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4 Ways to Scale Interactive BI and Analytics on a Data Lake

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Don’t let the headlines fool you. Apache Hadoop and Apache Spark are redefining data architectures and are being widely adopted in most organizations. The final step is to provide secure, self-service big data analytics and visualization for end users to get the insights they need to make more informed decisions and test new hypotheses.

Sounds easy, but is it really? There are four different ways to provide analytics on the data lake. What are the pros and cons of each, such as connecting your existing BI tools to Hadoop-based platforms? What are the best deployment strategies for supporting 100s or 1000s of concurrent users across many business units?

Watch the recording of this complimentary webinar with industry experts from Arcadia Data and MapR Technologies who will cover:

  • The pros and cons of four methods: BI servers, fast SQL engines, OLAP cubes, and data native
  • Example use cases in marketing optimization, financial services, and health care
  • How Arcadia integrates with MapR to support multi-tenant, high user concurrency applications


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Steve Wooledge
Sameer Nori
Saurabh Mahapatra
VP, Marketing
Director of Partner Marketing
Senior Product Manager
Arcadia Data
MapR
MapR


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