This whitepaper by eminent data experts BARC Research & Eckerson Group outlines Data Warehouse and Data Vault Adoption Trends describes how to design and build them, and explains how they increase both productivity and business agility. It recommends that companies double down on fundamentals such as data quality, adopt commercial automation tools, and learn more about the data vault. Best-in-class companies set an example for others to follow.
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Examines data warehouse and data vault adoption trends in modern analytics environments, including architecture types, priorities, and automation.
Why changes in the data landscape now demand sustainable architectures
Which technologies a more agile architecture will allow you to adopt
How to react faster to requests from the business
Steps to double down on strong fundamentals such as data quality, adopt commercial automation tools, and learn more about data vault.
The role of end-to-end data architecture automation tools
The changing role of data leads to new requirements for IT systems. Additionally, innovative new data processing and storage technologies are creating new business opportunities. Nowadays, systems can be developed that were almost unimaginable or too expensive to develop years ago.
Modern Data Architecture
To support all these new requirements and to leverage these new technologies, organizations are rethinking and redeveloping the data architectures of their current data platforms. It is not an option to develop new data platforms with architectures that will be as outdated within a few years.
Data Vault Adoption
The data vault has strong adherents among best-in-class companies, even though its usage lags the alternative approaches of third-normal-form and star schema. Compared with laggards, a higher portion of best-in-class companies adopt the data vault, embrace its standards, and intend to expand their use of it. They plan to expand their use of this modeling technique and methodology.
Data Warehouse Automation
Data warehouse automation (DWA) helps IT teams deliver and manage much more than before, much faster, with less project risk and at a lower cost by eliminating repetitive design, development, deployment and operational tasks within the data warehouse lifecycle.
Data warehouse automation has been credited with boosting developer productivity by fivefold. With the ability to automate as much as 80 percent of the data warehouse lifecycle, IT teams can more quickly deliver data warehouses, as well as more easily adapt existing data warehouses as business needs change.
Reduce the Learning Curve
When designed for a specific data platform, or data warehouse software, data warehouse automation can also greatly reduce the learning curve associated with implementing a new data platform within an organization. Whereas traditionally developers hand-coding projects would need deep knowledge of many aspects of the new platform, data warehouse automation specifically designed for the platform can mask much of the complexity working behind the scenes.
Standardize Best Practices
Data warehouse automation solutions have also been credited with providing organizations with the best practices standardization that can easily be lacking when working with a variation in development approaches, methodology understanding and other staffing characteristics. Thorough documentation is also a valuable takeaway for organizations using data warehouse automation, and often a luxury for those who are not