Tableau Desktop Free Edition opens the door to more teams. The real advantage is pairing it with live data access.
CData connectors provide connectivity to 350+ sources, so you can build dashboards on current data without pipelines or replication.
Learn more: https://bit.ly/418fuHj
AI agent design is often framed as LLM workflows vs code execution. That framing breaks down fast in real systems.
Workflows span an autonomy spectrum, from exploratory reasoning to deterministic actions.
Your architecture needs to support all of it.
Read more: https://bit.ly/4szKNWG
Most MCP migrations do not fail at the protocol layer. They fail when legacy systems, identity, and governance come into play.
This guide walks through a phased approach: https://bit.ly/3PGZYj7
Start with a focused pilot, wrap existing systems, enforce security early, and scale deliberately.
ERP migrations introduce schema drift that break pipelines and downstream workflows.
This demo shows an AI agent identifying those changes, rebuilding mappings, and executing order-to-cash across CRM, ERP, and billing systems with human approval in the loop: https://youtu.be/FXU9ED2y6IQ
Agentic retrieval is a shift from retrieval to execution.
Instead of single-pass queries, agents:
• Decompose complex questions
• Run parallel searches across sources
• Validate outputs before responding
• Call APIs to complete tasks
A foundation for enterprise AI systems: https://bit.ly/4tJHjlu
MCP solved fragmented agent-to-data connectivity. But it introduced a new issue.
Tool sprawl. Context bloat. Higher latency and token waste.
Dropping MCP for CLI is not a fix. It creates security and governance gaps.
What matters is how tools are exposed and loaded: https://bit.ly/3OC9QdK
Deploying MCP across multiple systems often turns into a mix of custom adapters, duplicated auth, and hard-to-maintain integrations.
A managed MCP approach standardizes this into a single endpoint with governed access and reusable adapters.
Step-by-step deployment guide: https://bit.ly/4c5bNHt
Most software providers are asking: how do we connect AI to our customers’ systems?
Better question: what does AI actually need from those systems?
• Live access to data
• Consistent business context
• Reliable write-back
That’s a different model than what iPaaS provides: https://bit.ly/4c2Sn5X
APIs come with different schemas, limits, and edge cases. Managing that across systems creates real overhead.
Euphonic AI standardized access through a unified ODBC interface, reducing complexity and improving how they tune performance: https://bit.ly/3Os0d18
AI works… until it needs access to real data.
That’s where most teams get stuck:
– disconnected systems
– inconsistent schemas
– access & governance challenges
We’re showing what this looks like in practice (and how to fix it) → https://bit.ly/4ciD0HO
ODBC is still the foundation for SAP connectivity, but small configuration details determine success.
Endpoint type, driver compatibility, DSN setup, and encryption all impact performance and reliability. This guide walks through the full setup with CData Drivers: https://bit.ly/4safP7b
Moving SAP data into Snowflake requires more than connectors.
You need:
• A domain-driven rollout plan
• ELT pipelines built for scale
• Structured layers (raw, staging, curated)
• Governance from ingestion to analytics
A detailed guide for modern data teams: https://bit.ly/4dUupfI
MCP performance issues rarely come from the model.
They come from cold starts, connection overhead, and how tool calls are executed.
Caching, batching, parallel execution, and context trimming reduce latency and improve throughput.
See all 10 techniques: https://bit.ly/41C8ZfZ
Scaling AI agents isn’t about orchestration alone. It’s about data access.
Multi-agent workflows depend on consistent, governed access across systems. Point-to-point integrations don’t hold up at scale.
A practical 7-step breakdown for production: https://bit.ly/4lZrx3c
Traditional MFT pricing models charge for growth. New partners, new workflows, higher throughput all increase cost.
Arc MFT Unlimited removes that variable. Flat pricing, unlimited connectors, parallel processing: https://bit.ly/4cdvblJ
Design for performance, not billing constraints.
New model releases get the attention.
But agents fail because of the harness: Context management, tool execution, retries, and security are the real challenges.
Claude Managed Agents is @anthropic.com's answer: bit.ly/4eeBaZS
CData Syncが新機能を追加、データパイプラインを革新する #宮城県 #仙台市 #CData #オープンテーブル #データパイプライン
CData Software, Inc.が発表したCData Syncの新機能は、企業のデータパイプラインを革新する。最新のオーケストレーション機能によりリアルタイムでデータ管理が可能に。
If your AI agent can’t access real enterprise data, it’s not useful.
The hard part isn’t the model — it’s:
– connecting to systems like SAP or SQL Server
– managing access control at scale
– making schemas usable for agents
We’re demoing how this works → https://bit.ly/4ciD0HO
Time-to-first-query determines how fast agent development actually moves.
With self-hosted MCP, every source adds setup work across auth, APIs, and schemas.
Managed MCP provides a single endpoint so agents can query live enterprise data within minutes.
Full comparison: https://bit.ly/3NMXFdP
Salesforce ETL challenges show up in production. API limits, rising data volume, and unpredictable pricing models.
CData Sync addresses this with CDC, flexible ETL and ELT, and connection-based pricing designed for scale.
Full breakdown of top tools here: https://bit.ly/4sPb2JG
Multi-tenant integration isn’t just about shared infrastructure.
At scale, teams are managing connector updates, schema drift, tenant isolation, and noisy neighbor risk, all while supporting AI workloads.
This playbook breaks down the architecture tradeoffs: https://bit.ly/4tirDp1
AI agents are no longer just querying data.
They’re writing to systems, coordinating workflows, and operating across environments.
That requires identity control, audit trails, and standardized communication between agents.
Here’s what that looks like in practice: https://bit.ly/4s1x5eV
Working with APIs often means dealing with rate limits, schema gaps, and inefficient queries.
This release adds new integrations and improves how large queries and binary data are handled, helping teams run faster and with more control.
Explore the update: https://bit.ly/4chdUZT
AI teams are spending too much time wiring systems together.
When integrations are custom and one-off, progress slows fast.
MCP standardizes how agents access data and tools so teams can move from prototypes to production faster: https://bit.ly/4v7osCs
Most enterprise AI projects don’t stall because of the model.
They stall because the data layer isn’t built for production —
on-prem data is unreachable, schemas are inconsistent, and governance breaks at scale.
We’re demoing how to fix that → https://bit.ly/4ciD0HO
Most organizations experimenting with AI agents aren’t blocked by model quality.
They’re blocked by connectivity.
Agents need reliable access to data, APIs, and other agents to operate in production. That’s where architectures are failing today.
What’s changing in 2026: https://bit.ly/4s1x5eV
Custom MCP tools are now available in Connect AI.
Define inputs, logic, and outputs. Reuse across users and applications. Monitor every execution centrally.
Drive more consistent AI actions.
See how: youtu.be/m2d6dwSd7tg 👀
CData Drivers and Connectors 2025.2 introduces new ways to work with API-driven data.
26 new API profiles, faster Salesforce record counts, and improved query control with vertical slicing.
Built to reduce latency and improve pipeline reliability.
Learn more: https://bit.ly/4chdUZT
“One API for everything” sounds efficient.
Until your team is supporting two integration layers.
“A year after implementing a unified API, we had a parallel code base…”
Common models cover the basics. Real use cases do not. So you build twice.
🔗 https://bit.ly/3Pv4nFR
