Data Warehouse Automation

What is data warehouse automation?

Data warehouse automation (DWA) is defined as the automation of each and every part of the entire data warehouse lifecycle. It helps ensure that the numerous tasks that a data warehouse does—discovery, designing, developing, deploying, provisioning, and scaling are automatically managed.

By automating every step of the data warehouse lifecycle, these automation tools require less amount of time to manage it. Data personnel can spend more time on critical tasks instead of managing the data warehouse 24/7.

Benefits of using data warehouse automation 

• Increased productivity and ROI: DWA solutions help businesses deliver projects quickly by consuming fewer resources since the process is fully automated from start to finish, driving productivity and ROI.
• Increased business agility: Traditional data warehouse processes could take weeks for a project to complete, causing delays for business decision-makers to access real-time data. Using DWA tools shortens the time to get access to analytics reports. 
• Better data quality: The introduction of automation into the enterprise data warehouse processes helps reduce manual errors. Automation for data warehouses includes data preparation, cleaning of data, and data integration automatically, helping save hours of manual work. This helps businesses ensure they have quality data when making decisions, thereby driving reliability.
• Improved data management processes: The number of data requests or analytics requests that come in outnumber the speed at which data can be processed. To solve this challenge, DWA software automates the entire process, speeding up the time to evaluate analytics requests.
• More time for developers: Automated enterprise data warehouse processes allow developers to get more time back in their day, allowing their expertise to be utilized elsewhere. Developers can spend more time on other critical projects. Operations become much more self-serving in nature.
• Standardization and compliance: A common feature of DWA solutions is documentation, which ensures companies remain transparent and compliant since the data is documented at every step. Privacy teams can use this documentation and aligned methodologies to ensure how data flows internally and externally for a company and raise any concerns if observed.

Impacts of using data warehouse automation

The field of business intelligence (BI) could be positively impacted by DWA in the following ways:

• Reliability: BI needs reliable data. With DWA tools, a BI analyst can access clean, prepared, and processed data that would help them make data-driven decisions wherever possible. BI analysts can also use these tools to move warehouse data into other systems, such as data visualization and cloud-based BI tools.
• Building analytical models: Business users can use DWA to provide data-driven business insights. Data warehouse users can build analytic models to help achieve fast and accurate business intelligence reporting. Without DWA, it would take weeks or months to deliver insights which would be inaccurate since the data is not real time anymore.

Data warehouse automation best practices

To make DWA work, users should follow these best practices:

• Ensure DWA offers checkpoint support: Several DWA tools can add checkpoints through the entire data pipeline process to keep things running smoothly. If at any point the automation fails, only that checkpoint would be paused and corrected without impacting the entire process.
• Support different deployment types: DWA software can be deployed on-premises, in the cloud, or a hybrid approach based on the customer’s requirements.
• Ensure code reusability: Data warehouse developers create several lines of code for various processes. A good practice is to ensure that DWA allows the reuse of the code across different platforms when required.

Data warehouse automation vs. extract, transform, and load (ETL)

DWA software differs from traditional extract, transform, and load (ETL) tools since the latter is used to transfer data between databases or for external use. ETL tools are primarily used to transform data sets to operationalize via querying and analysis, whereas DWA software automates all data-related processes from start to finish.