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Day: July 9, 2020

Apache Flink 1.1.0 Released

Marta Paes announces Apache Flink version 1.11:

Change Data Capture (CDC) has become a popular pattern to capture committed changes from a database and propagate those changes to downstream consumers, for example to keep multiple datastores in sync and avoid common pitfalls such as dual writes. Being able to easily ingest and interpret these changelogs into the Table API/SQL has been a highly demanded feature in the Flink community — and it’s now possible with Flink 1.11.

Click through for the full list of updates.

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Query Scheduling with Apache Hive

Zoltan Haindrich and Jesus Camacho Rodriguez walk us through scheduled queries in Apache Hive:

To fulfill that purpose, recently Apache Hive introduced a new feature called scheduled queries. Using SQL statements, users can schedule Hive queries to run on a recurring basis, monitor their progress, and optionally disable a query schedule.

In a nutshell, every scheduled query in Hive consists of (i) a unique name to identify the schedule, (ii)  the actual SQL statement to be executed, and (iii) the schedule at which the query should be executed defined by a Quartz cron expression. In addition, a scheduled query belongs to a namespace, i.e., a collection of HiveServer2 instances that are responsible to execute the query.

Read on for examples of how you might create, use, and learn about scheduled queries running on a system.

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Iterating over JSON and XML Data in SQL Server

Steve Stedman explains how you can iterate through XML and JSON data using the APPLY operator:

The results are what we are looking for in this specific example, but where they break down is when there are more employees represented in the XML, for each employee we need to add another UNION to bring the results together. That is not very iterative and since the title of this post includes the word iterating, we need to focus on how to do that.

Now we introduce the CROSS APPLY functionality that can be used like a JOIN to take a value from one result set (table) and apply it to a function that gets called once for each row. You can reference my JOIN TYPES poster for using CROSS APPLY

Click through for the full set of examples.

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Moving a Semantic Model from SSAS to Power BI

Teo Lachev explains why it might make sense to move a semantic model from SSAS over to Power BI:

We decided to move the semantic model to Power BI so that Power BI owns the data. Besides potentially improving the report load time, this architecture has also other important advantages (to learn more, read my “Power BI Large Datasets: The Good, the Bad, and the Ugly” post). If you’re not on Power BI Premium, that “movement” might not easy if you have opted to use Visual Studio or Tabular Editor for development. That’s because Power BI Pro doesn’t expose the XMLA endpoint, so your only option is to migrate the model to Power BI Desktop. But migrating an SSAS Tabular project to Power BI Desktop is not officially supported and there is no automatic migration path.

Click through for the full explanation of why, as well as notes on the process.

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Dropping Temporary Tables at the Beginning of a Procedure

Richard Swinbank walks through a technique to drop temp tables as a procedure begins:

Local temporary tables – tables with names that begin with a single # character – are dropped automatically by SQL Server when they are no longer in scope. So why drop them explicitly at all?

I’m writing this from the perspective of a data engineer. ETL processes or their components are often implemented as T-SQL stored procedures (SPs) which might move significant amounts of data around and take a while to run. I’m not talking about the sort of SP that needs to run in 30ms to guarantee application responsiveness!

Sometimes it’s useful to store intermediate results in temporary tables, sometimes it’s necessary, and sometimes you find yourself maintaining code built by someone who just really loves them. Adding explicit DROPs to code like this can make development and maintenance a bit smoother.

My concern with doing this is that it prevents temporary object caching. Granted, Richard explicitly calls out that these are long-running procedures (and probably one of a particular procedure running at a time), so that does reduce the concern accordingly.

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Deploying ADF via Azure DevOps

Kamil Nowinski has part two on a series about releasing Azure Data Factory code:

Struggling with #ADF deployment? adf_publish branch doesn’t suit your purposes? Don’t have skills with PowerShell? I have good news for you. There is a new tool in the market. It’s a task for Azure DevOps Release Pipeline to deploy whole ADF from code (JSON files) to ADF instance in Azure. Behind the scenes, it runs the PowerShell module which does all job for you.
Sounds unbelievable? But it’s real! Check it out for yourself.

Click through for the video.

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Performing a Power BI Risk Assessment

David Eldersveld has a set of questions to ask when building Power BI dashboards:

As with any important endeavor, businesses should be in a position to document and have a plan to mitigate any possible risks associated with deploying software. It is no different with Microsoft Power BI.

Note that the purpose of this post is simply an encouragement to evaluate risk exposure and consider how you would overcome potential future issues that could arise from any cause–it is not a caution against using external tools created by the wider Power BI community.

Read the whole thing.

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When NULL Makes Sense

Chris Johnson shares some rules of thumb on when allowing a column to be nullable makes sense:

Now I want to talk a bit about when we should be using NULLs and when we shouldn’t, inspired again by some things I’ve seen in legacy code a few days ago. And when I say using, I mean when should we allow values to be NULL, in tables, and parameters, and anything else.

The first thing to remember is what does a NULL represent. It’s very simple, NULL means “I don’t know”. If you keep that in mind, and you’re practising some good development design where everything has a point, you should be able to tell if it’s possible to be unsure about any particular value for a row.

One of the tricky parts about NULL values and legacy code is that if I need to add a new column and there is not a good default, either I make the column nullable (regardless of whether it should be) or I take downtime by blocking table access until my change is in place. As a result, quite often, I simply need to make something nullable because I can’t afford to block the table that long.

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