Enabling Real Time Analytics For IoT

What is Fast Data?

A few years ago, we remember the time when it was just impossible to analyze petabytes of data. Then emergence of Hadoop made it possible to run analytical queries on our huge amount of historical data.

As we know Big Data is a buzz from last few years, but Modern Data Pipelines are constantly receiving data at a high ingestion rate. So this constant flow of data at high velocity is termed as Fast Data.

So Fast data is not about just volume of data like Data Warehouses in which data is measured in GigaBytes, TeraBytes or PetaBytes. Instead, we measure volume but with respect to its incoming rate like MB per second, GB per hour, TB per day. So Volume and Velocity both are considered while talking about Fast Data.

What is Streaming and Real-Time Data

Nowadays, there are a lot of Data Processing platforms available to process data from our ingestion platforms. Some support streaming of data and other supports true streaming of data which is also called Real-Time data.

Streaming means when we are able to process the data at the instant 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 ingestion layer.

But Real-time data needs to have tight deadlines in the terms of time. So we normally consider that if our platform is able to capture any event within 1 ms, then we call it as real-time data or true streaming.

But When we talk about taking business decisions, detecting frauds and analyzing real-time logs and predicting errors in real-time, all these scenarios comes to streaming. So Data received instantly as it arrives is termed as Real-time data.

Stream & Real Time Processing Frameworks

So in the market, there are a lot of open sources technologies available like Apache Kafka in which we can ingest data at millions of messages per sec. Also Analyzing Constant Streams of data is also made possible by Apache Spark Streaming, Apache Flink, Apache Storm.

Apache Spark Streaming is the tool in which we specify the time-based window to stream data from our message queue. So it does not process every message individually. We can call it as the processing of real streams in micro batches.

Whereas Apache Storm and Flink have the ability to stream data in real-time.

Why Real-Time Streaming

As we know that Hadoop, S3 and other distributed file systems are supporting data processing in huge volumes and also we are able to query them using their different frameworks like Hive which uses MapReduce as their execution engine.

Why we Need Real — Time Streaming?

A lot of organizations are trying to collect as much data as they can regarding their products, services or even their organizational activities like tracking employees activities through various methods used like log tracking, taking screenshots at regular intervals.

So Data Engineering allows us to convert this data into structural formats and Data Analysts then turn this data into useful results which can help the organization to improve their customer experiences and also 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 just analyze our historical data and predict future.

So processing of 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 any important event occurs. This is required in Intelligence and surveillance systems, fraud detection etc.

Earlier handling of these constant streams of data at high ingestion rate is managed by firstly storing the data and then running analytics on it.

But organizations are looking for the platforms where they can look into business insights in real-time and act upon them in real-time.

Alerting platforms are also built on the top of these real-time streams. But Effectiveness of these platform lies in the fact that how truly we are processing the data in real-time.

Use Of Reactive Programming & Functional Programming

Now when we are thinking of building our alerting platforms, anomaly detection engines etc on the top of our real-time data, it is very important to consider the style of programming you are following.

Nowadays, Reactive Programming and Functional Programming are at their boom.

So, we can consider Reactive Programming as subscriber and publisher pattern. Often, we see the column on almost every website where we can subscribe to their newsletter and whenever the newsletter is posted by the publisher, whosoever have got subscription will get the newsletter via email or some other 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 is made possible by using Reactive Programming model. In Reactive Programming, whenever any events occur, there are certain components (classes) that had registered to that event. So instead of invoking target components by event generator, all targets automatically get triggered whenever any event occurs.

Now when we are processing data at 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 in which CPU itself takes care of allocation and deallocation of data and also makes the best use of memory which results in better concurrency or parallelism.

Streaming Architecture Matters

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 which are used while building real-time pipelines.

Lambda Architecture:

• This architecture was introduced by Nathan Marz in which we have three layers to provide real-time streaming and compensate any data error occurs if any. The three layers are Batch Layer, Speed layer, and Serving Layer.

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