Confluent and Databricks are two of the most influential platforms in modern data architectures. Both have roots in open source. Both focus on enabling organizations to work with data at scale. And both have expanded their mission well beyond their original scope.
Confluent and Databricks are often described as serving different parts of the data architecture—real-time vs. batch, operational vs. analytical, data streaming vs. artificial intelligence (AI). But the lines are not always clear. Confluent can run batch workloads and embed AI. Databricks can handle (near) real-time pipelines. With Flink, Confluent supports both operational and analytical processing. Databricks can run operational workloads, too—if latency, availability, and delivery guarantees meet the project’s requirements.
This blog explores where these platforms came from, where they are now, how they complement each other in modern enterprise architectures—and why their roles are future-proof in a data- and AI-driven world.
This article is part of a blog series exploring the growing roles of Confluent and Databricks in modern data and AI architectures:
Stay tuned for deep dives into how these platforms are shaping the future of data-driven enterprises. Join the data streaming community and stay informed about new blog posts by subscribing to my newsletter and follow me on LinkedIn or X (former Twitter) to stay in touch. And download my free book about data streaming use cases, including technical architectures and the relation to analytical platforms like Databricks.
Confluent and Databricks were designed for different workloads, but the boundaries are not always strict.
Confluent was built for operational workloads—moving and processing data in real time as it flows through systems. This includes use cases like real-time payments, fraud detection, system monitoring, and streaming pipelines.
Databricks focuses on analytical workloads—enabling large-scale data processing, machine learning, and business intelligence.
That said, there is no clear black and white separation. Confluent, especially with the addition of Apache Flink, can support analytical processing on streaming data. Databricks can handle operational workloads too, provided the SLAs—such as latency, uptime, and delivery guarantees—are sufficient for the use case.
With Tableflow and Delta Lake, both platforms can now be natively connected, allowing real-time operational data to flow into analytical environments, and AI insights to flow back into real-time systems—effectively bridging operational and analytical workloads in a unified architecture.
From Apache Kafka and Spark to (Hybrid) Cloud Platforms: Both Confluent and Databricks both have strong open source roots—Kafka and Spark, respectively—but have taken different branding paths.
Confluent is well known as “The Kafka Company.” It was founded by the original creators of Apache Kafka over ten years ago. Kafka is now widely adopted for event streaming in over 150,000 organizations worldwide. Confluent operates tens of thousands of clusters with Confluent Cloud across all major cloud providers, and also in customer’s data centers and edge locations.
But Confluent has become much more than just Kafka. It offers a complete data streaming platform (DSP).
This includes:
Databricks has followed a similar evolution. Known initially as “The Spark Company,” it is the original force behind Apache Spark. But Databricks no longer emphasizes Spark in its branding. Spark is still there under the hood, but it’s no longer the dominant story.
Today, it positions itself as the Data Intelligence Platform, focused on AI and analytics.
Key components include:
Together, Confluent and Databricks meet a wide range of enterprise needs and often complement each other in shared customer environments from the edge to multi-cloud data replication and analytics.
A major point of comparison between Confluent and Databricks lies in how they handle data processing—real-time versus batch—and how they increasingly converge through shared formats and integrations.
A key difference between the platforms lies in how they process and share data.
Confluent focuses on data in motion—real-time streams that can be filtered, transformed, and shared across systems as they happen.
Databricks focuses on data at rest—data that has landed in a lakehouse, where it can be queried, aggregated, and used for analysis and modeling.
Both platforms offer native capabilities for data sharing. Confluent provides Stream Sharing, which enables secure, real-time sharing of Kafka topics across organizations and environments. Databricks offers Delta Sharing, an open protocol for sharing data from Delta Lake tables with internal and external consumers.
In many enterprise architectures, the two vendors work together. Kafka and Flink handle continuous real-time processing for operational workloads and data ingestion into the lakehouse. Databricks handles AI workloads (model training and some of the model inference), business intelligence (BI), and reporting. Both do data integration; ETL (Confluent) respectively ELT (Databricks).
Many organizations still use Databricks’ Apache Spark Structured Streaming to connect Kafka and Databricks. That’s a valid pattern, especially for teams with Spark expertise.
Flink is available as a serverless offering in Confluent Cloud that can scale down to zero when idle, yet remains highly scalable—even for complex stateful workloads. It supports multiple languages, including Python, Java, and SQL.
For self-managed environments, Kafka Streams offers a lightweight alternative to running Flink in a self-managed Confluent Platform. But be aware that Kafka Streams is limited to Java and operates as a client library embedded directly within the application. Read my dedicated article to learn about the trade-offs between Apache Flink and Kafka Streams.
In short: use what works. If Spark Structured Streaming is already in place and meets your needs, keep it. But for new use cases, Apache Flink or Kafka Streams might be the better choice for stream processing workloads. But make sure to understand the concepts and value of stateless and stateful stream processing before building batch pipelines.
Databricks is actively investing in Delta Lake and Unity Catalog to structure, govern, and secure data for analytical applications. The acquisition of Tabular—founded by the original creators of Apache Iceberg—demonstrates Databricks’ commitment to supporting open standards.
Confluent’s Tableflow materializes Kafka streams into Apache Iceberg or Delta Lake tables—automatically, reliably, and efficiently. This native integration between Confluent and Databricks is faster, simpler, and more cost-effective than using a Spark connector or other ETL tools.
Tableflow reads the Kafka segments, checks schema against schema registry, and creates parquet and table metadata.
Native stream processing with Apache Flink also plays a growing role. It enables unified real-time and batch stream processing in a single engine. Flink’s ability to “shift left” data processing (closer to the source) supports early validation, enrichment, and transformation. This simplifies the architecture and reduces the need for always-on Spark clusters, which can drive up cost.
These developments highlight how Databricks and Confluent address different but complementary layers of the data ecosystem.
Confluent and Databricks are not competing platforms—they’re complementary. While they serve different core purposes, there are areas where their capabilities overlap. In those cases, it’s less about which is better and more about which fits best for your architecture, team expertise, SLA or latency requirements. The real value comes from understanding how they work together and where you can confidently choose the platform that serves your use case most effectively.
Confluent and Databricks recently deepened their partnership with Tableflow integration with Delta Lake and Unity Catalog. This integration makes real-time Kafka data available inside Databricks as Delta tables. It reduces the need for custom pipelines and enables fast access to trusted operational data.
The architecture supports AI end to end—from ingesting real-time operational data to training and deploying models—all with built-in governance and flexibility. Importantly, data can originate from anywhere: mainframes, on-premise databases, ERP systems, IoT and edge environments or SaaS cloud applications.
With this setup, you can:
Both directions will be supported. Governance and security metadata flows alongside the data.
A great example of how Confluent and Databricks complement each other in practice is Michelin’s digital transformation journey. As one of the world’s largest tire manufacturers, Michelin set out to become a data-first and digital enterprise. To achieve this, the company needed a foundation for real-time operational data movement and a scalable analytical platform to unlock business insights and drive AI initiatives.
Confluent Cloud plays a critical role at Michelin by powering real-time data pipelines across their global operations. Migrating from self-managed Kafka to Confluent Cloud on Microsoft Azure enabled Michelin to reduce operational complexity by 35%, meet strict 99.99% SLAs, and speed up time to market by up to nine months. Real-time inventory management, order orchestration, and event-driven supply chain processes are now possible thanks to a fully managed data streaming platform.
Meanwhile, Databricks empowers Michelin to democratize data access across the organization. By building a centralized lakehouse architecture, Michelin enabled business users and IT teams to independently access, analyze, and develop their own analytical use cases—from predicting stock outages to reducing carbon emissions in logistics. With Databricks’ lakehouse capabilities, they scaled to support hundreds of use cases without central bottlenecks, fostering a vibrant community of innovators across the enterprise.
The synergy between Confluent and Databricks at Michelin is clear:
Together, Confluent and Databricks allow Michelin to shift from batch-driven, siloed legacy systems to a cloud-native, real-time, data-driven enterprise—paving the road toward higher agility, efficiency, and customer satisfaction.
As Yves Caseau, Group Chief Digital & Information Officer at Michelin, summarized: “Confluent plays an integral role in accelerating our journey to becoming a data-first and digital business.”
And as Joris Nurit, Head of Data Transformation, added: “Databricks enables our business users to better serve themselves and empowers IT teams to be autonomous.”
The Michelin success story perfectly illustrates how Confluent and Databricks, when used together, bridge operational and analytical workloads to unlock the full value of real-time, AI-powered enterprise architectures.
Confluent and Databricks are both leaders in different – but connected – layers of the modern data stack.
If you want real-time, event-driven data pipelines, Confluent is the right platform. If you want powerful analytics, AI, and ML, Databricks is a great fit.
Together, they allow enterprises to bridge operational and analytical workloads—and to power AI systems with live, trusted data.
In the next post, I will explore how Confluent’s Data Streaming Platform compares to the Databricks Data Intelligence Platform for data integration and processing.
Join the data streaming community and stay informed about new blog posts by subscribing to my newsletter and follow me on LinkedIn or X (former Twitter) to stay in touch. And download my free book about data streaming use cases, including technical architectures and the relation to analytical platforms like Databricks.
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