In the past, designing and developing a robust data warehouse that satisfied the need for timely and effective business intelligence (BI) was an overwhelmingly difficult task, as it required significant time, capital, and risk. However, the advent of agile data warehouse architecture has truly transformed how enterprises build their data architecture and enable business intelligence.
In essence, agile data warehousing is about building and updating your architecture through an iterative approach rather than developing a fully built solution in one go. Each iterative cycle, while building on the previous one, adapts the warehouse to current business needs. And each iteration is made by inclusive teams of business and technical users to ensure that the business intelligence requirements of all stakeholders are met. An agile data warehouse, unlike legacy architectures, is a living system that continuously evolves and adapts to changing data needs.
In this article, we’ll explore the need for an agile approach and the role of automation in facilitating its development.
The Need for an Agile Data Warehouse Approach
Modern organizations operate in highly competitive and ever-changing business environments. Moreover, their data needs to undergo rapid change as they grow and scale with their environment. Therefore, they require a dynamic data architecture that is robust enough to provide accurate insights for informed decision-making and malleable enough to adapt to changes swiftly. An agile data warehouse does precisely this.
Here are a few reasons why your organization needs an agile approach toward data warehousing:
- Faster Time to Value: An agile approach reduces the time required to design, develop, and deploy a data warehouse architecture. By starting with a smaller focus area or specific BI requirements instead of pursuing an all-encompassing data warehousing project, collaborative teams can quickly pull up a data architecture and then adapt it as the requirements grow or change.
- Mitigate Development Risks: General and waterfall techniques of building legacy data architectures entail a high risk of failure due to the complexity and inherent limitations of the long development process. By breaking down the project into smaller iterative tasks, the agile approach reduces complexity and mitigates failure risk affecting whole projects. Moreover, smaller failures during specific iterations can easily be resolved on the spot.
- Ensure Seamless Adaptability: An agile data architecture for a company makes it dynamic enough to adapt to changing data requirements. With a data warehouse built incrementally, adding new data sources, incorporating different pipeline technologies, or adding platforms and changing data models becomes just another task in the sprint cycle. The agile data warehouse can be updated rapidly with the requisite changes without disrupting the operations of the whole architecture.
- Benefit from Collaborative Teams: Agile development demands that business users and solution developers collaborate on data architecture projects. For example, business decision-makers can partake in the whole process cycle – including development – and see that their requirements are being met. Collaborative teams also mean greater transparency on what the assigned tasks are and how these being done; subsequently, this clarity helps everyone share ideas on how to deal with new challenges. Eventually, collaboration between stakeholders results in development of a data warehousing solution that delivers accurate and timely insights to decision-makers.
How Automation Can Help
Data warehouse automation (DWA) uses design patterns, job scheduling features, and code-generators to streamline and automate data warehousing tasks such as data modeling, ETL/ELT integrations, and platform deployment. Development teams can automate manual, code-based development tasks that are repetitive and time-consuming, reducing the time to market significantly.
Automation plays a key role in unlocking the true potential of agile data warehousing. By eliminating repetitive, code-based tasks using drag-and-drop interface and job scheduling, architects can automate entire ETL pipelines and create a self-serving data warehouse architecture. A self-serving data warehousing solution accelerates the data-and-insight journey.
Moreover, it is easier to make adaptive changes to the automated processes in data warehouse automation tools since they do not require manually generated code scripts to run.
Modern DWA tools come with pre-built connectors for a variety of popular databases and cloud platforms, making data integration much more efficient. Organizations can simply change their connections to access different destinations and platforms. Subsequently, the data warehouse architecture can be updated easily to facilitate any number of sources, destinations, cloud platforms, or on-premise architectures.
Data Warehouse Automation Tools for Your Architecture
The use of automation for fueling an agile data architecture relies primarily on finding the right data warehouse automation tools. The perfect data warehouse automation tool should have the following features to facilitate development:
- Support for automated data modeling that can cater to different types of model designs such as dimensional models, data vaults, etc.
- Robust ETL pipelines to handle large amounts of data and ensure real-time data capture.
- Code-free, easy-to-learn interface that diverse teams can use to execute (maintain and update is the right choice) complex data warehousing tasks that otherwise might require long lines of code (time, efforts, manpower, maintenance, cost are also the consequences).
- An extensive range of connectors for different databases and cloud platforms to make the data architecture platform independent.
- Job scheduling and workflow orchestration to automate data warehouse architecture pipelines.
Equipped with these capabilities, any organization can employ an agile approach to build a robust data warehouse architecture that delivers quality insights at speed.