Apache Spark vs Pig

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Pig

Stacks57

Followers111

Votes5

GitHub Stars686

Forks447

Apache Spark vs Pig: What are the differences?

Introduction

Apache Spark and Pig are both big data processing frameworks used in the Hadoop ecosystem. They offer similar functionalities but also have some key differences. In this article, we will explore these differences in detail.

Execution Engine: Apache Spark uses a general-purpose cluster computing framework, whereas Pig uses a scripting language called Pig Latin that is executed using a two-step process - compilation and execution. Spark's execution engine is more optimized and faster compared to Pig's two-step process.
Data Processing Model: Spark provides a distributed computing model called Resilient Distributed Datasets (RDDs) that allows in-memory processing, making it significantly faster than Pig. Pig, on the other hand, uses a data flow model, which is easy to understand and write, but it doesn't optimize for in-memory processing like Spark.
Language: Spark supports multiple programming languages like Scala, Java, Python, and R, making it more flexible for developers. Pig, on the other hand, only supports its own scripting language called Pig Latin. This limitation can be a disadvantage if developers are not familiar with Pig Latin.
Ease of Use: Spark provides a high-level API that makes it easy to write complex data processing pipelines. It also has built-in libraries for machine learning and graph processing. Pig, on the other hand, requires writing scripts in Pig Latin, which can be more difficult for beginners or developers who are not familiar with the language.
Optimization: Spark has a built-in optimizer that automatically optimizes the execution plan based on the data and operations performed. Pig, on the other hand, relies on the Pig Latin compiler to optimize the execution plan. As a result, Spark tends to have better performance and faster execution time compared to Pig.
Integration: Spark integrates well with other big data technologies like Hadoop, Hive, and HBase. It can read and write data directly from/to these systems. Pig also integrates with Hadoop ecosystem components but requires additional steps like loading and storing data using scripts.

In summary, Apache Spark has a more optimized execution engine, supports multiple programming languages, offers a high-level API, and provides better performance compared to Pig. Pig, on the other hand, has a simpler data flow model and easier syntax for beginners.

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Advice on Apache Spark, Pig

Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceon

Elasticsearch

Kafka

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

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Detailed Comparison

Apache Spark	Pig
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.	Pig is a dataflow programming environment for processing very large files. Pig's language is called Pig Latin. A Pig Latin program consists of a directed acyclic graph where each node represents an operation that transforms data. Operations are of two flavors: (1) relational-algebra style operations such as join, filter, project; (2) functional-programming style operators such as map, reduce.
Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk;Write applications quickly in Java, Scala or Python;Combine SQL, streaming, and complex analytics;Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3	-
Statistics
GitHub Stars 42.2K	GitHub Stars 686
GitHub Forks 28.9K	GitHub Forks 447
Stacks 3.1K	Stacks 57
Followers 3.5K	Followers 111
Votes 140	Votes 5
Pros & Cons
Pros 61 Open-source 48 Fast and Flexible 8 One platform for every big data problem 8 Great for distributed SQL like applications 6 Easy to install and to use Cons 4 Speed	Pros 2 Finer-grained control on parallelization 1 Open-source 1 Proven at Petabyte scale 1 Join optimizations for highly skewed data

What are some alternatives to Apache Spark, Pig?

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

Vertica

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

Azure Synapse

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

Related Comparisons

Apache Spark vs Pig: What are the differences?

Introduction

Execution Engine: Apache Spark uses a general-purpose cluster computing framework, whereas Pig uses a scripting language called Pig Latin that is executed using a two-step process - compilation and execution. Spark's execution engine is more optimized and faster compared to Pig's two-step process.
Data Processing Model: Spark provides a distributed computing model called Resilient Distributed Datasets (RDDs) that allows in-memory processing, making it significantly faster than Pig. Pig, on the other hand, uses a data flow model, which is easy to understand and write, but it doesn't optimize for in-memory processing like Spark.
Language: Spark supports multiple programming languages like Scala, Java, Python, and R, making it more flexible for developers. Pig, on the other hand, only supports its own scripting language called Pig Latin. This limitation can be a disadvantage if developers are not familiar with Pig Latin.
Ease of Use: Spark provides a high-level API that makes it easy to write complex data processing pipelines. It also has built-in libraries for machine learning and graph processing. Pig, on the other hand, requires writing scripts in Pig Latin, which can be more difficult for beginners or developers who are not familiar with the language.
Optimization: Spark has a built-in optimizer that automatically optimizes the execution plan based on the data and operations performed. Pig, on the other hand, relies on the Pig Latin compiler to optimize the execution plan. As a result, Spark tends to have better performance and faster execution time compared to Pig.
Integration: Spark integrates well with other big data technologies like Hadoop, Hive, and HBase. It can read and write data directly from/to these systems. Pig also integrates with Hadoop ecosystem components but requires additional steps like loading and storing data using scripts.

Apache Spark vs Pig

Overview

Apache Spark vs Pig: What are the differences?