Real-Time ML based Anomaly Detection in Azure Stream Analytics - YouTube

Azure Stream Analytics is a PaaS cloud offering on Microsoft Azure to help customers analyze IoT telemetry data in real-time. Stream Analytics now has embedded ML models for Anomaly Detection, which can be invoked with simple function calls. Learn how you can leverage this powerful feature set for your scenarios.

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#machine-learning #data-science #azure

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Ian  Robinson

Ian Robinson

1621644000

4 Real-Time Data Analytics Predictions for 2021

Data management, analytics, data science, and real-time systems will converge this year enabling new automated and self-learning solutions for real-time business operations.

The global pandemic of 2020 has upended social behaviors and business operations. Working from home is the new normal for many, and technology has accelerated and opened new lines of business. Retail and travel have been hit hard, and tech-savvy companies are reinventing e-commerce and in-store channels to survive and thrive. In biotech, pharma, and healthcare, analytics command centers have become the center of operations, much like network operation centers in transport and logistics during pre-COVID times.

While data management and analytics have been critical to strategy and growth over the last decade, COVID-19 has propelled these functions into the center of business operations. Data science and analytics have become a focal point for business leaders to make critical decisions like how to adapt business in this new order of supply and demand and forecast what lies ahead.

In the next year, I anticipate a convergence of data, analytics, integration, and DevOps to create an environment for rapid development of AI-infused applications to address business challenges and opportunities. We will see a proliferation of API-led microservices developer environments for real-time data integration, and the emergence of data hubs as a bridge between at-rest and in-motion data assets, and event-enabled analytics with deeper collaboration between data scientists, DevOps, and ModelOps developers. From this, an ML engineer persona will emerge.

#analytics #artificial intelligence technologies #big data #big data analysis tools #from our experts #machine learning #real-time decisions #real-time analytics #real-time data #real-time data analytics

Azure Streaming Analytics and Anomaly Detection

Let’s talk about this feature of Azure called stream analytics and how to detect an anomaly before it becomes a failure.

Data stream is a set of data that is coming through and is very transient, it’s not sitting in a traditional SQL database. If we had so , we can just run a batch job and run SQL query over that data and extract whatever insights we want under that data.

But what if we have data that is just passing through an event hub ?

How do we run queries, get reports, raise alerts if something becomes unusual ?

So we have this feature called Stream Analytics.

Streaming Analytics is a querying, alerting and monitoring tool that monitors streams instead of stationary databases. Now the use case that Microsoft presents is Internet of Things solution.

Say, if I have a watch or other devices and got millions of these devices out in the world and they are feeding back into an IOT Hub or a regular event hub. We may want to monitor those millions of events per second and detect something that is outside of a normal range and then to raise an alert or take a particular action based on detecting an above average quantity of those data.

Another example , say there are logs as people are visiting my website and writing log with 200 status codes and what resource they are viewing . It’s just a continuous stream of data of status code and resource path. So what if I can monitor log like that and be able to say if more than 20 of these 404/500 events happened within the last five minutes, I want to raise an alert by sending SMS message. It is real time analytics of a stream.

#anomaly-detection #machine-learning #streaming-analytics #azure

Gerhard  Brink

Gerhard Brink

1622108520

Stateful stream processing with Apache Flink(part 1): An introduction

Apache Flink, a 4th generation Big Data processing framework provides robust **stateful stream processing capabilitie**s. So, in a few parts of the blogs, we will learn what is Stateful stream processing. And how we can use Flink to write a stateful streaming application.

What is stateful stream processing?

In general, stateful stream processing is an application design pattern for processing an unbounded stream of events. Stateful stream processing means a** “State”** is shared between events(stream entities). And therefore past events can influence the way the current events are processed.

Let’s try to understand it with a real-world scenario. Suppose we have a system that is responsible for generating a report. It comprising the total number of vehicles passed from a toll Plaza per hour/day. To achieve it, we will save the count of the vehicles passed from the toll plaza within one hour. That count will be used to accumulate it with the further next hour’s count to find the total number of vehicles passed from toll Plaza within 24 hours. Here we are saving or storing a count and it is nothing but the “State” of the application.

Might be it seems very simple, but in a distributed system it is very hard to achieve stateful stream processing. Stateful stream processing is much more difficult to scale up because we need different workers to share the state. Flink does provide ease of use, high efficiency, and high reliability for the**_ state management_** in a distributed environment.

#apache flink #big data and fast data #flink #streaming #streaming solutions ##apache flink #big data analytics #fast data analytics #flink streaming #stateful streaming #streaming analytics

Real-Time ML based Anomaly Detection in Azure Stream Analytics - YouTube

Azure Stream Analytics is a PaaS cloud offering on Microsoft Azure to help customers analyze IoT telemetry data in real-time. Stream Analytics now has embedded ML models for Anomaly Detection, which can be invoked with simple function calls. Learn how you can leverage this powerful feature set for your scenarios.

Thanks for reading ❤

If you liked this post, share it with all of your programming buddies!

Follow me on Facebook | Twitter

Learn More

Machine Learning A-Z™: Hands-On Python & R In Data Science

Python for Data Science and Machine Learning Bootcamp

Machine Learning, Data Science and Deep Learning with Python

Deep Learning A-Z™: Hands-On Artificial Neural Networks

Artificial Intelligence A-Z™: Learn How To Build An AI

Machine Learning: how to go from Zero to Hero

A Complete Machine Learning Project Walk-Through in Python

Top 18 Machine Learning Platforms For Developers

Beginner’s Guide to Machine Learning with Python

Machine Learning Algorithms Tutorial - Full Course for Beginners

#machine-learning #data-science #azure

Teresa  Jerde

Teresa Jerde

1597452410

Spark Structured Streaming – Stateful Streaming

Welcome back folks to this blog series of Spark Structured Streaming. This blog is the continuation of the earlier blog “Internals of Structured Streaming“. And this blog pertains to Stateful Streaming in Spark Structured Streaming. So let’s get started.

Let’s start from the very basic understanding of what is Stateful Stream Processing. But to understand that, let’s first understand what Stateless Stream Processing is.

In my previous blogs of this series, I’ve discussed Stateless Stream Processing.

You can check them before moving ahead – Introduction to Structured Streaming and Internals of Structured Streaming

#analytics #apache spark #big data and fast data #ml #ai and data engineering #scala #spark #streaming #streaming solutions #tech blogs #stateful streaming #structured streaming