Pig vs Talend

Overview

Pig

Stacks57

Followers111

Votes5

GitHub Stars686

Forks447

Talend

Stacks297

Followers249

Votes0

Pig vs Talend: What are the differences?

Key Differences between Pig and Talend

Language and Approach: Pig is a high-level platform for expressing data analysis programs that are made up of series of data transformations whereas Talend is an open-source integration tool that provides a unified set of products for data integration and management. Pig uses a language called Pig Latin, which is similar to SQL, while Talend combines data integration, data quality, and metadata management in a single platform.
Data Processing: Pig is specifically designed for processing large datasets in a parallel, distributed environment like Hadoop, allowing users to handle big data tasks efficiently. On the other hand, Talend is more versatile in terms of data processing capabilities as it can connect to various data sources, not limited to big data environments.
Ease of Use: Pig requires users to have some coding knowledge as it involves writing scripts in Pig Latin, making it more suitable for programmers and individuals familiar with scripting languages. In contrast, Talend comes with a graphical interface which enables users to design data integration jobs through a drag-and-drop interface, making it more user-friendly for non-programmers.
API Support: Pig provides APIs for Java and Python, allowing developers to extend its functionality by writing custom UDFs (User Defined Functions) in their preferred programming language. Meanwhile, Talend offers a wide range of connectors and components that support various APIs for integration with different systems and technologies.
Scalability and Performance: Pig is optimized for processing large-scale data sets efficiently in a distributed environment, ensuring scalability and high performance for big data tasks. Talend also supports scalability but may require additional configurations to handle large data volumes effectively.
Community and Support: Pig has a more niche community compared to Talend, which has a larger user base and active community support. Talend provides documentation, forums, and training resources, making it easier for users to learn and troubleshoot issues with the platform.

In Summary, Pig and Talend differ in their language and approach, data processing capabilities, ease of use, API support, scalability, and community support.

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Advice on Pig, Talend

karunakaran

Consultant

Jun 26, 2020

Needs advice

I am trying to build a data lake by pulling data from multiple data sources ( custom-built tools, excel files, CSV files, etc) and use the data lake to generate dashboards.

My question is which is the best tool to do the following:

Create pipelines to ingest the data from multiple sources into the data lake
Help me in aggregating and filtering data available in the data lake.
Create new reports by combining different data elements from the data lake.

I need to use only open-source tools for this activity.

I appreciate your valuable inputs and suggestions. Thanks in Advance.

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Detailed Comparison

Pig	Talend
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.	It is an open source software integration platform helps you in effortlessly turning data into business insights. It uses native code generation that lets you run your data pipelines seamlessly across all cloud providers and get optimized performance on all platforms.
Statistics
GitHub Stars 686	GitHub Stars -
GitHub Forks 447	GitHub Forks -
Stacks 57	Stacks 297
Followers 111	Followers 249
Votes 5	Votes 0
Pros & Cons
Pros 2 Finer-grained control on parallelization 1 Open-source 1 Join optimizations for highly skewed data 1 Proven at Petabyte scale	No community feedback yet

What are some alternatives to Pig, Talend?

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

Pig vs Talend: What are the differences?

Key Differences between Pig and Talend

Language and Approach: Pig is a high-level platform for expressing data analysis programs that are made up of series of data transformations whereas Talend is an open-source integration tool that provides a unified set of products for data integration and management. Pig uses a language called Pig Latin, which is similar to SQL, while Talend combines data integration, data quality, and metadata management in a single platform.
Data Processing: Pig is specifically designed for processing large datasets in a parallel, distributed environment like Hadoop, allowing users to handle big data tasks efficiently. On the other hand, Talend is more versatile in terms of data processing capabilities as it can connect to various data sources, not limited to big data environments.
Ease of Use: Pig requires users to have some coding knowledge as it involves writing scripts in Pig Latin, making it more suitable for programmers and individuals familiar with scripting languages. In contrast, Talend comes with a graphical interface which enables users to design data integration jobs through a drag-and-drop interface, making it more user-friendly for non-programmers.
API Support: Pig provides APIs for Java and Python, allowing developers to extend its functionality by writing custom UDFs (User Defined Functions) in their preferred programming language. Meanwhile, Talend offers a wide range of connectors and components that support various APIs for integration with different systems and technologies.
Scalability and Performance: Pig is optimized for processing large-scale data sets efficiently in a distributed environment, ensuring scalability and high performance for big data tasks. Talend also supports scalability but may require additional configurations to handle large data volumes effectively.
Community and Support: Pig has a more niche community compared to Talend, which has a larger user base and active community support. Talend provides documentation, forums, and training resources, making it easier for users to learn and troubleshoot issues with the platform.

In Summary, Pig and Talend differ in their language and approach, data processing capabilities, ease of use, API support, scalability, and community support.

Pig vs Talend

Overview

Pig vs Talend: What are the differences?