AI is everywhere. Open source is evolving. Vendors are merging faster than your data syncs.  

Despite the rapid changes we're experiencing in the world of data and analytics, there's a reliable constant: a strong data strategy provides you the foundation to adapt without getting lost in the weeds.   

This issue cuts through the hype — with an updated framework for building your data strategy. These five elements outline how we help our clients build for the long term, not just chase trends. 

Plus we provide our perspective on what "open source" really means now and the Fivetran and dbt merger. 

Plus:

Our Recommended Read: 5 elements of a modern data strategy  

Hot Topic: The next phase of “open source” (and why it matters to you)  

Emerging Tech Insights: Databricks partnered with OpenAI??

From the Field: Fivetran + dbt - what it means for your stack

LOL Moment: Google engineers finding inner peace

Let's get to it!

Tracey Doyle

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Our Recommended Read 

5 Elements of a Modern Data Strategy 

This updated framework distills what's worked for 1000s of organizations into five essential elements that help you focus on what really drives results with your data. 

💡 Here’s what’s inside: 

• How to align data initiatives with business goals 
• What your modern data stack should include 
• Practical steps for governance that ensures AI readiness 
• Talent strategies and models 
• Plus: a free Stakeholder Interview Guide to help you ask the right questions to build a data strategy that gets adopted. 

👉 [Read the full framework →] 

Hot Topic 

💬 The Next Phase of Open Source 

Open source is moving from promise to practicality. Technologies like Apache Iceberg and Delta Lake are converging to make multi-platform data architectures more achievable and less dependent on any single vendor. But as that ecosystem expands, the definition of “open” is getting fuzzier — especially in AI. 

Here’s what to keep in mind: 

• Check the fine print. “Open” AI models often come with restrictive licenses or proprietary training data. Transparency claims don’t always equal freedom to use. 
• Watch for convergence. Iceberg and Delta’s alignment means format interoperability is finally real, and that could reshape how you plan data storage and governance. 
• Match maturity to complexity. Most open-source tools assume advanced engineering capacity. Evaluate your team’s bandwidth before you add operational overhead. 
• Stay curious, not reactive. Track these trends so you can recognize when open source genuinely gives you more control or cost advantage — not just because it sounds modern. 

Open source isn’t about ideology anymore — it’s about selective adoption that balances flexibility, governance, and readiness for what’s next. 

Emerging Tech Insights 

A few tech updates on our radar: 

Fivetran + dbt Labs Join Forces 

Fivetran and dbt Labs recently announced an all-stock merger — uniting automated data movement with modern transformation to create what they’re calling “open data infrastructure.” 

💡 What’s happening: 

• Expect tighter native integration between Fivetran pipelines and dbt models, reducing time spent managing syncs, schema changes, and transformation jobs. 
• Metadata and lineage will flow automatically between ingestion and transformation, making governance and impact analysis easier across your stack. 
• dbt Core stays open-source, ensuring teams can keep using it with their preferred warehouse while gaining deeper interoperability with Fivetran’s managed pipelines. 
• The unified platform aims to streamline AI and analytics readiness — no more stitching together tools to go from raw data to modeled, governed insights. 
• Recent acquisitions of Tobiko Data (sqlMesh/sqlGlot) and Census reinforce Fivetran’s move toward a true end-to-end SaaS data stack, spanning ingestion to reverse ETL. 

⚙️ Why it matters: 
This partnership could reshape the data stack as we know it. Combining Fivetran’s automation with dbt’s transformation layer creates an end-to-end data foundation — open, interoperable, and AI-ready. 

dbt Coalesce Highlights 

dbt Labs is sharpening its focus on efficiency and open standards. 

• dbt Fusion now emphasizes cost savings, using state-aware orchestration to reduce unnecessary compute. It’s still early days, but the direction signals dbt’s shift from developer convenience to tangible ROI. 
• MetricFlow is becoming open-source, with partners like Snowflake, Tableau, and Cube backing the new Open Semantic Interchange — a step toward consistent metric definitions across BI and AI tools. 
• New tools like dbt Insight, Agents, and the VS Code extension aim to simplify workflows and integrate AI-driven support into development. 

⚙️ Why it matters: 
dbt’s updates point to a more connected, cost-aware, and interoperable data ecosystem — giving teams more options to optimize their compute and storage performance while expanding visibility in how metrics flow from source to insight. 

LOL Moment 

When everyone’s dashboards are red, but you’re the only one still sipping coffee. 

That’s not uptime — that’s inner peace. 

Have any good memes? Send it to our content manager and get featured on our social channels.

Have a great week!

Tracey

Transform your business with data.

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