Laptop Displaying the GigaOm Research Portal

Get your Free GigaOm account today.

Access complimentary GigaOm content by signing up for a FREE GigaOm account today — or upgrade to premium for full access to the GigaOm research catalog. Join now and uncover what you’ve been missing!

Modern Enterprise Grade Data Integration 2017

Table of Contents

  1. Summary
  2. Introduction and Methodology
  3. Usage Scenarios
  4. Disruption Vectors
  5. Company Analysis
  6. Key Takeaways
  7. Footnotes
  8. About William McKnight

1. Summary

This Sector Roadmap is focused on data integration (DI) selection for multiple/general purposes across the enterprise. We eliminated all DI products that may have been well-positioned and viable for certain use cases or single vendor environments but would prove deficient for our use cases. In many cases, a multiple DI tool approach should be pursued for an enterprise. Note that this report will not include the emerging data preparation market, which has complementary viability in enterprises.

Organizations today need to take advantage of the numerous relevant data platforms to which they provision their data. Platforming the data correctly is a key indicator of workload success. This leaves the progressive organization with more data platforms than ever before and with a clear need to make them all work together. Throw in multiple clouds and the data management challenge moves exponentially.

Managing the data integration is many of my clients’ limiting factors to data—and consequently business success.

All platforms cannot perform all enterprise processing and must take advantage of the data that has been sourced into other platforms and refined there. Data integration is at an all-time high in terms of need and shows no signs of abating.

Vendor solutions are evaluated over six Disruption Vectors: SaaS Applications Connectivity, Use of Artificial Intelligence, Conversion from any format to any format, Intuitive and Programming Time Efficient, Strength in DevOps and Shared Metadata across data platforms.