Modernize Your DB2 Data Pipelines, Live Demo Dec 16, New Data Engineering Approach, Trustworthy Agents

Hi Data & AI Pros,

DB2 continues to run your critical workloads—but getting that data into modern platforms is still far too complex. 

Join us on December 16 at 10 a.m. ET for a live 45-minute demo on how CData Sync simplifies and accelerates DB2 data integration across iSeries, LUW, and z/OS.

You’ll see how Sync delivers high-quality extraction and near real-time Change Data Capture while supporting hybrid on-prem/cloud environments without disrupting operations. 

What we’ll cover: 

• Authentication options for all major DB2 variants 

• How CDC captures inserts, updates, and deletes 

• Delivering DB2 data to warehouses & lakehouses smoothly 

• Configuring Sync for high-volume workloads with lower cost & complexity 

• Save your spot and modernize how you move DB2 data. 

Save your spot and modernize how you move DB2 data.

I just read a very practical piece on what it really takes to move AI agents from concept to production. The big takeaway is simple. Trust in AI is not something you add at the end. It has to be designed into every stage of the agent lifecycle. From data to deployment to monitoring.

A few points that stood out to me:

- Trusted AI starts with strong data governance. Clean data, bias checks, and clear ownership matter more than model complexity.

- Transparency is non negotiable. If an AI agent makes a decision, teams must be able to explain how and why it happened.

- Human oversight is critical. AI agents should know when to pause, escalate, or hand control back to people.

- Trust is not static. Continuous monitoring and feedback loops are required to keep systems reliable over time.

- In regulated industries like finance and healthcare, these are not optional ideas. They are table stakes.

This is a solid reminder that building trustworthy AI is about discipline and intent, not just smarter models.

Hot take after 550+ conversations with Data & AI leaders: AI & Analytics isn't failing because the models are bad. It's failing because the data pipelines are.

Here's the uncomfortable truth no one likes to say out loud👇

- Most teams are pouring millions into data processing using platforms designed for a different era of cloud computing.

- And then we wonder why budgets can't keep up with demand, or why IT leaders are forced into hard tradeoffs.

- I used to hear "How do we speed up Spark?" constantly. Now the sharpest teams are asking something different: "Why are we running architectures that require endless tuning in the first place?"

This is the real shift happening right now. The best-performing teams aren't optimizing harder, they're eliminating the architectural friction that makes optimization necessary.

- No months-long migration.

- No rewrites.

- No breaking workflows.

Just removing the tax that's been slowing everyone down.

I'm curious — what's the ONE performance bottleneck no amount of optimization has helped you solve?

P.S. DataPelago is taking a completely new approach to eliminating the data processing tax — the early results are fascinating. Check them out here:

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Best,

Ravit Jain

Founder & Host of The Ravit Show