How Streambased is Transforming Real-Time Analytics with Kafka

Welcome to “The Executive Outlook,” where technology, business, and smart strategies come together to create a data-powered future. A discussion where we dive into real-time data streaming, operational analytics, and advanced technology with industry leaders. Our guest today is Leo Delmouly, Co-founder of Streambased, a platform that simplifies access to operational and analytical data.

With deep expertise in Kafka, real-time data processing, and distributed systems, Leo shares insights on scaling data infrastructure, exploring real-time analytics, and the future of streaming technologies.

Streambased’s Bold Move: Making Kafka Data Instantly Accessible Without ETL Hassles

Leo’s career began at Confluent in 2017, where he witnessed the transformative power of data streaming but also saw major challenges—complex architecture, multiple ETL jobs, and a disconnect between operational and analytical systems.

After working at Cockroach Labs and exploring batch processing, he co-founded Streambased in early 2024 to revolutionize how businesses interact with Kafka data. The goal? Allows direct querying of Kafka data without moving it around or waiting for batch processes.

Leo smiled and mentioned, “We founded Streambased to open up Kafka data like never before—allowing users to query it directly without moving data around or waiting for batch processes.”

Streambased is about bridging the gap between analytics and operational systems, making data insights faster, simpler, and more intuitive.

The Hidden Reality of Real-Time Data Challenges

Real-time data management remains a major challenge for businesses. Many organizations still rely on batch processing, leading to delayed insights.

He noticed “There’s a big disconnect between analysts and operational data. Kafka is a powerful tool, but unless you own a GitHub account, it’s tough to access!” A key issue is Kafka’s complexity, which makes data access difficult for those without deep technical expertise. Streambased aims to change this by allowing analysts to query real-time data without complex infrastructure.

A Day in the Life of a Startup Founder – Chaos, Coffee, and Innovation

Building a startup is not for the faint-hearted. It is a fast-paced journey filled with breakthroughs, long hours, and solving complex problems that others shy away from. No two days look the same, and that is what makes it exciting.

For Leo, every day is a mix of:

• Conversations with prospective clients to uncover their biggest real-time data struggles and show them a better way.
• Strategy sessions with his co-founder to brainstorm and refine the product, making real-time analytics effortless.
• Product messaging and refinement to ensure SQL on Kafka is accessible to a wider audience without being overly technical.

Most companies are stuck in the traditional way of handling data, moving massive volumes around, waiting for batch processing, and always being one step behind. Streambased is breaking that cycle by giving businesses instant access to live data without the delays of ETL processing.

How Streambased Solves Real-Time Analytics Challenges

One of the biggest problems Streambased solves is complexity in accessing real-time data for analytics. Traditionally, companies must move data from Kafka into a data lake or warehouse via slow, costly, and error-prone pipelines.

He believes, “We’re turning that on its head by enabling direct access to Kafka—no more waiting around for ETL jobs to finish.”

By allowing direct access to Kafka, Streambased is revolutionizing fraud detection and predictive analytics:

• Traditional fraud detection systems rely on pre-aggregated data, leading to incomplete insights.
• Analysts using Streambased can access historical and live data instantly, enabling real-time fraud investigations and faster predictive modeling.

This is not about millisecond-level fraud detection, which tools like Flink or KSQL handle. Instead, it’s about empowering data analysts and scientists to explore, investigate, and predict in real-time. By removing the barriers between operational and analytical data, Streambased is redefining what’s possible in real-time decision-making.

Event Sourcing: A Game-Changer for Analytics 

How Event Sourcing Gives Analysts a 360° View of Data? 

Event Sourcing vs. Traditional Analytics: Why Businesses Are Making the Shift

With event sourcing, analysts can see the full lineage of data events, revealing the why behind patterns and anomalies. Instead of relying on pre-aggregated reports, users can interact with raw event data in real-time for deeper insights.

By removing the silos between historical and live data, businesses move from reactive investigation to proactive risk management and predictive analytics—leading to faster, data-driven decisions.

Why Most Data Leaders Are Failing and How Real-Time Insights Can Save Them
Leo Delmouly exposes the hidden gaps in how businesses handle real-time data and why many data leaders are unknowingly setting themselves up for failure. Listen now to learn the one shift that unlocks faster, smarter decisions.

Bridging the Gap Between Operational and Analytical Systems

Many organizations have strong operational capabilities with Kafka but struggle to extract insights from their infrastructure. Kafka is the main ingestion point for most business data, ensuring clean, consistent, and reliable datasets for operations.

However, when companies want to use this data for analytics, they face challenges:

• Kafka’s topic-based format is not optimized for analytical workloads.
• Data duplication increases complexity, costs, and trust issues in the data pipeline.
• Moving data from Kafka to a lake or warehouse creates inconsistencies.

Streambased introduces the “Topic-to-Table” concept, transforming Kafka topics into structured formats for fast, interactive analytics. This ensures businesses access real-time data at the source instead of relying on outdated, pre-processed data.

Data Validation & Consistency Challenges

One major issue in traditional data pipelines is ensuring consistency between Kafka, data lakes, and warehouses.

Leo mentioned that the most important question for companies is “Which dataset do you trust—the one in Kafka, the data lake, or the warehouse? But with Streambased, you don’t have to choose. You access data directly where it’s created.

With Streambased, businesses eliminate these concerns by accessing real-time data directly in Kafka. This ensures they always work with the latest, most accurate version of their data, eliminating inconsistencies caused by multiple copies across different storage layers.

The Future of Data Streaming and Analytics

Leo highlights a major industry shift—leading data platforms like Confluent, Databricks, and Snowflake are moving toward converging analytical and operational workloads.

The key challenges businesses face today include:

• High costs of moving massive volumes of event data.
• Data silos causing inefficiencies.
• Delayed insights from batch processing.

Streambased is addressing this by bringing analysts closer to operational data, ensuring they can query and analyze real-time data directly at the source.

What Excites Leo About Building Streambased?

As a founder, Leo believes “There’s nothing more satisfying than seeing someone’s eyes light up when they realize they don’t have to wait hours for data anymore!”

• Customer interactions— understanding pain points and delivering solutions that feel like “magic.”
• Helping businesses reduce infrastructure costs and complexity while improving decision-making speed.
• Storytelling— translating complex data concepts into compelling narratives that resonate with clients.

Recently, Streambased launched a short video using a space exploration metaphor to explain real-time analytics. Simplifying these complex concepts is a key part of making their product accessible to a broader audience.

The startup journey is fast-paced, and Leo enjoys the agility that comes with it. Whether it’s building custom features for a financial services company or refining product messaging, every step of the process is about solving real-world data challenges in a smarter way.

Conclusion

Real-time data management is one of the biggest challenges in modern businesses. Companies relying on batch processing struggle with delays, inefficiencies, and incomplete insights. Streambased solves this by enabling direct, real-time access to Kafka data, bridging the gap between operational and analytical systems. Businesses using Streambased are moving from reactive, delayed insights to proactive, real-time decision-making. With faster analytics, lower costs, and simplified infrastructure, they gain a competitive edge in a data-driven world.

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