Data warehouse automation
Data warehouse automation (DWA) is replacing standard methods for building data warehouses. It automates the planning, modeling, and integration steps to keep pace with an ever-increasing amount of data and sources.
Data warehouse automation is crucial for standardizing the way data — from many sources and in many formats — is integrated and woven together into a consolidated view. Teams increase productivity, agility, and the quality of their output.
Introducing Data Vault automation
The Data Vault 2.0 data model is rapidly becoming the DWA standard. Its simple design architecture makes it much easier to streamline processes and meets both data engineers' and compliance and audit departments' needs.
- Source data is copied to and stored, over time, in a single data warehouse. Values can always be traced back to the source.
- Modeling breaks down and consolidates source data into three core components: hubs (unique entities important to the business such as ‘customer’, ‘product’, ‘store’), links (relationships between entities), and satellites (all the properties and history of an entity).
Automating other data management architectures
Organizations use a variety of tools to process and manage data. Data Vault automation also supports the automated build of a data hub, data lake, data lakehouse and data mesh.
Everything is a repeatable pattern
Watch this video series that explains in depth how VaultSpeed approaches data warehouse automation.
First, we automate multi-source data integration without our users having to write a single line of code. No-code data automation significantly increases the speed, productivity, and quality of output.
Then automate your business rules: VaultSpeed’s template engine allows you to develop industry-specific metrics stores. This helps to build and market data-intensive applications or to deliver state-of-the-art analytics in time.
Here's how VaultSpeed works
Automation
benefits
Automate ETL processes
Streamline automation processes to extract, transform and load (ETL) data (full load or incremental load). Use auto-mapping and job scheduling to eliminate repetitive steps.
Get the complete view
Extract disparate data from databases faster. Get complete, accurate data for reporting and reduce risks of budget overruns and project failures.
Reduce manual work
Developers no longer need to spend time on upfront manual work, so there’s no more temptation to take risky shortcuts.