What is data warehouse automation or DWA?

Feel like manual data work is dragging you down? Struggling with growing piles of data in a complex and costly mess? Data warehouse automation (DWA) can get you back on track with efficient data workflows that help extract the most value from your data warehouse.

What is data warehouse automation?

Data warehouse automation (DWA) is very much what it sounds like: the process of automating and streamlining the design, build, deployment, and management of data warehouses. 

This involves specialized automation software and workflows that perform core data tasks such as data integration, transformation, cleansing, and loading. As it says on the tin, DWA significantly reduces the need for manual intervention across the enterprise data warehouse lifecycle. 

You will notice the impact as a reduced amount of errors, improved efficiency, and more consistent data quality, which all play to the benefit of faster and more informed decision-making.

Speaking of unlocking the potential of your data...

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DWA vs. traditional manual data handling

Why consider DWA in the first place? Well, the explosion of data volumes has thrown traditional (and mostly manual) data handling methods into waters too deep. High volumes bring high complexity, which increases proneness to errors.

It is simply not feasible to rely on manual workflows at the level of data demand faced by organizations today. The speed and quality aren’t enough. There aren’t enough data engineers to manage the complexity. It’s all too expensive. And every day without data warehouse automation means losing business to your rivals.

Elements of data warehouse automation 

So DWA is responsible for automating different stages of the data warehouse lifecycle. Let’s slice it up a little:

ETL/ELT automation

DWA automates the Extract, Transform, Load (ETL) process, allowing data to be extracted from various sources, transformed into a consistent format, and loaded into the data warehouse without manual coding or scripting. (The same applies if you are operating in ELT, only the sequence is different, so data transformation happens in the target data warehouse.)

Data modeling automation

This part involves automating the creation and maintenance of data models, including schema design, entity relationships, and data mappings. Data warehouse automation tools can generate data models based on predefined rules or templates.

Code generation and deployment automation

DWA tools generate code or scripts for various data processes, such as SQL queries, (and transformations and data loading, as mentioned above), reducing the need for manual coding and scripting. Data warehouse automation software also assists in deploying these processes efficiently.

Metadata management

Data warehouse automation tools often include features for managing metadata (data about the data you’re working with), providing comprehensive documentation and data lineage of data sources, transformations, and data flow within the warehouse.

Workflow orchestration

Automation allows for the orchestration of complex workflows, enabling the scheduling and coordination of various data-related tasks and processes within the enterprise data warehouse environment.

What does a modern data warehouse look like,
and should you even bother?

Find out in this modern data warehouse guide →

Advantages of data warehouse automation

Most organizations dealing with vast data volumes face two fundamental challenges:

How to handle increasing complexity in data integration, transformation, and other key data management processes?

How to increase the speed and quality of data with leaner workflows, but at the same time keep operational data costs in check?

Data warehouse automation seeks to answer both of these questions. Especially enterprises operating numerous source data systems and a powerful EDW can expect the following benefits from DWA:

Efficiency and speed

Less manual intervention gives room for streamlined data processes, leading to faster time-to-value at every stage of the data warehouse lifecycle, and leaving more time for data analysis and insights.

Data quality and consistency

Automation minimizes human errors, ensuring data consistency and accuracy, which fosters more reliable data analysis and better-informed decision-making.

Scalability and flexibility

DWA facilitates scalable data handling even with expanding data volumes and complexity, which enables the organization to adapt and respond to new business needs.

Cost savings and resource optimization

Data warehouse automation helps reduce reliance on manual labor and therefore control operational data management costs, while also freeing up expertise from routine data tasks. 

Compliance and governance

Built-in features of data warehouse automation tools (incl. metadata management and data lineage tracking) aid compliance with regulatory standards and ensure data governance by providing comprehensive documentation of data processes.

Empowered business users

Data warehouse automation software (such as Agile Data Engine) comes with intuitive interfaces and self-service capabilities that allow non-technical users to perform data-related tasks and access data insights.

Hard to argue against that, eh?

Automate your data warehouse with ADE

“I’m sold on the idea of DWA, but how do I make it happen?”

Achieving all of the benefits listed above takes a little bit more than a flip of a switch. Your organization needs sufficient data maturity, in both technical capabilities and skill sets. And then, of course, you need specialized software for automating your data warehouse.

If you already have the maturity part covered, take a look at how Agile Data Engine can help you automate data warehouse processes →