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Reading Time: 4 minutes What do data virtualization, streaming, and extract, transform, and load (ETL) processes have in common? They all deliver data from disparate sources to the business intelligence (BI), analytics, and other applications used by businesspeople, data scientists, and analysts. Like utilities,…
Reading Time: 3 minutes Plastics Between Challenge and Opportunity K 2019, which took place end of October this year in Dusseldorf, is a gigantic trade fair for the plastics industry. If you were a data anthropologist, you would not merely visit to smell the…
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 In all of my conversations about data, the same words and phrases keep coming up – agility, performance, security, governance, the heterogeneity of data sources (data silos), and ease of access, just to name a few. I use them so…
Reading Time: 3 minutes Public clouds and on-premises data centers can now be strategically connected via a strong hybrid architecture, simplifying data integration. If you are in the process of building a virtual data services layer across all of your disparate data sources in…
Reading Time: 3 minutes In the ever-so-cyclical oil and gas industry, long gone are the good ol’ days of $100/barrel and partying like it’s 2008. At the most recent industry peak in June of 2008, featuring oil prices north of $150/barrel, energy companies directed…
Reading Time: 6 minutes The logical data warehouse is the data architecture of the future — fast, flexible and ideal to support self-service BI. In the past year, I have published quite a lot regarding the architectural advantages of the logical data warehouse (read…
Reading Time: 5 minutes Complex Questions that Need Simple … but not Simplistic … Answers Update: make sure you also read the first part of this content. Data Virtualization Performance and Source System Impact (Part 1) In part 1 of this article, we looked…
Reading Time: 6 minutes One could almost call it tunnel vision. The way that we consistently hold on to one way in which to fulfil each and every need for information: data copying, transformation and integration in one physical database. We consider it almost…
Reading Time: 5 minutes In my previous posts, we reviewed some of the main optimization techniques used by Denodo in Logical Data Warehouse and Logical Data Lake scenarios (see here and here). We also provided real numbers showing that a logical architecture can achieve…
