Nucleus Research analyzes the adoption of modern data warehouse technology in a new value matrix

MIAMI–(BUSINESS WIRE)–Enterprises are increasingly adopting large-scale cloud transformations to become more agile, resilient, and data-driven. At the same time, the adoption of modern data warehouse technology has accelerated in parallel, as companies seek to scale up data ingestion and analysis. As 2022 moves forward, Core search released its Data Warehouse Value Matrix, predicting an increase in customers prioritizing a solution’s respective scalability, serverless capabilities, multi-regional and multi-cloud completeness, and optimization of performance for large and highly parallel data jobs when selecting a data warehouse solution.

Last month, Nucleus identified several key trends to watch in the database and data warehousing market:

  • Elastic data warehousing will drive a greater move toward serverless, usage-based pricing models;

  • Increase vendor investments in distributed cloud storage and processing;

  • Massive adoption of real-time and in-database analytics;

  • Accelerated development of artificial intelligence and self-service analytics with data warehouses as fertile ground.

According to Nucleus Research Analyst Alexander Wurm, these trends play a major role in future interactions with customers and suppliers and in addressing issues that coincide with industry growth. Although 2022 is just beginning, vendors are increasingly investing in distributed technologies to improve processing efficiency and provide better analytics capabilities. And while this rapid increase in adoption is already generating reports of value for customers, this mass scaling has also highlighted some challenges that need to be addressed for customers to maximize the value and success of their database and data warehouse projects.

For example, many vendors have struggled to scale their services while ensuring data security and access to data for value-added analysis. To address these challenges, providers continue to develop new networking practices, such as multi-region and multi-cloud approaches to reduce the potential severity of cloud outages. Vendors also offer in-database analytics capabilities to minimize latency for time-sensitive tasks by avoiding extract, transform, and load (ETL) actions. To that end, Nucleus’ 2022 Data Warehouse Value Matrix offers a breakdown of data warehouse industry leaders, experts, enablers and key vendors for companies looking to adopt a data warehouse solution:

  • Leaders: Amazon Redshift, Oracle Autonomous Data Warehouse, Microsoft Azure Synapse Analytics and Teradata Vantage.
  • experts: Yellowbrick Data, IBM Db2 Warehouse and Databricks Lakehouse.
  • Facilitators: Snowflake Data Cloud, SAP Data Warehouse Cloud and Google BigQuery.
  • Main suppliers: Panoply Cloud Data Platform and Cloudera Data Platform.

“Data warehouses have evolved from complex, rigid hardware specialized for infrequent batch processing to highly managed, interconnected, and scalable systems,” Wurm said. “As the development of fully managed and serverless offerings accelerates, Nucleus expects customers to significantly reduce IT intervention and reduce the expense of over-provisioning compute resources.”

To create the 2022 Data Warehouse Value Matrix, vendors are positioned based on the relative usability and functionality of their respective solutions, as well as the value customers have realized from each product’s capabilities. and presented as a snapshot of the current market rather than an empirical ranking. of sellers.

For the full report, click here.

About Nucleus Research

Nucleus Research is a global provider of investigative and case technology research and consulting services. The company’s ROI-focused research approach provides unique insight into the real results that technology solutions deliver, allowing organizations to cut through the hype to understand real business value and select or renew the best technology solution. for their environment. For more information, visit NucleusResearch.com.

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