The IT Nerd

CData Software today announced major enhancements to CData Sync designed to meet the data pipeline demands of modern enterprises. The updates deliver coordinated pipeline orchestration, expanded change data capture (CDC) for mission-critical systems, and native support for open table formats, empowering data teams to operate continuously across legacy and modern architectures.

As organizations race to operationalize AI, they face mounting pressure to keep data fresh, coordinate dependencies across systems, and maintain governance at scale. CData Sync’s latest capabilities directly tackle these challenges by unifying real-time replication, workflow orchestration, and open standards within a single platform.

Pipeline-Based Workflow Orchestration
CData Sync now includes Pipelines, enabling teams to orchestrate multi-step workflows directly within Sync. Data engineers can sequence replication jobs, transformations, and events without external orchestration tools, reducing complexity while maintaining full visibility and control over dependencies.

Programmable Control via API 2.0
The redesigned API 2.0 provides a predictable, automation-friendly interface for managing Sync at scale. Organizations can programmatically configure pipelines, trigger executions, and monitor operations across distributed deployments, making it easier to integrate Sync into internal platforms or enable orchestration through external systems, including AI agents.

Enterprise-Grade CDC for IBM DB2 and SAP HANA
CData Sync expanded CDC support to include IBM DB2 (LUW and iSeries/AS400) and SAP HANA, enabling near-real-time replication from these widely deployed enterprise platforms. Organizations can now stream incremental changes from core systems of record directly into cloud analytics and AI platforms without impacting production workloads.

Open Table Formats for AI and Analytics
With native support for Delta Lake (including Microsoft Fabric via Open Mirroring) and Apache Iceberg, CData Sync allows teams to write data into open, ACID-compliant table formats. This eliminates vendor lock-in and ensures data remains accessible across analytics engines and AI platforms without proprietary dependencies.

Centralized Governance with Workspaces
New Workspaces provide a unified control plane for managing connections, jobs, and transformations across teams and environments. As pipeline counts grow, Workspaces ensure organizations can scale governance, enforce policies, and maintain visibility without losing operational control.

Learn More

To explore CData Sync’s new pipeline orchestration, expanded CDC support, and open table format capabilities, visit cdata.com/sync.

CData Software today announced major enhancements to CData Connect AI at the Gartner Data & Analytics Summit (Booth #308). The updates extend CData’s managed Model Context Protocol (MCP) platform with new capabilities across connectivity, context, and control, the three pillars required to move AI from experimentation to production.

Why AI Stalls Before Production

AI investment is accelerating. “Gartner®¹ says worldwide AI spending will total $2.5 trillion in 2026.” But spending isn’t translating into results. Most generative AI initiatives still stall before reaching production. The bottleneck isn’t model capability, it’s the data infrastructure underneath. Without live connectivity to business systems, semantic intelligence that gives data context to AI, and governance controls that enforce security at scale, AI initiatives fail to deliver business value.

CData’s own State of AI Data Connectivity Report reinforces this reality. Only 6% of organizations are satisfied with their current data infrastructure for AI. More than half still rely on custom-built integrations that can’t scale. And 71% of AI teams spend over a quarter of their implementation time on data integration alone, time spent wiring plumbing instead of building intelligence.

Connect AI: Connectivity, Context, and Control in a Single Platform

CData Connect AI is purpose-built to address the data infrastructure gaps that prevent AI from reaching production. Today’s enhancements extend the platform across all three pillars

Connectivity: Connect Gateway and 350+ Data Sources

Connect AI provides live, read-write access to more than 350 business systems, without replication or data movement. The new Connect Gateway extends this reach to data sources behind the firewall, with support for SAP, SQL Server, and PostgreSQL, and more. The result: AI systems can operate against live data regardless of where it resides.

Context: Expanded Agent Tooling and Toolkits

AI agents need business-aware context to choose the right actions and avoid unnecessary MCP tool calls. But exposing too much context creates new risks: increased token usage, model confusion, and unintended access to sensitive data or operations. Connect AI addresses this challenge with a scoped MCP architecture that precisely controls what each agent can see and do. This release introduces three complementary tool types:

• Universal Tools provide a normalized set of operations that work consistently across all 350+ connected systems. Instead of exposing hundreds of system-specific tools, agents receive a compact, schema-aware interface ideal for data exploration, ad-hoc analysis, and multi-source reasoning — without tool surface bloat.
• Source Tools expose tightly defined operations specific to each system. These tools map directly to approved system actions, allowing IT teams to enforce predictable execution, transactional safety, and auditability for production workflows.
• Custom Tools allow organizations to define purpose-built operations tailored to specific workflows. These tools execute pre-optimized queries with explicit data access limits — reducing token usage, improving performance, and eliminating unintended data exposure.

Workspaces define the data boundary for each agent by specifying exactly which datasets, schemas, or views are accessible. New Toolkits define the action boundary by determining which Universal, Source, or Custom Tools are available. Each Workspace and Toolkit combination can be deployed as a dedicated MCP server, ensuring that agents operate only within their intended scope; reducing context noise, strengthening governance, and delivering enterprise-grade control over agent behavior.

Control: SCIM and Custom OAuth Applications

Connect AI enforces per-user authentication with native source-system permissions applied dynamically at runtime, backed by full audit trails. New governance enhancements include SCIM 2.0 for automated identity lifecycle management and Custom OAuth Applications that enable organizations to use first-party credentials to meet internal security and compliance requirements. Every query is authenticated, authorized, and auditable.

The 25% Accuracy Gap: Why Architecture Matters

MCP is becoming the default interface between AI agents and business software. But how accurately do MCP providers actually return data? To find out, CData tested five MCP providers, representing the major architectural approaches in the market, across four sources (CRM, project management, data warehouse, and ERP) using 378 real-world prompts. Every response was scored against pre-established ground truth. No partial credit.

The results revealed a significant accuracy gap. CData Connect AI achieved 98.5% accuracy (67 of 68 correct responses). The other providers ranged from 65% to 75%—failing on one out of every three to four queries. The failures weren’t random: they clustered around relative date logic, multi-filter queries, semantic interpretation of business terms, and write operations, exactly the kinds of tasks AI agents need to perform reliably every day.

For organizations moving beyond copilots toward autonomous agents that read, write, and act on live business data, this gap is decisive. At 75% accuracy, an AI agent fails one out of every four actions. And that inaccuracy compounds: 75% accuracy across a five-step workflow means less than 24% of processes complete successfully. A 75% accuracy rate becomes a 75% failure rate.

Most MCP providers translate natural language directly into API calls, which works for simple lookups but breaks down when queries require date math, multi-condition filtering, or platform-specific business logic. Connect AI uses a relational abstraction layer with semantic intelligence that understands entity relationships, business conventions, and workflow rules. That’s why it maintained near-perfect accuracy across every platform tested, including ERP, where the vendor’s own native MCP server failed completely.

View the full benchmarking methodology and results here: https://www.cdata.com/lp/ai-accuracy-whitepaper/

Organizations deploying AI in production need an accuracy rate that prevents autonomous agents from creating more cleanup work than they save. Connect AI is built to clear that bar because connectivity, context, and control aren’t just platform features. They’re what makes accuracy at scale possible.

CData at Gartner Data & Analytics Summit

CData will be at the Gartner Data & Analytics Summit at Booth #308, where attendees can connect with the team and see the latest in universal data connectivity.

Speaking Session: AI Agents and the Future of Digital Work with Microsoft — CData Chief Product Officer Ken Yagen will take the stage alongside Microsoft Partner Director of Product Management James Oleinik on Wednesday, March 11 (11:15–11:45 AM EDT). The session will present a joint blueprint for moving from AI pilots to production-ready agentic AI, exploring how Copilot Studio and universal data connectivity can deliver the governed infrastructure enterprises need as Gartner predicts 40%+ of agentic AI projects will be canceled by 2027 without the right architecture in place.

Supporting Resources

• The 25% Accuracy Gap: MCP Provider Performance Across Enterprise Workloads — CData’s benchmark of five MCP providers across 378 enterprise queries found a 25+ percentage point accuracy gap, with CData Connect AI achieving 98.5% accuracy compared to 65–75% for other providers. Download the whitepaper: https://www.cdata.com/lp/ai-accuracy-whitepaper/
• The State of AI Data Connectivity Report: 2026 Outlook — Based on research with 200+ data and AI leaders and insights from AI pioneers at Microsoft, AWS, and Google, CData’s report found that only 6% of enterprises consider their data infrastructure fully ready for AI — establishing a direct link between data infrastructure maturity and AI success. Download the report: https://www.cdata.com/lp/ai-data-connectivity-report-2026/

¹ Gartner, Inc., “Gartner Says Worldwide AI Spending Will Total $2.5 Trillion in 2026,” Gartner.com (Jan. 15, 2026), accessed Feb. 20, 2026, https://www.gartner.com/en/newsroom/press-releases/2026-1-15-gartner-says-worldwide-ai-spending-will-total-2-point-5-trillion-dollars-in-2026
GARTNER is a trademark of Gartner, Inc. and/or its affiliates.

 CData Software today announced that it has been recognized in the 2025 Gartner® Magic Quadrant™ for Data Integration Tools. This marks the second consecutive year that CData has been included in the report.

The company’s unified platform delivers real-time access, semantic intelligence, and comprehensive governance across diverse data sources, empowering organizations to activate their complete data landscape for use in AI, and analytics. Guided by its vision to make data more accessible and actionable for both humans and AI, CData continues to advance innovation in data integration. Ongoing investments in AI integration are focused on addressing one of today’s most critical enterprise challenges: connecting fragmented data to AI systems to enable conversational analytics and agentic platforms.

CData continues to gain industry recognition for its innovation and momentum in data integration and connectivity. Based on real customer reviews, CData positioned again in the Strong Performers quadrant in the 2025 Gartner Peer Insights™. Other recent honors include 2025 Inc. 5000 list, the Accel 2025 Globalscape Top 100 report, The Software Report’s Top 25 Data Management and Analytics Companies of 2025, and the DBTA 100 2025: The Companies That Matter Most in Data.

Access a complimentary copy of the full report here: https://www.cdata.com/lp/gartner-magic-quadrant-data-integration-2025/

Only 6% of enterprise AI leaders say their data infrastructure is fully ready for AI: a readiness gap that has become one of the biggest constraints on AI progress. That’s a central finding of CData Software’s new report, The State of AI Data Connectivity: 2026 Outlook, which draws on independently collected survey data from more than 200 data and AI leaders at software providers and enterprise organizations. The report establishes a direct link between data infrastructure maturity and AI maturity, identifying the core capabilities that define AI-ready data infrastructure and revealing how gaps in data connectivity, context, and control are stalling AI initiatives across industries.

Infrastructure Gaps Hold Back AI Progress

The research exposes a stark divide: 60% of companies at the highest level of AI maturity have also invested in advanced data infrastructure, while 53% of organizations struggling with AI implementations are hampered by immature data systems. The gap is costing companies time, money, and competitive advantage.

Key Findings:

• AI teams are drowning in data plumbing: 71% of AI teams spend over a quarter of their time on data plumbing instead of innovation
• Connectivity complexity is exploding: 46% of organizations need real-time access to six or more data sources for a single AI use case
• Real-time data is universally critical — but still missing: 100% agree real-time data is essential for AI agents, yet 20% still lack real-time integration capabilities
• AI-Native Providers Are Outpacing Traditional Software in Integration Demands: AI-native software providers require 3x more external integrations than traditional companies (46% need 26+ integrations vs. 15%)
• Infrastructure maturity is the great divide: All high-AI-maturity organizations have built centralized, semantically consistent integration layers — 80% of low-maturity providers haven’t even started

Investment Priorities Shifting from Models to Infrastructure

The report signals a fundamental shift in AI strategy. Only 9% of organizations now rank AI model development as their top investment priority, while 83% are investing in or planning centralized, semantically consistent data access layers.

Download the full report: https://www.cdata.com/lp/ai-data-connectivity-report-2026/

About the Report

The State of AI Data Connectivity: 2026 Outlook provides benchmarks for both enterprises and software providers in two key areas:

1. Enterprise AI Adoption — How data infrastructure gaps are limiting AI success and what separates high performers from laggards
2. Product AI Strategy — How software companies are embedding AI capabilities and managing escalating integration complexity

The research references findings from the August 2025 MIT report, The Generative AI Gap: The State of Business AI in 2025.

 CData Software today announced the appointment of Ken Yagen as Chief Product Officer (CPO). Yagen will lead product strategy and engineering as CData scales its connectivity platform for enterprises deploying agentic AI internally and for software providers building AI into their products.

The appointment comes as CData experiences rapid growth in the AI connectivity space. With thousands of users already connecting enterprise data sources to AI systems through CData’s MCP Servers, and the recent launch of Connect AI—a managed Model Context Protocol (MCP) platform—Yagen’s leadership will accelerate the company’s product roadmap.

Advancing AI-Native Connectivity

Yagen joins CData as the company shapes the emerging category of AI-native connectivity. Connect AI provides the enterprise-scale infrastructure that AI systems and autonomous agents require: live, governed access to business systems combined with embedded system-level semantic intelligence that teaches AI the structure, relationships, and business logic native to each platform—transforming raw connectivity into operational fluency.

Yagen is an accomplished product management and technology leader with more than 25 years of experience driving innovation in enterprise software. Most recently at Warburg Pincus, he led AI and LLM initiatives across the firm’s portfolio companies, helping enterprises integrate emerging AI technologies into their business strategies. His career includes pivotal roles at MuleSoft, where he shaped product strategy for APIs and integration platforms that became foundational to modern enterprise architecture, as well as leadership positions at Box and Symphony, where he drove collaboration and enterprise SaaS innovation.

Dual Market Strategy: Enterprises and ISVs

Under Yagen’s leadership, CData will accelerate its dual go-to-market strategy, enabling both direct enterprise adoption and embedded use by independent software vendors (ISVs). Organizations are adopting CData’s managed MCP platform to standardize connectivity across departments and initiatives, while software providers are embedding CData’s connectivity into their products to deliver enterprise-ready AI capabilities without building integrations themselves.

CData Software today announced it ranked on the Deloitte Technology Fast 500™, a ranking of the 500 fastest-growing technology, media, telecommunications, life sciences, fintech, and energy tech companies in North America, now in its 31^st year.

CData Co-founder and CEO Amit Sharma attributes the company’s sustained growth and profitability to surging enterprise demand for real-time data connectivity and CData’s expanding ecosystem of global technology partners, including Salesforce, Google, Palantir, and SAP. As organizations accelerate their adoption of AI, analytics, and automation, CData’s solutions deliver the unified, secure, and scalable data access required to fuel those initiatives.

2025 Milestones & Momentum

In 2025, CData delivered a series of standout milestones that underscore its leadership in data connectivity:

• Expanded Partnership with Google Cloud: CData broadened its collaboration with Google Cloud, extending native connectivity across BigQuery, Looker, and Vertex AI to simplify real-time data access and analytics in Google Cloud environments.
• Launch of CData Embedded Cloud: The company introduced a new cloud-based connectivity platform enabling software providers to embed CData connectors without managing infrastructure, accelerating development cycles and time-to-market.
• Strengthened Partnership with Palantir Foundry: CData expanded its embedded integration capabilities within Palantir Foundry, enabling secure, governed access to hundreds of enterprise data sources directly through CData’s connectors.
• Introduction of CData Connect AI: The company launched Connect AI, the industry’s first managed Metadata, Connectivity & Processing (MCP) platform, empowering enterprises to connect AI applications to live, governed data across more than 300 enterprise systems.
• Expanded Integration Accelerator Portfolio: CData launched a suite of no-code Integration Accelerators for Snowflake, Microsoft Fabric, and Databricks, dramatically simplifying real-time, multi-cloud data integration and speeding time-to-insight for analytics and AI initiatives. 
• Strengthened Partnership with SAP: CData announced expanded connectivity support for SAP Datasphere and SAP Business Data Cloud, enabling enterprises to unify SAP and non-SAP data for enhanced analytics.
• Collaboration with Microsoft to Power Enterprise AI Agents: CData introduced Model Context Protocol (MCP) connectivity for Microsoft Copilot Studio and Microsoft Agent 365 through its Connect AI platform, enabling enterprises to build intelligent AI agents with real-time, semantic-rich access to 350+ data sources and enterprise-grade governance.
• Inc. 5000 Recognition: CData was once again named to the Inc. 5000 list, marking its second consecutive year of recognition for rapid growth and innovation.
• Named to Accel’s 2025 US AI 100: CData was recognized by Accel as one of the top companies shaping the future of AI and cloud innovation, underscoring the rising importance of seamless, governed data access as enterprises deploy AI assistants and intelligent agents at scale.

About the 2025 Deloitte Technology Fast 500

Now in its 31st year, the Deloitte Technology Fast 500 provides a ranking of the fastest-growing technology, media, telecommunications, life sciences, fintech, and energy tech companies — both public and private — in North America. Technology Fast 500 award winners are selected based on percentage fiscal year revenue growth from 2021 to 2024.

In order to be eligible for Technology Fast 500 recognition, companies must own proprietary intellectual property or proprietary technology that significantly contributes to the company’s operating revenues. Companies must have base-year operating revenues of at least US$50,000, and current-year operating revenues of at least US$5 million, with a growth rate of 50% or greater. Additionally, companies must be in business for a minimum of four years and be headquartered within North America (United States and Canada).

At its second annual Foundations conference, CData Software announced Connect AI, the first managed Model Context Protocol (MCP) platform that integrates AI assistants, agent orchestration platforms, AI workflow automation, and embedded AI applications with more than 300 enterprise data sources. With governed, in-place access to enterprise data, Connect AI preserves data semantics and relationships, giving AI complete understanding of the context. The solution also inherits user permissions and authentication directly from the source and can be deployed in the cloud or embedded within software products in minutes with point-and-click configuration.

Connect AI takes the same enterprise-grade connectivity technology already embedded by top technology companies including Palantir, SAP, Salesforce Data Cloud, and Google Cloud into their offerings, and reimagines it specifically for AI workloads with real-time semantic integration capabilities.

The solution builds on the momentum of the company’s MCP Servers, which have already seen thousands of users connect hundreds of data sources to AI assistants. The adoption validates AI’s need for governed enterprise data integration that understands context and relationships.

Breaking the Enterprise AI Deployment Barriers

Connect AI solves two core challenges MIT identified in its recent research. MIT reported that despite $30-40 billion in enterprise AI investment, 95% of AI pilots fail to deliver measurable business impact, primarily due to data access and governance challenges.

First, through data-in-place access, Connect AI preserves the rich contextual relationships that AI agents need for intelligent decision-making, delivering both immediate data access and meaningful data understanding.

Second, Connect AI inherits existing security and authentication protocols set in the source system ensuring AI access remains aligned with organizational controls. Data access is logged under the identity of the authenticated user or agent for comprehensive governance. Additional AI controls can be layered and managed within Connect AI.

Immediate Impact on Enterprises’ and Software Providers’ Use of AI

Enterprises use Connect AI with AI apps to get contextually-aware answers from business data in seconds; work that previously required days or weeks of report building. Its ability to handle complex queries across diverse systems with semantic understanding enables sales teams to use Claude for pipeline insights, marketing teams to prompt ChatGPT for campaign analysis, and finance teams to rely on Copilot for real-time budget updates and financial reports. IT and AI Engineering teams can also power agents built through AI workflow automation and agent orchestration platforms with direct, scalable access to semantically-rich enterprise data.

ISVs embed Connect AI directly within their products to provide their end-users with self-service integration between their data sources and the ISV’s agentic capabilities. The white-label offering gives tech companies an edge in the race for AI users because their agents can operate on the full semantic context of their end-users’ business, and not just on data brought directly into their products.

Industry Validation and Market Opportunity

This proven connectivity technology validates CData’s critical role in enterprise data connectivity and the company’s expansion into AI-native integration.

Availability

Connect AI is immediately available. More details are available here: www.cdata.com/ai

 CData Software today announced an expansion of its partnership with SAP focused on SAP Business Data Cloud–a  fully managed SaaS solution that unifies and governs all SAP data and seamlessly connects with third-party data. The collaboration aims to help customers connect to the data they care about most, by providing seamless, real-time access to the external data sources that modern enterprises depend on–without requiring data replication or custom code. 

Enabling Immediate, Self-Service Data Access

CData’s embeddable connectors provide direct, enterprise-grade integration to critical third-party platforms. Through the partnership, customers can access data, whether stored in cloud services, on-prem databases, or productivity tools, through a consistent, SQL-based interface.

By embedding CData connectors into SAP Business Data Cloud, SAP can provide its customers with access to non-SAP data, and improve their time-to-insight across AI, analytics, and operational workloads.

Driving the Shift Toward Open, AI-Ready Data

CData’s connector infrastructure helps ensure that every integration point meets the speed, security, and scale demands of AI-driven environments.

The expanded partnership comes at a time of significant growth for CData’s Embedded business, which has seen increased demand from software providers looking to offer connectivity to a growing variety of data sources. This momentum is fueled by the rising need for AI-ready data and the pressure on software providers to deliver immediate, self-service access to the data sources their users prioritize.

CData’s market leadership was recently validated by its inclusion in the 2024 Gartner Magic Quadrant for Data Integration Tools. This recognition reflects the differentiated business outcomes CData delivers to customers through its comprehensive connectivity solutions and integration capabilities.

This announcement follows CData’s strategic growth funding of approximately $350 million led by Warburg Pincus, with participation from Accel, announced in June 2024. The investment has allowed CData to accelerate its mission of simplifying data connectivity for enterprises, users, and applications through continued investments in operations, product development, and go-to-market strategy.

For more information about CData’s embedded connectivity solutions, visit www.cdata.com/embedded.