In recent years, the phrase “big data” has gained popularity in a variety of industries throughout the world. Regardless of what industry you work in or the size of your firm, the growing volume and complexity of big data necessitate data collection, data analytics, and data comprehension. When you have the right big data processing tools at your disposal, transforming raw data into a form that helps companies make better decisions becomes seamless. This is why it’s so important to have effective data processing tools. There are a variety of big data technologies on the market, including Apache Kafka, Apache Spark, Flink, Apache Storm, and others.
This article will focus on two prominent tools viz., Apache Kafka and Apache Spark.
Table of Contents
- What is Apache Kafka?
- What is Kafka Workflow?
- What is Apache Spark?
- What is Spark Workflow?
- Understanding Apache Kafka and Apache Spark Differences
- Basic understanding of Big Data.
What is Apache Kafka?
Kafka is a distributed, partitioned, and replicated log service that is available as an open-source streaming platform. Created by LinkedIn and later acquired by the Apache Foundation, Kafka has its messaging system. It is a platform that manages real-time data streams with low latency and high throughput.
Apache Kafka, which is written in Scala, facilitates pulling in data from a wide range of sources and storing it in the form of “topics” by processing the data stream. These topic messages can be stored for long periods by apps that can reprocess them to give them useful insights.
Advantages of Apache Kafka
Compared to traditional Message Brokers, Kafka has several advantages.
- It provides for load de-bulking because no indexes are required for the message.
- It improves stream efficiency and eliminates buffering for end-users.
- Without deleting any data, all data logs are maintained with a punched time. As a result, the danger of data loss is reduced.
What is Kafka Workflow?
Kafka is a popular publish-subscribe messaging system that can handle large amounts of data and manage both offline and online communications. Message producers are known as publishers, while message consumers are known as subscribers in the publish-subscribe system. Messages are exchanged in this domain through a destination known as a topic. A publisher creates messages for a topic, while subscribers who have subscribed to the topic consume the messages. This technology enables message broadcasting (having more than one subscriber, and each gets a copy of the messages published to a particular topic). Kafka messages are stored on a disk and replicated throughout the broker cluster to prevent data loss.
Further, Apache Kafka employs the distributed messaging paradigm, which entails non-synchronous message queuing between messaging systems and applications. Kafka allows you to transport messages from one end-point to another and is suitable for both online and offline message consumption. It has a strong queue capable of managing a large volume of data. For this, Kafka requires the assistance of a zookeeper, which stores metadata about the brokers in the cluster. It determines whether brokers have crashed or have just been introduced to the cluster and monitors their lifecycle.
Kafka also offers a queue-based messaging system that is quick, efficient, resilient, fault-tolerant, and has minimal downtime. Several consumers with the same group ID can subscribe to a subject in a queue messaging system. They are treated as a single unit and share the same messages. The system’s process is as follows:
At regular intervals, Kafka producers transmit messages to a topic. By storing messages in partitions defined for a certain subject, Kafka brokers guarantee that messages are distributed evenly among the partitions.
A single customer becomes a subscriber to a single topic. Until a new consumer subscribes to the same topic, Kafka communicates with the consumer in the same way as Pub-Sub Messaging. When a new consumer is introduced, Kafka switches to share mode and divides the data between the two. This sharing continues until the number of consumers reaches the defined number of partitions for that subject.
When the number of consumers exceeds the number of specified partitions, a new consumer will not receive any further messages. This predicament develops as a result of the need that each consumer has a minimum of one partition, and if no partition is available, new customers must wait.
What is Apache Spark?
Apache Spark is a cluster computing framework that is free and open-source. It’s a data processing solution for dealing with large data workloads and data collections. It can swiftly handle massive data volumes and divide jobs across several systems to reduce workload. With the assistance of its DAG scheduler, query optimizer, and engine, Spark is recognized for its high-performance quality for batch and streaming data processing.
Matei Zaharia, who is regarded as the inventor of Apache Spark, began developing it as an open-source research project at UC Berkeley’s AMPLab. It was later designated as an Apache Software Foundation incubated project in 2013.
Advantages of Apache Spark
Advantages of Spark include:
- Spark can leverage Hadoop’s cluster management (YARN) and underlying storage to run as a single-engine (HDFS, HBase, etc.). It can, moreover, work independently of Hadoop, collaborating with other cluster administrators and storage solutions (the likes of Cassandra and Amazon S3).
- Spark can help with advanced analytics like machine learning and graph processing. It comes with amazing libraries, like SQL & DataFrames and MLlib (for machine learning), GraphX, and Spark Streaming, that aid enterprises solve complex data issues with ease. Spark further improves analytics performance by storing data in the RAM of the servers, which is quickly accessible.
What is Spark Workflow?
The Spark architecture is based on RDDs (Resilient Distributed Datasets) and DAG (Directed Acyclic Graph). RDDs are collections of data objects that are partitioned and could be stored in memory on the Spark cluster’s worker nodes. Apache Spark supports two types of RDDs in terms of datasets: Hadoop Datasets, which are built from HDFS files, and parallelized collections, which are based on existing Scala collections. A DAG is a set of data-processing operations in which each node represents an RDD division, and each edge represents a data transformation. The DAG abstraction eliminates Hadoop’s multi-stage MapReduce execution model and improves performance over Hadoop.
Apache Spark leverages master/slave architecture which comprises one central coordinator (driver) and many distributed workers. When you make a Spark request, a driver program runs and seeks resources from the cluster manager while also launching the main program of the user function of the user processing program.
The execution logic is then processed, and a Spark context is built in parallel. The various transformations and actions are processed using the Spark context. So, until the action is encountered, all transformations will be stored in the Spark context as a DAG, which will establish RDD lineage.
After the action is called, a job is created. Here, “job” refers to a collection of several work phases. After these tasks are formed, the cluster manager launches them on the worker nodes with the aid of a class called task scheduler.
The DAG scheduler is in charge of converting RDD lineage into tasks. When the action is called, the DAG is constructed based on the different transformations in the program, and these are broken into distinct stages of tasks and sent to the task scheduler when tasks become ready.
The cluster manager then launches these on the various executors of the worker node. The job and task monitoring, as well as the allocation of resources, are all handled by the cluster manager.
When you submit a Spark request, your user program and any other configuration you specify are transferred to all of the cluster’s accessible nodes. As a result, on all worker nodes, the program becomes the local read. As a result, the parallel executors on the various worker nodes do not need to execute any network routing.
Simplify Kafka ETL with Hevo’s No-code Data Pipeline
A fully managed No-code Data Pipeline platform like Hevo helps you integrate data from 100+ data sources (including 40+ Free Data Sources) like Kafka to a destination of your choice in real-time in an effortless manner. Hevo with its minimal learning curve can be set up in just a few minutes allowing the users to load data without having to compromise performance. Its strong integration with umpteenth sources provides users with the flexibility to bring in data of different kinds, in a smooth fashion without having to code a single line.GET STARTED WITH HEVO FOR FREE
Check Out Some of the Cool Features of Hevo:
- Completely Automated: The Hevo platform can be set up in just a few minutes and requires minimal maintenance.
- Real-Time Data Transfer: Hevo provides real-time data migration, so you can have analysis-ready data always.
- Connectors: Hevo supports 100+ Integrations to SaaS platforms, files, databases, analytics, and BI tools. It supports various destinations including Amazon Redshift, Firebolt, Snowflake Data Warehouses; Databricks, Amazon S3 Data Lakes, Kafka, SQL Server, TokuDB, DynamoDB, PostgreSQL databases to name a few.
- 100% Complete & Accurate Data Transfer: Hevo’s robust infrastructure ensures reliable data transfer with zero data loss.
- Scalable Infrastructure: Hevo has in-built integrations for 100+ sources, that can help you scale your data infrastructure as required.
- 24/7 Live Support: The Hevo team is available round the clock to extend exceptional support to you through chat, email, and support calls.
- Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
- Live Monitoring: Hevo allows you to monitor the data flow so you can check where your data is at a particular point in time.
Simplify your Data Analysis with Hevo today!SIGN UP HERE FOR A 14-DAY FREE TRIAL!
Understanding Apache Kafka and Apache Spark Differences
Here are the key differences between Apache Kafka vs Spark:
- Apache Kafka vs Spark: ETL
- Apache Kafka vs Spark: Latency
- Apache Kafka vs Spark: Recovery
- Apache Kafka vs Spark: Processing Type
- Apache Kafka vs Spark: Programming Languages Supported
Apache Kafka vs Spark: ETL
As Spark allows users to pull the data, hold it, process and push from source to target, it enables ETL process. However, Kafka does not offer exclusive ETL services. Instead, it relies on Kafka Connect API and the Kafka Streams API for the building of streaming data pipelines from source to destination. Through the Kafka Connect API, Kafka enables the creation of streaming data pipelines (the E and L in ETL). The Connect API makes use of Kafka’s scalability, builds on Kafka’s fault-tolerance design, and provides a unified way to monitor all connections. The Kafka Streams API, which offers the T in ETL, can be used to implement stream processing and transformations.
Apache Kafka vs Spark: Latency
If latency isn’t an issue (compared to Kafka) and you want source flexibility with compatibility, Spark is the better option. However, if latency is a major concern and real-time processing with time frames shorter than milliseconds is required, Kafka is the best choice. Because of its event-driven processing, Kafka provides superior fault tolerance. However, its compatibility with other types of systems can seem quite complicated.
Apache Kafka vs Spark: Recovery
Real-time stream processing systems must be available 24 hours a day, seven days a week, which necessitates the ability to recover from a variety of system faults. Apache Spark can withstand worker node failures in your cluster thanks to Sparks RDDs, preventing data loss. All transformations and actions are continually saved, allowing you to retry all of these stages in the event of a failure and achieve identical outcomes. Meanwhile, Kafka provides data replication inside the cluster for recovery, which entails duplicating and distributing your data often to other Servers or Brokers. If one of the Kafka servers goes down, the data will be available on other servers, which you may access easily.
Apache Kafka vs Spark: Processing Type
Kafka analyses the events as they unfold. As a result, it employs a continuous (event-at-a-time) processing model. Spark, on the other hand, uses a micro-batch processing approach, which divides incoming streams into small batches for processing.
Apache Kafka vs Spark: Programming Languages Support
While no programming language is supported by Kafka for data transformation, Spark supports a variety of programming languages and frameworks. This means that Apache Spark can do more than just interpret data because it can employ existing machine learning frameworks and process graphs.
This article introduces two of Apache’s most popular big data processing tools, Apache Kafka and Apache Spark. It gives you an overview of their advantages, workflows, and fundamental distinctions to assist you in making better decisions and processing information according to varying needs before diving into Apache Kafka vs Spark.
Extracting complex data from a diverse set of data sources to carry out an insightful analysis can be challenging, and this is where Hevo saves the day! Hevo offers a faster way to move data from Databases or SaaS applications like Kafka into your Data Warehouse to be visualized in a BI tool. Hevo is fully automated and hence does not require you to code.VISIT OUR WEBSITE TO EXPLORE HEVO
Want to take Hevo for a spin?