What is the Difference Between Hadoop and Spark?

What is Hadoop? Apache Hadoop is a free and open-source software tool that enables users to handle large data sets (from gigabytes to petabytes) by allowing a network of computers (or "nodes") to solve huge and complex data issues. It's a highly scalable, low-cost system for storing and processing structured, semi-structured, and unstructured data.

What is Apache Spark?

Apache Spark is an open-source data processing engine for large data sets. Spark, like Hadoop, distributes massive jobs over several nodes. However, it is quicker than Hadoop and employs random access memory (RAM) to cache and analyze data rather than a file system. Spark can now handle use situations that Hadoop cannot.

Hadoop offers sophisticated analytics for stored data. It permits the division of large data analytics processing jobs into smaller ones. The little jobs are completed in parallel using an algorithm (e.g., MapReduce) and spread throughout a Hadoop cluster.

Four Major Components of the Hadoop Ecosystem

● Hadoop Distributed File System (HDFS): A primary data storage system that runs on commodity hardware and manages enormous data collections. It also has great failure tolerance and fast data throughput.
● Yet Another Resource Negotiator (YARN): A resource manager for a cluster that schedules tasks and distributes resources (such as CPU and memory) to applications.
● Hadoop MapReduce divides large data processing jobs into smaller ones, spreads the little tasks over several nodes, and then performs each task.
● Hadoop Core (Hadoop Common): The set of shared libraries and tools on which the previous three modules rely.

Apache Spark, the largest open-source data processing project, is the only processing framework that blends data with artificial intelligence (AI). This allows users to do large-scale data transformations and analysis before running cutting-edge machine learning (ML) and AI algorithms.

Five Main Modules of the Spark Ecosystem

● Spark Core: The underlying execution engine that organizes and dispatches jobs as well as coordinates input and output (I/O).
● Spark SQL: Gathers structured data information to help users optimize structured data processing. 
● Spark Streaming and Structured Streaming: Both provide features for stream processing. Spark Streaming separates data from several streaming sources into micro-batches for a continuous stream. Structured Streaming, which is based on Spark SQL, decreases latency while simplifying programming.
● Machine Learning Library (MLlib): A collection of scalable machine learning algorithms and tools for feature selection and developing ML pipelines. The major API for MLlib is DataFrames, which enables consistency across programming languages such as Scala, Java, and Python.
● GraphX: A simple compute engine that allows for the interactive creation, editing, and evaluation of scalable, graph-structured data.

Hadoop and Spark Comparison

Spark is a MapReduce improvement for Hadoop. The key distinction between Spark and MapReduce is that Spark analyzes and stores data in memory for later use, whereas MapReduce processes data on disk. As a result, Spark's data processing rates are up to 100x quicker than MapReduce's for lesser workloads.

Furthermore, unlike MapReduce's two-stage execution procedure, Spark uses a Directed Acyclic Graph (DAG) to plan jobs and orchestrate nodes throughout the Hadoop cluster. This task-tracking mechanism allows for fault tolerance by reapplying recorded activities to data from a previous state.

Let's Look at their Main Distinctions: Hadoop vs. Spark:

Spark is quicker because it stores intermediate data in random access memory (RAM) rather than reading and writing it to disks. Hadoop collects data from many sources and processes it in batches using MapReduce.

Cost: Hadoop is less expensive to run since it uses any sort of disk storage for data processing. Spark is more expensive to run since it relies on in-memory calculations for real-time data processing, which necessitates the utilization of large amounts of RAM to spin up nodes.

Though both technologies analyze data in a distributed setting, Hadoop excels at batch and linear data processing. Spark is suited for real-time analysis of unstructured data streams.

Scalability: When the volume of data increases rapidly, Hadoop swiftly expands to meet the demand using the Hadoop Distributed File System (HDFS). For big amounts of data, Spark relies on fault-tolerant HDFS.

Security: Authentication by shared secret or event logging improves security in Spark, whereas Hadoop employs several authentication and access control techniques. Though Hadoop is safer overall, Spark can interface with Hadoop to get a greater degree of security.

Machine learning (ML): Because it incorporates MLlib, which does iterative in-memory ML calculations, Spark is the best platform in this area. It also offers tools for regression, classification, persistence, pipeline building, and assessment, among other things.