Data warehouses allow businesses to view their data from all endpoints in a centralized location and process it for insights. The setup can be done either by using various development tools or with data warehouse automation software. With a traditional data warehouse, the company depends on the IT team to maintain data and provide business users the insights they need. This creates significant delays; as for each request, the IT team needs to extract and integrate data from the right sources, code ETL processes and ensure data accuracy before providing the report. By the time the report reaches the relevant department, business needs may have already changed. These time-consuming processes occur at every step of creating and managing a traditional data warehouse.
Data integration is also becoming steadily more difficult with businesses handling an average of 10-100 terabytes of data, which is expected to increase in the future. Using data warehouse automation, enterprises can speed up and simplify their processes by drastically cutting downtime on these recurring manual tasks.
What is Data Warehouse Automation?
Data warehouse automation uses technology to help create data warehouses and existing ones without spending most of the development time on it, and in a shorter time frame. It increases effectivity of a business by speeding up the design, deployment, and maintenance of data warehouses. As much as 80% of manual tasks in these processes can be automated, which allows users to take control of processes and reduce handoffs during implementation.
Why Automate Your Data Warehouse?
A business may need to create a new data warehouse for the enterprise, or even for a department or division by integrating its various data marts. However, creating a traditional data warehouse is a costly venture and would take months to complete. Validating the data and making adjustments to ensure accuracy is another challenge. Using DWH automation tools, companies can create data warehouses in just days or weeks.
With automation capabilities, you can do much more than just building a data warehouse from scratch. Enterprises looking to modernize their existing implementation can also reap the benefits of data warehouse automation and simplify their move to the cloud.
List of Benefits of Automating Enterprise Data Warehouse
Here are some of the benefits of data warehouse automation that will convince you to make it a priority this year.
1. Simplified Automation at Every Stage
DWA allows users to automate processes at each of the design, code, deployment, execution, monitoring and reporting stages. Users can extract data from multiple sources, build data models, apply transformations, check for data errors, and load into the destination—all without writing a single line of code.
With the manual, time-consuming tasks out of the way, the processes are simplified, empowering even business users to take charge of data while allowing IT teams to focus on solving more complex problems.
Data warehouse automation example: Deploying data models with a few clicks
2. Iterative Approach for Easy Maintainability
Once a data warehouse is up and running, you have to make provisions for updating and maintaining it. New business requirements bring about the need to add more dimensions, modify relationships and update new information. Data automation tools allow businesses to build iteratively. This allows IT teams to integrate new database tables without having to completely remodel the data warehouse structure.
SCD field update options in Astera DW Builder
3. Data Quality Assurance
Business decisions depend on assessments made on data, which can make or break projects. Research shows that over 40% projects fail due to bad data. Companies receive data from a multitude of sources and in different formats. It is important to profile and validate this data before using it for reporting and analysis.
For example, Astera DW Builder allows businesses to define actions based on errors with workflows, such as emailing error logs records to the relevant authorities for review or dropping erroneous records entirely.
This ensures that the insights gained from this data are reliable, and that decisions based on those insights are impactful.
4. Streamlined Data Warehouse Modernization
Businesses that have decided to move their legacy data warehouses to the cloud to leverage the benefits of better security, agility, elasticity and scalability need a strategic solution to streamline the process. Cloud data warehouse automation tools provide them a platform to plan and develop synchronization processes, transform code that is native to the target cloud database, and execute the migration processes. The native connectivity to legacy sources and cloud destinations allows users to set up ETL processes quickly and deliver the modernization project on time, without compromising on data quality.
List of database connectors available in Astera DW Builder
5. Quicker Integration and Faster Reporting for Business Agility
DW automation expedites data integration between internal enterprise systems and external third-party tools and helps minimize time-to-market for projects. Moreover, reports can be generated and sent to the relevant departments automatically. With faster access to data and insights, businesses can match the speed of market changes and act quickly. With the entire process simplified, even frontline business units like data analysts and customer integration teams can extract their own insights for quick decisions without explaining their needs to developers every time.
Now that you are aware of the benefits of data warehouse automation, it is time to learn about various data warehousing tools that can help you get it done effortlessly.
Which Data Warehouse Automation Tool is Right for You?
When selecting a data warehouse automation tool, certain considerations are important to keep in mind before making the choice. Here are a few:
- Connectivity Support: DWH automation tools offer varying degrees of support for connectivity to databases. Depending on what the business needs, it is important to select the tool that offers robust connectivity to databases used.
- Integration Support: Businesses may deal with multiple sources and file formats, and as the company scales, more applications may need to be integrated. An automation tool that offers support to a wide array of systems and formats would ensure that business processes run smoothly without a hitch.
- BI Support: Some DW automation platforms offer not just the database structure generation but also the generation of metadata for BI tools as well. This assists a business’ analysis and visual reporting processes.
Use Case: Data Warehouse Automation Example
Let’s take an example of a distribution company, Gamiphi, that sells a wide range of toys from various vendors and has retail chains all over US. The company stores the stock data from vendors, employee and customer data from countrywide stores, and other information like purchase items and invoices in their SQL databases. Requesting BI data from IT teams meant waiting weeks until the team writes and executes the code and provides the insights, and additional time if any changes or feedback needs to be incorporated. Gamiphi wanted to bring their time-to-insights faster and started researching data warehouse automation vendors. They found Astera Data Warehouse Builder (ADWB).
How Astera Makes DW Automation Easier?
ADWB is a metadata-driven data warehousing solution with a feature rich data modeler. Users can reverse-engineer databases in a matter of a few clicks or create schemas from scratch with the drag-and-drop options. It also provides the option to import your designed data models to use for subsequent processes.
Reverse-engineering feature in Astera DW Builder
Reverse-engineered database for Gamiphi
Once the schema is built as per requirements, the data can be populated in fact and dimension tables with the help of dataflows.
Dataflow to populate dimension table in Astera DW Builder
Astera DW Builder offers three different source options to populate fact tables. One is the traditional SQL query source option where complete relationships are defined in hand-written SQL code.
Another is the data model query source which is completely codeless.
In this option, users only need to identify the root entity and all the parent-child relationships will be populated automatically.
The last option is the Astera Query Language source, which works like the SQL SELECT statement—simply identify the columns and the root entity.
Once the schema is built and data is populated, the data model can be forward engineered just as easily to the business’ database.
Astera DW Builder offers a single platform to design, test, and build on-premise and cloud data warehouses from the ground up, and automate processes to deliver insights quicker, without writing ETL code. If you want to discuss your use case or want to see a live demo of the product, let us know and our experts will reach out to you.