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Hadoop vs PySpark: What are the differences?

Introduction

Hadoop and PySpark are two popular frameworks used for big data processing. While both are designed for distributed processing, they have some key differences. This article aims to highlight six significant differences between Hadoop and PySpark.

  1. Programming Language: Hadoop is primarily written in Java and supports other languages like C++, Python, and Ruby through language-specific APIs. On the other hand, PySpark is a Python library that provides a Python interface for Apache Spark, which is written in Scala. PySpark allows developers to write Spark applications using Python, leveraging the simplicity and expressiveness of the Python language.

  2. Data Processing Model: Hadoop uses the MapReduce model for data processing. It processes data in two phases: map and reduce. Map tasks perform filtering and sorting, while reduce tasks aggregate and summarize the map outputs. In contrast, PySpark uses a more flexible and efficient processing model called Resilient Distributed Datasets (RDDs). RDDs are fault-tolerant and distributed collections of objects that can be processed in parallel across multiple nodes.

  3. Ease of Use: Hadoop requires extensive configuration and setup, making it more complex to install and manage. Additionally, Hadoop developers need to write code in Java, which can be harder for those who are not familiar with the language. PySpark, on the other hand, provides a user-friendly API in Python, allowing developers to write concise and readable code. This makes PySpark more accessible to developers with Python expertise.

  4. Performance: Hadoop's MapReduce model is optimized for batch processing of large datasets. It is well-suited for tasks that involve processing large amounts of data sequentially. PySpark, on the other hand, provides in-memory processing capabilities through RDDs. This allows PySpark to perform iterative and interactive data processing tasks much faster than Hadoop. PySpark's performance advantage becomes apparent when dealing with complex data analytics and machine learning workloads.

  5. Ecosystem: Hadoop has a mature and extensive ecosystem with various tools and frameworks built around it. It includes components like HDFS (Hadoop Distributed File System), Hive, Pig, and HBase, which provide additional functionalities for data storage, querying, and processing. PySpark, being a part of the Apache Spark ecosystem, benefits from a rich set of libraries and tools for big data analytics, machine learning, and graph processing. This makes PySpark a more versatile and comprehensive solution for big data processing.

  6. Real-time Processing: Hadoop's MapReduce model is not suitable for real-time data processing as it is optimized for batch processing. While there are frameworks like Apache Storm that can provide real-time capabilities on top of Hadoop, it requires additional setup and configuration. PySpark, on the other hand, supports real-time processing through its Spark Streaming module. It allows developers to process and analyze data streams in real-time, enabling applications like real-time monitoring, fraud detection, and sentiment analysis.

In Summary, Hadoop predominantly uses Java and the MapReduce model, while PySpark is a Python library built on top of Spark, offering a more accessible programming language, flexible data processing model, better performance, a comprehensive ecosystem, and real-time processing capabilities.

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    What is Hadoop?

    The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

    What is PySpark?

    It is the collaboration of Apache Spark and Python. it is a Python API for Spark that lets you harness the simplicity of Python and the power of Apache Spark in order to tame Big Data.

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    What are some alternatives to Hadoop and PySpark?
    Cassandra
    Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.
    MongoDB
    MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
    Elasticsearch
    Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).
    Splunk
    It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.
    Snowflake
    Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.
    See all alternatives