How do you see the data warehouse fitting into modern data and analytics architectures?
Sort By:
Oldest
VP / CIO in Healthcare and Biotecha year ago
In modern data and analytics architectures, data warehouses serve as centralized, high-performance repositories for structured data, providing a single source of truth for analytics. They integrate with BI tools, offer scalability, data transformation capabilities, and prioritize security and governance. Data warehouses often work alongside data lakes to create comprehensive data ecosystems that accommodate diverse data sources and analytical needs.Senior Data Scientist in Miscellaneousa year ago
Data warehouses will remain an important cornerstone in D&A. As the vast majority of data sources are (still) basing on object-relational data warehouses and quite a demand exists for related analytics and results, the reason is obvious.As gartner graphs illustrate data lakes and data lakehouses may pose needed extensions to the currrent appraoches.
Senior Data and Analytics Leader in Government8 months ago
The role of the data warehouse may evolve as organizations adopt newer architectural patterns, such as data lakes or real-time streaming analytics. However, the data warehouse continues to be relevant and valuable, particularly for organizations that require a structured and reliable foundation for their data and analytics initiatives. Below are certain aspects where data warehouses are still going to be relevant and valuable to organizations.1. Centralized Data Storage and Integration
2. Historical Data Analysis
3. Performance and Scalability
4. Data Transformation and Cleansing
Senior Systems Analyst / Team Leader in Government8 months ago
In modern data and analytics architectures, the data warehouse remains a foundational element, albeit with evolving roles. While traditional warehouses focus on structured data, the modern approach integrates varied data types, embracing scalability and real-time processing. It serves as a centralized repository, facilitating analytics, reporting, and decision-making. However, its role has transformed; it now collaborates with data lakes, streaming platforms, and cloud-based services. The warehouse complements these components by providing curated, organized, and structured data sets, offering a reliable source for critical business insights. Its adaptability, incorporating data from diverse sources while ensuring security and governance, renders it pivotal in the contemporary data landscape, creating a synergy that harnesses the full potential of data for analytics and strategic decision-making.
The warehouse is still a component of the overall platform, but I think it's not the only component. Now, I think we've broadened it. The warehouse is designed around the queries we know we're gonna ask, and today that is still useful. That is still valuable, but we have to extend beyond that to other questions we're exploring and experimenting beyond that - which is where the concepts of data lakes and data lakehouses come into the picture.