Enterprises are rebuilding data architectures around open-source databases, streaming engines, and search to avoid lock-in and keep pace with AI and real-time demands.
Enterprise data architecture is under pressure. AI projects demand fresh data, edge systems generate continuous streams, and regulators expect fine-grained control over where data lives and how it is used. The monolithic data warehouse — or a single proprietary cloud database — is struggling to keep up.
In response, more organizations are rebuilding data platforms around open-source components: databases such as PostgreSQL and Cassandra, streaming engines like Apache Kafka and Flink, and search and analytics engines including OpenSearch and ClickHouse. These technologies are increasingly deployed as the “control plane” for data and AI, while storage and compute remain portable across clouds and on premises.
A series of recent surveys underscores this shift. The 2025 State of Open Source Report found that 96% of organizations maintained or increased their open-source use over the previous year, with more than a quarter reporting significant increases. An OpenLogic analysis noted that open-source databases and data technologies are now among the top investment priorities, alongside cloud-native and container platforms.
Similarly, Linux Foundation research reported 40–55% penetration of open source in mission-critical domains, including databases and AI.
Open ecosystems match the speed of data and AI
A modern data architecture rarely revolves around a single engine. Real-time analytics, AI workloads, and historical reporting each have different requirements for latency, consistency, cost, and data modeling. As a result, organizations are embracing polyglot persistence by combining several specialized data stores and engines behind a unified platform.
Open-source building blocks map naturally to these roles:
- Databases: Tools such as PostgreSQL, MySQL, Cassandra, and various NoSQL systems power transactional and operational workloads, including those that support AI-driven applications. A recent report on open-source database management highlighted these engines as foundational to cloud-native and multi-database strategies.
- Streaming platforms: Tools like Apache Kafka and Flink have become the de facto backbone for data-in-motion. RTInsights has chronicled Kafka’s evolution from a LinkedIn messaging system to a global standard for real-time streaming, noting its role as a core architectural component in many enterprises.
- Search and analytics engines: Tools like OpenSearch and ClickHouse provide low-latency queries over log data, metrics, and event streams, supporting observability, personalization, and fraud detection use cases. These tools are frequently paired with Kafka in streaming pipelines.
Open formats are equally important. Projects such as Apache Iceberg and related lakehouse technologies allow organizations to keep data in open table formats while swapping in different query engines or cloud services over time. RTInsights has explored how Iceberg and Project Nessie can help build lakehouses that avoid tight coupling to a single vendor or tool.
The result is a more modular data platform. Compute engines, storage layers, and orchestration tools can evolve independently, provided they adhere to open interfaces and standards.
Avoiding lock-in with composable data stacks
Vendor lock-in is not just a pricing problem. It’s an innovation problem. When the data platform is tightly bound to a single proprietary service, roadmaps and integration options are constrained by that provider’s priorities. In contrast, a composable data stack is built from interchangeable components:
- Data is stored in open formats and accessed via standard protocols
- Engines such as Kafka, PostgreSQL, or OpenSearch can be run on any major cloud or in a Kubernetes environment
- New tools — vector databases or AI feature stores, for example — can be introduced without redesigning the entire platform
Managed open-source providers are increasingly the way enterprises operationalize this composability at scale. Platforms such as NetApp Instaclustr, Confluent Cloud, and Aiven offer fully managed versions of popular open-source databases, streaming engines, and search technologies while preserving the underlying open standards and APIs. That combination allows teams to get help with day-two operations without giving up the ability to move workloads or swap components over time.
An RTInsights analysis of next-generation data stacks described this trend as a move toward architectures that process, analyze, and adapt data in real time, closer to where the data originates rather than centralizing everything in a single warehouse. In that model, open-source engines and formats become the stable substrate, while managed services come and go as operational needs evolve.
Where managed open source fits without losing control
Running a complex open-source data stack in production is not trivial. Organizations consistently cite skills shortages, operational complexity, and support concerns as obstacles to broad open-source adoption. The 2025 State of Open Source Report, for example, found that nearly half of organizations handling big data expressed low confidence in their ability to administer those technologies securely and at scale.
This gap has fueled interest in managed open-source services. A growing ecosystem of providers now operate Kafka, Cassandra, PostgreSQL, Spark, OpenSearch, ClickHouse, and related technologies on behalf of customers, offering installation, scaling, backups, and 24/7 monitoring across on-premises, public cloud, or hybrid deployments.
NetApp’s Instaclustr business, for example, positions its managed platform as a way to simplify the deployment and management of open-source data technologies such as Apache Kafka, Cassandra, and PostgreSQL while preserving the benefits of community-driven development and open standards.
Confluent Cloud takes a similar approach for Apache Kafka and related streaming components, offering a fully managed, cloud-native Kafka service that runs on AWS, Azure, and Google Cloud. The service abstracts cluster operations while exposing standard Kafka APIs, letting teams build event-driven and AI-ready applications without managing brokers and scaling logic themselves.
Aiven, meanwhile, focuses on multi-cloud managed open-source databases and streaming engines — including PostgreSQL, Kafka, OpenSearch, and ClickHouse — delivered as a vendor-neutral data platform. Its offering is designed so organizations can move workloads across clouds and services while keeping the same open-source engines and APIs at the core of their data architecture.
For architecture teams, the key distinction is that managed open source does not have to mean surrendering architectural control. When the underlying technologies remain truly open — governed by neutral foundations and released under standard licenses — organizations can change operators, move workloads between environments, or bring operations back in-house over time. That flexibility is central to any strategy that seeks to balance the convenience of managed services with the long-term goal of avoiding lock-in.
Patterns emerging in next-generation data architectures
Across industries, several reference patterns are emerging:
Streaming-first cores
Many organizations are centering their architectures on streaming platforms, using Kafka or similar engines as the system of record for business events while layering stateful processors and databases on top. RTInsights has shown how pairing Kafka with edge processing can deliver both local responsiveness and global intelligence.
Open lakehouse designs
By combining object storage, open table formats like Iceberg, and independent query engines, enterprises are building lakehouses that support BI, data science, and AI without committing to a single vendor’s proprietary format or pricing model.
Polyglot and multi-cloud data planes
Multiple open-source databases coexist within and across clouds, with Kubernetes and other orchestration tools providing a common operational substrate. The Cloud Native Computing Foundation now hosts more than 170 projects with hundreds of thousands of contributors, reflecting how broad this ecosystem has become.
As these patterns mature, the focus is shifting from “Can we run open source?” to “How do we govern, secure, and observe an open data platform at scale?”
What IT leaders should do next
For architects and data leaders, the strategic questions are less about specific projects and more about principles. Here are the key ideas to follow:
- Define openness as a first-class requirement. Specify open formats, standard APIs, and foundation-governed projects as non-negotiables in reference architectures and RFPs.
- Design around data products, not just infrastructure. Identify the key data products — real-time risk scores, personalization features, operational dashboards — and map them to a composable set of open-source engines.
- Decide where you need help. Retain direct control over the parts of the stack that differentiate your business, and consider managed open-source services where operational complexity does not add proprietary value.
- Invest in skills and community. Encourage teams to participate in open-source communities, contribute fixes, and stay close to upstream roadmaps; this is often the best insurance against future platform surprises.
Open source is not a silver bullet, and it introduces its own governance and security challenges. But as data and AI become central to competitive strategy, architectures built on open engines, open formats, and open communities offer a compelling path to scale and innovation without surrendering control of the most precious asset: the data itself.
Sidebar: Three routes to managed open-source data platforms
As enterprises standardize on open-source engines for databases, streaming, and search, many look for partners who can shoulder operational complexity without reintroducing lock-in. Three broad approaches are emerging:
NetApp Instaclustr: Deep focus on data infrastructure
NetApp’s Instaclustr business concentrates on a curated set of open-source data technologies — including Apache Cassandra, Kafka, PostgreSQL, and related components — delivered as managed services across public clouds and private environments. The emphasis is on predictable, production-grade operations: capacity planning, upgrades, monitoring, and incident response, guided by teams that specialize in those specific projects. For organizations that want opinionated best practices around a core set of technologies, Instaclustr offers a relatively tight, infrastructure-centric experience.
Confluent Cloud: Streaming-first data-in-motion platform
Confluent Cloud is built around Apache Kafka and a growing family of streaming components, including connectors, stream processing, and governance capabilities. The service abstracts Kafka cluster management on AWS, Azure, and Google Cloud while exposing standard Kafka APIs, which makes it appealing for organizations that see event streaming as the backbone of their data architecture. In many environments, Confluent Cloud becomes the managed “nervous system” that other databases, lakehouses, and analytics engines plug into.
Aiven: Multi-cloud portfolio of open-source engines
Aiven offers a broader portfolio of managed open-source services — PostgreSQL, Kafka, OpenSearch, ClickHouse, Redis, and more — with a strong emphasis on multi-cloud flexibility. Customers can deploy the same services across different cloud providers with consistent configuration and operations, which is attractive for teams that want to standardize on a single operational model while retaining the option to move workloads. In practice, Aiven often serves as a neutral layer that sits above the major clouds, providing a unified control plane for a polyglot data stack.
Across all three approaches, the common thread is that the core technologies remain open: projects are governed by community or foundation models, and APIs and data formats are not tied to a single vendor. That gives architecture teams the option to change operators, re-balance workloads, or bring critical components back in-house while still benefiting from managed operations where it makes sense.
