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Category: Cloud

NoSQL? No! MoSQL

Published 2018-12-07 by Kevin Feasel

Steve Jones points out a bit of a shift at Google:


Google is doing more SQL, or at least shifting towards relational SQL databases as a way of storing data. At least, some of their engineers see this as a better way to store data for many problems. Since I’m a relational database advocate, I found this to be interesting.
When Google first started to publish information on BigTable and other new ways of dealing with large amounts of data, I felt that these weren’t solutions I’d use or problems that many people had. The idea of Map Reduce is interesting and certainly applicable to the problem space Google had of a global database of sites, but that’s not a problem I’ve ever encountered. Instead, most of the struggles I’ve had with relational systems are still better addressed in a relational system.

Read the whole thing.  Note that this is slightly different than Feasel’s Law, as Steve is focusing more on the consistency side of things rather than the interface.

Also, just going to leave this here:

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Preventing Credential Compromise When Using AWS

Published 2018-12-06 by Kevin Feasel

Will Bengtston walks us through techniques Netflix uses to protect credentials in AWS:

Scope

In this post, we’ll discuss how to prevent or mitigate compromise of credentials due to certain classes of vulnerabilities such as Server Side Request Forgery (SSRF) and XML External Entity (XXE) injection. If an attacker has remote code execution (RCE) or local presence on the AWS server, these methods discussed will not prevent compromise. For more information on how the AWS services mentioned work, see the Background section at the end of this post.

Protecting Your Credentials

There are many ways that you can protect your AWS temporary credentials. The two methods covered here are:

  • Enforcing where API calls are allowed to originate from.

  • Protecting the EC2 Metadata service so that credentials cannot be retrieved via a vulnerability in an application such as Server Side Request Forgery (SSRF).

Read the whole thing if you’re an AWS user.

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SQL Managed Instance Business Critical Tier Now Available

Published 2018-12-05 by Kevin Feasel

Jovan Popovic announces Azure SQL Managed Instance Business Critical tier has reached GA:

We are happy to announce General availability of Business Critical tier in Azure SQL Managed Instance – architectural model built for high-performance and IO demanding databases.

After 5 months of public preview period Azure SQL Managed Instance Business Critical Service tier is generally available.

Azure SQL Managed Instance Business Critical tier is built for high performance databases and applications that require low IO latency of 1-2ms in average with up to 100K IOPS that can be achieved using fast local SSD that this tier uses to place database files.

Click through to see what Business Critical tier in particular has to offer.

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A Review Of AWS Managed Kafka Public Preview

Published 2018-12-04 by Kevin Feasel

Stephane Maarek is not impressed with AWS’s managed Kafka offering so far:

For me, the more people use Apache Kafka, the more business I get. As I teach Apache Kafka online on Udemy (links at https://kafka-tutorials.com/), the prospect of having an entire user base from AWS wanting to learn Apache Kafka is exciting! And as an Apache Kafka consultant, it’s always more fun to spend time deploying data pipelines than deploying infrastructure.

Unfortunately what AWS released today misses the mark. I think it’s reminiscent of managed services of open source software in AWS overall: they’re released early and lack features that I think should be MVP. In my opinion this will deter future users.

Based on Stephane’s reading, this is a product which should have sat in development for another 3-6 months to flesh out the features, upgrade the version of Kafka used, etc.  Definitely read this before jumping on AWS MSK.

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Working With Firewall Rules From Azure SQL Database

Published 2018-12-04 by Kevin Feasel

Arun Sirpal shows us how we can use T-SQL to set and modify firewall rules within Azure SQL Database:

For this post I want to actually show you the TSQL code to do this, hopefully it will become a good reference point for the future. Before we step into the code lets understand the differences between database level and server level rules.

For server level rules they enable access your entire Azure SQL server, that is, all the databases within the same logical server. These rules are stored in the master database. Database level rules enable access to certain databases (yes you could also run this within master) within the same logical server, think of this as you being more granular with the access where they are created within the user database in question.

Personally, I try and always use database level rules, this is especially true when I work with failover groups.

Click through for instructions on how to work with both server and database level rules.

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AWS Timestream: A Time-Series Database

Published 2018-12-03 by Kevin Feasel

Alex Woodie reports on a new entrant in the time-series database market:

Time-series databases have emerged as a best-in-class approach for storing and analyzing huge amounts of data generated by users and IoT devices. While relational and NoSQL databases are sometimes used for time-stamped and time-series data – such as clickstream data from Web and mobile devices, log data from IT gear, and data generated by industrial machinery — today’s massive data volumes from the IoT have outstripped the capability of those databases to keep up.

As the high-end time-series use cases piled up, AWS decided it was time to take action and make its entry into the still-specialized field, much as it did with last year’s launch of Neptune, a graph database, which is another specialized database field that’s emerging.

And here I was, just learning a bit about InfluxDB from Tracy Boggiano’s work.

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Azure Price Increases In CAD

Published 2018-11-30 by Kevin Feasel

Randolph West notes that Azure services priced in Canadian dollars will increase by five percent:

Starting December 1, 2018, prices for Azure services in the Canadian dollar will increase by 5 percent to more closely align to Azure pricing in US dollars. Even after this adjustment, customers buying in the Canadian dollar will continue to find Azure offerings highly competitive.

Microsoft periodically assesses its pricing of products and services across the globe to ensure reasonable alignment across regions. This change to Azure prices is an outcome of this assessment.

The first thing that came to mind was a particular joke from the Simpsons.  For those who don’t remember, the Simpsons was a hilarious cartoon for about ten years before it was quietly killed and replaced with something almost but not quite the same, lacking most of the humor.

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Azure SQL Database Supports R Integration

Published 2018-11-29 by Kevin Feasel

David Smith notes that Azure SQL Database now has (in preview) support for R:

Azure SQL Database, the database-as-a-service based on Microsoft SQL Server, now offers R integration. (The service is currently in preview; details on how to sign up for the preview are provided in that link.) While you’ve been able to run R in SQL Server in the cloud since the release of SQL Server 2016 by running a virtual machine, Azure SQL Database is a fully-managed instance that doesn’t require you to set up and maintain the underlying infrastructure. You just choose the size and scale of the database you want to manage, and then connect to it like any other SQL Server instance. (If you want to learn how to set up an Azure SQL database, this Microsoft Learn module is a good place to start.)

Python and Java are not yet supported, but I’d imagine that they’ll be on the way too.

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Using Azure ML To Approve Expenses Automatically

Published 2018-11-27 by Kevin Feasel

Isabelle Van Campenhoudt walks us through a scenario of using Azure ML to find expense reports which should automatically be approved, reducing the workload for approvers:

My partner in crime Serge Luca aka Doctor Flow is the author of a nice and complex expenses approval system in Microsoft Flow .
One year ago, he asked me to add analytics to his Flow.  This year he has the interesting idea to add a machine-learning based approval in his flow and suggest me to work on it. The idea is the following: Since we have a lot of approvals in our system, can a machine learn and found some decision pattern to apply automatically to each expenses request ?
I decided to use the Microsoft Azure Machine Learning Studio. In this tool you can build experiments and use some of the most common and useful machine learning algorithms. It was amazing to see how easy it is to create and consume machine learning .

This contrasts with Ginger Grant’s nightmare scenario pretty well:  instead of trying to get the ML process to do all of the work, create a process which takes care of the really easy stuff and leave harder tasks to specialists with a deeper understanding of the rules.  That way they don’t have to spend their time on trivialities.

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The Risk Of Shadow IT In The Cloud

Published 2018-11-23 by Kevin Feasel

Kenneth Fisher walks us through the risk of increased Shadow IT with migration to the cloud:

Shadow IT has been, well, maybe not the bane of the IT department, but certainly a pain in the neck. On the off chance you’ve never heard of shadow IT do any of these sound familiar?

  • A user asks you to restore a corrupt database on a SQL Server you’ve never heard of and isn’t in your inventory. (And 50/50 odds there’s never been a backup taken.)

  • You do a licensing true-up and dozens of new SQL Servers suddenly show up.

  • You hear from a user: “We have this mission critical Access database that suddenly isn’t working. I know you don’t support access but you’re the database person so we need you to fix it.”

It’s an interesting short essay and worth thinking about if you’re in the cloud or moving that way.

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