Need advice about which tool to choose?Ask the StackShare community!
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.
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.
Hi Karunakaran. I obviously have an interest here, as I work for the company, but the problem you are describing is one that Zetaris can solve. Talend is a good ETL product, and Dremio is a good data virtualization product, but the problem you are describing best fits a tool that can combine the five styles of data integration (bulk/batch data movement, data replication/data synchronization, message-oriented movement of data, data virtualization, and stream data integration). I may be wrong, but Zetaris is, to the best of my knowledge, the only product in the world that can do this. Zetaris is not a dashboarding tool - you would need to combine us with Tableau or Qlik or PowerBI (or whatever) - but Zetaris can consolidate data from any source and any location (structured, unstructured, on-prem or in the cloud) in real time to allow clients a consolidated view of whatever they want whenever they want it. Please take a look at www.zetaris.com for more information. I don't want to do a "hard sell", here, so I'll say no more! Warmest regards, Rod Beecham.
Pros of Pig
- Finer-grained control on parallelization2
- Proven at Petabyte scale1
- Open-source1
- Join optimizations for highly skewed data1