AWS Glue vs Pig

Overview

Pig

Stacks57

Followers111

Votes5

GitHub Stars686

Forks447

AWS Glue

Stacks461

Followers819

Votes9

AWS Glue vs Pig: What are the differences?

Introduction: When it comes to data processing in the cloud, AWS Glue and Pig are both popular tools. However, they have distinct differences in terms of their functionalities and use cases.

1. Data Processing Paradigm: AWS Glue is a managed ETL service that offers a serverless data integration solution, making it easier to extract, transform, and load data. On the other hand, Pig is a high-level platform for creating MapReduce programs and runs on the Apache Hadoop platform, allowing users to process large datasets efficiently.

2. Programming Language: AWS Glue uses PySpark, which is a high-level API for Apache Spark written in Python, enabling developers to write ETL jobs using Python scripts. In contrast, Pig uses its own scripting language called Pig Latin, designed to simplify the process of writing complex data processing tasks.

3. Data Catalog: AWS Glue provides a centralized metadata repository where users can store, search, and access metadata for all the data assets in their AWS account. Pig does not have a built-in data catalog, requiring users to manage metadata manually or use external tools for metadata management.

4. Scalability: AWS Glue automatically scales resources based on the workload, allowing users to process vast amounts of data efficiently without worrying about infrastructure management. While Pig can also scale to handle large datasets, users may need to manually configure the cluster size and resources for optimal performance.

5. Integration with AWS Services: AWS Glue seamlessly integrates with other AWS services such as Amazon S3, Amazon Redshift, and Amazon RDS, making it easy to extract and load data from these services. Pig, on the other hand, can integrate with AWS services but may require additional configuration and setup for seamless data transfer.

6. Real-time Processing: AWS Glue supports real-time data processing through integration with Apache Kafka, enabling users to stream and process data in real-time. Pig is primarily designed for batch processing and may not be as well-suited for real-time processing without additional tools or configurations.

In Summary, AWS Glue is a managed ETL service with native support for Python scripting, automatic scaling, and seamless integration with AWS services, while Pig is a platform for creating MapReduce programs using Pig Latin, requiring manual scalability and lacking built-in data catalog capabilities.

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Manual

Advice on Pig, AWS Glue

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

Vamshi

Data Engineer at Tata Consultancy Services

May 29, 2020

Needs adviceon

PySpark

Azure Data Factory

Databricks

I have to collect different data from multiple sources and store them in a single cloud location. Then perform cleaning and transforming using PySpark, and push the end results to other applications like reporting tools, etc. What would be the best solution? I can only think of Azure Data Factory + Databricks. Are there any alternatives to #AWS services + Databricks?

269k views269k

Comments

datocrats-org

Jul 29, 2020

Needs adviceon

Amazon EC2

Tableau

PowerBI

We need to perform ETL from several databases into a data warehouse or data lake. We want to

keep raw and transformed data available to users to draft their own queries efficiently
give users the ability to give custom permissions and SSO
move between open-source on-premises development and cloud-based production environments

We want to use inexpensive Amazon EC2 instances only on medium-sized data set 16GB to 32GB feeding into Tableau Server or PowerBI for reporting and data analysis purposes.

319k views319k

Comments

Detailed Comparison

Pig	AWS Glue
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.	A fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics.
-	Easy - AWS Glue automates much of the effort in building, maintaining, and running ETL jobs. AWS Glue crawls your data sources, identifies data formats, and suggests schemas and transformations. AWS Glue automatically generates the code to execute your data transformations and loading processes.; Integrated - AWS Glue is integrated across a wide range of AWS services.; Serverless - AWS Glue is serverless. There is no infrastructure to provision or manage. AWS Glue handles provisioning, configuration, and scaling of the resources required to run your ETL jobs on a fully managed, scale-out Apache Spark environment. You pay only for the resources used while your jobs are running.; Developer Friendly - AWS Glue generates ETL code that is customizable, reusable, and portable, using familiar technology - Scala, Python, and Apache Spark. You can also import custom readers, writers and transformations into your Glue ETL code. Since the code AWS Glue generates is based on open frameworks, there is no lock-in. You can use it anywhere.
Statistics
GitHub Stars 686	GitHub Stars -
GitHub Forks 447	GitHub Forks -
Stacks 57	Stacks 461
Followers 111	Followers 819
Votes 5	Votes 9
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 9 Managed Hive Metastore
Integrations
No integrations available	Amazon Redshift Amazon S3 Amazon RDS Amazon Athena MySQL Microsoft SQL Server Amazon EMR Amazon Aurora Oracle Amazon RDS for PostgreSQL

What are some alternatives to Pig, AWS Glue?

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

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.

Related Comparisons

AWS Glue vs Pig: What are the differences?

Introduction: When it comes to data processing in the cloud, AWS Glue and Pig are both popular tools. However, they have distinct differences in terms of their functionalities and use cases.

AWS Glue vs Pig

Overview