CData Software

Building an AI finance assistant usually means months of integration work. 🔗 bit.ly/4tHTQGy This approach connects an LLM to any source through a single MCP endpoint, so you can query live data and go from idea to prototype in days.

Most enterprise AI projects fail at the same point: data access. Connecting Claude to Workday is easy. Connecting it securely, in real time, w/ RBAC, audit logs + no data replication is the hard part: bit.ly/49gpaDY That’s where Connect AI’s MCP architecture fits.

We are hosting a live demo on smarter SQL Server → Snowflake ingestion with CData Sync. Join Manish Patel and Cameron Leblanc for a practical walkthrough focused on reducing lag, simplifying pipelines, and improving data freshness. bit.ly/4vUOd9y

That is where approaches like log-based CDC change the equation. Capture only what changed. Reduce unnecessary movement. Deliver data continuously instead of waiting on large batch windows.

Your dashboards are not slow. Your ingestion layer is. SQL Server → Snowflake pipelines often introduce just enough lag to create constant downstream friction. bit.ly/4vUOd9y

A consistent integration layer is key for agentic AI.    @thenoahai used CData’s MCP capabilities to standardize data access—cutting integration timelines by 80%: bit.ly/4tZA7SZ    Real-time, governed data is what makes scalable AI products possible.

Good overview of why many teams move @Workday data into @Azure Data Lake before reporting or AI projects: bit.ly/4ert9kD    Focus areas: • Scalable HR analytics • Incremental replication • Governance + lineage • Bronze/Silver/Gold modeling • Monitoring + reliability

CData Sync のアップデートがとても充実していたのでウェビナーを開催することにしました！ 6/4 14:00〜・AI Ready なデータ基盤作りを新機能でそれぞれサポート！ ① 鮮度 — CDC 強化 ② 自動化 — パイプライン機能 ③ ガバナンス — Git 連携 ④ 接続性 — ClickHouse対応 jp.cdata.com/resources/2026…

An audit of 17 popular MCP servers found an average security score of 34/100. Every server lacked permission declarations entirely. ➡️ bit.ly/4w4mymq That’s fundamentally an architecture problem. MCP security has to be designed in from day one.

AI agents running on replicated data will eventually act on stale state. That’s not a latency problem. It’s a consistency problem. 🔗 bit.ly/4tnysFA For agentic workflows, live reads and writes against source systems matter more than faster syncs.

【🔥ChatGPTアプリディレクトリにConnect AIが登場】 CData Connect AI が ChatGPT の公式アプリディレクトリに加わりました！ Snowflake、BigQuery、リレーショナルDB、データレイクハウスなど 350 以上のエンタープライズシステムへ、ChatGPT からリアルタイムにアクセスできるようになります。 ChatGPT アプリディレクトリからConnect AIにログインして直接接続を追加できます。詳細とConnect AI無償トライアルはリンク先から👇

Managing pipelines at scale means balancing performance, control, and flexibility.    CData Sync 26.2 introduces:  • Git-based versioning   • Faster parallel reads   • Python workflow automation   • ClickHouse destination support     See what’s new: bit.ly/3QHm0mx

The most effective enterprise AI copilots aren't the ones w/ the flashiest demos. They're the ones built around reliable data access, clear governance & grounded answers across systems like @salesforce, @NetSuite + @Snowflake. Implementation matters: bit.ly/4nhfrDp

From months of integration work to under a week. @thenoahai built a standardized, real-time data layer for AI—removing customized connector development and maintenance: bit.ly/4tZA7SZ That’s what it takes to scale AI across enterprise systems.

🔗 bit.ly/4woW7bw Here is a closer look at how CData Arc approaches modern secure file transfer and B2B integration workflows.

Modern MFT architecture is increasingly tied to broader integration strategy. The goal is not just moving files securely. It is improving how systems, partners, applications, and workflows operate together across environments.

Managed file transfer is becoming a broader architecture discussion. Many teams are reassessing whether older MFT approaches still align with modern operational and security expectations. 🧵