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Overview of Stream Analytics
Stream Analytics delivers the most powerful insights from the data. Nowadays, there are a lot of Data Processing platforms available to process data from our ingestion platforms. Some support streaming of data and others support real streaming of data called Real-Time data. Streaming means when we can process the data instantly as it arrives and then processing and analyzing it at ingestion time. But in streaming, we can consider some amount of delay in streaming data from the ingestion layer. But Real-time data needs to have tight deadlines regarding time. So we usually believe that if our platform can capture any event within 1 ms, we call it Real-Time Streaming data. But When we talk about taking business decisions, detecting frauds and analyzing real-time logs, and predicting errors in real-time, all these scenarios come to streaming. So Data received instantly as it arrives, termed as Real-time data.Stream Analytics Tools & Frameworks
So in the market, there are many open source technologies available like Apache Kafka, in which we can ingest data at millions of messages per sec. Also, Apache Spark Streaming, Apache Flink, Apache Storm analyzes Constant Streams of data.
Why Stream Analytics?
As we know, Hadoop, S3, and other distributed file systems support data processing in huge volumes. We can also query them using their different frameworks like Hive, which uses MapReduce as their execution engine.Why we Need Real-Time Stream Analytics?
Many organizations are trying to collect as much data as possible regarding their products, services, or even their organizational activities, like tracking employees' activities through various methods like log tracking and taking screenshots at regular intervals. So Data Engineering allows us to convert this data into basic formats and Data Analysts. Then turn this data into useful results, which can help the organization improve their customer experiences and boost their employee's productivity. But when we talk about log analytics, fraud detection, or real-time analytics, this is not the way we want our data to be processed. The actual value data is in processing or acting upon it at the instant it receives. Imagine we have a data warehouse like hive having petabytes of data in it. But it allows us to analyze our historical data and predict the future. So processing huge volumes of data is not enough. We need to process them in real-time so that any organization can take business decisions immediately whenever an important event occurs. This is necessary for Intelligence and surveillance systems, fraud detection, etc. Earlier handling of these constant data streams at a high ingestion rate is managed by firstly storing the data and then running analytics on it. But organizations are looking for platforms to look into business insights in real-time and act upon them in real-time. Alerting platforms are also built on top of these real-time streams. But the Effectiveness of these platforms lies in how honestly we are processing the data in real-time.Software testing helps to check whether the actual results match the expected results and to assure that the software system is Bug- free. Source: Guide to Functional Testing
Use of Reactive Programming & Functional Programming
Now, when we are thinking of building our alerting platforms, anomaly detection engines, etc., it is vital to consider the style of programming you are following on top of our real-time data. Nowadays, Reactive Programming and Functional Programming are at their boom.What is Reactive Programming?
So, we can consider Reactive Programming as a subscriber and publisher pattern. Often, we see the column on almost every website where we can subscribe to their newsletter. Whenever the editor posts the newsletter, whosoever has got a subscription will get the newsletter via email or another way. So the difference between Reactive and Traditional Programming is that the data is available to the subscriber as soon as it receives. And it makes possible by using the Reactive Programming model. In Reactive Programming, certain components (classes) had registered to that event whenever any events occur. So instead of invoking target elements by event generator, all targets automatically get triggered whenever an event occurs.What is Functional Programming?
Now when we are processing data at a high rate, concurrency is the point of concern. So the performance of our analytics job highly depends upon memory allocation/deallocation. So in Functional Programming, we don’t need to initialize loops/iterators on our own. We will be using Functional Programming styles to iterate over the data. The CPU itself takes care of the allocation and deallocation of data and makes the best use of memory, which results in better concurrency or parallelism.What is Stream Analytics and Processing in Big Data?
While Streaming and Analyzing the real-time data, there are chances that some messages can be missed, or in short, the problem is how we can handle data errors. So, there are two types of architectures that useful while building real-time pipelines.-
Lambda Architecture for Big Data

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Kappa Architecture for Big Data

What is Stream Processing and Analytics For IoT?
Internet of things is a scorching topic these days. So numerous efforts are going on to connect devices to the web or a network. In short, we should be monitoring our remote IoT devices from our dashboards. IoT Devices includes sensors, washing machines, car engines, coffee makers, etc., and it almost covers every machinery/electronic device you can think of.