Introduction to Azure Stream Analytics

Azure stream analytics are truely real-time analytics, from the cloud to the edge. Azure Stream Analytics is designed to analyze and process high volumes of fast streaming data from multiple sources simultaneously.

The simplicity of Azure Stream Analytics is that it uses familiar SQL syntax and is extensible with JavaScript and C# custom code.

Basics of Azure Stream Analytics

Azure stream analytics solutions will always have input, query, and output.

Basics of Azure Stream Analytics

Advantages of Azure Stream Analytics

• Ease of creating analytics pipelines

• Can be used for complex and large workloads

• As a cloud service, Stream Analytics is optimized for cost

• Can be used on the Edge

• Built-in machine learning (ML) models to shorten time to insights

• Azure Stream Analytics has built-in recovery capabilities in case the delivery of an event fails

• Azure Stream Analytics is a fully managed serverless (PaaS) offering on Azure

• Azure Stream Analytics encrypts all incoming and outgoing communications and supports TLS 1.2

• Stream Analytics can process millions of events every second 

• Stream Analytics can deliver results with ultra-low latencies

Who are Data Scientists and What they Do

Now-a-days the buzz word is Data Analytics. Customers are savvy and want to know how we can help analyze their data.

I feel Data Analytics should be part of every medium to large Dynamics 365 project. This is first of many blogs I will start writing for Data Analytics.

Good news is that Microsoft is already leading the way in Data Analytics through extreme investments in Dynamics 365, Windows, Azure, Machine Learning, SQL Server and IoT. 

Data scientists are a special breed who work on data and have the art of making some sense out of it. Having said that it is not as simple as picking customer's SQL Server and start writing TSQL queries. 

Data scientists should have the following

• Constant learning of current state of any project or business

Customer's data is the representation of its business in the form of numbers, text, dates and images.

• Fully understand the expectations and deliverable 

Planning and the execution both depend on the end goal. A data scientist should be clear of what is expected so that the analysis and transformation can be performed accordingly.

• Knowledge of methodologies and tools 

A data scientist should be aware of various methodologies and tools available at his/her disposal. For example, Microsoft has many tools for data analysis and they work with all kinds of data.

• Curiosity, Persistent, Patient and Focused 

A data scientist sometimes need to churn Terabyte or Petabyte or Exabyte of data. Therefore they need to have constant curiosity and be persistent, patient & focused to explore, visualize, slice and dice data.

• Technical Savvy

Data scientists should know how to work with raw data. They should be able to transform it to an easy format & visualization so that correct analysis can be made and timely decisions can be reached. Data scientists should be good in math and statistics. Statistics is a branch of mathematics dealing with the collection, analysis, interpretation, presentation, and organization of data.

What does a Data Scientist Do?

• Data scientists spend most of the time preparing data

Data scientists need to clean, prepare and process raw data so that it can be analyzed. 

• Run software programs against data

Data scientists use various software programs and languages to analyze data. For example SQL query language, R, Python, Hive, Hadoop, Microsoft R Server, Power BI, Excel, etc.

• Prepare Reports and Visualizations

Data scientists produce reports, charts and tables for easy understanding.