Data Warehouse

Aveek Das

An overview of ETL and ELT architecture

April 21, 2020 by

This article explains what the basic features and differences between ETL and ELT are. I’m also going to explain in detail what an ELT pipeline is and a relevant architecture for the same in Azure. So far, we have come a long way dealing with ETL tools which basically are Extract, Transformation and Load technique used in populating a data warehouse. ELT, on the other hand, is another way to load data into a warehouse that implements the process of Extract, Load and Transform.

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Sifiso Ndlovu

How to handle SSRS multi-value parameter filtering in SQL Server Parallel Data Warehouse

August 23, 2018 by

Experienced business intelligence (BI) developers would tell you that as you move from one project to another, some requirements start becoming repetitive like you have dealt with them before. One such repetitive requirement occurs during SQL Server Reporting Services (SSRS) development wherein a client would request that a report parameter be configured to allow multiple values from a dataset that is populated by stored procedure, as illustrated in Figure 1.

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Sifiso Ndlovu

Replace bridge tables in a Data Warehouse with SQL Server 2017 graph database

March 8, 2018 by

Just like in Santa’s Bag of Goodies, every release of SQL Server often has something for everyone – be it enhancements to DMVs for the DBAs, new functions for T-SQL developers or new SSIS control tasks for ETL developers. Likewise, the ability to effectively support many-to-many relationships type in SQL Graph has ensured that there is indeed something in it for the data warehouse developers in SQL Server 2017. In this article, we take you through the challenges of modelling many-to-many relationships in relational data warehouse environments and later demonstrate how data warehouse teams can take advantage of the many-to-many relationship feature in SQL Server 2017 Graph Database to effectively model and support their data warehouse solutions.

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Minette Steynberg

Introduction to Azure SQL Data Warehouse

August 29, 2017 by

Introduction

Azure SQL Data Warehouse is a new addition to the Azure Data Platform. When I first heard about it I wasn’t quite sure about what exactly it would be. As it turns out it is relational database for large amounts of database and really big queries as a service. This is essentially the equivalent of the APS (Analytics Platform System) in the cloud.

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Sifiso Ndlovu

How to use Columnstore Indexes to improve your Data Warehouse Staging Environment

May 4, 2017 by

My team and I were recently tasked with refactoring older data marts, particularly those that were created with SQL Server 2008 in mind. As we all know, SQL Server has undergone significant changes since the release of SQL Server 2008. One of those changes relates to the introduction of columnstore as an alternative to the traditional B-tree index (rowstore). Whilst most of the existing documentation relating to columnstore seem to focus on the benefit of columnstore against data warehouse workloads, in this article I argue that the usage of columnstore index should not be limited to facts and dimensions instead let’s introduce it in our data warehouse staging environments too.

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Thomas LeBlanc

SQL Server Data Warehouse design best practice for Analysis Services (SSAS)

April 4, 2017 by

Before jumping into creating a cube or tabular model in Analysis Service, the database used as source data should be well structured using best practices for data modeling. Some might say use Dimensional Modeling or Inmon’s data warehouse concepts while others say go with the future, Data Vault. No matter what conceptual path is taken, the tables can be well structured with the proper data types, sizes and constraints.

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Sifiso Ndlovu

Two methods for restoring a data warehouse/data mart environment

November 8, 2016 by

Implementing best data warehouse designs and practices such as data lineage reduces the need to ever have to restore an entire relational data warehouse. However, sometimes there are instances whereby you have inherited poorly designed data warehouse environments that leaves you with no other options but to perform an entire database restore in an event of a sudden disaster. I recently found myself in a similar situation of having to recover one of my data mart following a data integrity issue wherein all data of a type 1 dimension was updated/overwritten using an incorrect source file. In this article I take a look at how different approaches can be utilised to restore the compromised SQL Server-based data mart back to its “good state”.

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Evan Barke

SQL Server Business Intelligence – Expanding fact tables in an established data warehouse

May 30, 2014 by

Introduction

As in often the case in life, things that sound simple are not always the easiest things to do. In computer science this is even more often the case. In fact, one of the most challenging things about information technology work is often the communication with bosses that know little about technology and require justification for time spent on seemingly simple tasks. However, by the same token, tasks that seem impossible to the untrained eye are often fairly straightforward and quick to implement and can earn you easy respect. Read more »