Published: 08 January 2024
Summary
Data and analytics leaders looking for analytic data solutions will find many choices to meet their needs. The market for cloud-based DBMS solutions for analytical use cases has matured and settled in the past year, with a mix of modernized, traditional, and newer offerings.
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Overview
Key Findings
The next wave of disruption in the DBMS market will be the emergence of the data ecosystem as the overall data platform. As data ecosystems mature, the ability to use multiple DBMS offerings together will become commonplace, reducing the need to find a single DBMS offering or vendor.
Some DBMS provide data science functions within their environment, leading to more efficiency and performance and some DBMS systems also offer data science capabilities such as model training and life cycle management. Although data science workloads are usually smaller in number than traditional workloads, increased attention on data science, brought about
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Strategic Planning Assumptions
- Alibaba Cloud (AnalyticDB)
- Amazon Web Services (Amazon Redshift)
- Cloudera (CDP)
- Couchbase (Couchbase Capella)
- Databricks (The Databricks Lakehouse Platform)
- EDB (BigAnimal)
- Google (BigQuery)
- IBM (Db2 Warehouse as a Service)
- InterSystems (InterSystems IRIS)
- Microsoft (Azure Synapse Analytics)
- Neo4j (Neo4j Aura Enterprise Graph Database)
- Oracle (Oracle ADW for Analytics and Data Warehousing)
- SAP (SAP Datasphere)
- Snowflake (Snowflake Data Cloud)
- Teradata (Teradata Vantage)
- Analytics
- Application Development Support
- Auto Perf Tuning and Optimization
- Data Science and ML
- Distributed Capabilities
- Financial Governance
- Management and Administration
- Multicloud/Intercloud/Hybrid
- Performance Features
- Relational Attributes
- Resource Usage
- Operational Intelligence
- Traditional Data Warehouse
- Logical Data Warehouse
- Data Lake
Critical Capabilities Methodology