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How can you manage constantly changing data in a data warehouse?

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Data warehouses are centralized repositories of integrated data from various sources, designed to support business intelligence (BI) and analytics. However, data is not static, and it can change over time due to various factors, such as updates, deletions, corrections, or additions. How can you manage constantly changing data in a data warehouse without compromising its quality, consistency, and performance? Here are some tips and best practices to consider.

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mohammad mohammadsharifi

Data Analyst and BI developer
Rohan Dalvi

Microsoft Certified PL-300 | DP-203 | MSBI(SSIS,SSAS,SSRS) | ADF | Databricks | PYTHON | ADLS | AZURE SQL | BIG DATA |…

1 Choose the right data warehouse architecture

The first step is to choose the right data warehouse architecture that suits your data sources, business requirements, and analytical goals. There are different types of data warehouse architectures, such as the star schema, the snowflake schema, the data vault, and the lambda architecture. Each of them has its own advantages and disadvantages in terms of flexibility, scalability, complexity, and maintenance. You should evaluate the pros and cons of each option and select the one that best fits your needs and resources.

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Rohan Dalvi

Microsoft Certified PL-300 | DP-203 | MSBI(SSIS,SSAS,SSRS) | ADF | Databricks | PYTHON | ADLS | AZURE SQL | BIG DATA | PY SPARK | AZURE DEV-OPS | SYNAPSE | POWER BI | SAP BODS | SPOTFIRE | PENTAHO | POSTGRESQL | C# .NET
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Design flexible data models that can accommodate changes in data structure without requiring significant modifications to the warehouse schema. Techniques like schema-on-read, where the schema is applied at the time of querying rather than during data ingestion, can be useful in this context.

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2 Implement a data quality strategy

The second step is to implement a data quality strategy that ensures the accuracy, completeness, validity, and timeliness of the data in your data warehouse. Data quality is essential for reliable and actionable BI and analytics. You should define the data quality rules and standards for your data sources, data warehouse, and data marts. You should also use data quality tools and techniques, such as data profiling, data cleansing, data validation, and data monitoring, to identify and resolve any data quality issues before they affect your data warehouse.

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Rohan Dalvi

Microsoft Certified PL-300 | DP-203 | MSBI(SSIS,SSAS,SSRS) | ADF | Databricks | PYTHON | ADLS | AZURE SQL | BIG DATA | PY SPARK | AZURE DEV-OPS | SYNAPSE | POWER BI | SAP BODS | SPOTFIRE | PENTAHO | POSTGRESQL | C# .NET
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Implement data quality monitoring processes to detect and address data anomalies and inconsistencies in real-time. Automated data cleansing procedures can help ensure that only high-quality data is stored in the warehouse.

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3 Use a change data capture (CDC) method

The third step is to use a change data capture (CDC) method that tracks and captures the changes in your data sources and applies them to your data warehouse. CDC is a way of minimizing the data load time and ensuring the freshness and consistency of the data in your data warehouse. There are different types of CDC methods, such as trigger-based, log-based, or query-based. You should choose the one that best suits your data source characteristics, data volume, and data latency requirements.

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mohammad mohammadsharifi

Data Analyst and BI developer
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Beside CDC, Slowly Changing Dimensions (SCD) is an important topic when we are dealing with data that changes over time. Besides keeping the latest data, we may also need to keep the history of data over time, depending on the business and our company strategy and the importance of historical data. Therefore, we need to implement SCD. After that, deciding on which type of SCD fits our data is another important decision that a BI specialist should make.

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Rohan Dalvi

Microsoft Certified PL-300 | DP-203 | MSBI(SSIS,SSAS,SSRS) | ADF | Databricks | PYTHON | ADLS | AZURE SQL | BIG DATA | PY SPARK | AZURE DEV-OPS | SYNAPSE | POWER BI | SAP BODS | SPOTFIRE | PENTAHO | POSTGRESQL | C# .NET
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Implement mechanisms to capture and integrate data in real-time or near real-time. This ensures that the data in the warehouse is always up-to-date. Technologies like Change Data Capture (CDC) can help capture changes from the source systems as they occur and propagate them to the data warehouse.

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4 Design a robust ETL process

The fourth step is to design a robust extract, transform, and load (ETL) process that efficiently and effectively moves and transforms the data from your data sources to your data warehouse. ETL is a crucial component of any data warehouse project, as it determines the quality, performance, and usability of the data warehouse. You should follow the best practices of ETL design, such as using metadata, modularizing the ETL code, handling errors and exceptions, logging and auditing the ETL activities, and optimizing the ETL performance.

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Rohan Dalvi

Microsoft Certified PL-300 | DP-203 | MSBI(SSIS,SSAS,SSRS) | ADF | Databricks | PYTHON | ADLS | AZURE SQL | BIG DATA | PY SPARK | AZURE DEV-OPS | SYNAPSE | POWER BI | SAP BODS | SPOTFIRE | PENTAHO | POSTGRESQL | C# .NET
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Utilize cloud-based data warehouses such as Amazon Redshift, Google BigQuery, or Snowflake, which offer automatic scalability and resource provisioning. Leverage serverless computing options for data processing tasks to scale resources dynamically based on workload demands.

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5 Adopt a data warehouse testing approach

The fifth step is to adopt a data warehouse testing approach that verifies and validates the functionality, reliability, and accuracy of your data warehouse. Data warehouse testing is a vital part of any data warehouse project, as it ensures the correctness, completeness, and consistency of the data in your data warehouse. You should perform different types of data warehouse testing, such as unit testing, integration testing, system testing, and user acceptance testing. You should also use data warehouse testing tools and frameworks, such as SQL queries, BI tools, or test automation tools, to facilitate and automate the testing process.

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6 Incorporate data warehouse governance

The sixth step is to incorporate data warehouse governance that defines and enforces the policies, standards, roles, and responsibilities for managing and using your data warehouse. Data warehouse governance is a way of ensuring the security, compliance, and quality of your data warehouse. You should establish a data warehouse governance framework, such as a data warehouse steering committee, a data warehouse team, a data warehouse documentation, and a data warehouse audit, to oversee and control the data warehouse lifecycle and operations.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Rohan Dalvi

Microsoft Certified PL-300 | DP-203 | MSBI(SSIS,SSAS,SSRS) | ADF | Databricks | PYTHON | ADLS | AZURE SQL | BIG DATA | PY SPARK | AZURE DEV-OPS | SYNAPSE | POWER BI | SAP BODS | SPOTFIRE | PENTAHO | POSTGRESQL | C# .NET
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Embrace agile development practices to quickly adapt to changing business requirements and data sources. This involves iterative development, continuous integration, and collaboration between data engineers, analysts, and business stakeholders.

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How can you manage constantly changing data in a data warehouse?

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2

3

4

5

6

7

1 Choose the right data warehouse architecture

2 Implement a data quality strategy

3 Use a change data capture (CDC) method

4 Design a robust ETL process

5 Adopt a data warehouse testing approach

6 Incorporate data warehouse governance

7 Here’s what else to consider

Business Intelligence

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How can you manage constantly changing data in a data warehouse?

1

2

3

4

5

6

7

1 Choose the right data warehouse architecture

2 Implement a data quality strategy

3 Use a change data capture (CDC) method

4 Design a robust ETL process

5 Adopt a data warehouse testing approach

6 Incorporate data warehouse governance

7 Here’s what else to consider

Business Intelligence

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