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Data Warehouse

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Snowflake and Anodot announced a strategic partnership that brings predictive analytics to Snowflake’s enterprise data warehouse. With this integration, customers can discover anomalies in data streams and raise alerts in real-time. Snowflake Computing, a San Mateo-based startup, is one of the first few companies to realize the potential of building a native, cloud-based data warehouse. Founded in 2012, Snowflake exploited the capabilities of the cloud to design a SQL data warehouse from the ground up. Snowflake’s data…

As technology evolves at a rapid rate – especially technology that incorporates artificial intelligence (AI) capabilities – so too does the potential for bias, disconnect, misuse of data, and the automation of impersonal actions or decisions. With the vast amounts of data collected, stored, and exchanged, capitalist societies risk the commoditization of personal data at the expense of the individual, instead of using personal data to foster valuable individual and societal relationships. In business, AI…

We all have a friend who’s bragged about getting the greatest deal ever on a used car, right up until they’re calling us from the side of the highway because their new hot rod doesn’t work quite as well as advertised. Some of us, too, have been that unlucky buyer, watching the smoke curl from our engine and wondering where we went wrong. Making a decision on a big purchase is tough. Apart from trying…

Data lakes are cool, but you don’t have to jump in head-first. It’s easy to start by dipping a toe: Integrating a legacy data warehouse into a data lake leverages the structured systems that have been built over the years while taking advantage of the ever-increasing volume of unstructured data across the organization. A Familiar Structure Data warehouses are familiar. Generally, IT teams know how to work with them. Over the past two decades, companies…

Gartner estimates that close to 70 to 80 percent of newly initiated business intelligence projects fail. This is due to myriad reasons, from bad tool choice to a lack of communication between IT and business stakeholders. Having successfully implemented BI projects across industries, I hope to share my experiences in this blog post and highlight key reasons why business intelligence projects fail. This article will present counter-measures to failure based on three principles that should govern…

It is surprisingly rare for IT people (in general) to appreciate that data warehousing requires a lot of disk space. Non-IT people, on the other hand, is more appreciative. They understand that if we collect data from many different places and put them in 1 place, then naturally it will require a big disk space.   But IT people are quite the opposite: they can appreciate that email and file servers are disks hungry, but…

What are the advantages of creating a data warehouse in a normalized format? Some people including myself have been exploring the answer to this question. I’m going to rewrite the answer that I posted in Kimball forum here. But before I do that, let’s clarify the terms: a) normalised, and b) normalised data warehouse. Normalised: “Normalisation” is the process of removing data redundancy by implementing normalisation rules. I am referring to Codd’s normalisation rules and…

A Data warehouse is a central repository of all data of an organisation/business from various sources. DW is done by a process which involves extraction, transformation, and loading of the data. This process is commonly known as ETL (Extraction Transform Load). Extraction- The data is collected from multiple sources.Transformation- The data is converted from its previous form to a form which is required so that it can be placed into another database.Loading- The transformed data…