February 2026 Summaries

Selecting an ETL tool should focus on compatibility with an organization’s existing infrastructure rather than headline connector counts or feature lists, particularly for enterprises combining legacy on-premises systems, cloud applications, and strict compliance requirements. ETL extracts, transforms, and loads data for analytics and operational use cases across industries, while effective tool evaluation should consider connector depth, scalability at projected volumes, deployment flexibility, predictable pricing, real-time and CDC capabilities, security certifications, governance, and usability. Hybrid architectures benefit from processing data near its source with centralized orchestration, while regulated environments require encryption, role-based controls, immutable audit logs, and lineage tracking. The discussion compares several enterprise tools, including CData Sync, Azure Data Factory, Fivetran, Airbyte, and AWS Glue, and recommends a structured selection process based on documented requirements, weighted priorities, and proof-of-concept testing with real data and pipelines.

Enterprise AI is increasingly dependent on agent connectivity—the infrastructure, protocols, and governance that allow autonomous agents to securely access data, communicate, coordinate tasks, and trigger actions across cloud, edge, and on-premises systems. Although many organizations are experimenting with AI agents, comparatively few have scaled them to production, largely because of challenges involving connectivity, architecture, security, and operational governance rather than model capability. Key trends for 2026 include low-latency networks, distributed edge computing, 5G Advanced network slicing, multi-agent orchestration, and interoperability standards such as Model Context Protocol and Agent2Agent, which can help specialized agents work together across vendor ecosystems. Organizations are advised to optimize costs through smaller task-specific models, hybrid edge-cloud deployment, and telemetry, while applying identity-based access controls, least-privilege permissions, audit trails, monitoring, and lifecycle governance to address the greater risks posed by agents with system-level access. The discussion positions secure, standardized connectivity platforms such as CData Connect AI as a way to simplify governed connections between agents and enterprise APIs, databases, and data platforms.

Model Context Protocol (MCP) is presented as a critical connectivity layer for enterprise AI agents, handling secure tool access, permissions, and context while often becoming a larger performance constraint than the underlying model. The discussion outlines ten complementary optimization approaches: retaining warmed models and storage connections through caching, grouping requests through batching and pipelining, running independent tools concurrently, streaming partial results, containing failures with circuit breakers and backoff policies, reusing connections with pooling and efficient protocols, limiting accumulated context, maintaining databases and vector stores, caching tool definitions for faster startup, and decomposing services for targeted autoscaling. It cites benchmark figures suggesting caching can substantially reduce repeated-call latency and recommends operational practices such as dependency analysis before parallel execution, idempotent streaming, monitoring, query pushdown, storage maintenance, and distributed tracing. The piece concludes that combining these methods can improve MCP reliability, latency, and throughput under enterprise workloads, while promoting CData Connect AI as a managed platform that provides several of these infrastructure capabilities across more than 350 data sources.

Salesforce often requires dedicated ETL, ELT, or reverse ETL tools for complex analytics, cross-system joins, compliance controls, and scalable data synchronization beyond its core CRM capabilities. Key selection factors include support for Salesforce APIs and change data capture, transformation approach, deployment options, compliance certifications, and predictable pricing as data volumes grow. The overview compares platforms including CData Sync, Fivetran, Matillion, Integrate.io, Hevo Data, Airbyte, Informatica, Talend, and Skyvia, positioning them for differing needs such as enterprise governance, no-code use, warehouse-native analytics, or self-hosted engineering control. ELT is presented as well suited to cloud data warehouses and high-volume analytics because transformations occur after loading, while ETL remains useful where data must be validated, masked, or governed before reaching downstream systems. The discussion also contrasts managed platforms, which reduce maintenance but may have variable usage-based costs, with self-hosted tools, which offer greater control but require internal operational resources. CData Sync is highlighted throughout as a compliance-certified option offering cloud or on-premises deployment, real-time incremental replication, reverse ETL, extensive connectors, and connection-based pricing intended to improve cost predictability.

CData and Microsoft are promoting a joint approach to scaling agentic AI that emphasizes enterprise data connectivity, business context, and governance rather than further model development. Citing research that 73% of organizations view data connectivity as the main obstacle to AI scaling, the announcement argues that many projects remain unprepared for production because of shadow AI, weak governance, and architectural gaps, with Gartner forecasting that more than 40% of agentic AI initiatives could be canceled by 2027. Microsoft’s Work IQ, Copilot Studio, and Agent 365 are presented as the intelligence layer, while CData provides more than 350 data connectors, semantic capabilities, inherited identity, and audit trails. The companies will demonstrate agents coordinating order-to-cash workflows across Salesforce and Dynamics 365 at the Gartner Data & Analytics Summit, with presentations from CData Chief Product Officer Ken Yagen and Microsoft product leader James Oleinik.

CData Driver and Connector 2025.2 expands its connectivity portfolio with new production drivers for SAP SuccessFactors Learning and Adobe Experience Manager, while promoting Oracle Eloqua Reporting, Jira Assets, Salesforce Data Cloud, Adobe Target, and Klaviyo from beta status. The release also introduces 26 API profiles spanning developer tools, marketing platforms, productivity software, AI services, and infrastructure products. Platform-wide improvements include SQL functions such as REGEXP_REPLACE and CURDATE, more efficient Base64 streaming, enhanced query slicing, and Salesforce-specific features for rapid record counts and dependent picklist analysis. CData additionally extends ODBC driver support to Linux ARM64, enabling deployment on ARM-based cloud infrastructure, and adds compatibility with Visual Studio 2026, Entity Framework Core 10, and related SSIS workflows. Updates for major services including Zendesk, Facebook Ads, MongoDB, HubSpot, Shopify, GitHub, Jira, ServiceNow, and others provide new APIs, authentication options, performance enhancements, and expanded data-access capabilities across more than 350 supported sources.

Real-time Salesforce data access is presented as increasingly important for enterprise analytics, AI agents, customer personalization, and operational decision-making, particularly as organizations contend with fragmented application ecosystems and limited integration coverage. The text compares several approaches, including managed connectivity platforms such as CData Connect AI for governed live queries across Salesforce and other sources, Salesforce Data Cloud for native identity resolution and data activation, Streaming APIs and Change Data Capture for event-driven synchronization, reverse ETL for returning warehouse-derived insights to Salesforce, embedded dashboards for in-CRM operational reporting, federated zero-copy queries for accessing distributed data without replication, collaboration integrations such as Slack for event alerts, and hybrid asynchronous processing for handling large background workloads without slowing the user interface. Each method involves trade-offs among latency, scalability, licensing or infrastructure costs, governance, and technical complexity, with native Salesforce tools emphasizing integrated security and external platforms supporting broader cross-system use cases. Effective selection depends on the specific business scenario, required consistency, compliance and audit needs, expected data volumes, API quotas, and the organization’s tolerance for replication, network dependence, and operational maintenance.

A CData case study compares AI-assisted development of a Java application that synchronizes Salesforce Accounts, Contacts, Products, and filtered Orders with an existing, undocumented MySQL database, using standard JDBC drivers at runtime. It reports that using Claude Code alone led to numerous runtime problems involving incorrect key mappings, missing fields, differing table names and schemas, absent destination Order tables, foreign-key ambiguity, and driver-specific SQL limitations, requiring roughly 2.5 hours and 13 compile-run-debug cycles for only partial success. In contrast, CData Code Assist MCP gave the AI live access to Salesforce and MySQL schemas, query testing, and driver behavior during development, allowing it to identify that Salesforce IDs map to dedicated MySQL fields, exclude unavailable columns, create custom Product2-to-products mappings, detect that no MySQL Order table exists, and validate filtered Salesforce Order queries before code generation. The reported MCP-assisted workflow took about 35 minutes, required one or two execution cycles, and achieved an estimated 90% first-run success rate, while producing a conventional Java/JDBC application with no MCP or LLM dependency in production.

SQL Server replication can offload reporting and business intelligence workloads from production databases by maintaining synchronized secondary copies, preventing complex analytical queries from locking source tables or degrading application performance. The guide explains snapshot replication for periodic full refreshes, transactional replication for near-real-time one-way reporting copies, and merge replication for bidirectional or disconnected environments, while also noting peer-to-peer and Always On read-only replicas as alternatives for certain availability and reporting needs. Effective deployment requires defining latency, update, schema-change, and capacity requirements; preparing SQL Server Agent, permissions, connectivity, security, and compliance controls; and configuring a Distributor, publications, articles, and Subscribers. It recommends filtering replicated data, using backup-based initialization for large databases, tailoring indexes and aggregates on reporting servers, isolating reporting hardware resources, and monitoring latency, agent health, queues, storage, and transaction logs. The material also emphasizes scripting configurations for recovery, testing schema changes outside production, limiting unnecessary data and large objects, documenting failover procedures, and considers CData Sync as a simplified no-code option for replication across SQL Server and other data sources.

CData Code Assist MCP is an add-on for AI coding environments such as Cursor, Claude Code, and Gemini Code Assist that gives ISVs real-time, schema-aware access to enterprise data sources supported by CData Drivers and Connectors. By sharing the same underlying data model as the runtime drivers, it enables developers to generate code, queries, connection interfaces, and workflows that align with live source schemas and behave consistently in production. Using Salesforce examples, the add-on can expose objects, fields, relationships, authentication requirements, valid picklist values, record types, and system constraints, helping teams design adaptive configuration screens, relationship-aware interfaces, validated query templates, and input controls. It also supports support and sales teams by making source-specific metadata available for troubleshooting integration issues and demonstrating compatibility with custom objects.

Sales managers can use Claude Cowork with CData Connect AI and Anthropic’s Model Context Protocol to access governed enterprise data conversationally, analyze pipeline health, and automate CRM-related work. In a Vibe Querying episode, CData Sales Manager David demonstrated querying Salesforce for high-value opportunities missing SPICED qualification data, producing results that would otherwise require custom reports or RevOps support. Claude also consolidated Gong call transcripts and email threads to create a detailed SPICED assessment for a deal, allowing managers to review synthesized context before updating records. When direct data access was read-only, Claude Cowork used browser automation to update Salesforce opportunity fields, populate SPICED information, draft an Outlook follow-up email without sending it, and create a calendar reminder. David reported saving at least 10 hours in roughly a week and a half, while estimating that sales representatives could save about an hour daily by reducing call preparation and follow-up administration.

IBM Db2 remains central to mission-critical systems in sectors such as finance, healthcare, and government, creating demand for ETL and ELT tools that modernize data access without disrupting performance, security, or compliance. Effective Db2 integration must account for differences among Db2 LUW, z/OS, and Db2 for i, including platform-specific drivers, EBCDIC or CCSID encoding, DRDA connectivity, RACF security, CDC methods, and bulk-load utilities such as LOAD and DSNUTILB. The guide recommends evaluating tools based on cross-platform support, log-based change data capture, pushdown processing, bulk-load capabilities, governance, deployment flexibility, and pricing, comparing CData Sync with IBM DataStage, Informatica, Talend, Oracle Data Integrator, Pentaho, and Apache NiFi. It presents CData Sync as a flexible SaaS or self-hosted option with broad connector coverage, CDC, parallel loading, and audit features, while outlining example pipelines that transform GraphQL’s nested JSON and Redis key-value data into relational Db2 tables. It also emphasizes practices such as batching, idempotent upserts, schema-drift handling, TLS encryption, least-privilege access, and preserving Redis expiration data to build reliable, scalable Db2 data pipelines.

Embedded data integration places connectivity, synchronization, and access to external data directly within software products, making it a core product capability rather than supporting infrastructure. The approach enables real-time reporting, automation, analytics, and AI features, but also makes security, latency, reliability, governance, and compliance immediately visible to end users. The text recommends secure-by-design development with strong authentication, least-privilege access, encryption, masking, tokenization, and continuous security testing, alongside event-driven architectures, change data capture, APIs, data virtualization, and ELT for responsive and controlled data movement. It emphasizes observability through freshness, lineage, anomaly detection, automated incident response, and carefully bounded AI-assisted monitoring, while advocating policy as code, audit logging, and human oversight for high-impact automated actions. Organizations are also advised to manage technical debt through phased migrations, validated automation, protected replication, and deployment choices ranging from customer-managed connectors to managed cloud and AI-ready services; CData Embed is presented as a provider supporting these deployment models.

Multi-agent AI systems require stronger governance than single-agent deployments because specialized agents need controlled access to enterprise tools and data while generating accountable, traceable actions. The Model Context Protocol (MCP), introduced by Anthropic, is presented as an open-source governance layer that complements rather than replaces conventional API gateways by providing schema validation, per-agent identities, least-privilege permissions, declarative policies, and live audit trails. The recommended implementation process begins by defining agent roles and narrowly scoped tool access, then selecting a framework such as FastMCP or official MCP SDKs, enforcing SSO, SCIM, OAuth 2.1, scoped tokens, and identity mapping, and applying deny-by-default policy-as-code controls for tool access, data classifications, rate limits, and token lifecycles. Production MCP servers should remain private behind secured gateways, use tool version pinning, cloud or network isolation, and additional monitoring or PII safeguards, while comprehensive logs and metrics should capture each agent’s identity, tool calls, parameters, results, errors, costs, and behavioral anomalies. Testing should use schema validation, role-based simulations, edge cases, sensitive-data checks, tracing, and sandbox-first deployments before gradual scaling across separate development, staging, and production environments. The text also promotes CData Connect AI as a managed platform for MCP-related identity, connectivity, policy enforcement, auditing, observability, and deployment governance.

Model Context Protocol (MCP) is presented as an open standard for connecting AI agents and applications with enterprise data, tools, and workflows in real time while enforcing security, governance, and compliance. The discussion outlines seven architecture patterns: centralized hub-and-spoke deployments for unified access and policy enforcement; distributed or federated servers for data residency, domain ownership, and isolation; embedded or white-label implementations for software vendors; federated SQL interfaces that standardize access to diverse systems; semantic-layer-first designs that align business definitions across sources; hybrid ETL and MCP environments that combine historical analytics with live operational context; event-triggered workflows that use events to initiate agent actions and MCP queries for current context; and API gateway-based governance for authentication, authorization, rate limits, auditing, and monitoring. Organizations may combine these approaches based on factors such as scale, latency, regulatory requirements, operational control, and the need for real-time data, with managed platforms positioned as a way to reduce integration and administrative complexity.