SQLBits 2017

The purpose of this post is to summarize my thoughts and points I came across the SQLBits (web|twitter) conference. The points are not explained into detail as they serve as reminder of topics that are important and have to be looked into. I use them to possible key words for deeper research on the topic covered during the presentation. This is just a tip of iceberg I gained during the four days, so if you are interested in any of these topics in more detail feel free to reach out to me. I’ve included a link to each session, which should be available as recording for free, each speaker’s blog and twitter account where you can find as well a lot more on each topic. Enjoy!

 

Day 1

 

 

From 0 to DAX

 

Speaker: Alberto Ferrari (blog|twitter)

Notes

  • Mind shift between Multidimensional and Tabular
    • Use more calculated measures (CPU) rather than calculated columns (RAM)
    • CPU is much, much faster than RAM
    • Smaller models are faster
  • Filter context
    • Automatically on table filtered by selections
      • Row
      • Column
      • Filter
      • Slicer
    • Not changed by FILTER function
    • FILTER changes the table, but not the filter context
  • Row context
    • Automatically for each row
    • Iterates through rows in table
    • Does not filter anything
  • FILTER function
    • Contexts and Relationships
    • Go by default from many to one not from one to many
    • Cross-filter direction filters as well from fact table to another dimension
    • Alberto: “Result is correct but nobody understands it. It’s a strange definition of correct.”
  • Context Transition
    • Performed by CALCULATE
    • Row context transforms into filter context
    • Calling measure is automatically surrounded by CALCULATE
    • Never add a table name in front of a measure – Alberto: “If you do it, you go to hell!”
  • Troubleshooting issues
    • Stop thinking
    • Look at filter context and how it is applied
  • Use only one Date table in model

 

Day 2

 

 

Columnstore Indexes-the practical solutions

 

Speaker: Niko Neugebauer (blog|twitter)

Notes

  • Columnstore Archive Compression
    • Better
    • For data not used frequently
    • Can separate on partitions
  • Dictionaries
    • Do not use strings on fact tables
    • Dictionary pressure
      • If dictionary is over 7MB it cuts down your row groups and slows down the performance
      • If you have too large dictionaries (more than 100k rows) they are not used to use predicate pushdown for strings
    • Global dictionary
      • Each partition has its own dictionary
  • Niko: “Strings are evil!” 🙂
  • Use bulk load to load data directly to CI
    • Use magic number to avoid Deltastore
  • Do not use CI with In-memory optimized tables
    • not production ready, more problems than solutions
  • Hammer DB
  • Batch mode is the gamechanger not compression
  • Partitioning
    • Make sure how big the row-groups are in your partitions
    • Each partition = min. 1 rowgroup
    • Be wise with partitioning
    • It’s not a performance feature, but a data management feature
  • In case of large strings and data dictionary pressure
    • Create a NCI and avoid the large column
    • Look at trim_reason_desc = NO_TRIM => this is ideal

 

Day 3

 

Columnstore killed the OLAP Star-Did it really?

 

Speaker: Gabi Munster (blog|twitter) and Niko Neugebauer (blog|twitter)

Notes

  • Hard to find people skilled in both
  • Hardware is not as easy to get as everybody says
  • If going to Azure check your requirements
  • Beware of dictionary pressure with Columnstore and page trimming
  • CI can perform on complex queries, but requires lot of knowledge writing it
  • Three factors for evaluation
    • Performance
    • Hardware cost
    • Expert person cost and availability
  • In some cases, Tabular can run circles around columnstore. #performance
  • Semantic model is the main aspect for SSAS
  • Modern Datawarehouse requires combination of various data sources
    • Tabular is comfortable here

 

T-SQL : Bad Habits and Best Practices

 

Speaker: Aaron Bertrand (blog|twitter)

Notes

  • Check out SQL Plan Explorer
  • Presentation for this on SentryOne blog
  • Great to review your T-SQL habits
  • Act 1 Stay consistent
  • Act 2 Use dates properly
  • Act 3 Get predictable behavior
  • Act 4 Query efficiently

 

Introduction to Azure Analysis Services

 

Speaker: Chris Webb (blog|twitter)

Notes

  • SSAS Tabular on cloud
  • Be aware which subscription you use
    • Might happen that you deploy with wrong cached account
  • Using standard development tools
  • REST API for automation
  • Main drawback is the cost
    • It’s in preview
  • Opens new usage scenarios of maturing Power BI

 

Optimizing multi-billion row tables in Tabular

 

Speaker: Alberto Ferrari (blog|twitter)

Notes

  • Do not store primary keys of fact tables
  • Care much more about cardinality rather than data type
  • Segments not smaller than partition
  • Parallelism is driven by segments not partitions
  • Create benchmark for optimizing columns
    • Time of Sum
    • Time of DistinctCount
  • Hardware is super-important
    • Have the fastest cores you can have
  • Be aware of NUMA mode of your server
  • Be aware of relationships
    • 1M on dimension

 

In-database Predictive Analytics with SQL Server vNext

 

Speaker: Sacha Tomey (blog|twitter)

Notes

  • Be a careful to select direct query vs. import
  • If you do not have gateway you can have some options with OneDrive
  • Carefully name the data sources in gateway according to actual server and database names

Power BI Gateway

Speaker: Adam Saxton (blog|twitter)

Notes

  • Map users between AAD a AD

 

Day 4

 

 

Database Design Throwdown

 

Speaker: Karen López (blog|twitter) and Thomas LaRock (blog|twitter)

Notes

 

Building Blocks of Cortana Intelligence Suite in Azure

 

Speaker: Melissa Coates (blog|twitter)

Notes

  • Overview of Azure services forming Cortana Intelligence Suite
  • Presentation on Melissa’s site
  • Azure Data Analytics
    • Currently not usable for ad-hoc queries
  • Azure Machine Learning
    • Look at it as Import/Export Wizard in SSIS
    • Helps you to start quickly and learn advanced analytics before you advance to own customized algorithms

 

Using M for data access in Analysis Services Tabular v.next

 

Speaker: Chris Webb (blog|twitter)

Notes

  • Azure adventure works from Redgate
  • Query folding for load performance
    • SQL server
    • SSAS
    • Odata
  • Lot of new small SSAS model
    • Will grow up and will need to scale
  • Direct query
    • SSAS as new semantic layer on Big Data / Cloud sources
  • Partitioning with M in SSAS
Previous Post
SSAS Tabular – Another DAX IF/SWITCH Performance Story
Next Post
Kdo se skrývá za JC?

Related Posts

No results found.

Leave a Reply

Your email address will not be published. Required fields are marked *

Fill out this field
Fill out this field
Please enter a valid email address.
You need to agree with the terms to proceed

Menu