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Category: Cloud

Kinesis Data Generation

Published 2017-05-19 by Kevin Feasel

Allan MacInnis shows off a new data generation tool for Amazon’s Kinesis:

Amazon Kinesis Streams and Amazon Kinesis Firehose enable you to continuously capture and store terabytes of data per hour from hundreds of thousands of sources. Amazon Kinesis Analytics gives you the ability to use standard SQL to analyze and aggregate this data in real-time. It’s easy to create an Amazon Kinesis stream or Firehose delivery stream with just a few clicks in the AWS Management Console (or a few commands using the AWS CLI or Amazon Kinesis API). However, to generate a continuous stream of test data, you must write a custom process or script that runs continuously, using the AWS SDK or CLI to send test records to Amazon Kinesis. Although this task is necessary to adequately test your solution, it means more complexity and longer development and testing times.

Wouldn’t it be great if there were a user-friendly tool to generate test data and send it to Amazon Kinesis? Well, now there is—the Amazon Kinesis Data Generator (KDG).

Check it out if you’re using Kinesis and need to do some load testing.

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Machine Learning At Build 2017

Published 2017-05-19 by Kevin Feasel

Adnan Masood looks at some of the new machine learning offerings in Azure:

Language Understanding Intelligent Service (LUIS) is one of the marquee offerings in cognitive services which contains an entire suite of NLU / NLP capabilities, teaching applications to understand entities, utterances, and genera; commands from user input. Other language services include Bing Spell Check API which detect and correct spelling mistakes, Web Language Model API which helps building knowledge graphs using predictive language models Text Analytics API to perform topic modeling and do sentiment analysis, as well as Translator Text API to perform automatic text translation. The Linguistic Analysis API is a new addition which parses and provide context around language concepts.

In the knowledge spectrum, the Recommendations API to help predict and recommend items, Knowledge Exploration Service to enable interactive search experiences over structured data via natural language inputs, Entity Linking Intelligence Service for NER / disambiguation, Academic Knowledge API (academic content in the Microsoft Academic Graph search), QnA Maker API, and the newly minted custom Decision Service which provides a contextual decision-making API with reinforcement learning features. Search APIs include Autosuggest, news, web, image, video and customized searches.

There are some nice products available on the Azure platform and Adnan does a good job of outlining them.

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So You Want A FAQ-Bot

Published 2017-05-16 by Kevin Feasel

Steph Locke shows how easy it can be to create a Q&A bot:

Now we need to go to Azure and finish our bot.

Add a new Bot Service. You’ll need to give it a name and set which region you want to host it in. It will then setup everything in the background, and takes a couple of minutes.

Once it is successfully deployed, navigate your bot service and Create an App, making sure to copy and paste the values from the new tab into the interface. Select the Q&A Bot type. It should bring up a poppup that allows you to select your bot from a dropdown.

Bots are fun and worth learning, but that technology is still in its infancy, regardless of whose bot platform you’re using.

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Using Azure Cloud Shell

Published 2017-05-16 by Kevin Feasel

Jeffrey Verheul shows off a bit of Azure Cloud Shell:

Connecting to a database
Now that your Cloud Shell is ready to go, you can start using Bash. This means you can also use sqlcmd from within Bash.

You can connect to a database with sqlcmd, by using the following command:

sqlcmd -S servername.database.windows.net -U username -P password
Once the connection to your database has been made, you can run queries against it.

There’s no Powershell support yet, but Bash is currently supported and Powershell is in the works.

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Creating An Azure Database For MySQL

Published 2017-05-16 by Kevin Feasel

Arun Sirpal shows how to create a new MySQL database in Azure:

Here you have the concept of compute units. No such thing as DTUs here but just as confusing.

Compute Units are a measure of CPU processing throughput guaranteed to be available to a single Azure Database for MySQL server. A Compute Unit is a blended measure of CPU and memory resources. In general, 50 Compute Units equate to half-core, 100 Compute Units equate to one core, and 2000 Compute Units equate to twenty cores of guaranteed processing throughput available to your server. I am not going to rehash official documentation on these concepts so I recommend reading https://docs.microsoft.com/en-gb/azure/mysql/concepts-compute-unit-and-storage

Different database product, different metric, it seems.  Check out Arun’s post as he walks you through the process step by step.

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Azure MySQL Backups

Published 2017-05-16 by Kevin Feasel

Grant Fritchey focuses on an area where Azure’s MySQL Platform as a Service offering really makes sense:

Why Is MySQL Platform as a Service Important?

I am going to answer this question. There are a lot of advantages to creating, using and developing against data storage within a PaaS offering. One of the biggest for me is backups. Microsoft is automatically taking backups of the MySQL databases you create within Azure. These are real, full backups. Microsoft validates the backups. As I write this, you’ll have the ability to restore your entire database, to any point in time, at intervals of five minutes, over a 35 day preceding period. By programming against a MySQL database within Azure, you are gaining protection of the information you’re storing within your database, and you don’t have to do anything to benefit from this. It’s all part of the service.

Read the whole thing.

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Azure SQL Data Warehouse Max Size Property

Published 2017-05-15 by Kevin Feasel

Joey D’Antoni says not t freak out if you look at the Max Size property on an Azure SQL Data Warehouse database:

One of the promises of Azure SQL Data Warehouse is the ability to have petabyte scale. The ability to quickly scale data, and have that data scale independently of compute resources. So when one I my clients emailed me yesterday with this screenshot, needless to say I was concerned.

As you can see, when  the properties screen shows a max size of 5 GB.

Click through for the reason why.

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Azure Data Lake Tools For VS Code

Published 2017-05-12 by Kevin Feasel

Jenny Jiang announces that Azure Data Lake Tools for Visual Studio Code is now generally available:

ADLA Integration

The ADL Tools for VSCode integrate well with ADLA. Azure Data Lake includes the capabilities required to make it easy for developers, data scientists, and analysts to store data of any size, shape, and speed, and do all types of processing and analytics across platforms and languages. U-SQL on ADLA offers Job as a Service with the Microsoft invented U-SQL language. Customers do not have to manage deployment of clusters, but can simply submit their jobs to ADLA, an analytics platform managed by Microsoft.

Click through for the full announcement.

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Azure Cosmos DB

Published 2017-05-11 by Kevin Feasel

Victoria Holt discusses a new Azure product announcement:

Another database annoucment today from Microsoft. The first globally distributed, multi-model database service. Microsoft describe Azure Cosmos DB as containing a write optimized, resource governed, schema-agnostic database engine that natively supports multiple data models: key-value, documents, graphs, and columnar.

The troll in me really wants this to be SQL Server, but it is instead a very much beefed-up DocumentDB.

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