Overview

Pig

Stacks57

Followers111

Votes5

GitHub Stars686

Forks447

Amazon Athena

Stacks519

Followers840

Votes49

Amazon Athena vs Pig: What are the differences?

Introduction

Amazon Athena and Pig are two popular tools used in big data analytics and processing. While both tools serve similar purposes, there are key differences between them in terms of functionality and usage. In this article, we will explore these differences and understand when to leverage each tool based on specific requirements.

Data Querying Language: Amazon Athena is built on the Presto distributed SQL engine, and it uses a SQL-like query language for data querying and analysis. On the other hand, Pig is a high-level procedural language, known as Pig Latin, which allows users to write complex data transformation and analysis tasks using a set of predefined operators.
Data Processing Paradigm: Amazon Athena operates on the data in a serverless manner by running SQL queries directly on the data stored in Amazon S3. It is a query-based service that allows users to interactively analyze data and get results quickly. In contrast, Pig operates on data using a batch processing paradigm, where users need to specify the entire data processing pipeline before executing it.
Scalability and Performance: Amazon Athena is a fully managed service that automatically scales the underlying compute resources based on the query workload. It can handle large datasets and complex queries efficiently, providing fast results. Pig, on the other hand, relies on the Apache Hadoop ecosystem for distributed processing and can be scaled based on the available resources in the cluster. It requires users to optimize the Pig scripts for improved performance.
Integration with Ecosystem: Amazon Athena seamlessly integrates with other AWS services, such as Amazon S3 for data storage, AWS Glue for metadata cataloging, and Amazon QuickSight for data visualization. It provides a unified experience with the entire AWS ecosystem. Pig is part of the Apache Hadoop ecosystem and integrates well with other Hadoop components, such as HDFS, YARN, and Hive, enabling users to leverage the entire Hadoop stack.
Ease of Use and Learning Curve: Amazon Athena is designed to provide a user-friendly interface for running SQL queries on data without the need for infrastructure management. Users familiar with SQL can quickly start using Athena for data analysis. Pig, on the other hand, requires users to learn Pig Latin, a specialized scripting language, and understand the concepts of the Hadoop ecosystem, which may have a steeper learning curve for beginners.
Flexibility and Extensibility: Amazon Athena allows users to write complex SQL queries with support for various built-in functions, aggregations, and joins. It also provides custom functions using the Presto function interface. Pig offers a wide range of built-in operators and functions that can be used for complex data transformations. Additionally, Pig provides the flexibility to write user-defined functions (UDFs) in Java, allowing users to extend its functionality to suit specific requirements.

In summary, Amazon Athena and Pig differ in their data querying language, data processing paradigm, scalability, integration with the ecosystem, ease of use, and flexibility. While Amazon Athena is a serverless SQL-based query service for interactive analysis, Pig is a high-level procedural language for batch data processing in the Hadoop ecosystem.

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Advice on Pig, Amazon Athena

Aditya

Mar 13, 2021

Review

you can use aws glue service to convert you pipe format data to parquet format , and thus you can achieve data compression . Now you should choose Redshift to copy your data as it is very huge. To manage your data, you should partition your data in S3 bucket and also divide your data across the redshift cluster

220k views220k

Comments

Kevin

Co-founder at Transloadit

Dec 18, 2020

Review

Hey there, the trick to keeping costs under control is to partition. This means you split up your source files by date, and also query within dates, so that Athena only scans the few files necessary for those dates. I hope that makes sense (and I also hope I understood your question right). This article explains better https://aws.amazon.com/blogs/big-data/analyze-your-amazon-cloudfront-access-logs-at-scale/.

5.07k views5.07k

Comments

Pavithra

Mar 12, 2020

Needs adviceon

Amazon S3

Amazon Athena

Amazon Redshift

Hi all,

Currently, we need to ingest the data from Amazon S3 to DB either Amazon Athena or Amazon Redshift. But the problem with the data is, it is in .PSV (pipe separated values) format and the size is also above 200 GB. The query performance of the timeout in Athena/Redshift is not up to the mark, too slow while compared to Google BigQuery. How would I optimize the performance and query result time? Can anyone please help me out?

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Comments

Detailed Comparison

Pig	Amazon Athena
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.	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.
Statistics
GitHub Stars 686	GitHub Stars -
GitHub Forks 447	GitHub Forks -
Stacks 57	Stacks 519
Followers 111	Followers 840
Votes 5	Votes 49
Pros & Cons
Pros 2 Finer-grained control on parallelization 1 Open-source 1 Join optimizations for highly skewed data 1 Proven at Petabyte scale	Pros 16 Use SQL to analyze CSV files 8 Glue crawlers gives easy Data catalogue 7 Cheap 6 Query all my data without running servers 24x7 4 No data base servers yay
Integrations
No integrations available	Amazon S3 Presto

What are some alternatives to Pig, Amazon Athena?

Apache Spark

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.

Presto

Distributed SQL Query Engine for Big Data

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.