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Category: Cloud

Limiting Index Sizes in Cosmos DB

Hasan Savran explains why you might want to exclude columns from Cosmos DB indexes:

If everything is indexed already; Why do we want to exclude some of indexes? Indexes are saved on disk, you pay for the storage in Azure. If you keep indexing everything, your index file gets larger and you pay more for storage.

     Also; write operations to index file takes longer if index file is larger. By keeping only what you need in index file will improve the latency of write operations. If you will need to change your indexing policies, Rebuilding indexes will take less time.

This behavior is quite different from the way SQL Server behaves, where indexing is more of an opt-in philosophy.

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Using Azure Kubernetes Services for Big Data Clusters

Mohammad Darab explains why it’s a good idea to use Azure Kubernetes Service when building out a Big Data Cluster:

According to the Microsoft documentation, there are three ways to deploy a Big Data Cluster:

1. Minikube
2. Kubeadm
3. AKS

I’ll go into each and list the pros and cons.

Of course, if you have a great Kubernetes admin, on-prem is certainly a viable option, but AKS is definitely easier to get started with.

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Architecting a Data Lake in AWS

Gaurav Mishra takes us through data lake architecture on AWS:

Landing zone: This is the area where all the raw data comes in, from all the different sources within the enterprise. The zone is strictly meant for data ingestion, and no modelling or extraction should be done at this stage.

Curation zone: Here’s where you get to play with the data. The entire extract-transform-load (ETL) process takes place at this stage, where the data is crawled to understand what it is and how it might be useful. The creation of metadata, or applying different modelling techniques to it to find potential uses, is all done here.

Production zone: This is where your data is ready to be consumed into different application, or to be accessed by different personas. 

This is a nice overview of data lake concepts and worth the read if you’re using AWS. Even if not, the same principles (if not the same technologies) apply for Azure, other clouds, and on-prem.

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Installing Python Libraries on EMR Clusters with Notebooks

Parag Chaudhari shows how we can install Python libraries on existing ElasticMapReduce clusters using EMR Notebooks:

The notebook-scoped libraries discussed previously require your EMR cluster to have access to a PyPI repository. If you cannot connect your EMR cluster to a repository, use the Python libraries pre-packaged with EMR Notebooks to analyze and visualize your results locally within the notebook. Unlike the notebook-scoped libraries, these local libraries are only available to the Python kernel and are not available to the Spark environment on the cluster. To use these local libraries, export your results from your Spark driver on the cluster to your notebook and use the notebook magic to plot your results locally. Because you are using the notebook and not the cluster to analyze and render your plots, the dataset that you export to the notebook has to be small (recommend less than 100 MB).

Read the whole thing.

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Ordered Clustered Columnstore Indexes in Azure SQL DW

Niko Neugebauer takes us through a new feature in preview for Azure SQL Data Warehouse:

After creating (or dropping and recreating a Clustered Columnstore Index we can specify the reserved word ORDER and then one or !!!MULTIPLE!!! columns. This looks like an extremely promising feature!

On Azure SQL Data Warehouse one can of course define table as a Columnstore and with that specification it is also possible to define an ORDER option with one or multiple columns.

For the syntax and basic functionality testing purposes on Azure SQL Data Warehouse, let us then create a table with a Clustered Columnstore Index, load some data and see if by recreating an Ordered Clustered Columnstore Index we can achieve some improvements.

Niko has a few hard-earned lessons from this post.

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SQL Server on Azure: Performance Optimized Storage Config

Mine Tokus announces a new feature when using Azure to host IaaS SQL Server instances:

Today, we are excited to announce Performance Optimized Storage Configuration capabilities for the VM’s registered with SQL VM RP. This feature automates storage configuration according to performance best practices for SQL Server on Azure virtual machines through Azure Portal or Azure Quick start Templates when creating a SQL VM. Automated performance best practices include separating Data and Log filescache configuration for premium disks hosting data and log filessupport for Temp DB on local disksupport for Ultra disks to host data, log or Temp DB files and database engine only images. In this article, we will discuss each automated performance best practice in detail.

Read on for the description and check out those links for additional information.

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Migrating Databricks Workspaces

Gerhard Brueckl has made DatabricksPS better:

I do not know what is/was the problem here but I did not have time to investigate but instead needed to come up with a proper solution in time. So I had a look what needs to be done for a manual export. Basically there are 5 types of content within a Databricks workspace:

– Workspace items (notebooks and folders)
– Clusters
– Jobs
– Secrets
– Security (users and groups)

For all of them an appropriate REST API is provided by Databricks to manage and also exports and imports. This was fantastic news for me as I knew I could use my existing PowerShell module DatabricksPS to do all the stuff without having to re-invent the wheel again.

I’ve used DatabricksPS and really like it for cases where I’d have to loop with the Databricks REST API—for example, when uploading files.

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Backing Up Cosmos DB

Josh Smith takes us through backing up Cosmos DB yourself:

Unfortunately if you are restricting access to your Cosmos DB service based on IP address (a reasonable security measure) then Data Factory won’t work as of this writing as Azure Data Factory doesn’t operate like a trusted Azure service and presents as IP address from somewhere in the data center where it is spun up. Thankfully they are working on this. In the meantime however the next best thing is to use the Cosmos DB migration tool (scripts below) to dump the contents to a location where they can be retained as long as needed. Be aware in addition to the RU cost of returning the data that if you bring these backups back out of the data center where the Cosmos DB lives you’ll also incur egress charges on the data.

Having a plan for this kind of thing is important, even if you normally rely on service-provided automated backups.

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Building an Azure Usage Report with Powershell

June Castillote shows us how we can use Powershell to get usage data from Azure for our subscriptions:

In the section above, it would be common for the command to return many thousand objects especially for long date ranges. To prevent overwhelming the API, the Get-UsageAggregates command only returns a maximum of 1000 results. If you’ve saved the $usageData variable as covered in the previous section, you can confirm it by using running this command $usageData.UsageAggregations.count.

What if there are more than 1000 results? You’re going to have to do a little more work.

Knowing how much you’re spending is critical in an Op-X world like Azure or AWS.

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SQL Server Monitoring with Grafana and Telegraf

Denzil Ribeiro shows how you can use Telegraf and Grafana to monitor Azure SQL Database databases:

SQL DB Storage
This is the dashboard for monitoring file size and space used, IO latency, and IO throughput, for each file in the database. When using Standard or General Purpose databases, which use data files in Azure blob storage, storage performance depends on several blob properties, exposed via the FILEPROPERTYEX() function.

Aside from a couple Azure SQL DB-specific steps, it’s basically the same process that Tracy Boggiano has for monitoring on-prem and IaaS SQL Server instances.

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