Author Archives: fdtki

About fdtki

Sr. BI Developer | An accomplished, quality-driven IT professional with over 16 years of experience in design, development and implementation of business requirements as a Microsoft SQL Server 6.5-2014 | Tabular/DAX | SSAS/MDX | Certified Tableau designer

Problem unsolvable for Data Lake Analytics

One of the tasks of Database Forensics is to detect events (or categories of events) that began too frequent. From programming perspective, this means that for each date there should be searches categories appearing more than certain number of times … Continue reading

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Lake and Blob – how far from each other?

Two competing cloud storage products by Microsoft are defined the next way: Azure Blob Storage is a general purpose, scalable object store that is designed for a wide variety of storage scenarios. Azure Data Lake Store is a hyper-scale repository that … Continue reading

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Hive 2.0 – The Solution For Data Warehousing

Slides from presentation by Hortonworks’ founder Alan Gates describing at high-level new features of Hive 2.0 tailored for the kind of queries typical for Data Warehousing: Apache Hive 2.0: SQL, Speed, Scale from Hadoop Summit Hive with LLAP is available … Continue reading

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BIModal IT: Unheeded Silver Bullet?

Gartner gives the next definition for it’s new Bimodal approach: The essence of an enterprise bimodal capability is marrying more predictable evolution of products and technologies (Mode 1) with the new and innovative (Mode 2): Mode 1 is optimized for … Continue reading

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Restoring objects in SQL Server

This is a code googled in panic after occasional deletion of a stored procedure. Does real magic saving hours of work that could be spent on re-writing:  

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Governed Data Discovery vs BI

Governed Data Discovery vs BI: Is there the best strategy? Governed Data Discovery is one of the trending buzzword since 2014. The term made its debut in Gartner’s 2014 Magic Quadrant Report. Gartner first popularized the term data discovery and … Continue reading

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Smart Data – Extracting Quality From Quantity

Amit Sheth defined “Smart Data” as “realising productivity, efficiency, and effectiveness gains by using semantics to transform raw data into Smart Data.” Clarifying “Smart Data” concept can be understood as automation of the process represented by the arrow linking “Information” and … Continue reading

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