Building a Hadoop Data Warehouse with Impala

Explore how Impala's architecture supports query speed over Hadoop data that not only convincingly exceeds that of Hive, but also that of a proprietary analytic DBMS over its own native columnar format, understand the current state of, and roadmap for, Impala's analytic SQL functionality, and see an example configuration and benchmark suite that demonstrate how Impala offers a high level of performance, functionality, and ability to handle a multi-user workload, while retaining Hadoop’s traditional strengths of flexibility and ease of scaling.

Date: Wednesday, Apr 09 2014

Description

Impala raises the bar for SQL query performance on Apache Hadoop. With Impala, you can query Hadoop data – including SELECT, JOIN, and aggregate functions – in real time to do BI-style analysis. As a result, Impala makes a Hadoop-based enterprise data hub function like an enterprise data warehouse for native Big Data.

In this webinar featuring Impala architect Marcel Kornacker, you will explore:

  • How Impala's architecture supports query speed over Hadoop data that not only convincingly exceeds that of Hive, but also that of a proprietary analytic DBMS over its own native columnar format.
  • The current state of, and roadmap for, Impala's analytic SQL functionality.
  • An example configuration and benchmark suite that demonstrate how Impala offers a high level of performance, functionality, and ability to handle a multi-user workload, while retaining Hadoop’s traditional strengths of flexibility and ease of scaling.

You will also learn how enterprise conformed dimensions can be used as the basis for integrating Hadoop and conventional data warehouses.

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