Streaming Data Analytics for IoT and Big Data in Real-time
We all remember the time a few years ago when it was impossible to analyze petabytes of data. Hadoop’s emergence made it simple to run analytical queries on vast amounts of data.
We’ll know that “Big Data is a buzz” from last few years, but advanced data pipelines are receiving data at a high ingestion rate every time. Therefore, this constant data flow at high velocity is known as Fast Data.
Fast data is not only about data volumes such as Data Warehouses which measures information in GigaBytes, TeraBytes or PetaBytes. Preferably, we can estimate amount but concern its incoming rate like GB per hour, MB per second, TB per day. Therefore, both Velocity and Volume are both considered while considering Fast Data.
Real-time Data Streaming
In recent times, several data processing platforms are available for data processing from our ingestion platforms. Some support data streaming and others support real data streaming that is probably known as Real-Time data.
Streaming is instant processing and analyzing data as it arrives at ingestion time. Whereas in streaming, a certain amount of delay in data streaming from this layer.
But, the data in real-time needs to have tight time deadlines. Hence, we usually believe that if our platform is capable of capturing any event within 1ms, then we call it as real streaming or real-time data.
While considering the case of business decisions, at analyzing real-time logs, detecting frauds and predicting real-time errors, etc. comes under streaming. So, the data that is received instant as it arrives can be Real-time data.
Data Streaming Tools and Frameworks in Real-Time
There are several open source technologies available like Apache Kafka which enables data ingestion at millions of messages per second. Also analyzing constant data streams is also made possible by Apache Spark Streaming, Apache Storm, and Apache Flink.
Data Streaming Frameworks in Real-Time
Apache Spark Streaming is one among the tools which specify the time-based window to stream data from the message queue. Therefore, it does not process every message individually. We can name it as the real streams processing in micro batches. Whereas Apache Flink and Apache Storm can stream data in real-time.
Why is Real-Time Streaming Essential?
We know that S3, Hadoop, and many other distributed file systems support vast volumes of data processing and can also query those using distinct frameworks like Hives uses MapReduce as their execution engine.
Why we Need Real-Time Streaming?
Several organizations are trying to collect as much as they can regard services, products or even their organizational activities like employees tracking movements through distinct methods used like log tracking, taking screenshots at frequent intervals.
For instance, let’s imagine that we have a data warehouse having petabytes of data in it. But it allows us to just to analyze our historical data and to predict future.
Hence, processing substantial data volumes is not enough. It is essential in Intelligence and Surveillance systems, Scam detection and many more.
Earlier, handling these constant data streams at high ingestion is maintained using the process storing and running analytics sequentially on it. But these days organizations are in search of platforms where they can look into business insights in real-time and act instantly.
The alerting platforms built on the top of these real-time streams. But the effectiveness of these platforms lies in real-time data processing.
When we think of building alerting platforms, fraud detection engines, etc. on top of real-time data, it is essential to consider the programming style. These days, Reactive Programming and Functional Programming are at their boom.
What is Reactive Programming?
We can consider Reactive Programming as publisher and subscriber pattern. Often, this column is in almost every website where we can subscribe to their newsletter, and whenever the editor posts the newsletter, will deliver to all the people whosoever got accepted via email or some other way.
Therefore, the difference between Traditional and Reactive Programming is that the data that is available to the subscriber as soon as it receives and is made possible using the model of Reactive Programming. Whenever an event occurs, there are some classes or components in Reactive Programming registered. So in spite of invoking target elements by generator automatically triggers whenever an event occurs.
Whenever we are processing data at a high rate, the primary concerning point is concurrency. Therefore, the performance of our analytics job depends mostly on the allocation or deallocation of memory. So in Functional Programming, it is not essential to initialize loops or iterators on our own.
Real-Time Streaming of Big Data
While Streaming and Analyzing data in real-time, there are chances that some messages can be missed or just handling the data errors. Therefore, there are two main types of architectures used while building real-time pipelines.
Lambda Architecture for Big Data
Nathan Marz introduced this architecture in which we can have three layers to provide real-time streaming and can compensate for the occurrence of any data errors. The three layers are Batch, Speed, and Serving Layers.
Many organizations use Hadoop and Apache Storm as a batch layer and speed layer respectively. And NoSQL data store such as Cassandra and MongoDB are serving layers which stores analyzed results.
Kappa Architecture for Big Data
The founders of Apache Kafka raise the question on Lambda architecture, they loved the benefits provided the lambda architecture but stated that it is tough to build the pipeline and maintain analysis logic in both speed and batch layers.
So if we use frameworks like Apache spark streaming, Beam, Flink, support is ensured for both batch and real-time streaming. Therefore, it is essential for developers to maintain the logical part of the data pipeline.
Real-Time Streaming and Data Analytics for IoT
IoT is a very hot topic nowadays. Therefore numerous efforts are going on to connect devices to the network or the web. We must monitor our remote IoT devices from dashboards. IoT devices include washing machines, sensors, etc. and cover almost every electronic equipment and machinery we can think.
Now, let’s consider building a local production in which we need to provide real-time alerts regarding their electricity consumption to organizations using their meters. Hence, there are sensors in thousands, and our data collectors were ingesting data at a high rate, i.e., millions of events per second.
Altering platforms need to provide monitoring in real-time and alerts to the organizations regarding the status and usage of sensors. To meet these requirements, the platform we created should give streaming of data in real-time and should ensure results accuracy.
Fast Data Processing
The Kappa architecture is getting more popular nowadays for data processing with more flexibility and less overhead. Our data pipeline should be able to handle any volume of data at any velocity. The platform should be intelligent enough to scale automatically up and down according to the data pipeline’s load.
The modern data integration solutions enable organizations to use the platforms of Big Data with Microservices architecture using Kubernetes and Docker. Real-Time data Analytics and Streaming with Microservices for IoT with platforms like Apache Hive, Apache Hadoop, and Apache Spark.
I’m Prasanthi, currently working as a Tech content contributor and digital marketing analyst at Tekslate.com, a global training platform. I’m passionate about being updated on recent IT and business technology innovations. I was wondering if I could get an opportunity to participate in the communities in any topic based on the niche. I believe I can add value to this community with my participation. I’m available at email@example.com. Connect with me on LinkedIn and Twitter.