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Report

Analytics in the Cloud: Minimize Pain, Maximize Success v1.0

Table of Contents

  1. Summary
  2. Strategies for Moving On-Premises Data to the Cloud
  3. Hurdles Impeding a Successful On-Premises Analytics Migration to the Cloud
  4. A More Agile, Less Risky Approach
  5. How a Third-Party Platform Can Accelerate Your Cloud Migration
  6. Conclusion: Plan Wisely, In Both Tools and Approach
  7. About Andrew Brust
  8. About Yiannis Antoniou

Summary

There can be no doubt: the cloud is king. Following the cautious, initial adoption of cloud services a few years ago, the cloud is quickly becoming the new normal for organizations. But as it becomes the de facto solution for new analytics initiatives and migrated legacy solutions alike, it becomes increasingly evident that the path to the cloud is not as easy as it is made out to be, particularly when it comes to data. And the challenge is compounded by the proliferation of data silos in most organizations.

In this paper we discuss the cloud data journey and its associated issues, focusing on the use case of deploying new cloud analytics requiring existing on-premises data. We look at how organizations should use modern data platforms such as Datameer Spotlight and Spectrum to help avoid these issues. We describe best practices for deploying and using analytics data in the cloud, and explore how the right third-party tool can help implement data analytics securely and at scale in the cloud. Finally, we provide generalized advice for migrating from on-premises to the cloud and provide a rationale for using a third-party toolset to enable and accelerate successful migrations.

Findings include:

  • “Big Three” cloud providers offer only basic data management capabilities to deliver hybrid cloud analytics
  • Both data virtualization and data pipelines have emerged as significant features for optimized cloud analytics
  • Third-party platforms such as Datameer offer more extensive capabilities
  • We can learn practices from the field that promote successful data management for analytics in the cloud

In conclusion, we would advise organizations looking to bridge large quantities of data with cloud analytics to look beyond the default functions, and to plan carefully.

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