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Category: Cloud

A Review of AWS Athena

Published 2021-01-29 by Kevin Feasel

John McCormack updates an older review:

AWS’s own documentation is the best place for full details on the Athena offering, this post hopes to serve as further explanation and also act as an anchor to some more detailed information. As it is a managed service, Athena requires no administration, maintenance or patching. It’s not designed for regular querying of tables in a way that you would with an RDBMS. Performance is geared around querying large data sets which may include structured data or semi-structured data. There are no licensing costs like you may have with some Relational Database Management Systems (RDBMS) such as SQL Server and costs are kept low, as you only pay when you run queries in AWS Athena.

Click through for an overview of product benefits.

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Using the Cloudshell Code Editor

Published 2021-01-25 by Kevin Feasel

Hamish Watson shows off the cloudshell code editor in Azure:

This post is about code which is an online editor you can use with cloudshell.

I live in the Azure platform all day (almost) every day. What this means is I need to be able to run scripts from just about any device – there are times where I will be scaling a Hyperscale database at 10:30pm from my phone….

So I use cloudshell a LOT in Azure

Click through for more details.

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AzureCosmosR

Published 2021-01-22 by Kevin Feasel

Hong Ooi takes us through an R library for working with Cosmos DB:

Among other features, Azure Cosmos DB is notable in that it supports multiple data models and APIs. When you create a new Cosmos DB account, you specify which API you want to use: SQL/core API, which lets you use a dialect of T-SQL to query and manage tables and documents; MongoDB; Azure table storage; Cassandra; or Gremlin (graph). AzureCosmosR provides a comprehensive interface to the SQL API, as well as bridges to the MongoDB and table storage APIs. On the Resource Manager side, AzureCosmosR extends the AzureRMR class framework to allow creating and managing Cosmos DB accounts.

AzureCosmosR is now available on CRAN. You can also install the development version from GitHub, with devtools::install_github("Azure/AzureCosmosR").

Hong provides examples for us using three of the Cosmos DB APIs, so check it out.

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Logical Separation in Azure Data Factory

Published 2021-01-22 by Kevin Feasel

Rayis Imayev is at a crossroads:

I was raised listening and reading fairy tales where the main character would reach a crossroad with a large stone that had some directions written on it – turn right and you will lose your horse, turn left and you will lose your life, walk straight and you will find your happiness. 

Also, growing up in a small Ukrainian industrial city, closely situated to a railroad hub, I was always fascinated to see many colorful rail traffic lights, trying to imagine where a myriad of rail tracks would lead trains on them.

Similarly, Azure Data Factory (ADF) provides several ways, to control/direct/filter your pipeline workflows; it’s all conditioned and constrained to the boundaries of my “crossroad stone” writings.

As one of my intellectual heroes is purported to have said, if you see a fork in the road, take it.

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Deciding to Use Dataflows or Power Query

Published 2021-01-22 by Kevin Feasel

Paul Turley explains a choice:

When should you use dataflows vs regular Power Query? I didn’t jump on the dataflows bandwagon and struggled to adopt them at first. Frankly, Power Query is easier to use. The browser-based dataflows designer is quite impressive but it is not as responsive and convenient as the desktop app, so this is a bit of a trade-off. The power and value of dataflows becomes apparently when the business reaches a certain stage of data culture maturity.

Before we can address the question of whether to use Power BI Dataflows, conventional Power BI queries, or any other approach to source and transform data; we need to briefly review different options for orchestrating a Business Intelligence solution in the Microsoft cloud ecosystem.

Read on for a bit of architecture and the explanation.

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Using Azure Cloud Shell

Published 2021-01-21 by Kevin Feasel

Joey D’Antoni shows off some of the benefits of using Azure Cloud Shell:

One of the challenges of being a consultant is having to work with a number of clients, and having different login credentials and accounts. In the early days of Azure, this was exceptionally painful, but over time the experience of using the portal with multiple identities and connecting to Azure tenants has gotten much easier. However, when writing PowerShell or Azure CLI code, switching accounts and contexts is slightly more painful. Also, when you are doing automation, you may be touching a lot of resources at one time, you want to be extra careful that you are in the right subscription and tenant.

Enter cloud shell.

Read on to see how to use it, get an idea of its cost, and see some of the benefits.

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Enabling Multiple Lifecycle Policies on S3

Published 2021-01-21 by Kevin Feasel

Sheldon Hull has a hoarding problem to solve:

In my case, I’ve run into 50TB of old backups due to tooling issues that prevented cleanup from being successful. The backup tooling stored a sqlite database in one subdirectory and in another directory the actual backups.

I preferred at this point to only perform the lifecycle cleanup on the backup files, while leaving the sqlite file alone.

Click through to see how to do this using Powershell.

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DTUs and vCores in Azure SQL Database

Published 2021-01-20 by Kevin Feasel

Arun Sirpal gives us one take on whether to use DTUs or vCores for Azure SQL Database:

If you are building database solutions in Azure , using Azure SQL Database then you will know that you have a purchasing option decision to make. That being should you use a vCore model or DTU approach?

Arun prefers the vCore model for solid reasons. I agree with the sentiment when we’re talking about production databases. For small-scale, personal stuff, however, the bottom end of the DTU model is much less expensive.

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Azure Data Factory Deployment Methods

Published 2021-01-18 by Kevin Feasel

Kamil Nowinski contrasts two methods for deploying Azure Data Factory pipelines:

Turned out that two-third of people use Microsoft’s deployment way, according to their answers on that poll (including few people who publish the code manually). In 1/3 cases people prefer to deploy directly from code. You may ask: what’re the differences? What characterizes both methods? Which one is better?
Before I start answering these questions, let me present both methods of publishing.

Read on to learn more about these approaches.

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Auto-Pausing Dedicated SQL Pools in Azure Synapse Analytics

Published 2021-01-18 by Kevin Feasel

Fonseca Sergio automates an important cost-saving measure when working with Azure Synapse Analytics dedicated SQL pools:

As Synapse engineer or Synapse Support Engineer you may need to start and test some Pools, and you want this to be the most cost efficient possible. Leaving some Synapse with a lot of DWU left turned on during the weekend because you forget to pause the DW after you shutdown your computers is not a good approach and we can quickly resolve this by using Powershell + Automation accounts.

This is also a good introduction to Azure Automation if you aren’t familiar with it.

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