November 2025 Summaries

CData has enhanced its managed MCP platform, Connect AI, with source-specific instructions for hundreds of enterprise connectors, aiming to help AI applications understand schemas, metadata, entity relationships, dynamic objects, and common operations before querying data. The enhancement addresses common large language model limitations, including difficulty navigating large catalogs, discovering relationships, applying exact filters, avoiding invalid queries, and safely performing write operations, which can otherwise cause failed requests, excessive API use, high token consumption, and costly full-dataset scans. Drawing on more than a decade of connector development, the instructions provide mapped core entities, dynamic-schema discovery steps, progressive query guidance, stored-procedure examples, and relationship models tailored to systems such as Salesforce, NetSuite, and ServiceNow. CData says the update improves accuracy, response speed, cost predictability, and time to value through semantic metadata, query push-down, derived views, curated multi-source data collections, custom tools, and document access within AI workflows.

Bengaluru Tech Summit 2025, held November 18–20, highlighted the central role of clean, connected, secure data in making AI, digital identity, and enterprise modernization practical at scale. Sessions on fintech, governance, foundation models, generative AI, and social impact emphasized that fragmented data systems, unsafe datasets, and weak integration remain major barriers to effective AI deployment. Speakers also stressed the need for multilingual, culturally relevant Indian foundation models, domain-specific benchmarks, people-first AI initiatives, and responsible applications in healthcare, education, fraud prevention, and public services. Networking conversations with technology companies reinforced demand for improved access to operational data, cross-platform analytics, and compliant integration capabilities. The recap concludes that enterprise AI progress depends less on increasingly sophisticated models than on reliable data pipelines that deliver the right information securely and efficiently, positioning CData’s connectivity tools, including CData Connect AI, as a potential solution for these needs.

Real-time enterprise connectivity is presented as an alternative to batch, streaming, and vector-database pipelines for supplying LLMs with current operational data. Rather than replicating information into separate stores, permission-aware connectors and Model Context Protocol servers can let models query live systems such as ERP, CRM, HRIS, SQL databases, and SaaS applications at request time, improving freshness, reducing synchronization drift, lowering maintenance, and limiting governance risks from duplicated sensitive data. The approach supports on-demand transformations, role-based access controls, filtering or masking, audit logging, and monitoring of requests, permissions, availability, and response times. It is described as particularly useful for regulated industries and applications including financial risk analysis, healthcare workflows, manufacturing inventory and equipment monitoring, and customer service, while integrating with AI frameworks such as LangChain, CrewAI, Copilot Studio, and function-calling tools. CData positions its Connect AI managed MCP service as a unified platform for this architecture, arguing that direct live access can produce more grounded LLM responses than pipeline-based retrieval systems.

Real-time data integration is presented as a growing requirement for enterprise LLM applications by 2026, enabling AI systems to generate timely, accurate responses from continuously refreshed operational information. Organizations should first catalog internal systems such as CRM, ERP, HR, financial, and support platforms alongside external APIs, IoT telemetry, market feeds, webhooks, and event streams, then prioritize sources by structure, update frequency, and relevance. Effective integration platforms should provide low-latency ingestion, continuous synchronization, security controls, broad connectors, transformations, schema validation, and support for RAG, vector databases, and scalable LLM workloads. Continuous pipelines can ingest, normalize, enrich, and deliver live data through methods including Model Context Protocol, event streaming, RAG, and function calling, supporting uses such as fraud detection, clinical assistance, personalized customer service, and industrial monitoring. Maintaining these systems requires testing latency, schema consistency, throughput, data freshness, and model outputs, followed by iterative improvements to pipelines, prompts, mappings, and source connections. Security and compliance depend on encryption, least-privilege access, inherited authentication, input validation, audit logs, and adherence to standards such as SOC 2, GDPR, HIPAA, and CCPA. The material promotes CData Connect AI as a managed MCP-based platform that provides governed access to live enterprise data without replication or complex ETL workflows.

Embedded data connectivity integrates secure, direct access to external databases and services within SaaS applications, helping users access live data, analytics, and workflows without switching tools or relying on exports and custom pipelines. As organizations use more SaaS applications and face integration, security, and IT-capacity challenges, providers can use embedded connectivity to reduce data silos, improve user experience, and differentiate their products. Successful implementation requires assessing data sources, freshness requirements, user permissions, multi-tenant needs, and dependent workflows; selecting suitable ETL, iPaaS, or embedded-iPaaS tools; and designing for authentication, encryption, compliance, governance, scalability, and monitoring. Platforms such as CData Embed, Workato Embedded, Prismatic, Paragon, and Tray Embedded offer different strengths in data connectivity, automation, customization, and developer tooling, while deployment models range from downloadable connectors to managed cloud and AI-focused embedded cloud services. Emerging trends include AI-enabled data access, semantic context for large language models, embedded no-code builders, intelligent transformations, identity-centric controls, and expanded governance for AI-driven workflows.

AI adoption is compared to the early electrification of factories, where productivity gains were delayed because organizations applied new technology to workflows designed for older systems rather than redesigning operations around its capabilities. Although many companies have modernized through cloud platforms, APIs, data lakes, ETL pipelines, and governance, these architectures are often not optimized for AI’s much greater requirements for context, reasoning, data variety, volume, and real-time information. The passage argues that organizations achieving stronger AI results are treating AI as an active participant in workflows that can retrieve context, support decisions, validate outputs, identify inconsistencies, and initiate actions across systems, rather than as a reporting layer added to business-intelligence-era infrastructure. It advocates selectively reworking the architectural components most stressed by AI, potentially including refined pipelines, live-data layers, and semantic discovery, and promotes Mark Palmer’s e-book, Six Moves to Rewire Software for the AI Age, as a blueprint for AI-native data architecture.

Model Context Protocol (MCP) is presented as a standardized framework for connecting large language models, AI agents, and automation tools to live enterprise data while preserving business context, security controls, and auditability. CData Connect AI is described as a managed MCP platform that provides governed, real-time access to more than 300 data sources without replicating data, inheriting authentication, permissions, and security policies from underlying systems. The platform aims to address limitations of traditional APIs and batch ETL, including delayed data, lost semantics, fragmented integrations, and inconsistent governance, through centralized connectivity, identity-based logging, role-based access, and secure tool invocation. It supports legacy, cloud, on-premises, and hybrid environments through standard protocols such as SQL, ODBC, and JDBC, and is positioned for uses ranging from embedded product features to sales, finance, and IT automation. The material also emphasizes compliance alignment, risk mitigation for threats such as permission leakage and data exfiltration, and future support for multi-agent workflows, lifecycle management, semantic intelligence, and evolving AI governance requirements.

Microsoft and CData announced a collaboration at Microsoft Ignite 2025 to integrate CData’s managed MCP platform, Connect AI, with Copilot Studio, Azure AI Foundry, and Agent 365. The partnership focuses on enterprise AI agent requirements for connectivity, context, and control, using more than 350 prebuilt connectors, semantic metadata about source systems, and identity-based security controls such as OAuth/SSO, role-based access, least-privilege permissions, and audit trails. Within Copilot Studio, the integration is intended to extend agent access to enterprise applications and databases including SAP, Salesforce, Oracle, ServiceNow, Snowflake, and legacy systems, while Azure AI Foundry users can build applications that analyze data across multiple sources. Agent 365 is positioned as the governance layer for agent behavior, complemented by Connect AI’s controlled source-system access and monitoring capabilities. CData argues that combining broad data access, system-level context, and security oversight can help organizations move AI agents from experimental demonstrations to production tools for cross-system analysis, workflow automation, and business insights.

Real-time data pipelines give LLM applications continuous access to current enterprise data, improving response relevance, accuracy, and adaptability compared with traditional batch ETL processes. Effective pipelines combine multi-source ingestion, data cleansing and validation, text embedding and vector storage, workflow orchestration, and continuous monitoring to support reliable retrieval-augmented generation and other AI uses. Organizations must address fragmented systems, latency, scalability, and compliance through governed connectivity, source-level permissions, federated queries, semantic layers, and data lineage controls. Technologies such as Kafka, dbt, Pinecone, LangChain, Airflow, Kubernetes, Arize AI, and Weights & Biases support different pipeline stages, while CData Sync and CData Connect AI are presented as platforms that provide low-latency, permission-aware connectivity and replication across more than 350 data sources. These capabilities can enable applications including current financial reporting, consolidated customer insights, and LLM responses grounded in live business knowledge.

CData Sync has added change data capture support for IBM DB2 iSeries (AS400), extending its existing CDC capabilities for DB2 on z/OS and Linux, Unix, and Windows to cover the full IBM DB2 family. The hybrid replication platform is positioned as a way for organizations to move data securely and near real time from long-standing on-premises IBM systems into cloud and analytics destinations such as Databricks, Snowflake, Microsoft Fabric, and SQL Server without disruptive migrations. Its iSeries connector uses IBM i’s native journal records to capture inserts, updates, and deletes, avoiding triggers and full-table scans while aiming to preserve transactional accuracy, limit production impact, and maintain continuity through restarts or outages. CData argues that this support helps enterprises retain the operational value and historical context of IBM databases used in sectors such as banking, manufacturing, logistics, government, and telecommunications while making their data more accessible for reporting, analytics, AI, and machine learning initiatives.

AI agents operating on behalf of users should be treated as applications rather than independent non-human identities, using established OAuth, SSO, and delegated authorization patterns instead of new IAM accounts and credentials. Assigning agents separate identities can produce excessive privileges, account sprawl, weak attribution, and greater risk because LLM-driven agents are less predictable than conventional software. The proposed approach enforces permissions when an agent executes a specific action on a resource for a particular user, with access governed by scoped connections and auditable APIs. Connect AI applies this model by using credentials linked to users, preventing agents and models from handling access tokens, and limiting actions according to data-source and platform-level permissions. The company argues that separating user identity, model reasoning, and application execution allows developers to avoid embedding identity logic, gives security teams centralized control, and enables scalable, traceable agent deployments without creating new IAM constructs.

CData’s 2025 Q4 Driver and Connector release adds support for Klaviyo, Databricks Lakebase, and Oracle Eloqua Reporting, expanding SQL-based access to marketing, database, and reporting data for analytics, integration, and AI workflows. The release also modernizes Neo4j connectivity for Neo4j 5.x and AuraDB, improves WordPress compatibility, and enhances MCP server capabilities for Google Drive and Sheets, including direct Google Docs content access and expanded file-management features. SQL Gateway now supports PostgreSQL endpoints, Microsoft Graph drivers gain OAuth scope configuration for sovereign cloud environments, and Databricks, Sage Intacct, Shopify, BigCommerce, Salesforce Account Engage, and QuickBooks Online receive significant updates, including API migrations, authentication changes, and expanded data operations. Additional improvements span platforms such as HubSpot, Snowflake, Google BigQuery, Amazon Athena, LinkedIn, Mailchimp, SharePoint, SurveyMonkey, and SAP BusinessObjects BI, reflecting hundreds of portfolio-wide updates and compatibility enhancements.

CData has introduced Klaviyo drivers and connectors that provide live, SQL-based access to marketing data for BI tools, databases, custom applications, and automation platforms. Klaviyo is a marketing automation and customer data platform that organizes customer information and activity through profiles, campaigns, lists, segments, and events, enabling targeted communications and performance analysis across channels such as email and SMS. CData’s offerings include JDBC and ODBC drivers, a .NET provider, Excel Add-In, Tableau and Power BI connectors, Python support, and PowerShell cmdlets, with authentication through API keys or OAuthPKCE and read/write capabilities. Its relational data model exposes objects including accounts, campaigns, campaign statistics, profiles, events, segment statistics, and profile analytics as database-like tables and views. A Power BI example explains how users can install the connector, configure an ODBC data source with a Klaviyo API key, select Import or DirectQuery mode, and create visualizations such as a revenue-by-channel donut chart using campaign and campaign-statistics data.

As enterprises expand use of large language models such as ChatGPT, Microsoft Copilot, and Google Gemini, secure managed access to live enterprise data is presented as essential for preventing data exposure, maintaining compliance, and preserving operational oversight. Effective LLM governance includes identity-based authentication, role-based access controls, encryption, audit logs, rate limiting, continuous monitoring, incident response, and testing against threats such as prompt injection, model inversion, and excessive agency. Organizations are advised to align LLM security with business priorities including data protection, cost control, and stakeholder trust, while mapping controls to regulations such as GDPR, HIPAA, and CCPA and using frameworks including OWASP, MITRE ATLAS, and NIST. The text contrasts real-time, governed data connectivity with data-replicating ETL approaches, arguing that live access can reduce latency, duplication, and compliance complexity. It highlights CData Connect AI as a no-code platform using the Model Context Protocol to connect multiple AI models to more than 270 enterprise data sources while retaining source-system permissions, supporting cloud and hybrid environments, and providing security features such as OAuth, SSO, MFA, RBAC, logging, and encryption.

CData has introduced drivers and connectors for Databricks Lakebase, a fully managed, PostgreSQL-compatible database that adds OLTP capabilities to the Databricks Lakehouse and supports querying both native and federated external data. The new tools provide live, bidirectional access to Lakebase through standard SQL, OAuth authentication, SQL-92 querying, and compatibility with more than 200 BI, analytics, reporting, AI, integration, and development platforms. Available options include JDBC, ODBC, ADO.NET, MCP Server, Excel, Tableau, Power BI, Python, and PowerShell support, with CData Sync and Connect AI planned for future release. The connectors map a broad range of PostgreSQL and Lakebase data types into CData schemas and can be used to create Power BI reports by installing the Lakebase connector, configuring an OAuth-based DSN, selecting the connection in Power BI, and visualizing imported data.

CData has been named an Innovative Provider in the ISG Buyers Guide for Data Integration 2025, an independent evaluation that assesses vendors’ product capabilities and customer experience, including usability, reliability, validation, and return on investment. The recognition highlights CData’s AI-driven platform and its role in helping enterprises make fragmented data more accessible for analytics, conversational AI, and agentic workflows. As organizations move beyond traditional ETL toward flexible approaches such as ELT, change data capture, and data virtualization, CData supports these patterns alongside reverse ETL through broad connectivity across applications, databases, APIs, and cloud services.

Real-time SAP-to-Databricks integration can improve decision-making, analytics, automation, and AI by making operational data quickly available in Databricks, but it requires careful planning around latency, schema changes, security, and data quality. The recommended approach begins by defining measurable business outcomes and KPIs such as freshness, processing latency, completeness, and integrity, then selecting a batch, event-driven, or hybrid architecture based on operational needs. The guide positions CData Sync as a low-code tool for connecting SAP and Databricks, supporting secure authentication, role-based access, governance, full loads, and timestamp- or integer-based incremental replication. It advises preparing SAP data and Databricks governance through accessible, standardized source tables and Unity Catalog, choosing suitable SAP change-data-capture methods such as ODP, SLT, or database logs, and establishing a clean historical baseline before transitioning to incremental updates. Ongoing validation should reconcile record counts, monitor freshness and latency, and verify relational consistency, while dashboards, alerts, logging, and scalable pipeline design help maintain reliable performance as data volumes and use cases expand.

CData’s comparison of Claude Skills and the Model Context Protocol (MCP) argues that the two approaches serve complementary roles rather than replacing one another. MCP enables AI agents to discover and explore external systems, schemas, tools, and data sources, while Claude Skills package known, repeatable workflows into instructions and code that can execute tasks with less model context. In controlled tests using Salesforce and Zendesk data through CData Connect AI, MCP combined with Skills reduced token use by 65% for data discovery, 34% for a simple revenue query, and 58% for a cross-system join compared with MCP alone. The savings resulted from using MCP initially to identify available data and construct requests, then moving established requests into Skills that call APIs directly and return streamlined results rather than requiring the model to process extensive schema metadata and verbose JSON. The proposed workflow is therefore to use MCP for initial discovery and understanding, then create Skills for efficient, reliable execution of recurring tasks.

CData announced its recognition as a Major Player in the IDC MarketScape: Worldwide Data Integration Software Platforms 2025 Vendor Assessment, highlighting its focus on simplifying secure, scalable, and governed data access across cloud and on-premises systems. The company positions its platform as a unified solution for connecting data sources, managing data movement, and supporting analytics, operations, automation, and AI initiatives. Its recently launched Connect AI extends this approach by linking enterprise systems with AI models and agentic platforms including Claude, ChatGPT, Microsoft Copilot Studio, and n8n, enabling natural-language data queries and automated actions across connected systems. CData states that its platform supports real-time data flows, governance, lineage, policy controls, and consistent data quality to help organizations build trusted foundations for AI and decision-making. IDC MarketScape evaluates technology vendors through qualitative and quantitative criteria to compare their offerings, strategies, capabilities, and market position.

CData Connect AI is presented as a no-code, governed integration layer that connects Anthropic’s Claude to Salesforce through the Model Context Protocol, enabling Claude to query live CRM data, generate reports, summarize opportunities, and answer natural-language business questions without creating separate data copies. The setup involves configuring a Salesforce connection with appropriate authentication, limiting Claude’s access to necessary objects and permissions, and using Salesforce sandbox environments to validate queries and controls before production deployment. The integration relies on Salesforce authentication policies alongside TLS encryption, OAuth authorization, audit logging, and existing roles, SSO, and MFA to support security and compliance requirements. Ongoing monitoring of query performance, API usage, security alerts, and user feedback is recommended, along with periodic permission reviews, data masking for sensitive fields, and stakeholder training. Suggested applications include sales forecasting, personalized customer engagement, real-time analytics, automated reporting, and operational recommendations, while CData’s platform can also connect Claude to more than 300 enterprise data sources.

CData announced that its Connect AI platform is a featured Model Context Protocol launch partner in the Databricks Marketplace, intended to extend Databricks Agent Bricks with governed access to live enterprise data and operational systems. The integration is designed to let AI agents combine historical analysis in Databricks with real-time information and actions across more than 350 systems, including Salesforce, ServiceNow, and NetSuite. Connect AI provides connectors that expose system data, metadata, relationships, and business logic, while supporting both read and write operations such as updating sales opportunities, creating support tickets, and adjusting inventory forecasts. The platform also emphasizes enterprise controls including existing access permissions, row- and field-level security, audit logs, and compliance certifications, and is available through a 14-day free trial in the Databricks Marketplace.

Context engineering is presented as the practice of supplying an LLM with the right minimum amount of information, tools, and data at the appropriate time, extending beyond prompt engineering to include system instructions, memory, external data, and tool access. As RAG and the Model Context Protocol make live enterprise data more accessible to LLMs, managing context has become important for controlling costs, improving reliability, and reducing hallucinations. CData positions its Connect AI platform as a context-engineering solution built on its SQL-based data connectivity foundation, offering access to more than 350 enterprise sources. Its approach aims to limit tool selection complexity by using eight tools regardless of connected systems, provide semantic understanding of standard and custom data objects, and process complex queries through source systems or the platform before returning results to the LLM. According to the text, these capabilities reduce token use and data-model exploration while leaving more of an LLM’s context window available for analysis and actions.

Production AI agents require authorization beyond user authentication, because they must act on behalf of individual users across external services while being constrained to specific actions and resources. Generic service accounts can bypass user-level permissions, while granting agents full user credentials can create risks from errors, hallucinations, or prompt injection. The text presents CData Connect AI as a managed MCP platform that addresses this gap through user-specific, connection-level permissions enforced when tools are executed, enabling controlled actions such as querying Salesforce, updating Jira, or sending messages. It provides generalized tools for accessing and acting on business systems, abstracts OAuth flows, token management, credential storage, auditing, and service integrations, and can be used with MCP-compatible agent frameworks such as LangChain to securely retrieve data and perform permitted tasks.