Analyzing Data Streaming using Spark vs Kafka
By: Cuelogic Technologies
Tags: Data Streaming, Big Data, Data Processing, Kafka Data Streaming, Spark Data Streaming

Before we draw a comparison between Spark Streaming and Kafka Streaming and conclude which one to use when, let us first get a fair idea of the basics of Data Streaming: how it emerged, what is streaming, how it operates, its protocols and use cases.

How Data Streaming came into existence?

Data has ever since been an essential part of the operations. Data forms the foundation of the entire operational structure, wherein it is further processed to be used at different entity modules of the system. That is why it has become quintessential in the IT landscape.

As technology grew more substantial, the importance of the data has emerged even more prominently. The methodologies that are used in data processing have evolved significantly to match up with the pace of growing need for data inputs from the software establishments.

As time grew, the time frame of data processing shrank dramatically to an extent where an immediately processed output is expected to fulfill the heightened end-user expectations. With the emergence of Artificial Intelligence, there is a strong desire to provide live assistance to the end user that seems much like humans.

This requirement solely relies on data processing strength. The faster, the better. Thus, as a result, there has been a change brought in the way data processed. Earlier there were batches of inputs that were fed in the system that resulted in the processed data as outputs, after a specified delay.

Currently, this delay (Latency), which is a result of feeding the input, processing time and the output has been one of the main criteria of performance. To ensure high performance, the latency has to be minimum to the extent of almost being real time.

This is how the streaming of data came into existence. In Data Streaming process, the stream of live data is passed as input that has to be immediately processed and deliver a flow of the output information in real time.

What is Data Streaming?

Data Streaming is a method in which input is not sent in the conventional manner of batches, and instead, it is posted in the form of a continuous stream that is processed using algorithms as it is. The output is also retrieved in the form of a continuous data stream.

This data stream is generated using thousands of sources, which send the data simultaneously, in small sizes. These files when sent back to back forms a continuous flow. These could be log files that are sent in a substantial volume for processing.

Such data which comes as a stream has to be sequentially processed to meet the requirements of (almost) continuous real-time data processing.

Why is Data Streaming required?

With the growing online presence of enterprises and subsequently the dependence on the data has brought in, the way data has been perceived. The advent of Data Science and Analytics has led to the processing of data at a massive volume, opening the possibilities of Real-time data analytics, sophisticated data analytics, real-time streaming analytics, and event processing.

Data Streaming is required when the input data is humongous in size. One needs to store the data before we move it for the batch processing. This involves a lot of time and infrastructure as the data is stored in the forms of multiple batches. To avoid all this, information is streamed continuously in the form of small packets for the processing. Data streaming offers hyper scalability that remains a challenge for batch processing.

Another reason, why data streaming is used, is to deliver a near-real-time experience, wherein the end user gets the output stream within a matter of few seconds or milliseconds as they feed in the input data.

Data streaming is also required when the source of the data seems to be endless that cannot be interrupted for the batch processing. IoT sensors contribute to this category, as they generate continuous readings that need to be processed for drawing inferences.

How Data Streaming takes place?

For making immediate decisions by processing data in real-time, data streaming can be done. Depending upon the scale, complexity, fault tolerance and reliability requirements of the system, you can either use a tool or build it yourself.

Building it yourself would mean that you need to place events in a message broker topic such as Kafka before you code the actor. An actor here is a piece of code that is meant to receive events from problems in the broker, which is the data stream, and then publish the output back to the broker.

Spark is a first generation Streaming Engine that requires users to write code and place them in actor, and they can further wire these actors together. To avoid this, people often use Streaming SQL for querying, as it enables the users to ask the data easily without writing the code. Streaming SQL is extended support from the SQL to run stream data. Moreover, as SQL is well practiced among the database professionals, performing Streaming SQL queries would be much easier, as it is based on the SQL.

Here’s the streaming SQL code for a use case where an Alert mail has to be sent to the user in an event when the pool temperature falls by 7 Degrees in 2 minutes.