2026 Global: Cloud Analytics Market-Competitive Review (2032) report
Description
The 2026 Global: Cloud Analytics Market-Competitive Review (2031) report features the global market size and projected growth/decline data for the period 2021 through 2032. The report primarily provides an examination of the business strategies for the ten largest global companies in the market and how their strategies differ.
Perry/Hope Partners' reports provide the most accurate industry forecasts based on our proprietary economic models. Our forecasts project the product market size nationally and by regions for 2021 to 2032 using regression analysis in our modeling. and Perry/Hope is the only market research publisher that utilizes both longitudinal (historical) and vertical (from market section to market division to market class) analysis, since we study every manufactured product in the countries we analyze. The report also provides written analysis on the market definition, market segments, and SWOT analysis (market strengths, weaknesses, opportunities, and threats).
The market study aims at estimating the market size and the growth potential of this market. Topics analyzed within the report include a detailed breakdown of the global markets for cloud analytics market by geography and historical trend. The scope of the report extends to sizing of the cloud analytics market market and global market trends with market data for 2024 as the base year, 2025 and 2026 as the estimate years with projection of CAGR from 2027 to 2032.
The report also features a list of the top ten largest global players in the market. A review of each company includes 1) an estimate of the market share, 2) a listing of the products and/or services in the market, and 3) the features of these products and/or services in the market. The report has a chapter on Comparative Business Strategies for the largest four players. An example of the Comparative Business Strategies analysis would be -- How does Netflix's business strategy to expand its market share in the global online streaming compare to Amazon Prime's business strategy through its video products and services?
The ten market players in this report and a brief synopsis of their participation in the market are:
Snowflake, Databricks, Google (BigQuery), Amazon (Redshift), Microsoft (Azure Synapse/Power BI), Oracle, IBM, Snowflake, Teradata, and SAP are widely recognized among the top companies shaping the cloud analytics market today. Snowflake leads with a cloud-native data platform that separates storage and compute to enable scalable warehousing, data sharing, and support for data lakes and data engineering workloads, making it a preferred choice for enterprises pursuing a single source of truth for analytics. Databricks advances unified data and AI through its lakehouse architecture that combines data engineering, machine learning, and analytics with strong support for open formats and collaborative notebooks, appealing to organizations that need end-to-end model development and production. Google’s BigQuery provides serverless, highly scalable analytics with deep integration of AI and real-time ingestion, favored for fast, SQL-based analysis of massive datasets and tight coupling with Google Cloud’s ML tools. Amazon Redshift emphasizes performance and cost optimization within the AWS ecosystem, offering serverless and RA3 instances, zero-ETL integrations, and features tuned for enterprises that want analytics tightly integrated with their operational AWS services. Microsoft’s Azure Synapse and Power BI pair cloud-scale data integration and analytics with pervasive BI, enabling hybrid scenarios and strong enterprise governance while leveraging Microsoft’s broad cloud footprint and productivity ecosystem. Oracle’s Analytics Cloud and Autonomous Data Warehouse deliver automated tuning, integrated analytics inside the database, and enterprise-grade security and governance that suit regulated industries and mission-critical workloads. IBM positions Cloud Pak for Data and Watsonx as hybrid-first analytics and AI platforms with emphasis on data lineage, responsible AI, and enterprise controls, attracting customers with strict compliance requirements. Teradata’s Vantage platform targets large-scale, enterprise analytics and data warehousing with a focus on performance, complex analytics, and multi-cloud deployment options for organizations that require heavy-duty throughput and concurrency. SAP leverages its data management and business process integration to offer analytics that tightly connects operational ERP data with cloud analytics capabilities, serving enterprises that seek embedded analytics within business applications. Qlik, Tableau, SAS, Domo, and Snowplow also appear across industry lists as significant players offering visualization, self-service BI, streaming analytics, and specialized data pipelines that complement the major cloud-native platforms.
Market differentiation among these vendors centers on architecture, ecosystem alignment, AI readiness, and governance. Platforms built as cloud-native lakehouses or serverless warehouses prioritize elastic scale, separation of storage and compute, and open data formats, while established database vendors emphasize in-database automation, security, and verticalized solutions for regulated sectors. Integration with cloud provider services, real-time ingestion and streaming stacks (e.g., Pub/Sub, Kafka, Kinesis), and embedded ML/AI tooling have become table stakes; choice often depends on existing cloud commitments, data gravity, and whether teams prioritize self-service BI, advanced ML lifecycle support, or strict compliance controls. Emerging themes include AI-native features such as natural-language querying and automated insights, hybrid and multi-cloud deployment options to avoid vendor lock-in, and managed services that reduce operational overhead for streaming and real-time analytics.
Perry/Hope Partners' reports provide the most accurate industry forecasts based on our proprietary economic models. Our forecasts project the product market size nationally and by regions for 2021 to 2032 using regression analysis in our modeling. and Perry/Hope is the only market research publisher that utilizes both longitudinal (historical) and vertical (from market section to market division to market class) analysis, since we study every manufactured product in the countries we analyze. The report also provides written analysis on the market definition, market segments, and SWOT analysis (market strengths, weaknesses, opportunities, and threats).
The market study aims at estimating the market size and the growth potential of this market. Topics analyzed within the report include a detailed breakdown of the global markets for cloud analytics market by geography and historical trend. The scope of the report extends to sizing of the cloud analytics market market and global market trends with market data for 2024 as the base year, 2025 and 2026 as the estimate years with projection of CAGR from 2027 to 2032.
The report also features a list of the top ten largest global players in the market. A review of each company includes 1) an estimate of the market share, 2) a listing of the products and/or services in the market, and 3) the features of these products and/or services in the market. The report has a chapter on Comparative Business Strategies for the largest four players. An example of the Comparative Business Strategies analysis would be -- How does Netflix's business strategy to expand its market share in the global online streaming compare to Amazon Prime's business strategy through its video products and services?
The ten market players in this report and a brief synopsis of their participation in the market are:
Snowflake, Databricks, Google (BigQuery), Amazon (Redshift), Microsoft (Azure Synapse/Power BI), Oracle, IBM, Snowflake, Teradata, and SAP are widely recognized among the top companies shaping the cloud analytics market today. Snowflake leads with a cloud-native data platform that separates storage and compute to enable scalable warehousing, data sharing, and support for data lakes and data engineering workloads, making it a preferred choice for enterprises pursuing a single source of truth for analytics. Databricks advances unified data and AI through its lakehouse architecture that combines data engineering, machine learning, and analytics with strong support for open formats and collaborative notebooks, appealing to organizations that need end-to-end model development and production. Google’s BigQuery provides serverless, highly scalable analytics with deep integration of AI and real-time ingestion, favored for fast, SQL-based analysis of massive datasets and tight coupling with Google Cloud’s ML tools. Amazon Redshift emphasizes performance and cost optimization within the AWS ecosystem, offering serverless and RA3 instances, zero-ETL integrations, and features tuned for enterprises that want analytics tightly integrated with their operational AWS services. Microsoft’s Azure Synapse and Power BI pair cloud-scale data integration and analytics with pervasive BI, enabling hybrid scenarios and strong enterprise governance while leveraging Microsoft’s broad cloud footprint and productivity ecosystem. Oracle’s Analytics Cloud and Autonomous Data Warehouse deliver automated tuning, integrated analytics inside the database, and enterprise-grade security and governance that suit regulated industries and mission-critical workloads. IBM positions Cloud Pak for Data and Watsonx as hybrid-first analytics and AI platforms with emphasis on data lineage, responsible AI, and enterprise controls, attracting customers with strict compliance requirements. Teradata’s Vantage platform targets large-scale, enterprise analytics and data warehousing with a focus on performance, complex analytics, and multi-cloud deployment options for organizations that require heavy-duty throughput and concurrency. SAP leverages its data management and business process integration to offer analytics that tightly connects operational ERP data with cloud analytics capabilities, serving enterprises that seek embedded analytics within business applications. Qlik, Tableau, SAS, Domo, and Snowplow also appear across industry lists as significant players offering visualization, self-service BI, streaming analytics, and specialized data pipelines that complement the major cloud-native platforms.
Market differentiation among these vendors centers on architecture, ecosystem alignment, AI readiness, and governance. Platforms built as cloud-native lakehouses or serverless warehouses prioritize elastic scale, separation of storage and compute, and open data formats, while established database vendors emphasize in-database automation, security, and verticalized solutions for regulated sectors. Integration with cloud provider services, real-time ingestion and streaming stacks (e.g., Pub/Sub, Kafka, Kinesis), and embedded ML/AI tooling have become table stakes; choice often depends on existing cloud commitments, data gravity, and whether teams prioritize self-service BI, advanced ML lifecycle support, or strict compliance controls. Emerging themes include AI-native features such as natural-language querying and automated insights, hybrid and multi-cloud deployment options to avoid vendor lock-in, and managed services that reduce operational overhead for streaming and real-time analytics.
Table of Contents
32 Pages
- 1.0 Scope of Report and Methodology
- 2.0 Market SWOT Analysis and Players
- 2.1 Market Definition
- 2.2 Market Segments
- 2.3 Market Strengths
- 2.4 Market Weaknesses
- 2.5 Market Threats
- 2.6 Market Opportunities
- 2.7 Major Players
- 3.0 Competitive Analysis
- 3.1 Market Player 1
- 3.2 Market Player 2
- 3.3 Market Player 3
- 3.4 Market Player 4
- 3.5 Market Player 5
- 3.6 Market Player 6
- 3.7 Market Player 7
- 3.8 Market Player 8
- 3.9 Market Player 9
- 3.10 Market Player 10
- 4.0 Comparative Business Strategies
- 4.1 Comparative Business Strategies of Player 1 and 2
- 4.2 Comparative Business Strategies of Player 1 and 3
- 4.3 Comparative Business Strategies of Player 1 and 4
- 4.4 Comparative Business Strategies of Player 2 and 3
- 4.5 Comparative Business Strategies of Player 2 and 4
- 4.6 Comparative Business Strategies of Player 3 and 4
- 5.0 Appendix
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