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Hadoop vs Teradata : When to Use Which

While there are certain use cases that are distinct to Hadoop or the data warehouse, there is also overlap where either technology could be effective. The following table is a good starting place for helping to decide which platform to use based on your requirements.


Requirement Data Warehouse   Hadoop
Low latency, interactive reports, and OLAP YES
ANSI 2003 SQL compliance is required YES
Preprocessing or exploration of raw unstructured data YES
Online archives alternative to tape YES
High-quality cleansed and consistent data YES
100s to 1000s of concurrent users YES YES
Discover unknown relationships in the data YES YES
Parallel complex process logic YES
CPU intense analysis YES YES
System, users, and data governance YES
Many flexible programming languages running in parallel YES
Unrestricted, ungoverned sand box explorations YES
Analysis of provisional data YES
Extensive security and regulatory compliance YES
Real time data loading and 1 second tactical queries YES YES


Hadoop and the data warehouse will often work together in a single information supply chain. When it comes to Big Data, Hadoop excels in handling raw, unstructured and complex data with vast programming flexibility. Data warehouses also manage big structured data, integrating subject areas and providing interactive performance through BI tools. It is rapidly becoming a symbiotic relationship. Some differences are clear, and identifying workloads or data that runs best on one or the other will be dependent on your organization and use cases. As with all platform selections, careful analysis of the business and technical requirements should be done before platform selection to ensure the best outcome. Having both Hadoop and a data warehouse onsite greatly helps everyone learn when to use which.

Source : Teradata white paper

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