📝 usncan Note: AI And Open Source Redefine Enterprise Data Platforms In 2025
Disclaimer: This content has been prepared based on currently trending topics to increase your awareness.
In 2025, enterprise data platforms support business operations by combining analytics, governance and orchestration in one system, increasingly with generative and agentic AI-enabled features to improve autonomy and speed.
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In 2025, enterprise data platforms are the backbone of how businesses run and manage data across cloud, on-prem and edge environments. They power everything from finance and supply chains to customer experience and strategic planning. With AI increasingly baked into daily workflows and compliance rules tightening, companies need data that’s clean, easy to find and ready to use on the spot. Enterprise data vendors are adapting rapidly to meet these needs and stay competitive.
I published an overview of this market earlier this year, but this landscape is shifting so quickly that it deserves a new installment focusing on the biggest vectors of change happening right now. One of them is that open formats such as Apache Iceberg and Delta Lake are making it easier to move data across systems without getting stuck in vendor silos. There’s also a push for more AI-ready tools enabled by technologies such as retrieval-augmented generation and vector search that can pull answers from live data — and that’s before we get to the breathtakingly rapid uptake of agentic AI in these systems. On top of that, instead of juggling disconnected solutions, more enterprises are turning to unified platforms that bring orchestration, governance and metadata into one place. Vendors with the scale and feature sets to serve as platforms are flexing those capabilities to improve their competitive positions.
In this context, data architecture is increasingly not just a technical IT concern, but a critical strategic consideration for staying fast, smart and competitive. So let’s dive into how these emerging vectors of change are making a difference in the enterprise data management market.
What Matters For Enterprise Data Platforms In 2025
Enterprise data platforms are evolving into more modular, standards-driven systems rather than single-vendor stacks. For starters, open table formats such as Apache Iceberg and Delta Lake are now widely supported, making it easier to build architectures that work across clouds and adapt over time; this also helps reduce vendor lock-in by making data easier to move and query across different platforms. And instead of handling ingestion, transformation and governance as separate steps, data platforms are increasingly treating them as connected, continuous processes. Snowflake, IBM, Cloudera and Informatica support Iceberg, while Databricks supports both formats via its Unity Catalog, and Delta Lake UniForm enables cross-format access. The use of open standards gives organizations more control over their data and makes it easier to switch tools without starting from scratch.
Today’s data platforms are also built for AI from the ground up. Agentic systems handle tasks like metadata tagging and data quality checks on their own. RAG keeps AI grounded in trusted enterprise data, while tools like vector search and embedding management are now standard. Low-code features and policy automation are also becoming standard — used not just for efficiency but for practical needs like identifying data quality issues early, enforcing compliance rules and preparing for audits without heavy manual work. At this point, AI has moved past pilot projects; copilots, agents and domain-specific automation are embedded in everyday tasks, from streamlining supply chain adjustments to flagging fraudulent transactions. This allows both technical and non-technical teams to get faster, more consistent results.
In terms of infrastructure, hybrid and edge deployments have become the norm. Companies need to process data closer to where it’s created, especially in industries like healthcare, manufacturing and finance, where speed, privacy and control matter. With increasing data generation outside of traditional datacenters, seamless edge integration has also become a necessity. Vendors like Microsoft, IBM and Cloudera now offer edge-ready options that support this shift.
Financial operations, or FinOps, functions have become increasingly important due to the increasing cost of AI workloads. Vendors now offer various solutions that provide visibility into these costs across complex environments. AWS’s Cost Optimization Hub, Microsoft’s enhanced Fabric controls and IBM’s integration of FinOps tools into its data stack are examples of these solutions. Financial governance is evolving into full lifecycle planning, with tools that track usage, forecast costs and help teams make informed decisions about workload management.
At the same time, sovereign AI is picking up steam as governments and enterprises often want their AI systems within national or regional borders to meet privacy laws and regulatory expectations. This focus on control, particularly in defense, healthcare and government sectors where trust and accountability are crucial, is driving the development of new regulations like the U.S. Department of Justice’s 2025 Data Security Program. The real advantage will come from platforms that can flex with policy and geography. Think model auditing, boundary-aware deployment and support for hybrid environments that mix cloud, on-prem and edge. The more adaptable your platform, the easier it’ll be to keep moving fast — even in a world of complex rules and rising expectations.
On the data side, strong governance is now the default. Features like lineage tracking, policy enforcement and metadata tagging aren’t nice-to-haves — they’re expected. More teams are also starting to treat data as a product: something that’s reusable, well-documented and governed from the start.
Put it all together, and by this point vendors can’t be focused on the potential of their platforms anymore. Today, it’s about actual capabilities. Buyers are seeking tangible real-world performance at scale, along with robust governance and observability and the flexibility to adapt. The platforms that meet these expectations are poised to shape the next phase of enterprise data strategy.
Comparing Enterprise Data Vendors
Enterprise data platform vendors continue to take different paths, shaped by their backgrounds and strategic priorities. Snowflake has added AI to its SQL-native platform through Cortex AI-SQL, letting users embed AI directly into queries. It now supports Apache Iceberg via the open-source Polaris Catalog and recently introduced OpenFlow to handle real-time pipelines and combine structured and unstructured data for event-driven use cases. Cittabase used Cortex AI-SQL to automatically transform unstructured visual data into structured text summaries, enabling teams to join image-derived insights with relational tables for richer analytics.
Databricks focuses on data science and AI-first workflows. As mentioned above, it supports Delta Lake and Iceberg, and its Unity Catalog now provides governance across multiple formats and engines. Databricks is doubling down on interoperability and agent-driven automation; this is backed by its LakehouseIQ, a knowledge engine that enables natural-language queries by learning an organization’s data context, and Mosaic AI, a platform for building and governing AI models and agents — not to mention its acquisition of Tabular (the team behind Iceberg). DraftKings built a real-time fraud detection system using machine learning on Databricks. And Coinbase uses the platform to monitor blockchain transactions and flag suspicious activity at scale. Both of these examples suggest the platform’s strength in real-time processing, vector search and ML tooling.
Informatica continues to lead with metadata-driven governance. Its Claire AI engine now includes Claire Agents — autonomous tools for managing data beyond chat-style interactions. It supports Iceberg and offers hybrid deployment flexibility, appealing to enterprises needing strong policy controls. For instance, Holiday Inn Club Vacations used Claire to consolidate customer data from disconnected systems, improving accuracy. And Paycor modernized its pipelines with Informatica’s cloud tools, speeding up analytics and AI delivery. (More on Informatica in the Salesforce item below.)
Cloudera plays to its strengths in hybrid and edge deployments. It relies on open-source technologies such as NiFi for streaming and Spark for processing, and it supports Iceberg with ACID transactions and time travel — the ability to query historical versions of data tables for auditing, recovery or point-in-time analysis. Recent updates add GPU observability, Nvidia H100 support and Hugging Face model integration (including Llama 3.2) for AI-enabled lakehouse use cases. Manufacturers use it at the edge for predictive maintenance, while retailers and banks use it to secure customer data and detect fraud in real time — balancing local processing with centralized oversight.
Teradata is still the go-to for large-scale analytics in industries like finance and retail. Its VantageCloud Lake and ClearScape Analytics platforms now support generative and agent-based AI, with new tools for cost tracking and workload management meant to make life easier for both technical and business teams. Banks and telecom firms use it for compliance, risk modeling and auditing due to its strong workload management and scalability, which are well suited for regulated industries with heavy data demands.
IBM has been expanding watsonx to cover more complex and regulated AI workloads. The June 2025 update brought unstructured data support, real-time Cassandra integration via DataStax and Spark acceleration through Apache Gluon. Today, watsonx supports Iceberg, edge deployments and enhanced vector search, which includes modern pipeline tools and FinOps features. Vodafone uses watsonx to simulate customer interactions, while insurers automate claims processing by extracting key info from forms and documents — suggesting watsonx’s value in hybrid, compliance-focused settings.
Salesforce is expanding its enterprise data strategy with a proposed $8 billion acquisition of Informatica, expected to close in fall 2026. This would likely extend Informatica’s governance and AI capabilities across Salesforce’s stack — integrating with Data Cloud, Tableau and MuleSoft — while positioning Salesforce more directly against competitors such as Snowflake and Databricks. In August 2025, Salesforce also completed its acquisition of Waii, a startup that translates natural-language queries into optimized SQL using a metadata knowledge graph. Waii’s technology is expected to enhance Data Cloud, Agentforce and Tableau Next, enabling users and AI agents to interact with enterprise data through conversational queries.
Enterprise Data Management Offerings From Cloud Service Providers
The major cloud providers continue to take distinct approaches to delivering their own enterprise data platforms, shaped by their strengths in AI, infrastructure and developer tools. AWS offers a broad toolkit, including Redshift for warehousing, Glue for ETL, SageMaker for machine learning and Athena for ad hoc querying. While powerful, these services often need to be stitched together. To help, AWS introduced DataZone for governance and Cost Optimization Hub for better financial tracking. Meanwhile, Greengrass supports edge deployments in manufacturing, retail and field operations.
Microsoft Azure focuses on integration through Microsoft Fabric, which combines Synapse, Data Factory and Power BI into one SaaS platform on OneLake. (For more on how Fabric simplifies data management for AI, see my analysis from late 2024.) Fabric now has more than 17,000 customers, including most of the Fortune 500. Recent updates added Materialized Lake Views, improved mirroring and tighter OneLake integration. Azure Arc extends Azure data services to on-prem and sovereign environments, supporting hybrid use cases. The real-world use cases span many industries. For example, Melbourne Airport uses Microsoft Fabric for unified analytics to manage operational data efficiently. Chanel integrates Fabric into its analytics workflows, balancing decision support with strong governance. And Microsoft itself uses Fabric internally to manage complex, large-scale data environments.
Google Cloud emphasizes AI and data flexibility. Its stack — BigQuery, Vertex AI and Looker — supports Iceberg and Delta Lake, allowing open, cloud-agnostic architectures. Anthos enables hybrid and edge orchestration, and Google’s updated FinOps dashboards are designed to offer better cost visibility. The platform’s open AI tooling appeals to engineering teams building custom workflows. Bayer uses AlloyDB alongside BigQuery to deploy real‑time analytics on open Iceberg‑formatted data, resulting in faster response rates and higher throughput compared to its previous architecture.
Oracle Cloud Infrastructure focuses on performance for transactional and application-integrated workloads. With Autonomous Database and AI Vector Search, OCI is tightly aligned with Oracle’s ERP and SaaS stack. While its edge capabilities are still maturing, OCI offers stable pricing and built-in integration for enterprises already standardized on Oracle. As one example of a customer use, DeweyVision deployed Oracle Autonomous Database together with AI Vector Search to deliver fast, AI-powered semantic media searches across diverse data types, improving discoverability and user experience.
Strategic Outlook For Enterprise Data Platforms
The enterprise data platform market is expected to double in the next seven years — from $111.3 billion in 2025 to $243.5 billion by 2032, growing at 11.8% CAGR. This growth is fueled by rising data complexity, AI adoption, tighter regulations and continued cloud expansion.
Today’s enterprises want platforms that simplify operations, cut costs and make AI useful. Features like catalog federation, agent-based orchestration and AI-aware cost modeling are starting to meet those needs. New “cognitive” platforms treat AI agents as active data users — capable of taking action without constant human oversight.
Sovereign AI and edge computing are also shaping platform design. AI systems increasingly need to stay near regulated data sources, while edge capabilities support fast, local processing. Most vendors are adapting to support both. Sustainability is starting to matter more, too. Companies are beginning to factor in the environmental impact of data infrastructure when evaluating platforms. Going forward, platform choice will hinge less on name recognition and more on technical fit. The strongest contenders will offer flexible deployment, open standards, transparent cost controls and baked-in governance — helping businesses move faster and make smarter decisions in manufacturing, healthcare, finance, retail and beyond.
