Data warehouse automation is more complex than robots zooming around your company warehouse, completing tasks. Processes including, but not limited to, design, development, operations, impact analysis, and testing are involved in data warehouse automation systems.
What exactly is data warehouse automation? Does it differ from an understanding of warehouse automation? Let’s dive into what data warehouse automation is and how it’s different to understand than warehouse automation.
From a definitional state, data uses metrics from a digital form to produce growth or spark discussion. In a warehouse, data is vital to the survival of the company. Without data, there are no logistics, and it’s hard to optimize a warehouse. Data warehouse automation uses development to automate the lifecycle processes with the help of artificial intelligence (AI).
A warehouse lifecycle goes from a repetitive process to one that is much more automated and more user-friendly with data warehouse automation. This can help the warehouse automation tools such as robots and sensors
Data warehousing is the process of using metrics and other statistics to not only generate code but deploy code. By doing this, it is easier to develop the best design of the warehouse inside and out for the project at hand.
Storage in the database for a warehouse uses real-time data. Think of orders going in and out, or for a manufacturing line, new products going in and out, all in real-time, not stalled by delays in reporting. That database keeps up with those numbers so that humans don’t have to, but that is only the beginning. This not only eliminates errors but also helps with labor costs and frees up those working to help in more prominent parts of each project.
Examples of Data Warehouse Automation:
Used to streamline data computerization, data warehouse automation systems use extraction, transformation, and loading (ETL for short) tools to complete the job. The cost of these tools for projects ranges between 20K and 20M, depending on the complexity of the project and the tool you use.
Below are 7 of my favorite data warehouse automation tool systems, but there are plenty more where these came from.
As a cloud-based analytical and BI tool, Amazon Redshift has a lot of useful functions, making it one of the more popular data warehouse automation tools. This service is easy to customize as well as easy to integrate with previous databases. The custom functionality of Amazon Redshift is limitless due to its storage, processing, and optimization. The service even has a two-month free trial to make sure you love it before taking the plunge into its data warehouse automation tool.
Data-driven is at the forefront of Oracle, which is also a cloud-based tool. Machine learning analysis, auto-tuning, and data visualization are some of the biggest and most desirable features of Oracle. This tool is best for heavily analytical warehouses and projects. Oracle will generate reports and predictions based on your received data and help your company to grow through warehousing projects.
ActiveBatch uses end-to-end solutions to ensure a real-time database for users. It features advanced scheduling and a job library, which is helpful for automating designs and quickly moving through projects. You can add multiple checkpoints to increase usability. It also has a free demo and 30-day trial, which is useful for getting started with ActiveBatch.
Redwood is great for companies expecting growth. It’s user-friendly and has unparalleled scalability to be able to grow with you rather than needing to change your automated tools as you grow. It has incredible visibility features, great for keeping up with everything behind the scenes, and has integration capabilities for multiple sources to keep all your data together. It also has the intelligence to feed data onto dashboards and build reports. This can give even more time back to you as a manager, or employee, by not having to develop those reports and dashboards yourself.
Zap DataHub is extremely user-friendly. There is no coding involved in setup and use. It is wizard-based automation, meaning the interface itself will lead the user through the necessary steps for setup or any features. Also, Zap offers a free demo to get companies started, not to mention it is one of the lower-priced options for data warehouse automation tools on the market. Another key feature of Zap is the intuitive modeling it offers, meaning you can drag and drop within the interface.
Popular for its infrastructure automation, WhereScape automates designs and streamlines projects. Doing this can reduce the overall timeline for production. WhereScape focuses on design, development, deployment, and operation. It also has add-on services; WhereScape 3D, WhereScape® Red, and WhereScape® Data Vault Express.
Astera is an agile management automation tool that implements design patterns through its data automation software. They use a code-free design, making it extremely user-friendly similar to Zap DataHub. The biggest difference between the two comes down to Astera being metadata-driven, and Zap DataHub being wizard-based. You can also request a free trial for Astera before you decide to invest.
How to Know it’s Time
Warehouses will know that data needs an automation element to it when their processes are starting to check the wrong boxes. If you are finding that all of your or your employee’s time is being spent working on data warehousing projects, or your projects are taking much longer than anticipated due to the volume of data, it may be time to consider some of the above options to automate the process a bit. As you can see, there is a lot of flexibility and many options to choose from.
This blog has given you insider information into the world of data warehouse automation. Additionally, why it’s important to the overall structure of a warehouse automation system. You also gained knowledge on several popular data warehouse automation tools to help you whenever the time may come to use one for your own warehouse projects.
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