The Basics Of Automated Data Warehouse Lifecycle Management

In the past, designing a data warehouse and data warehouse architecture has taken too long to complete. Executing numerous semi-automated steps results in a data warehouse that was limited and inflexible. This limitation caused businesses to seek out other solution providers like AWS. Data warehouse solution providers came up with an alternative solution to automate the data warehouse that includes every step involved in the life-cycle, thus reducing the efforts required to manage it. Data warehouse automation works on the principles of design patterns. It comprises a central repository of design patterns, which encapsulate architectural standards as well as best practices for data design, data management, data integration, and data usage. The solution addresses the goal of having a fully automated data warehouse environment and document data warehouses and data marts.

Every industry has used automation to increase productivity, improve quality and consistency, reduce costs and speed delivery. There is a definitive need to introduce automation to data warehousing. Data warehouse automation (DWA) Data Warehouse Automation provides benefits in a greater magnitude to each team player involved in any organization including Customers, Business Users, Project Managers, Developers, Testers and Quality Analysts.

DWA increases developer productivity by automating and accelerating the routine tasks associated with building and managing decision support infrastructures, and gradually improves the time and reduces the repetitive manual efforts associated with each step of a data warehouse lifecycle. Additionally, it provides insight into source data exploration, warehouse data models, ETL generation, metadata management, managed deployment, scheduling, change impact analysis and easier maintenance and modification of the data warehouse.

Concurrently, data warehouse automation adds benefits to the testers by providing testing automation from DWA tools. It also strengthens the quality and consistency of decision support infrastructures and provides a timely response to changing business requirements, thus providing a huge benefit to the Business team. More important than the technical features of DWA tools, however, is the ability to deliver projects faster and with fewer resources.

There are various DWA platforms available to use, but there are also toolsets such as scripting and development environments that can provide much of the implementation value of a data warehouse automation solution. The right environment for team skills and budget will go a long way to either validating a DWA practice or showing its limitations. Just as DWA is designed to iterate the development of analytical environments, data warehouse automation practices should have the same level of iteration.

DWA is an emerging class of tools that improve the efficiency and effectiveness of your data warehousing processes. The beauty of automation tools is that they provide a huge return on investment (ROI) since they simply eliminate so many moving parts and arrange those all together in an automated package. Which DWA tools should you buy for your organization? There are many attributes to evaluate when selecting DWA tools; the most important is design approach which forms the basis of an organizational framework for evaluating DWA products.

As part of your research in making a tool selection consider the following questions:

  • Does your organization want to design a data warehouse using a data model?
  • What will be the best way to capture business requirements and create a conceptual or logical model?
  • Does your organization wants to design a data warehouse using an actual data rather than a data model?
  • Does your organization want to get engaged with business users based on the data model? You should choose a model-driven approach. Or does your organization wants to collaborate with business users by interactively prototyping BI solutions with actual data? A data-driven approach would be the best choice.
  • What are the important data warehouse concepts out there to consider?

    Benefits of (DWA) Data Warehouse Automation:

  • It’s fast. Dramatically reduce your team development time.
  • It’s flexible. Respond to changing business requirements quickly and easily.
    Stay focused. On what matters and concentrate on reporting and analytics instead of getting stuck in ETL code.
  • Quality. DWA tools produce tested high performance, complete and readable code.
  • Developers come and go, but if they keep using the same tool, it’s easy for one developer to understand the work of another.
Automation can be a huge help, but automating concepts before you understand them can put you and your organization in a loss. Automating a broken process leads to making mistakes faster. Your organization will benefit by using the right design process and engage the analytical implementation teams. Without this level of forethought, communication, and cultural buy-in, the process becomes more of an issue than a benefit and takes longer to implement than a traditional approach. Most importantly choose the right tools to use.

The need is growing for data warehousing professionals to implement DWA tools. Automation is crucial to the success of data warehousing projects. Integrated DWA tools eliminate hand coding and custom design for planning, designing, building, and documenting decision support infrastructure. The tools let you work smarter at the speed of business. DWA can save organizations a huge amount of money in development and maintenance costs and more importantly, increases the productivity of an organization with customers and the IT market.

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