Cloud Data Warehouses

Pure-Plays and Cloud Service Providers Dominate Cloud Data Warehouse Market

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This report focuses on Cloud Data Warehouses, with data on the following vendors:

For decades, organizations have relied on data warehouses to integrate data from multiple sources into a single location with a common schema, enabling access to a more complete picture of an organization’s business data in an organized and governed manner. Data warehouses make possible the business reporting and analytics activities critical to how modern organizations operate. Data lakes offer another approach to data management that can handle a wider range of structured and unstructured data, and data lakehouses have emerged as a hybrid paradigm in recent years that combines strengths of both the warehousing and lake strategy. In each of these veins, though, data management and storage has moved from on-premises to the cloud in recent years, taking advantage of the flexibility, scalability, security, and (potential) cost-savings of cloud computing.

The big public cloud providers are prominent players in the cloud data warehousing game, offering data warehousing solutions that integrate seamlessly with other data, analytics, governance, and security offerings in their suite of products. They join cloud-native data warehousing providers, such as Snowflake and Databricks, as well as companies with decades of on-premises database and data warehousing experience who have rolled out quality cloud warehousing solutions. The market for data warehousing software is competitive, with each of these players jockeying for dominance and continually enriching their offering with new data management, governance, and ML/AI capabilities.

ETR’s Observatory for Cloud Data Warehouses focuses on enterprise-grade, cloud-based, full-featured products in this market that specialize in supporting structured data, which includes cloud data warehouses and cloud lakehouses. Tools that function primarily as relational of NoSQL databases, as well as tools primarily designed as data lakes, are not included here.

Companies are starting to get more into this multi-cloud type of strategy. The biggest challenge when having a cloud service provider or hyperscaler giving you that data warehouse is, you create challenges around data transferring.

Snowflake Overtakes Databricks for Top Spending Momentum, as Big Three Public Cloud Vendors Round Out Top Echelon

The 2025 ETR Observatory for Cloud Data Warehouses surveyed 321 IT decision makers. Most (64%) represent Large enterprises of more than 1,200 employees, with more than a fifth (21%) at Fortune 500 firms and nearly a third (31%) at Global 2000 enterprises. The three most representative industry verticals are Services/Consulting, IT/TelCo, and Financials/Insurance, collectively comprising more than half (58%) of the sample. Almost three-quarters (73%) of respondents are in North America and 17% are in EMEA, with the remainder representing APAC (9%) and Latin American (1%) regions. About half (54%) of respondents hold VP or Director-level titles, and the remainder are split between C-level roles (27%) and practitioner roles (19%).

Positioning for the above was determined purely by ETR’s proprietary surveys powered by the ETR Community. The full methodology and graphic explanation are available on our Methodology page.

The report categorizes vendors across different categories, reflecting their Momentum and Presence within the Cloud Data Warehousing space:

Leaders like Snowflake, Databricks, and Microsoft Azure Synapse show strong adoption and market share, driven by broad capabilities and ease of use.
Sole Advancing vendor Google BigQuer is gaining Momentum but still lags in Presence compared to market leaders.
Tracking vendor Oracle is a long-established name in enterprise tech stacks, with stronger Presence but relatively lower Momentum.
Pursuing Vendors, including SAP Data Warehous Cloud and Cloudera, are experiencing slower growth, with less impact in the market.

Snowflake and Databricks lead the cloud data warehousing market in spending plans, with Snowflake posting a Net Score of 72% and Databricks with a Net Score of 67% (see Figure 2). Net Scores are a snapshot of positive spending plans (Adoption and Increase indications) minus negative spending plans (Replacement and Decrease indications). Data warehouse offerings from the big three public cloud platforms sit closely together in spending plans behind Snowflake and Databricks: Microsoft Azure Synapse (57%), Google BigQuery (56%), and Amazon Redshift (56%). Net Score falls more sharply after this group, and on the lower end is IBM (4%), OpenText (12%), and Oracle (14%).

Comparing Net Scores from last year’s Observatory to the present iteration, Snowflake saw the largest increase, growing from 58% in 2024 to 72% in 2025, or a 14 percentage-point jump, to take over the top spot from Databricks. Databricks and Amazon Redshift posted sizable year-over-year gains, as well, both growing 8 percentage points. On the other hand, OpenText and Cloudera saw the largest Net Score declines, dropping 24 and 21 percentage points from 2024, respectively.

As with the spending intentions data in this study, the three public cloud vendors along with Snowflake and Databricks lead in expected return on investment (ROI). About three-quarters of respondents say they expect ROI within the first three years for Google BigQuery (76%), Microsoft Azure Synapse (75%), Amazon Redshift (74%), Databricks (74%), and Snowflake (73%). All five of these leading vendors saw expected ROI grow year-over-year, too. Despite low ranking in spending intentions and in expected length of use, OpenText (64%) and IBM (62%) fall in line behind these five leaders when it comes to ROI. In this analysis, vendors that had low citations for actual use were included if respondents had done an evaluation on the tool, so several more vendors join this analysis. On the lower end of the spectrum, Panoply (41%), Dremio (46%), Actian (47%), and Alibaba Cloud (48%) posted the lowest rates of expected ROI within the first three years.

Snowflake and Databricks Most Recommended, Snowflake Most Innovative, Microsoft Most Desired

Investing in one or more cloud data warehouse is a major decision for organizations, as these tools take considerable effort to implement, migrating data can be a costly and tedious endeavor, and developing schema and governance rules that align to business needs takes considerable time and cross-departmental collaboration. Understanding which vendors are seen as the innovators in the market sheds light on where the market may be going in terms of its technical features, while knowing which vendors are the most desired for centering in a tech stack speaks more to deeper strategic needs for organizations as they evolve with the market. In the Observatory study, we ask respondents to provide open-ended answers to which vendor they view as the most innovative, and which vendor they could most prioritize if given the opportunity to rebuild their tech stack from scratch. Analyzing these open-ended responses shows that Microsoft is seen as the most desired vendor, followed by Snowflake, while Snowflake is seen as the most innovative, followed by Microsoft. In the next few spots, it is Amazon, Databricks, and Google in line for most desired vendor behind Microsoft and Snowflake, and for most innovative it is Databricks, Amazon, and Google in 3rd through 5th place.

Among many attributes, what makes a product desired is a tool’s completeness or ability to do what is expected of it, as well as its ability to integrate with an organization’s existing technical ecosystem. Appropriately, then, these top desired vendors rank high in the individual vendor strengths analysis in their rates of agreement with the statements “this product does everything I expect a cloud data warehouse to do” and “this product integrates easily with our existing ecosystem.” Snowflake, Databricks, and Microsoft Azure Synapse, in order, have the highest rates of agreement in doing everything expected of it as a cloud data warehouse, and Microsoft Azure Synapse, Snowflake, and Amazon Redshift hold the top three spots in ease of integration with an existing ecosystem. A Sr. Director of IT Architecture for a large financial services firm notes an important tradeoff when it comes to the ease of integrating with the ecosystems of the big cloud providers, however. He said, “The biggest challenge when having a cloud service provider or hyperscaler giving you that data warehouse is, you create challenges around data transferring,” noting especially the costs associated with data movement in or out of cloud providers in an increasingly common multi-cloud architecture. Microsoft, Google, and AWS, he adds, are “working really hard to create this ecosystem, […and] from a commercial point of view, that means cost savings” if you are able to stick with one cloud ecosystem. He sees these cloud provider strategies to capture users in a single ecosystem as a direct response to vendors like Snowflake and Databricks.

Conclusion – Hyperscalers Jockeying for Pure-Play Market Share in an Age of AI

The cloud data warehouse market is a crowded one, with a handful of mature players with sizable market share vying to be the dominant technology for enterprise data management. The public cloud giants Microsoft, AWS, and Google are innovating their data and analytics products with incredible speed, leveraging an ecosystem of companion tools and capabilities from security and ML/AI to provide a one-stop-shop experience for customers. All the while, bundled sales strategies are capturing sizable market share and pushing organizations into their cloud data warehouse paradigms. Cloud data warehouse behemoth Snowflake is hard at work expanding into new territory in ML/AI, on a collision course with rival Databricks, which is disrupting the data warehouse market with its lakehouse paradigm and existing strength in ML/AI.

Long-time, established megavendors and on-premises data warehousing companies are seeing the gap widen between them and the major public cloud players and pure-play cloud data warehouses, though these legacy tools remain quite sticky and satisfy established use cases just fine for many organizations.

As the cloud hyperscalers continue to make their data warehousing offerings more appealing by nestling them within tightly integrated ecosystems, it’s the pure-plays like Databricks and Snowflake that users see as having the competitive edge long-term, and the spending intentions data and “most innovative” rankings in this Observatory support this notion. As AI continues to dominate the focus of many organizations’ IT strategies, the realities of consolidating, organizing, and making available mountains of data have put data warehouses and data lakes – and data quality and storage – into the spotlight as important foundations to build future AI applications upon.

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ETR Insights Team Contact a member of our ETR Insights team to discuss all the details from this analysis or request custom research.

Erik Bradley, Chief Strategist & Research Director epb@etr.ai
Daren Brabham, PhD, VP Research Analyst dbrabham@etr.ai
Jake Fabrizio, Principal Research Analyst jf@etr.ai
Doug Bruehl, Principal Research Analyst dbruehl@etr.ai

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