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Month: October 2019

When Power Query Hits Data Sources Repeatedly

Published 2019-10-21 by Kevin Feasel

Chris Webb answers an age-old question:

If you’re developing in Power BI Desktop and you think that refresh is taking a long time, you should definitely check whether the Power Query engine is hitting your data source more than once. There are lots of ways to do this. Some data sources have tools that show when they are queried, such as the Run History screen in Microsoft Flow that I show in the video or SQL Server Profiler. Other ways include using Fiddler for web services or Process Monitor for files.

Read the whole thing.

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Backing Up SQL Server on Azure VMs

Published 2019-10-21 by Kevin Feasel

Arun Sirpal looks at three techniques for backing up SQL Server running on Azure virtual machines:

In the previous blog post I did a quick overview building a SQL VM (imaged) in Azure. It is now time to clarify some backup techniques because it can get confusing.

At a high level there are 3 techniques.
– Automated backup.
– Azure backup for SQL VM (that’s what MS call it).
– Manual backup, for example backup to URL.

Read on to learn more about each.

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MLFlow on Databricks Community Edition

Published 2019-10-18 by Kevin Feasel

Jules Damji and Siddharth Murching have an interesting announcement:

Today, we are excited to extend Databricks Community Edition with hosted MLflow for free, as part of our ongoing commitment to help developers learn about machine learning lifecycle. With the Community Edition, you can try tutorials that demonstrate how to track results and experiments as you build machine learning models—a crucial stage in the machine learning model’s development lifecycle.

MLflow is an open-source platform for the machine learning lifecycle with four components: MLflow Tracking, MLflow Projects, MLflow Models, and MLflow Registry. MLflow is now included in Databricks Community Edition, meaning that you can utilize its Tracking and Model APIs within a notebook or from your laptop just as easily as you would with managed MLflow in Databricks Enterprise Edition.

I like showing off Databricks Community Edition, and I’m glad to see them extend it a bit.

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Streaming ETL of Rail Data with Kafka

Published 2019-10-18 by Kevin Feasel

Robin Moffatt has an interesting architecture and implementation for Kafka:

Trains are an excellent source of streaming data—their movements around the network are an unbounded series of events. Using this data, Apache Kafka® and Confluent Platform can provide the foundations for both event-driven applications as well as an analytical platform. With tools like KSQL and Kafka Connect, the concept of streaming ETL is made accessible to a much wider audience of developers and data engineers. The platform shown in this article is built using just SQL and JSON configuration files—not a scrap of Java code in sight.

The code is also available in a GitHub repo.

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Determining Your Isolation Level

Published 2019-10-18 by Kevin Feasel

Bob Pusateri explains different transaction isolation levels and where you can figure out your current settings:

Isolation level settings for the entire database exist only as a default. All connections created will inherit the database’s isolation level. Furthermore, a database only has two possible isolation levels:
– read committed
– read committed snapshot (also known as “RCSI”)

There are four additional isolation levels (read uncommitted, repeatable read, serializable, snapshot) which can only be selected at the connection or statement level. In SQL Server and Azure SQL Database Managed Instance, the default database isolation level is read committed. In Azure SQL Database, the default is read committed snapshot.

Read the whole thing.

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Upgrading Servers in an Availability Group

Published 2019-10-18 by Kevin Feasel

Thomas Rushton has a checklist for upgrading servers connected by an Availability Group:

This is a checklist put together and followed for an upgrade of a pair of physical SQL Server 2012 servers which hosted a single availability group of several terabytes of data with minimal downtime.

The availability group was configured with synchronous commit and automatic failover.

Minimizing downtime here is great, but it’s not automatic: you still need to do work on your end to get this right.

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Making the SQL Server 2019 Edition Cut

Published 2019-10-18 by Kevin Feasel

Brent Ozar noticed an update to Microsoft’s “what’s in which edition” document:

The most important stuff:
– Standard Edition is still capped at 128GB RAM.
– Accelerated Database Recovery is in Standard Edition.
– Automatic tuning, batch mode for row store, adaptive memory grants, adaptive joins, and memory-optimized TempDB are Enterprise Edition only.

Click through for more of Brent’s thoughts. Of interest to me is that PolyBase control nodes may now run in Standard Edition. It makes me wonder if they’ll cap that feature somehow, where you get N nodes maximum in a Standard Edition scale-out group, or if it’s uncapped like Enterprise Edition.

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AGs and Redo Latency

Published 2019-10-18 by Kevin Feasel

David Fowler explains that even in synchronous mode, there can still be redo latency for Availability Groups:

There’s a misconception that if you have a synchronous replica, that replica has to commit any transactions that are passed to it by the primary before the primary can then commit those same transactions. This ensures that there is no possibility of data loss and that the data would always be available on the secondary.

With this misconception comes our problem.

Read on to understand the full issue.

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Scala Views

Published 2019-10-17 by Kevin Feasel

Girish Bharti takes us through a performance-tuning technique in Scala:

We all know the power of lazy variables in Scala programming. If you are developing the application with huge data then you must have worked with the Scala collections. Some mostly used collections are List, Seq, Vector, etc. Similarly, you must be aware of the power of Streams. The streams are a very powerful tool for handling the infinite flow of data and streams are powerful because of there lazy transformations. As we know most of the Scala collections are strict so applying an operation on immutable collections creates a new collection. The size of the collection can be huge in the big data world. So, what if you have to apply a lot of transformations to the collection? Is there a way to handle collections in a lazy way? What if you can find a way to apply operations on your usual collections lazily? In this blog, we will be talking about the Scala views and how to use them.

Read the whole thing.

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Delta Lake to Become an Open Standard

Published 2019-10-17 by Kevin Feasel

Michael Armbrust and Reynold Xin have exciting news about Delta Lake:

At today’s Spark + AI Summit Europe in Amsterdam, we announced that Delta Lake is becoming a Linux Foundation project. Together with the community, the project aims to establish an open standard for managing large amounts of data in data lakes. The Apache 2.0 software license remains unchanged.

Delta Lake focuses on improving the reliability and scalability of data lakes. Its higher level abstractions and guarantees, including ACID transactions and time travel, drastically simplify the complexity of real-world data engineering architecture. Since we open sourced Delta Lake six months ago, we have been humbled by the reception. The project has been deployed at thousands of organizations and processes exabytes of data each month, becoming an indispensable pillar in data and AI architectures.

Read on to see what this means for Delta Lake.

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