Data warehouse automation (DWA) software automates and streamlines every part of the entire data warehouse lifecycle. It helps ensure the automation software automatically manages a data warehouse's numerous tasks—discovery, designing, developing, deploying, provisioning, and scaling.
Automating data warehousing ensures that there is a reduction or a complete elimination of repetitive tasks. Data warehouse software usually provides built-in templates or uses data modeling (patterns to ensure functionality) to automate. Automating these repetitive tasks helps companies develop data-driven strategies and provide data-driven insights and hence jump on the digital transformation bandwagon.
By automating each step of the data warehouse lifecycle, there is much less time required to manage it, thereby providing data engineers with more time on other tasks instead of managing the data warehouse 24/7.
For businesses, data is at the core of decision-making. However, it's not just the data that is important, but the workflow. Specifically, how business users can access the data and the speed to access that data also matters, driving the need for DWA solutions.
Traditional data warehouse architecture has intensive manual code writing for data modeling, design, etc. DWA helps eliminate these steps and allows clean data preparation and integration without requiring engineers to write code.
Data in a data warehouse goes through three stages primarily:
- Extraction, where data is extracted from numerous internal and external data sources (big data sources). SQL scripts/code written by data engineers is used to extract all data from the database. In this step, data preparation (cleansing the data) also occurs.
- Data modeling is done using different schemas, and the data sets are transformed. This data is then loaded into the data warehouse.
- Data can then be exported into analytics or business intelligence (BI) tools to make data-driven decisions.
The extract, transform, and load (ETL) or extract, load, and transform (ELT) process in the first two steps above used to be a manual process, but the introduction of different ETL tools and DWA processes makes the process much more efficient. DWA tools help optimize the ETL/ELT process for real-time data warehousing. The difference between ETL and ELT is that ELT uses the target system to transform the data instead of pre-processing the data like in ETL.
As shared earlier, all the above steps, from extraction to exporting to business intelligence (BI) tools, happen automatically within the DWA software.
What Does DWA Stand For?
DWA stands for dData wWarehouse aAutomation. The main task of this software is automating multiple processes, ensuring the speed and agility of the entire data warehouse lifecycle.
What are the Common Features of Data Warehouse Automation Software?
The following are some core features within DWA solutions that can help users in several ways:
Automation: The key feature of DWA tools is the introduction of automation into a traditionally manual data warehouse process. Automating the numerous steps involved helps reduce manual error and the time for the data to be used by BI tools to drive analytics.
Batch processing and scheduling: DWA tools support businesses to schedule and run any of their data warehousing jobs automatically, reducing any need for manual support. Automating batch processing and scheduling ensures resources are being allocated judiciously.
Consolidation of the data management process: Since DWA ensures that data warehouse processes are automated from start to finish, companies may not require specific ETL tools or even additional BI platforms since the DWA software can offer the same. DWA solutions can exist as a one-stop shop for several data management processes, making it much easier for admins and developers to handle them as it exists in a single platform.
Checkpoint support: Although automation is key here, any automation failure could cause numerous problems. To support this, many DWA tools can add checkpoints throughout the 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.
Analytics support: As shared earlier, a key outcome of using DWA tools is providing data-driven business insights. A key feature of any DWA solution is ensuring the user can build analytic models to help achieve fast and accurate business intelligence reporting. Without DWA, it would take weeks, or even months, to deliver insights. And by the time those insights are received, the data would be old, hence not real time and accurate.
Built-in connections: DWA tools also support built-in connections to various on-premises databases or cloud services such as Microsoft Azure, Amazon Web Services (AWS), etc.
