Datameer vs Talend Data Integration

• It leverages scalability, flexibility and cost-effectiveness of hadoop to deliver an end-user focused analytic platform for big data without involvement of IT.
• It overcomes Hadoop`s complexity by providing GUI interface with pre-built functions across integration, analytics and data visualization .
• Excel feature is awesome for business users which is already provided by Datameer.
• Using datameer now user can do smart analytic using Decision Trees, Column dependency and recommendation.
• Recently HTML5 inclusion is making application to available on a wider range of devices, including the iPad and other mobile devices which does not support Flash.
• It can be used in premise or in a cloud computing environment.
• Wizard-based data integration designed for IT and business users to schedule and do transformation of large sets of structured, semi-structured and unstructured data without any knowledge of Hadoop ecosystem.

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• JSON parsing, if you are into highly nested JSON object parsing Talend has the ability to display the structure of JSON and allows you to define the extraction logic in the metadata. Same with XML source.
• Customization, For mostly all ETL work you will have a component in Talend. And in case you have a very specific requirement you can end up easily designing a custom component that you can code and reuse and share with others.
• Talend has a connectivity option to almost all the databases (relational or NOSQ)L and sources available. It also has generic JDBC/ ODBC drivers in case you need it.
• I like the ease of deployment across environment (DEV and PROD) with the use of context variables.

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• Data visualization and graphics. Right now it's not very user friendly. Creating dashboards requires a lot of work that can be done by the application

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• Syncing with Git should be made easier
• Can face issues with certain big data spaces
• Support find it difficult to resolve complex issues

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• Datameer definitely helped to increase employee efficiency, provide quick results for our data analytic teams.
• Quick data injection and data partitions allowed quick data scans.
• Integration with the Java sdk allowed us to reuse existing Java code.

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• Easy to build complex data transformations.
• Licensing model isn't that flexible.
• Memory mangement for huge volumes of information. You have to modify ETL designs to handle it properly.

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