Data is the new oil in today's world, and the need to store, manage and analyze it effectively has become paramount for businesses. Data Warehouse Automation (DWA) is a technology that helps organizations improve their data management capabilities. In this blog post, we'll take a closer look at what DWA is, its different levels of automation, what can't be automated, and how it benefits individuals in various roles.
What Exactly is Data Warehouse Automation?
In simple terms, Data Warehouse Automation is the process of automating data warehouse development, deployment, and maintenance. DWA replaces traditional manual processes with automated ones, speeding up the entire data warehousing process and eliminating manual coding. This is done through templates, capturing metadata, and automating tedious tasks, such as hand-coding functionality in dimensional modeling or in data vault models.
With DWA, organizations can create data warehouses in a fraction of the time it would take with manual processes. It also helps to reduce the risk of human errors, as the automation process eliminates the chances of mistakes in coding. Moreover, DWA enables organizations to make their data warehouses more agile and scalable, thus enabling them to adapt quickly to the changing needs of their business. Common standards and development processes mean that modifications and enhancements can be done faster and easier.
What are the Different Levels of Automation?
DWA has different levels of automation, ranging from basic to advanced. At the basic level, DWA automates simple tasks like data loading and schema creation. The next level of automation is where DWA automates the entire data warehouse design process. This includes everything from creating data models to ETL (extract, transform, load) processing and report generation.
DWA can automate the entire data warehousing process at the advanced level, from design to deployment and maintenance. It includes self-optimizing and self-tuning the data warehouse, ensuring that it always operates at peak performance levels.
What CAN'T be automated?
Although DWA is a compelling technology, certain aspects of data warehousing cannot be automated. For example, while DWA can automate the design and deployment of data warehouses, it cannot replace the need for human input in data analysis.
Similarly, DWA cannot replace the need for skilled professionals in data warehousing. The technology can help automate specific tasks, but it cannot replace the need for developers, architects, managers, and analysts with the expertise to understand and analyze data.
How Exactly is this Good for Me in My Role?
DWA can bring significant benefits to individuals in different roles.
For developers, DWA can reduce the manual coding required, allowing them to focus on more complex tasks. DWA can also speed up the development process, enabling developers to create data warehouses in a fraction of the time it would take with manual processes.
For architects, DWA can enable them to design more complex data warehouses while reducing the risk of human errors. DWA can also help architects create more agile and scalable data warehouses that adapt quickly to changing business needs.
DWA can give managers more visibility into the data warehousing process. This can help them make better decisions about resource allocation, project timelines, and budgeting.
For analysts, DWA can provide them with faster access to data, enabling them to make more informed decisions. DWA can also help analysts focus more on analyzing data than spending time on data collection and preparation. In addition, DWA provides full lineage, so analysts can understand exactly where the data came from.
How does this Change How I do Work?
Implementing DWA can change how individuals work in their respective roles.
For developers, it can mean focusing more on complex tasks, such as creating custom ETL processes, rather than manual coding. It also means working directly with analysts, creating quick prototypes that allow the analyst to try out new ideas.
For architects, it can mean spending more time designing complex data warehouses and less worrying about coding errors. It also ensures that the physical data warehouse is built according to their design!
For managers, it can mean having more visibility into the data warehousing process, enabling them to make better decisions. Budgeting and scoping of projects is easier, as the development and enhancement processes are more clearly understood.
For analysts, it can mean spending more time analyzing data and less time on data preparation. And, as said earlier, the analysts will have more confidence in the data they are viewing. They will be able to see full lineage and the entire pipeline of transformations from the source system to their analysis tools.
Overall, DWA can make the data warehousing process more efficient, enabling individuals to focus on tasks requiring unique skill sets. It can also enable organizations to make better decisions based on data insights, which can drive business growth and success.
What Examples Show these Benefits?
Several organizations have already benefited from implementing DWA. For example, Teradata, a data warehousing company, implemented DWA and reduced their development time for data warehouses by 70%. This enabled the company to deliver data warehouses to their clients in a fraction of the time it would take with manual processes.
Another example is BigPanda, a technology company that provides an event correlation and automation platform for IT operations. By implementing DWA, BigPanda reduced their ETL processing time by 80%, providing their clients with faster data insights.
DWA is a powerful technology that can help organizations improve their data warehousing capabilities. It can make the data warehousing process more efficient, reduce the risk of human errors, and enable organizations to make better decisions based on data insights. While certain aspects of data warehousing cannot be automated, DWA can benefit individuals in different roles, including developers, architects, managers, and analysts. By implementing DWA, organizations can improve their agility, scalability, and adaptability, enabling them to stay ahead of the competition in today's data-driven world.