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Author: Kevin Feasel

Power BI the Right Way

Published 2020-07-13 by Kevin Feasel

Paul Turley is starting a series on doing Power BI the right way:

Any attempt to apply universal best practices to Power BI solution design is a slippery slope. The tools are so flexible and powerful, and the requirements of each project are so varied that it is challenging to establish a set of steps or rules that, if followed, will always yield the absolute best design for a given scenario. With that out of the way, I’ll say this: In my job, I see a lot of poorly-designed Power BI projects. I’ve worked with dozens or scores (maybe even hundreds?) of consulting clients who bring us projects – some partially completed, some finished, and many that are just broken – to be fixed or completed. My reactions range from “that’s just downright wrong” to “hmmm… I wouldn’t have done it that way but I guess it will work for the time being”. I try not to cast stones and do, on occasion, realize that others have found a better way to solve a problem. I don’t have all the answers but I do have a lot of experience with Microsoft Business Intelligent solution design. I have learned many good practices and design patterns from community leaders over the past twenty or so years.

This is only the introductory post in the series, but I’m excited to see what Paul has in store.

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A Brief Overview of Azure Synapse Analytics

Published 2020-07-13 by Kevin Feasel

Ginger Grant gives us the nickel tour of why Azure Synapse Analytics is interesting:

In the past few months, I have been examining Azure Synapse and what it can do.  When it was first released in November of 2019, the first functionality that was rolled out was an update of Azure SQL DW.  For this reason, many people think that Synapse is just an improved version of a cloud data warehouse.  Microsoft did improve SQL DW when it moved it to Synapse.  The biggest architectural design change is the separation of the code from the compute, a theme with many web projects, which allows the compute power to be increased when need dictates and scaled down when computing needs change.  Within Synapse, resources are allocated as Pools and you can define a sql pools to run data warehouse and later change the compute to a different resource.  You will still need to partition your DW as large datasets require partitioning to perform well.  Subsequently Microsoft Released the Azure Synapse Studio to be a container for a larger environment of tools and notebooks to interact with them.

But it’s more than that. Read on to see what else is available.

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Areas of Improvement for DROP TABLE

Published 2020-07-13 by Kevin Feasel

Michael J. Swart points out a few foibles about the DROP TABLE syntax:

I was looking at the docs for DROP TABLE and I noticed this in the syntax: [ ,...n ]. I never realized that you can drop more than one table in a statement.

I think that’s great. When dropping tables one at a time. You always had to be careful about order when foreign keys were involved. Alas, you still have to care about order.

That is a shame. Michael also includes a few other places where DROP TABLE could be made better, so check it out.

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Schema References and Multiple Event Types in a Kafka Topic

Published 2020-07-10 by Kevin Feasel

Robert Yokota updates some prior knowledge:

In the article Should You Put Several Event Types in the Same Kafka Topic?, Martin Kleppmann discusses when to combine several event types in the same topic and introduces new subject name strategies for determining how Confluent Schema Registry should be used when producing events to an Apache Kafka® topic.

Schema Registry now supports schema references in Confluent Platform 5.5, and this blog post presents an alternative means of putting several event types in the same topic using schema references, discussing the advantages and disadvantages of this approach.

Click through to see how this works out.

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Performance Tuning for Cloudera’s Operational Database

Published 2020-07-10 by Kevin Feasel

Liliana Kadar, et al, show us the tools we can use to tune Cloudera’s Operatioanl Database:

A query optimizer determined the most efficient way to run a query. Query optimization helps you to reduce the hardware resources required to run a query and also speeds up your query-response time. Cloudera’s Operational Database provides you with various tools such as plan analyzers to make optimal use of your computing resources. 

Cloudera’s OpDB provides various cost-based and rules-based optimizers. You can use different optimizers based on your use cases. OpDB is primarily used for Online Transactional Processing (OLTP) use cases with Apache Phoenix in the OpDB used as a SQL engine. But you can also use Hive and Impala for Online Analytical Processing (OLAP) use cases. 

Read on for recommendations on platform choice as well as indexing and tuning options.

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SQL Server Assessment Reports

Published 2020-07-10 by Kevin Feasel

Dave Bland shares the results of a lot of effort:

When you click the SQL Server 2012 or newer you will be taken to a report that will return a great deal of information that will be useful when doing an assessment.  This is very similar to the report you will be taken to if you click the 2008r2 or older button.  Since many of these data points are not options in an Azure SQL DB, that button will take you to the Report Library.

Below are the 21 data points that will be returned in just a few seconds.  A number of these will be helpful when doing an assessment for performance reasons.  The boxes will also change color to yellow or red if issues are found, just like the image above.

Click through for samples as well as the download link.

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Changing Power BI Models with Visual Studio Code

Published 2020-07-10 by Kevin Feasel

Phil Seamark has a integration I hadn’t expected to see:

Visual Studio Code is a reasonably new development environment which is a lightweight and quick install and get up and running. There is nothing you can do in VS Code that you can’t also do in another tool using TOM. I just thought it would be fun to show how quick and easy it is to get up and running in very few steps.

The following exercise uses VS Code to connect and manage a Power BI Desktop model. You can also connect to models hosted in Azure Analysis Services as well as models hosted in Power BI Premium.

Read on to see how to get everything going.

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The Importance of Power BI Deployment Pipelines

Published 2020-07-10 by Kevin Feasel

Marc Lelijveld explains the importance of Power BI Deployment Pipelines:

You might have seen the announcement, Power BI Deployment Pipelines have been released in May 2020. It is around for about two months now. On different social channels I have seen a lot of buzz around it already, both positive and negative honestly. Though, I think this is a great step forward!

Back in 2018, I posted a blog about multi-tier architecture and continuous delivery with Power BI. If you are not familiar with a DTAP approach and why this helps you to structure your development processes, I advise you to first read that blog. Personally, I am really excited about Deployment Pipelines! With this functionality, Microsoft starts offering an out-of-the-box functionality that helps you to easier move your Power BI content through you DTAP pipeline.

I think it’s a pretty big step in the right direction, though the “Why this isn’t so great” section is a bit lengthy.

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Migrating From Cosmos DB to SQL Server

Published 2020-07-10 by Kevin Feasel

Eitan Blumin builds an app:

The general idea is this:

The app executes a Cosmos DB query and collects a number of records into its “buffer”.

Once that “buffer” reaches a certain number of records (configurable), it’s time to “flush” it into the SQL Server. That could be either a database table receiving a Bulk Copy stream, or a stored procedure receiving a table valued parameter (again, configurable).

After the buffer is flushed, we have the option to execute a “merge” procedure. This is a stored procedure that would implement an “upsert” logic from the “staging” table and into the actual destination table.

Read on for more explanation and check out Eitan’s GitHub repo.

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