This article compares and contrasts two well-known technologies that are both associated with the processing of large amounts of data and are renowned for their capacity to work in real-time or with streaming data: Kafka vs Spark Streaming.
How does Kafka work is a free and open-source software application that, in most cases, follows the publish-subscribe workflow architecture and serves as an intermediary component in streaming data pipelines?
Kafka streaming and spark streaming is a well-known framework in the big data space, and one of its primary functions is the processing of massive volumes of unstructured data in a short amount of time.
It is well known that the subject for producer and consumer events in Kafka vs Spark Streaming is the fundamental storage component. Spark, on the other hand, processed the data sets using something called a resilient distributed dataset structure (RDD) as well as data frames.
When it comes to data streaming, it's important to have a firm grasp of the basics, like how it came about, what streaming is and how it works, the protocols it utilizes, and what use cases it supports before diving into a comparison of Spark Streaming versus Kafka vs Spark Streaming.
Since that time, having accurate data has been an integral aspect of operations. The data serves as the basis for the whole operational structure, and after being subjected to additional processing, it is then used by the many entity modules that make up the system.
Because of this, it has developed into a crucial component of the overall IT environment.
Since the development of technology has progressed to the point where it is now, the significance of the data has become even more apparent.
In order to keep up with the rapidly increasing demand for data inputs from software firms, the approaches that are utilized in data processing have undergone a substantial amount of evolution in recent years.
The amount of time required to process data has greatly decreased over the course of time, to the point where an instantaneously processed output is anticipated to be sufficient to meet the elevated standards set by end-users.
Since the advent of artificial intelligence (AI), there has been a growing interest in the provision of real-time help to end users that is indistinguishable from that offered by actual people. Data processing capability is the only factor considered for fulfillment of this prerequisite.
The quicker something is, the better. As a consequence of this, there has been an adjustment made to the manner in which data is handled. In the past, there were batches of inputs that were sent into the system, and after a certain amount of time had passed, the system would then provide the processed data as outputs.
At the moment, one of the most important criteria for performance is called "latency," and it refers to the amount of time that elapses between the time an input is given and when it produces an output.
In order to guarantee a high level of performance, the latency must be kept to a minimum and come as close as possible to being real-time. The transmission of data in this way was how it first came into being.
The stream of live data is provided as input into the Data Streaming process. This stream of live data then has to be quickly processed, and it must produce a flow of the output information in real-time.
Data Streaming is a method in which input is not sent in the traditional manner of batches, but rather, it is posted in the form of a continuous stream that is processed using algorithms as they are.
This method differs from the traditional method of batch input in that it does not use the term "batch." A nonstop data stream may likewise be accessed as part of the output in its entirety.
This data stream is produced utilizing thousands of sources, each of which sends the data in tiny amounts concurrently. When these files are transmitted one after the other in succession, a continuous flow is formed.
It's possible that there are log files being sent in a significant quantity for processing. In order to fulfill the criteria of continuous real-time data processing, this kind of data, which arrives in the form of a stream, has to be handled sequentially.
The way that data is regarded has changed as a result of the expanding online presence of businesses and, as a consequence of this, the dependency on the data that has been brought in.
The development of Data Science and Analytics has allowed for the processing of a huge number of data, which in turn has opened the door to the possibility of real-time data analytics, sophisticated data analytics, real-time streaming analytics, and event processing.
When dealing with enormous amounts of input data, data streaming is an absolute need. Before we can transfer the data to be processed in batches, we need to store it somewhere first. Due to the fact that the data is stored in a variety of different batches, this takes a significant amount of both time and infrastructure. In order to circumvent all of this, the information that has to be processed is continually broadcast in the form of little packets.
The issue of hyper scalability is one that continues to plague batch processing, but data streaming provides a solution to this problem.
Another reason why data streaming is employed is to give a near-real-time experience to the end-user. This means that the end-user will obtain the output stream within a few milliseconds or seconds after they have fed the input data into the system.
When the source of the data seems to be infinite and cannot be halted for batch processing, data streaming is also necessary. Sensors that are connected to the internet of things are a part of this category since they provide continuous readings that have to be processed before conclusions can be drawn.
The streaming of data allows for the processing of data in real time, which enables users to make instant judgments. You have the option of using a tool or building it yourself, and the decision should be made based on the size, complexity, fault tolerance, and reliability needs of the system.
If you want to build it yourself, you will need to put events in a message broker topic first, such as Kafka, before you can begin coding the actor.
An actor is a piece of code that is supposed to accept events from issues in the broker, which is the data stream, and then publish the result back to the broker. In this context, the data stream is the actor.
Spark is a first-generation Streaming Engine, which means that users are required to create code and put it in an actor. Users also have the ability to wire these actors together. People often use Streaming SQL for querying since it allows the users to quickly ask for the data without having to write the code, which is why this problem is avoided.
The expanded functionality provided by SQL to execute stream data is known as "streaming SQL." Since SQL is so widely used in the database industry, executing Streaming SQL queries, which are built on top of SQL, would be much simpler. This is because Streaming SQL is based on SQL.
The following code is the streaming SQL that should be used for a use case where an Alert message should be sent to the user in the event that the temperature of the pool drops by 7 degrees in only 2 minutes.
Spark SQL offers a Domain Specific Language (DSL) that may be used in conjunction with a variety of computer languages, including Scala, Java, R, and Python, to facilitate the manipulation of DataFrames. Using SQL or the DataFrame API, it is possible to run queries on structured data from inside the Spark applications itself.
Streaming SQL is supported by newer generations of Streaming Engines like as Kafka, which also supports Streaming SQL in the form of Kafka vs Spark Streaming SQL or KSQL.
The choice of the Streaming Engine, which must consider both the needs of the use case and the infrastructure that is now available, is the most important consideration here, despite the fact that the method of stream processing has more or less remained the same.
Before we get to a conclusion on whether to use Spark Streaming and when to use Kafka vs Spark Streaming, let's first examine the fundamentals of Kafka vs Spark Streaming so that we have a better grasp of both of these technologies.
Cluster computing is facilitated by Apache Spark, an open-source platform. The Spark codebase was first created at the Amp Lab at the University of California, Berkeley. Subsequently, it was given to the Apache Software Foundation to be maintained.
Spark offers a programming interface that is implicitly data-parallel and fault-tolerant. This interface may be used to program whole clusters.
When Hadoop was first released, the MapReduce framework served as the foundational execution engine for any Job tasks that needed to be completed.
The Read-Write activity that took place during the Map-Reduce execution was carried out on a physical hard disk. This is the explanation for the increased consumption of both time and space throughout the execution process.
Apache Spark is a platform that is available for free. Increases the quality of execution compared to the Map-Reduce method. It is an open platform that supports a number of programming languages, including Java, Python, Scala, and R, among others.
In-memory execution with Spark is provided at a speed that is 100 times quicker than with MapReduce. The RDD definition is being used here. RDD is a resilient distributed data set that gives you the ability to store data on memory in a way that is completely transparent and to only keep it on disk when it is absolutely necessary to do so.
At this point, it would be more efficient to read the data stored in memory rather than on the disk.
Spark is the technology that enables us to process the data while it is still stored in Data Frames. Spark is a tool that may be used by application developers, data scientists, and data analysts to quickly handle massive amounts of data in a short amount of time.
Spark provides us with the capability to do features such as interactive and iterative data processing.
Spark streaming is another capability that allows us to handle data in real time. The financial industry must monitor real-time transactions in order to provide the best possible deal to customers while also monitoring any questionable financial activity.
Spark streaming is particularly well-liked among Hadoop users of a younger age. A developer will be able to more quickly work on streaming projects by using Spark, which is a lightweight API that is straightforward to design.
Once the infrastructure is in place, Spark streaming will simply be able to recover lost data and will be able to offer precisely what is requested. We are also able to work with real-time spark streaming data as well as historical batch data simultaneously without any additional coding efforts being required (Lambda Architecture).
The basic Spark API has been extended to include the functionality of Spark Streaming, which enables users to execute stream processing on live data streams.
It gathers information from several sources such as Kafka, Flume, Kinesis, and TCP sockets. Further processing of this data may be done using complicated algorithms that are defined with high-level functions such as a map, reduce, join, and window.
The processed data, which is the final output, may be sent to destinations such as HDFS filesystems, databases, and live dashboards.
Let's take a more in-depth look at the operation of Spark Streaming, shall we?
Spark Streaming receives live input from the data sources in the form of data streams, which it then separates into batches before sending them to the Spark engine to be processed. This process results in the generation of output in the form of quantities.
Spark Streaming gives you the ability to use sophisticated data processing techniques like machine learning and graph processing for the data streams they manage. In addition to this, it offers a high-level abstraction that stands in place of a continuous data stream.
The term "discretized stream," often written as "DStream," refers to this particular abstraction of the data stream. Any data streams from sources like Kafka, Flume, and Kinesis or other DStreams may be used to build this DStream, and then high-level operations can be performed on either of those streams to produce this DStream.
These DStreams are sequences of RDDs, which stands for resilient distributed datasets. An RDD is a collection of several sets of data items that can only be read and is spread over a cluster of computers.
These RDDs are maintained in a way that allows for errors, which gives them a high level of robustness and reliability.
Streaming analytics are carried out by Spark Streaming via the use of the quick data scheduling capabilities of Spark Core. In order to conduct the RDD transformations necessary for the data stream processing, the data that is ingested from sources such as Kafka, Flume, and Kinesis, etc. in the form of mini-batches is employed.
These sources include:
You are able to develop programs in Scala, Java, or Python to handle the data stream (also known as DStreams) generated by Spark Streaming in accordance with the requirements.
The fact that the code that is used for batch processing can also be used here for stream processing makes it much simpler to construct a Lambda architecture by utilizing Spark Streaming.
This kind of architecture is a hybrid of batch processing and stream processing. However, this comes at the expense of a latency that is equivalent to the period of the micro batch.
Spark is capable of reading primary data from sources like file systems and socket connections. On the other hand, it is also capable of supporting more complex sources like Kafka, Flume, and Kinesis.
Adding additional utility classes is the only way to get access to these outstanding sources.
Using the following artifacts, you may create a connection between Kafka, Flume, and Kinesis.
- Kafka: spark-streaming-kafka-0-10_2.12
- Flume: spark-streaming-flume_2.12
- Kinesis: spark-streaming-kinesis-asl_2.12 [Amazon Software License]
The Apache Software Foundation created the open-source stream processing framework known as Kafka. For a real-time streaming process in which the data may be persisted for a certain amount of time, it acts as a mediator between the source and the destination of the data.
Kafka vs Spark Streaming is a communications system that operates on a distributed basis. Where we are able to make advantage of the data that has persisted in the real-time process. It operates as a service on one or more servers, depending on the number.
The Kafka vs Spark Streaming database organizes the stream of records it receives into folders known as topics. Each record in the stream is made up of a key, a value, and a timestamp.
We are able to utilize the Kafka vs Spark Streaming Sink flume. In such a case, the record will be triggered if there is a CDC (Change Data Capture) or a New insert flume, and it will then push the data to a Kafka vs Spark Streaming topic.
In order to do it, we need to adjust the channel. HDFS, JDBC, and sinks are all possible options, much as with the flume and the Kafka sink. kafka streaming vs spark streaming is the greatest choice for large-scale message or stream processing applications since it offers higher throughput and capabilities such as built-in segmentation, replication, and fault-tolerance.
The term "kafka streaming vs spark streaming" refers to a client library that gives you the ability to process and analyze the data inputs that have been received from Kafka.
It then transmits the results of its processing either back to Kafka or to some other external system that has been defined. The following topics related to stream processing are used by Kafka:
- Determining the difference between the event time and the processing time in an accurate manner
- Assistance with Windowing
- Management of the application state that is both effective and easy to use
It makes the process of developing applications easier by expanding on the producer and consumer libraries that are already present in Kafka in order to harness the inherent capabilities of Kafka.
This makes the process clearer and more expedient. Kafka streaming is able to provide data parallelism, distributed coordination, fault tolerance, and operational simplicity because of the inherent potential of Kafka.
The primary application programming interface of Kafka Streaming is a domain-specific language (DSL) for stream processing that provides a number of high-level operators.
The following operators are considered to be "operators": filter, map, grouping, windowing, aggregation, joins, and the concept of tables.
Within Kafka, the messaging layer is responsible for partitioning the data that will later be stored and transmitted. In preparation for future processing, the data is partitioned inside the Kafka Streams according to the state events.
The topology is scaled by dividing it up into several jobs, where each task is given with a list of partitions (Kafka Topics) from the input stream. This provides parallelism as well as fault tolerance for the system.
In contrast to Spark Streaming, which operates on batches, Kafka operates on state transitions. It does this by storing the states inside its topics, which are then used by the stream processing applications for the purposes of data storage and querying.
As a result, the state maintains authority over all of its activities. These states are then used in the process of connecting subjects in order to produce an event task.
It is Kafka's state-based operations that make it fault-tolerant and allow for automated recovery from the local state stores. Both of these features are made possible by Kafka.
The idea of tables and Streams is used in the construction of data streams in Kafka Streaming; this enables the data streams to offer event-time processing.
Spark Streaming gives you the freedom to use whatever sort of system you want, even ones with a lambda design, as your data pipeline backbone. Spark Streaming, on the other hand, has a variable latency that may vary from milliseconds to many seconds.
Spark Streaming is the ideal choice to go for if you are seeking for flexibility in terms of the source compatibility and latency is not a key concern for you.
It is possible to operate Spark Streaming using its standalone cluster mode, on EC2, on Hadoop YARN, on Mesos, or on Kubernetes. All of these options are also available.
It is able to access data from a wide variety of data sources, including HDFS, Alluxio, Apache Cassandra, Apache HBase, and Apache Hive, among many others.
It provides fault tolerance and moreover provides Hadoop distribution. In addition, you won't need to develop distinct programs separately for batch and streaming applications if you use Spark streaming since the same system can handle both scenarios.
On the other hand, if latency is a big problem and one is required to adhere to real-time processing with time frames that are fewer than milliseconds, then you should seriously consider Kafka Streaming.
Although event-driven processing, which is what Kafka and spark streaming uses, provides increased fault tolerance, interoperability with various kinds of systems continues to be a key area of concern.
In addition, when there are significant needs for scalability, what is apache Kafka used for is the ideal solution since it is very scalable.
If you are working with an application that is native to both what is apache Kafka used for and Kafka (meaning that both the input and output data sources are in Kafka), then Kafka and spark streaming is the best option for you to go with.
The difference between Kafka streaming and spark streaming allows for code to be written in Scala, Python, and Java, in contrast to apache Kafka tutorial Streaming, which is exclusively accessible in Scala and Java.
Alongside the development of new technologies came a concurrent explosion in the amount of data that needed to be stored. The need to handle such a large amount of data together with the expanding requirement to process data in real-time has resulted in the use of data streaming.
Because there are a variety of data streaming techniques, including Kafka and spark and apache Kafka tutorial Streaming, it is vital to have a comprehensive understanding of the use case in order to choose the one that will fulfill the requirements in the most effective manner.
It is very essential to place a priority on the criteria included within the use cases before selecting the Streaming technology that is most suited to your needs.
In light of the fact that both Kafka and spark Streaming and how does kafka work Streaming are quite dependable and come highly recommended as Streaming techniques, achieving the greatest possible outcomes is primarily dependent on the use case and application.
In this post, we have highlighted the areas of expertise for both of the streaming techniques in order to provide you with a better categorization of them, which may assist you in prioritizing and deciding upon one over the other.
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