The concept of data warehousing has evolved from age-old single stack repositories to logical warehouses. Archaic data warehouses don’t address the needs like multi-dimensional data processing and real-time data virtualization. Unfortunately, many enterprises are still practicing old-school approaches for contemporary data warehousing initiatives. This is leading to some major setbacks.
It is crucial to understand that conventional approaches such as the Enterprise Service Bus (ESB), Extract Transform and Load (ETL), etc. aren’t compatible with multi-dimensional data. These approaches don’t support cloud-born data and structured/unstructured data.
Legacy modernization is important for aligning the old data warehouses with the latest IT landscape. Modern enterprises must learn to restructure their warehouse structure to extract more value out of the data. With legacy modernization, it is easier to drive innovative strategies and governance. It gives teams the privilege to make the best use of conventional IT and embracing new technologies without disruption. However, data warehouse modernization is easier said than done. Teams face many issues that come from the integration side. Here are some best practices for smoothening the path to legacy modernization.
What are the Best Practices for Successful Data Warehousing Adoption?
Know Difference Between Conventional Data vs. Modern Data: With the advent of cloud technologies, modern data warehousing has undergone a huge transformation. Data warehouses of today emphasize more on value instead of transaction processing. These warehouses are mainly constructed for analysis.
Modern data warehouses are differentiated by design thinking. Conventional data warehouses, on the other hand, cover mainly four important functions: applications, data sources, analytics, and infrastructure. On the contrast, modern data warehouse covers the following core functions:
- Programming Languages
Knowing these differences is important and it enables teams to make the best use of both the concepts.
1. Define Data Storage and Compression Formats
There may be multiple options for data storage. Each option offers distinct benefits. Detailed evaluation of data storage and formats within applications is important.
2. Consider Multi-tenancy Support
This is crucial for Business Intelligence (BI) environment. It provides benefits of using a single software stack for serving thousands of clients and partners. Making upgrades and customizations becomes easier during legacy modernization.
3. Assess the Schema
Make sure you assess the nature of databases that you store. It is also important to verify the way it gets loaded, processed, and orchestrated for optimizing schema objects.
4. Confirm Metadata Management
It is crucial to ensure end-to-end meta-data management for data warehousing initiatives. Effective metadata management simplifies the path to modernization. It allows in capturing necessary info for building, utilizing, and interpreting elements of the data warehouse.
5. Agile Deliveries
This becomes possible with an enterprise-class integration solution. Integration facilitates the efficient building of data warehouse projects. However, finding the right solution that effectively addresses start-to-end integration requirements is a huge challenge. A cloud-based integration solution can help teams in building the right strategy for data warehousing.
Conclusion: Old software tools and data warehousing techniques like ESB & ETL, etc. are leading to increased costs and inefficiencies in B2B operations. Business should rethink their data warehousing from scratch. However, modernizing the legacy can be challenging. Enterprises need to tread carefully on this path with a phased approach.
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