GigaOm Radar for Evaluating Data Warehouse Platformsv1.0

Table of Contents

  1. Summary
  2. About the GigaOm Radar Report
  3. Key Criteria Comparison
  4. GigaOm Radar
  5. Vendor Roundup/Overview
  6. Conclusion
  7. About Andrew Brust

1. Summary

For decades data warehouses have been the trusted technology for large-scale data storage and analytics in the enterprise. And that role has become only more vital, as traditional data warehouse vendors have modernized their products to provide advanced scaling capabilities, massive parallelism, enhanced ease of use, and reduced total cost of ownership. At the same time, vendors are evolving new features and advancing their architectures to leverage the native capabilities of the cloud.

Today, vendors are extending their products, moving from core data warehouse offerings to more integrated platforms with warehouse capabilities at their core. These include integrations with formerly discrete technologies such as data lakes, Hadoop and Spark, as well as AI operations, deep integration with data analytics and other BI tools, and easier integration with data engineering, data science and machine learning workflows. Built-in data governance, data quality and data preparation are also included.

Managing the full data cycle is now virtually impossible for an organization without a data warehouse platform. Evaluating the market’s most important vendors is therefore vital for any organization. This process needs to examine both technical and non-technical considerations, and should be approached holistically.

This GigaOm Radar report examines and evaluates the most important data warehouse platforms in the market today. It looks at each vendor’s approach, capabilities and, crucially, its ongoing development, and explores how each is poised to evolve over the next twelve months.

This report is designed to help you evaluate both the current and future position of solutions within the market. The aim is to help your organization make the best possible decision about the vendor it selects for its data warehouse.

Key findings:

  • New platform development is focused on the cloud. Several cloud-native platforms have emerged, and more traditional vendors have modernized their platforms to transition from on-premises to the cloud.
  • Vendors are investing heavily in hybrid cloud capabilities. Data sets spanning on-premises and the cloud can now be processed and analyzed, and disaster recovery capabilities have been enhanced.
  • Most vendors are integrating data science, ML, and artificial intelligence (AI) capabilities into their base data warehousing architecture.
  • Data warehouses are able to store and analyze data more quickly, allowing huge amounts of data to be explored more readily, and this trend is accelerating.
  • SQL is the dominant querying language, though vendors do make their own additions or incorporate special variations.
  • Integration with data lakes, query federation, and processing of remote data sets in-place are slowly becoming more prevalent, with several vendors implementing similar performance-enhancing features.

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