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Category: Storage

Loading Data into Delta Lake

Prakash Chockalingam takes us through auto-loading Delta Lake from various sources:

Auto Loader is an optimized file source that overcomes all the above limitations and provides a seamless way for data teams to load the raw data at low cost and latency with minimal DevOps effort. You just need to provide a source directory path and start a streaming job. The new structured streaming source, called “cloudFiles”, will automatically set up file notification services that subscribe file events from the input directory and process new files as they arrive, with the option of also processing existing files in that directory.

This does look interesting.

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Storing Power BI Audit Logs in Blob Storage

Gilbert Quevauvilliers works around a built-in constraint with Power BI Audit Logs:

With the new Power BI Get-PowerBIActivityEvent I wanted to find a way where I could automate the entire process where it all runs in the cloud.

One of the current challenges with the Audit logs is that they only store 90 days, so if you want to do analysis for longer than 90 days the log files have to be stored somewhere. Why not use Azure Blob Storage?

Whilst these steps might appear to be rather technical if you follow them and you have access to an Azure Subscription you can do this too.

Gilbert warns us up-front that this will be a lengthy post and that is quite true. But if you need to hold those audit logs more than 90 days, this is a great way of doing so.

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Finding the Right Disk and Data Node Sizes in HDFS

Lokesh Jain has some advice when it comes to disk and data node size:

There are two factors to keep in mind when choosing node capacity. These will be discussed in detail in the next sections.

1. Large Disks – total node capacity being the same, using more disks is better as it yields higher aggregate IO bandwidth.
2. Dense Nodes – as nodes get denser, recovery after node failure takes longer.

These factors are not HDFS-specific and will impact any distributed storage service that replicates data for redundancy and serves live workloads.

Click through for specific advice on maximum disk and node sizes.

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Tips for Using Azure Storage

James Serra takes us through Azure Data Lake Store Gen2 and Azure Blob Storage:

Azure Data Lake Store (ADLS) Gen2 should be used instead of Azure Blob Storage unless there is a needed feature that is not yet GA’d in ADLS Gen2.

The major features that are missing from ADLS Gen2 are premium tiersoft deletepage blobsappend blobs, and snapshots. The major features that are in preview are archive tierlifecycle management, and diagnostic logs. Check out all the missing features at Known issues with Azure Data Lake Storage Gen2.

Note that underneath the covers, ADLS Gen2 uses Azure Blob Storage and is simply a layer over blob storage providing additional features (i.e. hierarchical file system, better performance, enhanced security, Hadoop compatible access).

Click through for a bullet point list of useful information.

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Creating a Gen-2 Azure Data Lake Store

Cecilia Brusatori shares how to build a generation-2 data lake in Azure:

Finally, you’ve decided that Data Lake Gen 2 is good for your Data Analytics Scenario and you’ve started the journey, went to the Azure Portal and searched for it. Mhh you don’t see it in the options to create it, let’s try the search bar [typing Data Lake Gen2….] Nothing… Ok maybe you’ve missed something…. nope!
So what is in fact a Data Lake Gen 2? it is a blob storage account, optimized for Data Analytics.
Let’s take a look at how you are able to create it!

If you’re used to the first generation, where Azure Data Lake Storage was its own thing, it might take a minute to realize where it went.

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Testing SQL Server Storage Performance

Brent Ozar walks us through CrystalDiskMark 7 to check whether storage speed is up to snuff:

The Peak Performance + Mix setting runs a pretty cool mix of tests that will push your storage hard. Note that I don’t try to get CDM to replicate exactly how SQL Server does IO: I’m just trying to get a quick 5-minute idea of whether my storage is hot or not.

Click through for the demo. Mind you, this is something you want to do before setting up SQL Server…

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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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ADLS Gen2 Navigation in Power Query

Chris Webb shows off hierarchical navigation in Power Query against Azure Data Lake Storage Gen2:

While the documentation on how to import data from Azure Data Lake Gen2 Storage into Power BI is pretty detailed, the connector (which at the time of writing is in beta) that supports this functionality in the Power Query engine has some useful functionality that isn’t so obvious. If you look at the built-in documentation on the AzureStorage.DataLake M function in the Power Query Editor you’ll see there are a lot of options that aren’t in the documentation on the web yet:

Click through for an example.

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HBase and S3

Krishna Maheshwari, et al, explain how we can allow Apache HBase to use S3 for storage:

Cloudera Data Platform (CDP) provides an out-of-the-box solution that allows Apache HBase deployments to use Amazon Simple Storage Service (S3) as its main persistence layer for saving table data. Amazon S3 is an object store which offers a high degree of durability with a pay-per-use cost structure. There is no server-side component to run or manage for S3 — all that is needed is the S3 client library and AWS credentials. However, HBase requires a consistent and atomic filesystem which means that it cannot directly use S3 because it is an eventually consistent object store. Both CDH and HDP have only provided HBase solely using HDFS because there have been long-standing impediments that prevented HBase from natively using S3. To address these issues, we’ve built an out-of-the-box solution which we are delivering for the first time via CDP. When you launch an Operational Database (HBase) cluster on CDP, HBase StoreFiles (the backing files for HBase tables) are stored in S3 and HBase write-ahead-logs (WAL) are stored in an HDFS instance run alongside HBase per usual.

I hadn’t thought of using S3, but it’s an interesting post.

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VM Storage Performance in the Cloud

Joey D’Antoni explains how storage architecture has changed from on-prem to the cloud:

This architecture design dates back to when a storage LUN was literally a built of a few disks, and we wanted to ensure that there were enough I/O operations per second to service the needs of the SQL Server, because we only had the available IO of a few disks.

As virtualization became popular storage architectures changes and the a SAN lun was carved out into many small extents (typically 512k-1MB depending on vendor) across the entire array. What this meant was that with modern storage there was no need to separate logs and data files, however some DBAs did, however in an on-premises world there was no penalty for this.

It’s important to keep up on these changes.

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