The new data standard: Lakehouse, streaming, and zero-ETL

For years, businesses have wrestled with fragmented data systems that couldn’t keep pace with their needs.

Data warehouses worked well for reporting but struggled with flexibility. Data lakes offered scale but often lacked reliability and governance. Meanwhile, ETL pipelines required endless transformation work before information was usable.

That era is ending. As enterprises push into AI-driven applications, real-time analytics, and global-scale operations, the traditional data stack is showing its limits. A new model is emerging as the default: the open lakehouse, powered by streaming and zero-ETL pipelines.

Why legacy approaches fall short

Legacy architectures were built for a world where data was structured, workloads were predictable, and insight could wait until tomorrow’s report. But today’s environment is different.

• Businesses need real-time insights to respond to customer behavior or supply chain disruptions instantly.
• AI models demand fresh, contextual, and well-governed data to perform reliably.
• Regulatory frameworks require lineage and transparency that patchwork ETL jobs often can’t provide.

The result is mounting frustration: too many data silos, too much duplication, and too much lag between raw data and usable information.

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The rise of the open lakehouse

The lakehouse model has gained traction because it blends the best of both worlds. It combines the scalability of a data lake with the reliability and governance features of a warehouse. Technologies such as Delta Lake, Apache Iceberg, and Hudi allow businesses to manage large volumes of structured and unstructured data without compromising consistency.

Crucially, lakehouses are open. They’re built on standards that prevent lock-in and support interoperability. This openness is essential in multi-cloud and hybrid environments, where organizations want freedom to choose the right tool without being trapped in a single ecosystem.

Streaming and zero-ETL

Lakehouses alone aren’t enough. To support AI and real-time decision-making, businesses also need:

• Streaming pipelines that deliver continuous flows of fresh data rather than relying on nightly batch jobs.
• Zero-ETL patterns that let data flow seamlessly between systems without costly, error-prone transformations.

Together, these capabilities mean organizations no longer wait days or weeks to make data useful. Information can move directly from source to insight, dramatically reducing friction.

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Adoption across industries

The momentum is clear across sectors:

• Financial services firms are moving to lakehouse and streaming models to power real-time fraud detection and regulatory reporting.
• Retailers are embracing zero-ETL approaches to integrate customer data instantly across channels, enabling personalized experiences at scale.
• Healthcare providers are building governed lakehouses that balance accessibility with compliance, giving researchers and clinicians trusted information without breaching privacy.

Each case shows the same pattern: businesses that modernize their platforms can deliver insights and AI applications faster, with greater confidence.

What this means for leaders

For executives, the lesson is straightforward. If your business is still relying on legacy warehouses, siloed lakes, or heavy ETL, you’re falling behind. Modern data platforms are no longer experimental—they are becoming the default standard.

Investing in open lakehouse architectures, streaming, and zero-ETL pipelines is not just a technical upgrade. It’s a strategic move that enables faster insight, better AI performance, and compliance-readiness in an increasingly regulated landscape.