Guilty of Over-Customising

Am I guilty of over-customising software? Read this and let us know what you think.

The Value of Surveys

Phil Factor on the perils of trying to squeeze good insights out of bad surveys.

Two Approaches to Addressing Circular References in SSDT

Both options have their advantages and disadvantages. Neither is universally right for all situations. Understand the differences before picking the model that works for your situation.

Guide to enhancing privacy and addressing GDPR requirements with the Microsoft SQL platform

A new whitepaper published today gives Microsoft SQL customers technical guidance for how to approach GDPR compliance with Microsoft SQL technologies.

Scala and Apache Spark in Tandem as a Next-Generation ETL Framework

Scala and Apache Spark might seem an unlikely medium for implementing an ETL process, but there are reasons for considering it as an alternative. After all, many Big Data solutions are ideally suited to the preparation of data for input into a relational database, and Scala is a well thought-out and expressive language. Krzysztof Stanaszek describes some of the advantages and disadvantages of a scala-based approach to implementing and testing an ETL solution.

Understanding OPENJSON and FOR JSON in Azure and SQL 2016

An example of exporting and importing table data with JSON in Azure and SQL Server 2016.

Statistics in SQL: Pearson’s Correlation

Some people will assure you that you can't do any serious statistical calculations in SQL. In the first of a series of articles, Phil factor aims to prove them wrong by explaining how easy it is to calculate Pearson's Product Moment Correlation.

SQL Server is Getting Smarter

SQL Server is becoming more capable all the time, requiring fewer human resources for basic management.

How to partition data in Tabular SSAS

Partitioning data is a standard SQL Server administration practice. Partitions enable independent administration of different slices of data. When a SQL Server Analysis Services (SSAS) tabular data model is developed and processed, data is read from the source system and loaded into the tabular data model configured in In-Memory processing mode. Every time the model is processed, the entire data set may not require re-processing. Only certain slices of data containing changes may require re-processing which can be achieved by partitioning data into logical slices. In this post, Siddharth Mehta looks at how to partition tables in Tabular SSAS.

Byte Me: Diagnosis Data