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Reading Time: 2 minutes Too often the design of new data architectures is based on old principles: they are still very data-store-centric. They consist of many physical data stores in which data is stored repeatedly and redundantly. Over time, new types of data stores,…
Reading Time: 3 minutes The star of SnowflakeDB keeps rising. This cloud-based, analytical SQL database server offers a set of powerful features, not in the least of which is high performance, end-to-end encryption, cloud platform independency, a self-driving engine, automatic scalability (up or down),…
Reading Time: 3 minutes Apache Spark and data virtualization enables us to extract data from heterogeneous data sources, integrate and process that data, and make it available for a wide range of reporting and analytics tools. Both do so without the need to store…
Reading Time: 3 minutes Some still think it makes sense to compare ETL tools with data virtualization servers. Let me be clear, it really doesn’t. Unquestionably, both tool categories belong to the class of data integration tools, like data replication tools, data wrangling tools,…
Reading Time: 3 minutes According to Gartner, 60% of all the big data projects fail and according to Capgemini 70% of the big data projects are not profitable. There can only be one conclusion, big data projects are hard! There is not one specific…
Reading Time: 3 minutes Some still see data virtualization as an alternative technology to ETL. For them it’s nothing more than another style of data integration. However, data virtualization is much more. Data virtualization is about abstraction. It’s about data consumers being decoupled from…
Reading Time: 3 minutes When E.F. (Ted) Codd developed the relational model in the early 1970s, he had a dream. He wanted applications to be independent of the data storage and data access layer. Changes made to one should not lead to changes to…
Reading Time: 2 minutes The concepts of logical data warehouse and logical data lake are getting traction. Organizations are starting to understand that these forms of data warehouses and data lakes are more practical and flexible than their more traditional physical counterparts. The technology…
Reading Time: 3 minutes There was a time when developing a data warehouse was sufficient to quench the thirst for data, reporting, and analytics of most business users. Not anymore. Organizations have discovered that data can be a valuable business asset. It has taken…
Reading Time: 3 minutes The strength of Hadoop and NoSQL systems in supporting big data systems is indisputable. These big data technologies are designed, built, and optimized specifically for large-scale and high-performance environments. Their support for massively parallel hardware architectures and cheap commodity hardware…
