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Apache Spark is a super-fast, open-source, unified analytics engine that’s used for large-scale analytics and implanting data engineering, data science, artificial intelligence applications, and machine learning on single-node machines and clusters. It can run against diverse data sources. It is known to deliver three important requirements for big data – programmability, scalability, and computational speed.
Apache Spark started in 2009 as a research initiative at the AMPLab at UC Berkeley. Spark was developed to solve the challenges experienced while using Hadoop MapReduce.
The focus was to support faster, iterative tasks like machine learning and interactive analytics – while keeping the benefits of Hadoop, like handling large quantities of data and being fault-tolerant. Apache can run on its own on Apache Mesos, or most frequently, on Apache Hadoop.
How Apache Spark Works
Apache Spark follows a hierarchical architecture that’s based on a primary and secondary node setup. At the center you’ll find the Spark Driver which is the primary node. It interacts with the Cluster Manager to handle resource allocation and distributes tasks to the secondary nodes in the cluster.
Once the processing is done, the driver will send the results to the client application. Whenever any Spark application runs, the driver initiates SparkContext. It interacts with the cluster manager to distribute tasks and track their execution across worker nodes.
How Apache Spark solves the limitations in Hadoop MapReduce
As mentioned earlier, Apache Spark was built to overcome the traditional challenges of MapReduce systems. Instead of writing data to disk after every step, Spark will process everything in memory. Result – super fast data processing!
Spark will perform jobs in just one step. It will load the data into memory, perform operations, and write results. One of Spark’s core strengths is its ability to reuse data. It uses Resilient Distributed Datasets (RDDs) which are fault-tolerant collections of data elements across a cluster.
Now, the DataFrames and Datasets built on RDDs make it easier to work with structured data and allow caching and reusing data across operations.
Many machine learning algorithms run multiple operations over the same data and Apache’s Spark caching and in-memory model makes these processes faster than traditional disk-based engines. Hence, we can say that Apache Spark is optimized for machine learning and analytics.
Apache Spark Components
Spark Core
Foundation of Apache Spark that enables distributed task scheduling, execution, and basic I/O operations
Spark SQL
Module for handling structured data using either SQL syntax or the DataFrame API
Spark Streaming
Supports real-time, fault-tolerant stream processing with the same intuitive API used for batch data
MLlib
A scalable machine learning library offering algorithms and tools to build and deploy end-to-end ML solutions
GraphX
A graph analytics framework that enables in-memory processing of graph data alongside traditional data sets
Apache Spark Benefits
Simplified Processing
100+ built-in operators for processing and transforming large datasets
All-in-One Engine
Standard libraries for SQL queries, streaming data, machine learning & graph processing to build complex workflows
Multi-Language Support
Developers can use Java, Scala, R, and Python for developing applications
High-Speed Performance
Delivers up to 100x faster processing than Hadoop by leveraging in-memory computing
