Introduction

We live in a world where an enormous load of data is generated every second. This data obtained can provide meaningful and useful results if it is accurately analyzed and can also offer solutions for various industries at the right time. 

It’s useful for industries such as Travel Services, Retail, Media, Finance, and Health Care. Many reputable companies have adopted Data Analysis, such as Tracking Customer Interaction and various types of productions done by Amazon on its platform or viewers receiving personalized recommendations in real-time, provided by Netflix. 

It can be used by businesses that use a large amount of data. They can analyze it to improve their business’s overall process and increase customer satisfaction and user experiences. Better user experiences and customer satisfaction benefit the organization and expand the business and profit in the long run. 

  1. What is Spark Streaming?
  2. How does Spark Streaming Work?
  3. Why Use Spark Streaming?
  4. What is the Need for Streaming in Apache Spark
  5. Spark Streaming Advantage & Architecture
  6. Goals of Spark Streaming

1. What is Spark Streaming?

When data continuously appears in an unbound sequence, it’s called a data stream. When input data is flowing steadily, it is divided by streaming. Additionally, the processing of data is done after it is apportioned into discrete units. Analyzing and processing the data at low latency is known as stream processing.

In 2013, Apache Spark was supplemented with Spark Streaming. Spark is deemed a high-speed engine to process high volumes of data and is 100 times faster than MapReduce. It uses the distributed data and splits that data into even smaller chunks of data computed parallel across the machines, saving time. Also, it uses in-memory processing rather than disk-based processing, which allows the computation to be faster. 

There are several sources from which the data ingestion can surface, such as TCP Sockets, Amazon Kinesis, Apache Flume, and Kafka. By applying sophisticated algorithms, the processing of data is done. Using a high-level function such as window, join, reduce, and map to express the processing, and Live Dashboards, Databases, and file systems are managed to push the processed data to the file systems. 

A) The Four Major Aspects of Spark Streaming are: 

  • Swift restoration from damage and stragglers
  • Regular load balancing and resource usage
  • Combining streaming data with static datasets and interactive queries
  • Native integration with advanced processing libraries (machine learning, graph processing, SQL)

This alliance of disparate data processing capabilities is the leading cause behind Spark Streaming’s rapid adoption. It makes it very accessible for developers to use a single framework to satisfy all their processing needs. 

2. How does Spark Streaming Work?

The stream’s data is divided into small batches, which are called DStreams in the Spark Streaming. It is a sequence of RDDs internally. RDDS uses spark APIs for data processing, and as a result, shipments are returned. The API of Spark Streaming is accessible in Python, Java, and Scala. 

Stateful computations are called a state maintained by the Spark Streaming based on the stream’s incoming data — the data flowing in the stream is processed within a time frame. The developer should specify this time frame, and it is to be authorized by Spark Streaming. The work should be completed within the time frame. The window of time is updated within a time interval, known as the sliding interval in the window. 

3. Why Use Spark Streaming?

Learning real-time processing with Spark streaming from different sources like Facebook, Stock Market, and Geographical Systems can be advantageous to conduct powerful analytics and support businesses. There are five principal aspects of Spark Streaming that makes it so unique, and they are:

1. Integration — Advanced Libraries like graph processing, machine learning, and SQL can be easily integrated.

2. Combination — The data that is getting streamed can be done in conjunction with interactive queries and static datasets.

3. Load Balancing — Spark Streaming has a perfect balancing of load, making it very special.

4. Resource usage — Spark Streaming uses the available resource in a very optimum way.

5. Recovery from stragglers and failures — Spark Streaming can quickly recover from any loss or straggler.

4. What is the Need for Streaming in Apache Spark

While designing the system, a continuous operator model is used for conventionally processing streams to process the data. The functioning of the system is as follows:

  1. Data sources are manipulated to stream the data. These streaming data are incorporated into data ingestion systems like Amazon Kinesis, Apache Kafka, and several others.
  2. In a cluster, the data is parallel processed. 
  3. Downstream systems such as Kafka, Cassandra, HBase are utilized to transfer the result.

5. Spark Streaming Advantage & Architecture

Processing one data stream at a time can be exhausting and complicated; Hence, Spark Streaming distributes the data into small sub batches that are easily manageable. This is done because Spark workers get buffers of data in parallel accepted by the Spark Streaming receiver. As a result, the whole system runs the batches in parallel and then accumulates the final arrangements. These short tasks are then processed in batches by Spark engine, and the outcomes are communicated to other systems. 

In Spark Streaming architecture, the calculation is not statically allocated and loaded to a node but based on the resources’ data locality and availability. Thus it is reducing loading time as compared to previous traditional systems. Hence the use of the data locality principle is also easier for detecting faults and for its recovery. 

6. Goals of Spark Streaming

Following are the Goals achieved by Spark architecture:

A) Dynamic Load Balancing

The main aim of load balancing is to adjust the workload efficiently across the workers and similarly put everything, that there is no wastage of resources available. And also responsible for dynamically allocating resources to the worker nodes in the system.

B) Failure and Recovery 

As in the traditional system, when an operation failure occurs, the whole system must reconfigure to get the lost information back. But the problem arises when one node is handling all this recovery and making the entire system wait for it to complete. Whereas in Spark, the information lost is computed by other available nodes and it brings the system back to track without any extra waiting like in the traditional methods. 

C) Batches and Interactive Query

The set of RDDs in Spark is called DStream in Spark; these batches are stored in Spark’s memory, which presents an efficient way to query the data present. 

Spark’s best part is that it includes a wide variety of libraries used when required by the spark system. The libraries’ names are MLlib for machine learning, SQL for data query, GraphX, and Data Frame, whereas Dataframe and questions can be converted to equivalent SQL statements by DStreams.

D) Performance

Since the spark system uses parallel distributions of the task, it improves its throughput capacity, leveraging the Spark engine capable of achieving low latency as low as up to a few 100 milliseconds.

Conclusion

In a data-driven world, tools to store and analyze data have proven critical in business analytics and growth. Big Data and the tools and technologies associated have proven to be in rising demand. Apache Spark has a significant market and offers excellent features for customers and enterprises.

If you are interested in knowing more about Big Data and Data Analytics, check out our 10-month online Integrated Program in Business Analytics, in collaboration with IIM Indore. It is ranked #1 among the ‘Top Part-time PG Programmes In India’ in 2020. It is designed for working professionals and provides extensive live online classes and comprehensively covers Data Science, Statistical Modeling, Business Analytics, Visualization, Big Data, and Machine Learning so that learners can become data-proficient Future Leaders. 

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