Open data platforms: Breaking free from vendor lock-in

How your data is ingested, stored, and transformed all influence how accessible it is. When it’s ingested through a vendor-neutral connector and stored in open formats, any tool can work with your data. When you opt for proprietary standards and platforms, that flexibility disappears.

An open data platform uses shared industry standards and vendor-neutral components to give companies complete control over their data. You can easily migrate data from one provider to another. And when you need to swap out a transformation or compute engine, simply plug in the new component and get started.

Especially for AI systems that must query data across different business functions and compute engines, open standards provide compatibility and consistent access.

This article outlines why open data platforms are a better choice compared to proprietary solutions, how openness impacts business, and how to build a data architecture for freedom.

What defines an open data platform?

Open data architecture relies on interoperable data formats and systems. Where possible, open systems choose vendor-neutral data platforms to avoid lock-in and preserve full organizational control.

From an architectural perspective, five main characteristics give open data platforms their flexibility:

• Open table formats: Formats like Apache Iceberg™ and Delta Lake allow any compliant compute engine to read and write data into tables. With no proprietary data formats, you avoid lock-in and costly export fees.
• Open APIs and SQL: Standard REST and SQL interfaces replace proprietary connectors, giving every tool a universal way to access your data.
• Decoupled storage and compute: Object storage and compute engines scale independently so you can upgrade only the required components as business needs evolve.
• Pluggable compute engines: Trino, Flink, Snowflake, Spark, or any engine compatible with open formats can all operate simultaneously. Instead of having to choose one vendor, you’re free to select the best engine for each workload.
• Open governance and standards: In an open data platform, standards are maintained by neutral bodies (such as Linux Foundation and Apache Foundation) rather than single vendors.

How to build an open data platform: Architecture and the five-layer stack

Building an open data platform requires full alignment with the main Open Data Infrastructure (ODI) principles. By implementing these flexible standards and systems, you future-proof your tech stack against vendor lock-in and cost escalation. For companies deploying AI agents internally, open data standards facilitate AI access and use of data for queries.

Here are the five layers required to build an open data platform:

• Open ingestion (Layer 1): Vendor-neutral connectors ingest data at scale without creating destination lock-in. Fivetran enables ingestion from any source while preserving full portability.
• Open storage (Layer 2): Scalable object storage, paired with open table formats, provides a flexible, accessible single source of truth.
• Open compute (Layer 3): Engines such as Spark or DuckDB can plug into your workflows and operate on the same source data, no duplication or conversion required.
• Open transformation (Layer 4): Transformation services like dbt offer portable transformation logic across different engines.
• Governance and metadata (Layer 5): Open catalogs like Apache Polaris™ enforce access, quality, and governance rules in an engine-independent way.

When all these layers remain open, data can flow through your organization freely. The moment one part of this system becomes proprietary, the entire system is affected. The strength of an open data platform is its holistic design, where every component freely connects and exchanges information with the next.

Open data platforms vs. proprietary platforms: Lock-in risks and long-term cost

Proprietary platforms offer short-term convenience as you can quickly onboard with full-size stacks across ingestion, storage, and compute. But as your data estate grows, that convenience turns into fragmentation. Without an open standard data platform, you often get tied to a single vendor’s ecosystem, with no leverage of your own.

Explore the main differences between open data platforms and proprietary platforms to see where their paths diverge. 

Pricing control

Open data platforms let you pick the right tool for each job. Because multiple compute engines can operate on the same open data, you can choose a high-performance engine for heavy workloads and a lighter, lower-cost option for routine queries. This flexibility directly reduces costs: You avoid overpaying for compute simply because your data is tied to one system.

Proprietary platforms remove that leverage. Once you store data in closed formats, it’s locked into that system. That means every workload, from light queries to heavy processing, must run on the same engine — at the vendor’s pricing. Switching to a different provider often means prohibitive costs or egress fees. And with no alternative engines that support the proprietary formats, the vendor holds full pricing power.

Data portability

Open data platforms keep and export the data in portable formats. By using customer-controlled storage, you can move data within and beyond your ecosystem without any additional cost.

Proprietary platforms limit data portability by using restrictive export formats and charging egress fees. Your flexibility and ability to migrate information are severely reduced.

Ecosystem flexibility

Open platforms allow any standards-compliant tools to integrate with your tech stack. You can add, remove, and replace components easily as business needs evolve.

Proprietary platforms limit the range of tools you can integrate, often blocking any tool that isn’t developed by the existing vendor. This limitation restricts growth, especially if you want to innovate by introducing new features or components.

This lack of flexibility is even riskier now with AI embedded into most company workflows. AI systems need access to company data on a large scale, but API limitations or incompatible data formats make this impossible in proprietary environments.

Business benefits of open data platforms

Open data platforms aim to democratize access to data. Using components that rely on ODI throughout the business provides complete control over data architecture and how it processes your company information.

Here are the main benefits of open data platforms:

• Cost control: Open data standards keep data portable, giving you leverage to negotiate better deals for every component in the data architecture.
• AI readiness: AI systems can easily access and use data stored in open formats, facilitating AI integration and information exchange. 
• Future-proofing: Open data platforms prioritize flexibility, letting you change or update infrastructure without disruption. When a new technology emerges in the future, you can adopt it as soon as components become available.

How Fivetran enables open data platforms

Fivetran provides a vendor-neutral ingestion layer for open data platforms. Import data from over 750 sources with pre-built connectors and direct incoming data to any destination of your choice. Fully automated ELT workflows handle extraction, loading, and schema management so you have full flexibility to transform and use data however you want.

Fivetran’s Managed Data Lake Service lands data in open table formats on customer-controlled object storage to keep your data portable across compute engines. With full support for REST catalogs and open metadata APIs, Fivetran enables governance and discovery without the need for vendor-specific metadata stores.

For a platform that fully commits to open standards and vendor neutrality, get started with a Fivetran demo today.

FAQ

What is the difference between an open data platform and an open-source platform?

An open-source platform refers to software whose source is publicly available, but that doesn’t automatically make it an open data platform. For example, open-source systems may still be closely coupled to vendor-specific infrastructure or standards. By contrast, open data platforms are built on open standards and formats that remain fully flexible without vendor lock-in.

What role does Apache Iceberg play in an open data platform?

Apache Iceberg is an open table format that allows compliant compute engines to access data consistently. It enables analytics at scale without vendor lock-in.

What open standards and technologies should enterprises adopt to build open data platforms aligned with AI-ready infrastructure?

Organizations should build out ingestion, storage, and transformation layers based on open standards. Using portable frameworks or compute engines that prioritize open access creates long-term flexibility and an AI-ready foundation.

Apache Iceberg and Apache Polaris are trademarks of the Apache Software Foundation.

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