Comparing Data Streaming Frameworks | Scala - Knoldus Blogs

Comparing Data Streaming Frameworks

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In this Era of Technology, where the amount of data is growing exponentially and every bit of data holds value. Even, according to some reports, the number of bytes being generated and stored till now in the world has already exceeded the star counts in the sky. As every bit is useful so, it is very important to store them without losing any bit.

When the first thought of data strikes your mind, you might be thinking of piles of data residing in data warehouses or somewhere in databases. Such data can be extracted, process, or analyzed for future predictions or any use.

But this all can be done only when the data is in a resting state. In other words, data is resting somewhere. And when you need to process it, you run some queries or jobs (operations) against such data. But it doesn’t rely that the data must be in resting-state all time and you only can perform operations on the data. Since nowadays you easily can see that a number of systems i.e. sensors, CRMs, server logs, etc generate continuous streams of data.

Now, let’s assume a scenario where we have to process the data in real-time, while it moves or it is not in a resting state. In such a scenario, you can not wait for it to pile up somewhere like data warehouses or any of the databases and then run a query on it. Now, we need something that gives us access to data in its flowing state or streaming data. That platform will allow us to perform the operations quickly rather than waiting for it to store somewhere.

So, In this blog, we’ll compare some types of data streaming frameworks along with some use cases.

Different Data Streaming Frameworks –

So if we talk about the data streaming frameworks we have –

• Pub/Sub
• Apache Kafka
• Akka Streams
• Apache Spark
• Apache Storm
• Apache Samza
• Apache Flink
• Amazon Kinesis

Though we have much more frameworks apart from these being listed above, more specifically we’ll compare three data streaming frameworks i.e. Akka Stream, Apache Kafka, and Apache Spark.

Akka Streams –

As Akka is one of the most powerful features of Scala. This comes with a number of Libraries and modules as well for different purposes, and one of them is Akka Stream.

It is a library to process and transfer the sequence of data. Again, here the size may not be known or it may be infinite. Akka Streams implementations uses the Reactive Streams interface internally to pass the data between different operators. Akka Reactive is an initiative to provide a standard for asynchronous stream processing with non-blocking backpressure.

The feature that makes it more popular is that you have entire control over the processing of individual records and streaming topologies. This feature is independent of the amount of data being processed and the configuration. Also, it is built on top of a successful actor model of concurrency, and streaming components that are built can help you in processing the data in any way you want to.

Degree of Akka Streams –

• It is highly scalable and fault tolerant.
• It follows the Reactive Manifesto, i.e. elasticity, responsiveness, fault-tolerance and message-driven behavior.
• API’s is extremely powerful.
• It also offers the low-level GraphStage API that enhance you to get all the control for custom streaming logic.

Use-Case –

Akka Streams is best for high-performance systems if you want to implement Akka Streams into your application, as it has an extremely powerful API.

• Complex event Stream processing.
• BackEnd Services.
• Concurrency/Parallelism.
• Transaction Processing.

Kafka Streams –

Kafka Streams also known as Apache Kafka Streams, is a client library for building applications and microservices and unbounded data. We interact with the clusters to process a stream of data. It combines the simplicity of writing and deploying standard Java and Scala applications on the client-side with the benefits of Kafka’s server-side cluster technology.

The data is represented in it is as key-value records, which makes it easy to identify, and they are organized into topics, which are durable event logs.

The season behind choosing Kafka over other streaming platforms is its integration with Kafka security, deployment to containers, VM’s and cloud, etc., no separate processing cluster required.

Degree of Kafka Streams –

• It comes with Kafka Cluster that provides high-speed, fault-tolerance and high scalability.
• Kafka also provides exactly-once message sending semantics.
• It also encourage us to make the use of microservices using the same message bus to communicate.

Use-Case –

Apache Kafka works best as an external high-performance message bus for the applications.

• Messaging.
• Web Activity Tracking.
• Log Aggreagations.
• Stream Processing.

Spark Streaming –

Spark Streaming is also known as Apache Spark Streaming. It is a scalable fault-tolerant streaming processing system that natively supports both batch and streaming workloads. It is a natural streaming extension of the massively popular Spark distributed computing engine with the main purpose to use is to process endless big data at scale.

The point to remember and to be noted is that it will need a dedicated compute cluster to run, which could usually be costly in production.

The abstraction key of Spark streaming is a Discretized Stream or, in short, a DStream, that represents a stream of data divided into small batches. DStreams are built on top of RDDs, Spark’s core data abstraction. This allows Spark Streaming to seamlessly integrate with any other Spark components like MLlib and Spark SQL.

Degree of Spark Streaming –

• It is mainly built for big-data.
• Ont of most feature of spark is the ability to deal with late data based on event time and watermarks, which is very powerful in real life.
• It can also be quickly spun up locally for smaller data processing.
• Fast recovery from failures and stragglers.

Use-Case –

Undoubtedly, Spark Streaming is best when it comes to big data computation, thus making it easy to build scalable fault-tolerant streaming applications.

• Streaming Data
• Machine Learning
• Fog Computing

Conclusion –

So, in this blog we’ve discussed some streaming frameworks, their degree, their use-cases so far. This blog will be more beneficial when you’re gonna implement any of these techs. Just come back! and have a look, you’ll have your answer.