As technology landscapes evolve, software vendors must decide whether to specialize in a core area or offer a broad suite of services. Some companies take a highly focused approach, investing deeply in a specific technology, while others attempt to cover multiple use cases by integrating various tools and frameworks. Both strategies have trade-offs, but history has shown that specialization leads to deeper innovation, better performance, and stronger customer trust. This blog explores why focus matters in the context of data streaming software, the challenges of trying to do everything, and how companies that prioritize one thing—data streaming—can build best-in-class solutions that work everywhere.
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Data streaming enables real-time processing of continuous data flows, allowing businesses to act instantly rather than relying on batch updates. This shift from traditional databases and APIs to event-driven architectures has become essential for modern IT landscapes.
Data streaming is no longer just a technique—it is a new software category. The 2023 Forrester Wave for Streaming Data Platforms confirms its role as a core component of scalable, real-time architectures. Technologies like Apache Kafka and Apache Flink have become industry standards. They power cloud, hybrid, and on-premise environments for real-time data movement and analytics.
Businesses increasingly adopt streaming-first architectures, focusing on:
As data streaming becomes a core part of modern IT, businesses must choose the right approach: adopt a purpose-built data streaming platform or piece together multiple tools with limitations. Event-driven architectures demand scalability, low latency, cost efficiency, and strict SLAs to ensure real-time data processing meets business needs.
Some solutions may be “good enough” for specific use cases, but they often lack the performance, reliability, and flexibility required for large-scale, mission-critical applications.
The Data Streaming Landscape highlights the differences—while some vendors provide basic capabilities, others offer a complete Data Streaming Platform (DSP)designed to handle complex, high-throughput workloads with enterprise-grade security, governance, and real-time analytics. Choosing the right platform is essential for staying competitive in an increasingly data-driven world.
Many software vendors and cloud providers attempt to build a comprehensive technology stack, covering everything from data lakes and AI to real-time data streaming. While this offers customers flexibility, it often leads to overlapping services, inconsistent long-term investment, and complexity in adoption.
A few examples (from the perspective of data streaming solutions).
AWS has built the most extensive cloud ecosystem, offering services for nearly every aspect of modern IT, including data lakes, AI, analytics, and real-time data streaming. While this breadth provides flexibility, it also leads to overlapping services, evolving strategies, and complexity in decision-making for customers, resulting in frequent solution ambiguity.
Amazon provides several options for real-time data streaming and event processing, each with different capabilities:
Each of these services targets different real-time use cases, but they lack a unified, end-to-end data streaming platform. Customers must decide which combination of AWS services to use, increasing integration complexity, operational overhead, and costs.
AWS has introduced, rebranded, and developed its real-time streaming services over time:
As AWS expands its cloud-native services, customers must navigate a complex mix of technologies—often requiring third-party solutions to fill gaps—while assessing whether AWS’s flexible but fragmented approach meets their real-time data streaming needs or if a specialized, fully integrated platform is a better fit.
Google Cloud is known for its powerful analytics and AI/ML tools, but its strategy in real-time stream processing has been inconsistent:
Customers looking for stream processing in Google Cloud now have three competing services:
While each of these services has its use cases, they introduce complexity for customers who must decide which option is best for their workloads.
BigQuery Flink was introduced to extend Google’s analytics capabilities into real-time processing but was later discontinued before exiting the preview.
Microsoft Azure has taken multiple approaches to real-time data streaming and analytics, with an evolving strategy that integrates various tools and services.
While Microsoft Fabric aims to simplify enterprise data infrastructure, its broad scope means that customers must adapt to yet another new platform rather than continuing to rely on long-standing, specialized services. The combination of Azure Event Hubs, Stream Analytics, and Fabric presents multiple options for stream processing, but also introduces complexity, limitations and increased cost for a combined solution.
Microsoft’s approach highlights the challenge of balancing broad platform integration with long-term stability in real-time streaming technologies. Organizations using Azure must evaluate whether their streaming workloads require deep, specialized solutions or can fit within a broader, integrated analytics ecosystem.
I wrote an entire blog series to demystify what Microsoft Fabric really is.
Instaclustr has positioned itself as a managed platform provider for a wide array of open-source technologies, including Apache Cassandra, Apache Kafka, Apache Spark, Apache ZooKeeper, OpenSearch, PostgreSQL, Redis, and more. While this broad portfolio offers customers choices, it reflects a horizontal expansion strategy that lacks deep specialization in any one domain.
For organizations seeking help with real-time data streaming, Instaclustr’s Kafka offering may appear to be a viable managed service. However, unlike purpose-built data streaming platforms, Instaclustr’s Kafka solution is just one of many services, with limited investment in stream processing, schema governance, or advanced event-driven architectures.
Because Instaclustr splits its engineering and support resources across so many technologies, customers often face challenges in:
This generalist model may be appealing for companies looking for low-cost, basic managed services—but it falls short when mission-critical workloads demand real-time reliability, zero data loss, SLAs, and advanced stream processing capabilities. Without a singular focus, platforms like Instaclustr risk becoming jacks-of-all-trades but masters of none—especially in the demanding world of real-time data streaming.
Cloudera has adopted a distinct strategy by incorporating various open-source frameworks into its platform, including:
While this provides flexibility, it also introduces significant complexity:
Rather than focusing on one core area, Cloudera’s strategy appears to be adding whatever is trending in open source, which can create challenges in long-term support and roadmap clarity.
Splunk, known for log analytics, has tried multiple times to enter the data streaming market:
Initially, Splunk built a proprietary streaming solution that never gained widespread adoption.
Later, Splunk acquired Streamlio to leverage Apache Pulsar as its streaming backbone.This Pulsar-based strategy ultimately failed, leading to a lack of a clear real-time streaming offering.
Splunk’s challenges highlight a key lesson: successful data streaming requires long-term investment and specialization, not just acquisitions or technology integrations.
Some vendors take a more specialized approach, focusing on one core capability and doing it better than anyone else. For data streaming, Confluent became the leader in this space by focusing on improving the vision of a complete data streaming platform.
At Confluent, the focus is clear: real-time data streaming. Unlike many other vendors and the cloud providers that offer fragmented or overlapping services, Confluent specializes in one thing and ensures it works everywhere:
While Confluent is often recognized as “the Kafka company,” it has grown far beyond that. Today, Confluent is a complete data streaming platform, combining Apache Kafka for event streaming, Apache Flink for stream processing, and many additional components for data integration, governance and security to power critical workloads.
However, Confluent remains laser-focused on data streaming—it does NOT compete with BI, AI model training, search platforms, or databases. Instead, it integrates and partners with best-in-class solutions in these domains to ensure businesses can seamlessly connect, process, and analyze real-time data within their broader IT ecosystem.
Confluent is not just one product—it matches the specific needs, SLAs, and cost considerations of different streaming workloads:
Confluent is built for organizations that require more than just “some” data streaming—it is for businesses that need a scalable, reliable, and deeply integrated event-driven architecture. Whether operating in a cloud, hybrid, or on-premise environment, Confluent ensures real-time data can be moved, processed, and analyzed seamlessly across the enterprise.
By focusing only on data streaming, Confluent ensures seamless integration with best-in-class solutions across both operational and analytical workloads. Instead of competing across multiple domains, Confluent partners with industry leaders to provide a best-of-breed architecture that avoids the trade-offs of an all-in-one compromise.
A purpose-built data streaming platform plays well with cloud providers and other data platforms. A few examples:
Rather than attempting to own the entire data stack, Confluent specializes in data streaming and integrates seamlessly with the best cloud, AI, and database solutions.
Confluent is not alone in recognizing the power of focus. A handful of other vendors have also chosen to specialize in data streaming—each with their own vision, strengths, and approaches.
WarpStream, recently acquired by Confluent, is a Kafka-compatible infrastructure solution designed for Bring Your Own Cloud (BYOC) environments. It re-architects Kafka by running the protocol directly on cloud object storage like Amazon S3, removing the need for traditional brokers or persistent compute. This model dramatically reduces operational complexity and cost—especially for high-ingest, elastic workloads. While WarpStream is now part of the Confluent portfolio, it remains a distinct offering focused on lightweight, cost-efficient Kafka infrastructure.
StreamNative is the commercial steward of Apache Pulsar, aiming to provide a unified messaging and streaming platform. Built for multi-tenancy and geo-replication, it offers some architectural differentiators, particularly in use cases where separation of compute and storage is a must. However, adoption remains niche, and the surrounding ecosystem still lacks maturity and standardization.
Redpanda positions itself as a Kafka-compatible alternative with a focus on performance, especially in low-latency and resource-constrained environments. Its C++ foundation and single-binary architecture make it appealing for edge and latency-sensitive workloads. Yet, Redpanda still needs to mature in areas like stream processing, integrations, and ecosystem support to serve as a true platform.
AutoMQ re-architects Apache Kafka for the cloud by separating compute and storage using object storage like S3. It aims to simplify operations and reduce costs for high-throughput workloads. Though fully Kafka-compatible, AutoMQ concentrates on infrastructure optimization and currently lacks broader platform capabilities like governance, processing, or hybrid deployment support.
Bufstream is experimenting with lightweight approaches to real-time data movement using modern developer tooling and APIs. While promising in niche developer-first scenarios, it has yet to demonstrate scalability, production maturity, or a robust ecosystem around complex stream processing and governance.
Ververica focuses on stream processing with Apache Flink. It offers Ververica Platform to manage Flink deployments at scale, especially on Kubernetes. While it brings deep expertise in Flink operations, it does not provide a full data streaming platform and must be paired with other components, like Kafka for ingestion and delivery.
Each of these companies brings interesting ideas to the space. But building and scaling a complete, enterprise-grade data streaming platform is no small feat. It requires not just infrastructure, but capabilities for processing, governance, security, global scale, and integrations across complex environments.
That’s where Confluent continues to lead—by combining deep technical expertise, a relentless focus on one problem space, and the ability to deliver a full platform experience across cloud, on-prem, and hybrid deployments.
In the long run, the data streaming market will reward not just technical innovation, but consistency, trust, and end-to-end excellence. For now, the message is clear: specialization matters—but execution matters even more. Let’s see where the others go.
A well-defined focus provides several advantages for customers, ensuring they get the right tool for each job without the complexity of navigating overlapping services.
Rather than adopting a broad but shallow set of solutions, businesses can achieve stronger outcomes by choosing vendors that specialize in one core competency and deliver it everywhere.
For mission-critical workloads—where downtime, data loss, and compliance failures are not an option—deep expertise is not just an advantage, it is a necessity.
Data streaming is a high-performance, real-time infrastructure that requires continuous reliability, strict SLAs, and rapid response to critical issues. When something goes wrong at the core of an event-driven architecture—whether in Apache Kafka, Apache Flink, or the surrounding ecosystem—only specialized vendors with proven expertise can ensure immediate, effective solutions.
Many cloud providers offer some level of data streaming, but their approach is different from a dedicated data streaming platform. Take Amazon MSK as an example:
For companies that cannot afford failure, a data streaming vendor with direct expertise in the underlying technology is essential.
A complete data streaming platform is much more than a hosted Kafka cluster or a managed Flink service. Specialized vendors like Confluent offer end-to-end operational expertise, covering:
This level of deep, continuous investment in operational excellence separates a general-purpose cloud service from a true data streaming platform.
Software vendors will continue expanding their offerings, integrating new technologies, and launching new services. However, focus remains a key differentiator in delivering best-in-class solutions, especially for operational systems with critical SLAs—where low latency, 24/7 uptime, no data loss, and real-time reliability are non-negotiable.
For companies investing in strategic data architectures, choosing a vendor with deep expertise in one core technology—rather than one that spreads across multiple domains—ensures stability, predictable performance, and long-term success.
In a rapidly evolving technology landscape, clarity, specialization, and seamless integration are the foundations of lasting innovation. Businesses that prioritize proven, mission-critical solutions will be better equipped to handle the demands of real-time, event-driven architectures at scale.
How do you see the world of software? Better to specialize or become an allrounder? Let’s connect on LinkedIn and discuss it! Stay informed about new blog posts by subscribing to my newsletter. And download my free book about data streaming use cases.
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