, according to Gartner.

However, with data scattered across siloed sources, it's challenging for strategic decision-makers to get a single version of the truth to effectively evaluate opportunities and risks.  A well-designed data warehouse can help address this challenge by integrating data from disparate sources into a centralized repository.

Building a data warehouse can be complex. Integrating data from multiple systems, ensuring its quality, and designing a scalable architecture require careful planning and expertise. This blog explores practical strategies to help you navigate these challenges and establish a robust data warehouse solution – your key to unlocking actionable insights from your data.

How to build a data warehouse?

The key steps involved in building a data warehouse are:

• Planning: Thorough planning is essential to define goals, and requirements and develop an implementation roadmap. This helps ensure a structured approach.
• Data collection: Relevant data is gathered from multiple internal and external sources. This involves identifying critical data elements and sources.
• Data preparation: The collected data undergoes cleaning, data transformation, and consolidation using an ETL tool to organize it into a standardized format suitable for analysis.
• Data analysis: Tools like SQL server, data mining, and machine learning are used to analyze the prepared data and gain meaningful insights. Various reporting and visualization techniques help understand patterns.
• Decision-making: Business users leverage the insights generated to make strategic, tactical, and operational decisions. This helps improve processes, products, and overall business performance over time.
• Regular maintenance and optimization: Proper data warehousing implementation facilitates better business intelligence, informed choices, and competitive advantage through data-driven decision-making. 

What are the components of data warehouse implementation?

The core components involved in building a successful data warehouse include:

• Data marts: A focused data mart contains a subset of data from the overall data warehouse, tailored to the needs of a specific business unit or department like sales, marketing, finance, etc.
• Transaction processing: Online Transaction Processing (OLTP) systems manage real-time business transactions daily, like orders, shipments, payments, etc. They capture rapidly changing operational data.
• Analytics: Online Analytical Processing (OLAP) is used to analyze, examine, and retrieve data stored in the data warehouse in different views and perspectives to generate useful information for executives and managers.
• Extract, Transform, Load (ETL):  ETL is the process of extracting data from various source systems, transforming it to fit operational needs and structures, and loading it into the data warehouse.
• Metadata: Information about the data is stored as metadata in the data warehouse. This includes attributes such as descriptions, origins, and access restrictions.

9 best practices for data warehouse implementation

To build a data warehouse you need multiple considerations to ensure its effectiveness and efficiency. Here are nine best practices to guide you through the process:

1. Engaging stakeholders throughout the implementation

Departments like sales, marketing, finance, and IT produce a variety of reports and conduct diverse types of analyses. Engaging representatives from these groups upfront allows for incorporating the right data and building appropriate structures and tools they require.

Executive sponsorship is also important to align the data warehousing initiative with broader business objectives. Their ongoing guidance helps optimize the warehouse to support key strategic and operational decisions across the organization.

2. Choosing the right warehouse platform

When building a data warehouse, one of the first decisions is where the warehouse will be hosted. Some common options include:

• On-premises: The warehouse is hosted on local hardware within your company's infrastructure. This provides full control but requires managing your equipment.
• Cloud-hosted: Major public cloud providers like AWS or Microsoft Azure hosts the warehouse infrastructure. This shifts responsibilities like hardware maintenance to the cloud vendor but loses some control. In general, the public cloud provides an affordable and low-maintenance setup.
• Private cloud: Similar to public cloud, but the infrastructure is hosted either on a dedicated hardware or a trusted third-party provider. Offers more control than the public cloud.
• Hybrid cloud: Blends on-premises infrastructure with cloud-hosted components, leveraging local data storage alongside cloud resources for processing tasks.

3. Creating separate environments for seamless deployment

Establish separate development, testing, and production environments. The development allows building and validating features on test data. Testing evaluates changes on a larger dataset. And production acts as a live warehouse for end users.

Proper separation prevents untested changes from impacting production and users. It establishes a structured development process with development and testing phases before deployment. Additional environments like separate tests and QA may also help, depending on the needs.

4. Designing the data model

Choosing a data model is an important part of building a data warehouse. Sources may have different schemas, but the warehouse needs a single consistent schema. The model must fit existing data and scale for future additions.

Common schemas include:

• Star schema: Fact tables linked to dimensional tables.
• Snowflake schema: Adds another level to the star with sub-dimensions.
• Galaxy: Multiple fact tables sharing dimensions.
• Constellation: Galaxy with hierarchical dimensions.

Selecting the right data model is crucial for unlocking the full potential of your customer data.

Discover how to unleash customer intelligence with data by leveraging our proven strategies and best practices.

5. Extracting data for business efficiency and accuracy

Data is extracted from source systems through various methods such as API calls, file transfers, or direct database queries. This extraction pulls the raw data out of its original location. Automated data extraction reduces the need for manual data gathering and processing, freeing up valuable time and resources. Additionally, business leaders have access to comprehensive and accurate information.

6. Streamlining data transformation

An ETL process converts extracted data to the destination schema through transformation steps. The transformation includes validation, cleansing, harmonization, and enrichment to prepare data for loading.

Data warehousing service providers like Altudo streamline this process by automating schema mapping and implementing rule-based data hygiene checks. This reduces the time and effort required to transform raw source data into a clean, consistent format optimized for analytics and business intelligence uses.

7. Creating data marts

Data warehouses act as centralized repositories for all organizational data, ensuring comprehensive storage. Conversely, data marts offer specialized perspectives by segmenting the warehouse into smaller, subject-specific subsets tailored for departments, such as sales or finance. This segmentation allows records to be categorized with multiple identifiers, enabling their inclusion in various relevant marts.

8. Configuring analytics

Business Intelligence (BI) and analytics tools integrate simply with data warehouses and ETL platforms for fast insights. These tools rely on sufficient data volume, velocity, and veracity from the warehouse to provide an accurate picture through visualizations and dashboards, particularly for real-time analysis. Adhering to a data warehouse implementation plan ensures analytics teams receive needed data.

9. Opting cloud data warehouse

Cloud data warehousing has significantly advanced, offering flexibility and ease of management over on-premises systems. It enables the use of data lakes for large volumes of unstructured data before analytic preparation, supporting complex analytics. Partnering with a cloud data warehousing specialist can ensure a seamless transition.

Conclusion

Efficient data warehousing is essential for organizations to extract valuable insights from their data assets. The combination of lakehouse architecture and Databricks SQL offers cloud data warehousing capabilities directly within data lakes, facilitating the creation of highly performant and cost-effective data warehouses.

Altudo, in collaboration with Databricks, provides robust data warehousing services utilizing this architecture. Our team of experienced data consultants and engineers work closely with clients to understand their unique requirements, design scalable architectures and seamlessly migrate data from disparate sources.

Contact us for 1:1, no-obligation consultation session to explore the scope of data warehouse implementation for your business.