Apache Kafka is an open-source streaming platform developed by LinkedIn and donated to the Apache Software Foundation.

This project aims to provide an integrated platform, high power, and low latency for instant data.

Its storage layer is a highly scalable, engineered (pub/sub) message queue associated with distributed transactions. Apache Kafka is the open-source processing infrastructure of a stream written using the Scala and Java languages.

Kafka and Spark are among the essential tools machine learning engineers use extensively.

Apache Kafka

The project aims to provide a robust, integrated, low-latency infrastructure for the immediate manipulation of input information. Its storage layer is distributed in large quantities and scales for a server-queue architecture. Kafka is used for data processing (Stream Processing) and messaging broker (Message Broker).

In addition, Kafka allows connection to external systems (for input/output data) via Kafka Connect and provides Kafka Streams. Kafka works best for managing large volumes of data constantly being sent and not having enough time to be processed and stored. In addition, Kafka is well able to manage errors.

How to use Kafka?

The first step in using Kafka is to build a Topic. From now on, new messages can be sent via TCP connection for storage in the new Topic. This can be done easily through clients designed in different languages and for different platforms. These messages must then be stored somewhere. Kafka stores these messages in files called Logs.

New data is added to the end of the log files. Kafka can store incoming messages on a set of Kafka servers clustered together. If, for example, there are Kafka servers in a cluster, the data associated with each message sent will be copied to all supported servers after being stored on the Leader server.

However, even if n-1 servers are decommissioned, the Topic data in question will still be available and usable.

Hence, tolerance for error is well seen in Kafka.

Clients can also read information stored on Kafka. The message-consuming client, the Consumer, must subscribe to a Topic to receive messages.

With the implementation of the Poll method, the data will flow to the Consumer.

When defining a new Topic, related data can be stored in multiple partitions. In fact, Kafka stores all messages sent to a Topic in all partitions in the same order.

In this storage model, each partition is stored on a server, and the other servers in the Cluster will copy the backup of that partition. This Kafka feature allows the co-consumer to receive information in parallel

The Spark

Apache Spark is an open-source distributed computing framework. The software was originally developed by the University of California, Berkeley. The code was later donated to the Apache Software Foundation, which has maintained it ever since.

Spark provides an application programming interface for programming all clusters with parallel data parallelization and fault tolerance.

Spark stores program data in the main memory, which makes programs run faster (unlike the mapping/reduction model, which uses disk as a storage location for intermediate data).

Another factor that increases Spark’s performance is the use of the cache mechanism when using data that is to be reused in the program.

This will reduce the overhead caused by reading and writing to disk.

An algorithm to implement in the mapping/reduction model may be divided into several separate programs. Each time the data is read from the disk, it must be processed and rewritten to the disk.

However, using the cache mechanism in Spark, the data is read from the disk once, cached in the main memory, and various operations are performed on it.

Using this method also significantly reduces the overhead caused by disk communication in programs and improves performance.

What components are Spark made of?

Spark Core: The Spark Core contains Spark’s basic operations, including the components needed for task scheduling, memory management, error handling, storage system interaction, and more.

The Spark kernel also houses the development of APIs that define RDDs, which are the core concept of Spark programming.

RDDs represent a set of items that are distributed over multiple computational nodes and can be processed in parallel.

The Spark kernel provides several APIs for creating and manipulating these collections.

Spark SQL:

Spark SQL is a framework for working with structured data. This query system enables data through SQL and Apache Hyo, another type of SQL also called HQL, and supports data sources such as Hyo tables, Parquet data structures, CSV, and JSON.

In addition to providing a SQL UI for Spark, Spark SQL enables developers to combine SQL queries with data modification operations on RDDs supported in Python, Java, and Scala, and integrate SQL queries with complex analytics in one application…

This close coherence with the processing environment provided by Spark sets Spark SQL apart from other open-source data warehousing tools.

Spark Streaming:

The Spark Streaming data processing component is one of Spark’s components that provides data stream processing. Examples of data streams include log files created by web servers or messages containing status updates sent by web service users or on social networks, such as posts.

This component provides APIs for modifying data streams that are compatible with the APDs for RDDs in the Spark Core. This facilitates application Development and switches between applications that store data in main memory or on disk. The actual process can be. In the Development architecture of these APIs, to have fault tolerance, high productivity, and scalability, as in the Spark core component, attention has been paid to the points related to the Development of distributed systems.

MLlib:

Spark has a library of machine learning (ML) APIs called MLlib. MLlib offers a variety of machine learning algorithms, including classification, regression analysis, clustering, and group refinement, and supports features such as model evaluation and data entry.

MLlib also provides low-level machine learning structures, such as descending gradient optimization algorithms. These methods are designed to run programs at the Spark cluster level.

GraphX:

GraphX ​​is a library for processing graphs and performing parallel processing on graph data. Like the Spark Streaming and Spark SQL components, GraphX develops RDD APIs and enables us to create directional graphs by assigning specifications to each node and edge. GraphX ​​also provides various operators for changing graphs (such as subgraph and map vertices) and a library of graph algorithms (such as PageRank and counting graph triangles).