I hope you have time to attend this awesome free event on December 9, 2017. Here are the details behind the two presentations that I gave that day.
Effective Data Warehouse Storage Patterns
Many companies start off with a simple data mart for reporting. As the company grows, users become dependent on the data mart for monitoring and making decisions on Key Performance Indicators (KPI).
Unexpected information growth in your data mart may lead to a performance impacted reporting system. In short, your users will be lining up at your cube for their daily reports.
How do you reduce the size of your data mart and speed up data retrieval?
This presentation will review the following techniques to fix your woes.
1 – What is horizontal partitioning?
2 – Database sharding for daily information.
3 – Working with files and file groups.
3 – Partitioned views for performance.
4 – Table and Index partitions.
5 – Row Data Compression.
6 – Page Data Compression.
7 – Programming a sliding window.
8 – What is different in Azure SQL database?
Full Text Indexing Basics
Today’s large data fields (LDF) are full of unstructured information stored in varchar, text, varbinary or xml data types. How do you write an application to search the column for patterns? Traditional SQL techniques using a column INDEX and LIKE operator result in a query plan that contains a full table scan.
In this presentation, I will be introducing the brother’s grimm database that has the full text of each fairy tale. I will create a full text catalog / index, select a change tracking strategy, define optional stop list / thesaurus file, and then populate the index. I will use CONTAINS and FREETEXT operators in SELECT queries to leverage the FTI. This resulting query plan performs index seek.
1 – Creating a database from scratch.
2 – Creating a table with LOB field.
3 – Loading files via BULK INSERT.
4 – Performance via traditional techniques.
5 – Creating a full text index.
6 – Performance with the full text index.