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Month: January 2020

Data Lake File Formats and Security

Ashish Kumar and Jorge Villamariona continue a series on data lakes:

People from a traditional RDBMS background are often surprised at the extraordinary amount of control that data lake architects have over how datasets can be stored. Data Lake Architects, as opposed to the Relational Database Administrators, get to determine an array of elements such as file sizes, type of storage (row vs. columnar), degrees of compression, indexing, schemas, and block sizes. These are related to the big data oriented ecosystem of formats commonly used for storing and accessing information in a data lake.

It is a bit of a different world and it comes with trade-offs. The whole thing is worth reading.

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Parameterizing a Data Factory Linked Service to a REST API

Meagan Longoria had to parameterize a linked service connecting to a REST API recently:

In order to pass dynamic values to a linked service, we need to parameterize the linked service, the dataset, and the activity.

I have a pipeline where I log the pipeline start to a database with a stored procedure, lookup a username in Key Vault, copy data from a REST API to data lake storage, and log the end of the pipeline with a stored procedure. My username and password are stored in separate secrets in Key Vault, so I had to do a lookup with a web activity to get the username. The password is retrieved using Key Vault inside the linked service. Data Factory doesn’t currently support retrieving the username from Key Vault so I had to roll my own Key Vault lookup there.

Click through for the instructions.

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Tracing a Session with Extended Events

Jason Brimhall shows how you can trace a specific session using Extended Events:

The ability to quickly and easily trace a query is important to database professionals. This script provides one useful alternative to trace a specific spid similar to the method of using the context menu to create the trace within SSMS and Profiler.

This is yet another tool in the ever popular and constantly growing library of Extended Events. Are you still stuck on Profiler? Try one of these articles to help remedy that problem (here and here)

Read on to see how.

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Moving Data Around in Azure Synapse Analytics

Niko Neugebauer looks at some techniques for copying data into a table in an Azure Synapse Analytics SQL Pool:

First of all, let us list some of them (and I am not even attempting on providing all of them, of course):
– INSERT INTO … SELECT FROM … (the most well known one)
– SELECT INTO … FROM … (the most well-known to perform well, since it will create a HEAP while copying most of the properties from the original table(s))
– CREATE TABLE … AS SELECT … (the old way, which must be like 10 years old on PDW/APS & Azure SQL DW, but that has never gotten into a Box Product or Azure SQL Database)
– Polybase (that will use the External Tables & externally allocated data to transfer into Azure SQL DW)
– BCP (good old tested friend that will give you a pain in the neck until you dominate it)
– OPENROWSET / BULK INSERT (some very good and very old friends with complicated histories (who remembers all the code pages?, settings and uncertain future mostly because of their original restrictions, I guess)
– COPY INTO … (the brand new command that will allow you under very neat privileges to copy data from the external storage accounts, much like BULK INSERT)

In this blog post I will simply focus on those features that have not been ported (hopefully just yet): CTAS & COPY INTO.

Read on to see how these two work. Also, I too have wanted CTAS in on-premises SQL Server for years.

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Upgrading a SQL Server Big Data Cluster

Mohammad Darab shows how to upgrade an existing Big Data Cluster:

The above scenario was updating a Big Data Cluster from a supported release. Microsoft officially supports BDCs starting from SQL Server 2019 GDR1. But what if you have a previous version of BDCs, say CTP or release candidate? In that case you’ll have to backup any data you have, delete your cluster, uninstall azdata, install the updated azdata, and deploy your big data cluster anew. A little cumbersome but that’s how it is. In fact, no one should be running an unsupported release of Big Data Clusters anyway!

Click through for the instructions.

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What’s New with Delta Lake

Denny Lee and Tathagata Das announce Delta Lake 0.5.0:

With the following pull requests, you can now run even more Delta Lake operations concurrently. With finer grain conflict detection, these updates make it easier to run complex workflows on Delta tables such as:

– Running deletes (e.g. for GDPR compliance) concurrently on older partitions while newer partitions are being appended.
– Running file compactions concurrently with appends.
– Running updates and merges concurrently on disjoint sets of partitions.

Click through for the full changelog.

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Cloudera and R

Ian Cook shows us how Cloudera works for R users:

Our customers love it when they can use familiar syntax to work with data regardless of its size or its source. The popularity of sparklyr is a case in point: it enables R users to use either SQL or dplyr—both familiar to most R users—to work with large-scale data using Apache Spark. Two R packages developed at Cloudera—implyr and tidyquery—aim to provide this same choice of either SQL or dplyr when querying tables with Apache Impala and when manipulating R data frames.

The implyr package is new to me, but looks interesting.

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The Cost of Sorting in Stored Procedures

Monica Rathbun wants us to think about whether we really need that ORDER BY clause:

We know that sorting can be one of the most expensive things in an execution plan as shown below. However,  we continue to do ORDER BYs repeatedly. Yes, I 100% agree that there is a need to sort a results set and that this should be done in the procedure for good reason, but my concern is having  multiple sorts, erroneous sorts, and the sorts that can be done elsewhere. These are the ones that waste resources and can stifle performance.

Click through for a demo showing that this does make a difference.

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Chaos Engineering with SQL Server

Andrew Pruski is excited about Chaos Engineering:

Chaos Engineering is a practice in which we run experiments against a system to see if it reacts the way we expect it to when it encounters a failure.

We’re not trying to break things here…Chaos Engineering is not breaking things in production.

If I said to my boss that we’re now going to be experiencing an increased amount of outages because, “I AM AN ENGINEER OF CHAOS”, I’d be marched out the front door pretty quickly.

On the plus side, we will know Andrew’s supervillain origin story.


What Uses tempdb?

Jason Hall takes us through several SQL Server features which use tempdb behind the scenes:

Since SQL Server 2005, triggers use the version store, which is kept in tempdb. So, if you’re using triggers, they are implicitly using tempdb. Remember that triggers are set-based, and you’ll get version data for every affected row. For really big batches, this can turn out to be quite a bit of version data.

Click through for several more examples.

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