Open Data Infrastructure Article Library

Foundation

Foundation articles in the Open Data Infrastructure library.

What Is Open Data Infrastructure?

Define ODI as the architecture, standards, and ecosystem for portable, governed, AI-ready data.

Open Data Infrastructure vs. Open Data: What Is the Difference?

Separate public data access from infrastructure that makes enterprise data portable and usable.

The Case for Open Data Infrastructure

Make the strategic argument for ODI as a control, cost, and innovation lever.

175

What Is a Table Format? And Why Data Ownership Depends On It

Give the definitive plain-language answer and tie it directly to who controls the data.

176

Data Catalog vs Metastore vs Business Glossary

Disambiguate three terms people conflate and explain which one is ODI's control plane.

177

What Is a REST Catalog?

Answer the question directly, then explain why the API boundary matters for portability.

179

What Is a Lakehouse, Really?

Cut through marketing to a precise definition and its relationship to open infrastructure.

183

Is Apache Iceberg Actually Open?

Answer directly — license and governance — then name the catalog caveat buyers miss.

The Future of Data Platforms Is Open, Governed, and Interoperable

Connect market direction to durable platform design principles.

Open Data Infrastructure Is Not Just Open Source

Clarify the difference between licensing, standards, interoperability, and customer control.

178

What Is Compute-Storage Separation?

Explain the principle and why it is the precondition for engine and cost portability.

180

What Is Data Portability, and What It Is Not

Distinguish true portability from an export button that produces a one-way dump.

181

What Is Time Travel in Data Systems?

Explain snapshots, rollback, and audit use cases without assuming Iceberg internals knowledge.

182

Open Table Format vs File Format: What's the Difference?

Resolve the single most common confusion in the open data conversation.

184

Is a Lakehouse the Same as Open Data Infrastructure?

Clarify the overlap and the distinction so the terms stop being used interchangeably.

218

What Is Agentic Data?

Answer the definition directly and connect it to governed data access, metadata, lineage, and action boundaries.

Strategy

Strategy articles in the Open Data Infrastructure library.

Open Data Infrastructure and the End of Vendor Lock-In

Show how ODI reduces switching costs and restores architectural control.

The ODI Maturity Model

Create a staged maturity model and roadmap.

Why Data Ownership Is Becoming a Board-Level Issue

Frame data control as a business resilience and AI competitiveness issue.

The Interoperability Tax: Why Data Teams Pay for Closed Systems

Name and quantify the recurring labor created by closed data systems where possible.

Who Owns Your Data Infrastructure?

Prompt leaders to inspect who controls their data, metadata, policies, and exit paths.

How to Modernize a Closed Data Stack

Give a migration path from closed systems to open infrastructure.

The Data Infrastructure Exit Strategy Every Company Needs

Argue that exit paths should be designed before they are needed.

How Open Standards Become Strategic Infrastructure

Explain how standards compound into ecosystems and buyer control.

The New Data Platform Buyer: From Warehouse-First to Infrastructure-First

Describe how evaluation criteria shift from features to data control and interoperability.

AI

AI articles in the Open Data Infrastructure library.

Why Open Data Infrastructure Matters in the AI Era

Show why AI increases the strategic value of open, governed data foundations.

Why AI Makes Closed Data Infrastructure More Expensive

Explain how agentic systems magnify the cost of proprietary data boundaries.

Why Every AI Strategy Needs an ODI Strategy

Tie AI success to open access, metadata, governance, and trusted context.

Why Agents Need Governed Data Access

Show how permissions, policy, and auditability must move into agent tooling.

156

The Model Context Protocol and Open Data Infrastructure

Explain why MCP is the agent data interface and why the layer behind it must be open and governed.

163

Data Provenance for AI Training and the EU AI Act

Connect training-data provenance obligations to lineage that only open infrastructure makes durable.

208

Open Data Infrastructure Is the Foundation for AI

Make the foundational argument that production AI depends on governed access, metadata, lineage, and portability before model choice.

209

AI-Ready Context: The Missing Layer Between Data and Agents

Define AI-ready context as governed data plus metadata, semantics, lineage, freshness, and policy exposed through usable interfaces.

210

Agentic AI Needs Open Data Infrastructure

Argue that agentic AI raises the stakes for governed access, provenance, catalog context, and explicit data contracts.

211

Agentic Data: What Data Has to Become for Agents to Use It

Define agentic data as data packaged with the permissions, meaning, quality, provenance, and action boundaries agents need.

212

The Context Graph: Metadata, Lineage, Semantics, and Policy for AI

Introduce the context graph as the connected metadata layer that helps agents reason over data without guessing.

The Rise of AI-Ready Data Infrastructure

Define the infrastructure capabilities that make data usable by AI systems.

The AI Context Layer: Where ODI Meets Agentic Systems

Define context as a governed interface over data, metadata, lineage, and semantics.

Why RAG Needs Open Data Infrastructure

Explain why retrieval quality depends on open access, metadata, governance, and freshness.

The Difference Between AI-Ready Data and AI-Washed Data

Give buyers a practical test for whether data infrastructure can support production AI.

How to Design Data Access for AI Agents

Provide design patterns for permissions, tool interfaces, query scopes, and audit logs.

How Open Catalogs Help AI Systems Understand Data

Show catalogs as discovery and context infrastructure for AI systems.

Why Enterprise Agents Fail Without Data Governance

Connect AI project failures to weak governance and poor context infrastructure.

The Agentic Lakehouse: A Reference Architecture

Lay out an architecture for agents using lakehouse tables, catalogs, policy, and context services.

How to Make Your Data Stack Agent-Ready

Translate ODI principles into an adoption checklist for existing data stacks.

The Four Layers of AI-Ready Data Infrastructure

Offer a memorable model for access, metadata, governance, and context.

157

Why MCP Servers Need a Governed Data Layer

Show what breaks when MCP tools query data without policy, identity, and audit behind them.

158

Text-to-SQL on the Open Lakehouse

Explain why catalog and semantic grounding, not a bigger model, drive text-to-SQL accuracy.

159

Vector Search on Open Table Formats

Explore keeping embeddings beside governed data instead of in a siloed vector store.

162

Agent Memory Belongs in Open Data Infrastructure

Argue that durable, portable, governed agent memory should live in open infrastructure, not a vendor silo.

164

How to Assess Data Readiness for Fine-Tuning

Give a checklist for quality, lineage, and usage rights before data touches a fine-tuning run.

166

The Context Window Is Not Your Data Layer

Argue that larger context windows do not remove the need for governed, open data infrastructure.

217

From Semantic Layer to Context Graph

Show how semantic layers need to evolve into richer context graphs for agentic AI systems.

How Metadata Becomes Prompt Context

Describe patterns for translating metadata into safe, useful model context.

Agentic Workflows Need Lineage, Not Just Vectors

Argue that provenance and transformation history are essential for trustworthy agent outputs.

Why Semantic Layers Matter More in the Agent Era

Explain how semantics prevent agents from misusing ambiguous business data.

The AI Data Contract: What Agents Need Before They Query

Define the minimum metadata, policy, and reliability contract an agent should receive.

160

Feature Stores and Open Data Infrastructure

Position open tables as the offline feature store and explain the governance benefits.

161

Knowledge Graphs and Open Data Infrastructure

Explain how a knowledge graph adds context over governed open data rather than replacing it.

165

Synthetic Data and Open Data Infrastructure

Explain why synthetic data still needs provenance, lineage, and governance to be trustworthy.

223

DataFusion as an Embedded Query Engine for Agents

Explain why embeddable query execution matters when agents need governed local reasoning over open data.

229

Context Graph vs Knowledge Graph for AI-Ready Data

Separate business entity graphs from operational context graphs for agents that need metadata, policy, and lineage.

230

Data Modeling for Agentic Analytics

Explain how modeling changes when autonomous systems consume metrics, entities, policies, and lineage directly.

231

AI-Ready Data Quality Signals

Define the quality signals agents need beyond dashboard freshness, including provenance, uncertainty, and policy status.

234

Apache Parquet Metadata for AI-Ready Context

Explain what Parquet metadata can and cannot tell agents, and why table and catalog metadata still matter.

236

Open Data Infrastructure Reference Architecture for Agents

Describe the reference architecture that connects open tables, catalogs, context graphs, policy, retrieval, and agent tools.

238

Foundation Models Need Data Contracts

Argue that foundation model performance depends on governed data contracts as much as retrieval and prompt design.

Architecture

Architecture articles in the Open Data Infrastructure library.

The Open Data Infrastructure Stack

Map the ODI stack from access to AI-ready context.

A Reference Architecture for Open Data Infrastructure

Provide a concrete architecture that teams can adapt.

Why Catalogs Are the Control Plane for ODI

Explain why catalogs coordinate identity, metadata, permissions, and table operations.

Open Data Infrastructure vs. the Modern Data Stack

Explain why ODI is a design lens across the stack, not another tool category.

Lakehouse Architecture as Open Data Infrastructure

Frame lakehouse architecture as one implementation pattern for ODI.

How Open Table Formats Change Data Architecture

Show how table metadata moves capabilities out of proprietary engines.

Why Metadata Is the Real Infrastructure Layer

Argue metadata determines whether data can be found, governed, trusted, and reused.

How to Build a Vendor-Neutral Data Architecture

Offer architectural practices that preserve optionality.

The ODI Control Plane: Catalogs, Policies, and Metadata

Define the control-plane responsibilities in an open data architecture.

Why Bring Compute to Data Needs Open Metadata

Show why compute portability breaks without shared metadata and catalog semantics.

Designing for Interoperability in Data Platforms

Give design rules for avoiding brittle one-off integrations.

Data Portability as an Architecture Principle

Turn portability from a procurement concern into an architecture requirement.

The ODI Data Plane: Files, Tables, Streams, and APIs

Explain the physical and logical data interfaces that carry ODI workloads.

The ODI Application Plane: Analytics, AI, and Data Products

Connect infrastructure choices to the applications and data products they enable.

Governance

Governance articles in the Open Data Infrastructure library.

The Trust Problem in Enterprise AI Starts in the Data Layer

Connect hallucination, provenance, policy, and data quality to infrastructure choices.

How to Design an Open Metadata Architecture

Provide a reference approach for metadata collection, access, governance, and usage.

Governance Is Infrastructure, Not Compliance Theater

Argue governance should be designed into platform behavior.

How ODI Changes Data Governance

Show how openness changes governance from paperwork to infrastructure.

Access Control in Open Data Infrastructure

Explain patterns for identity, policy, and enforcement across open systems.

Data Lineage for AI-Ready Infrastructure

Explain why lineage becomes critical when outputs influence AI decisions.

Observability for Open Data Infrastructure

Define what needs to be observable across access, storage, catalogs, and pipelines.

How to Audit an Open Data Infrastructure Stack

Provide an audit method that maps to the ODI scorecard.

Open Data Infrastructure for Regulated Industries

Explain why openness and strong governance can reinforce each other.

OpenLineage and the Case for Portable Lineage

Explain why lineage needs portable event standards.

DataHub, OpenMetadata, and the Metadata Layer

Compare metadata platform roles through an ODI lens.

Policy Enforcement Across Open Data Systems

Discuss enforcement points and tradeoffs across catalogs, engines, and services.

Data Quality Signals Agents Can Actually Use

Turn data quality into machine-readable agent context.

Secure Data Sharing Without Platform Lock-In

Show how policy, catalogs, and open formats support secure sharing.

Privacy, Consent, and Control in Open Data Infrastructure

Explore how open infrastructure must still respect privacy and consent boundaries.

219

Apache Iceberg REST Catalog Security Patterns

Map authentication, authorization, credentials, audit, and policy boundaries for REST catalog deployments.

220

Apache Polaris Governance Patterns for ODI

Explain how Polaris fits the open catalog control-plane story without turning ODI into one product.

235

Governed Data Sharing With Open Table Formats

Explain how open table formats support data sharing only when catalog, policy, and audit boundaries are designed with them.

Technical Architecture

Technical Architecture articles in the Open Data Infrastructure library.

Apache Iceberg as Open Data Infrastructure

Explain Iceberg's role in open, interoperable lakehouse architecture.

Apache Polaris and the Future of Open Catalogs

Position Polaris as a key open catalog implementation for Iceberg ecosystems.

ADBC Explained: Why Database Connectivity Needs a Rethink

Explain ADBC and how columnar connectivity changes data movement.

141

Apache XTable: Cross-Format Interoperability Explained

Explain how XTable translates between Iceberg, Delta, and Hudi metadata and where the limits are.

142

Project Nessie: Git-Style Catalog Versioning Explained

Explain branch/tag/merge semantics for data and where Nessie fits in an open catalog strategy.

147

Apache Parquet Explained: The Foundation of the Open Lakehouse

Explain the columnar file format every open table format is built on, in plain terms.

201

StarRocks and Open Data Infrastructure

Explain where StarRocks fits in an open lakehouse stack and how to evaluate its Iceberg, catalog, and query-engine role.

202

Apache Doris and Open Data Infrastructure

Position Apache Doris as a real-time analytical engine in ODI and name the control boundaries buyers should inspect.

203

Lakekeeper: Open Catalog Operations for Apache Iceberg

Explain Lakekeeper through the ODI control-plane lens: catalog operations, governance boundaries, and self-hosted ownership.

204

Apache Flink and the Streaming Layer of Open Data Infrastructure

Show how Flink fits the ODI data plane for streaming writes, CDC, and event-driven lakehouse workloads.

205

SQLGlot and the Case for Portable SQL

Explain why SQL translation and lineage-aware parsing matter when ODI spans many engines instead of one warehouse.

206

SQLMesh and Open Data Infrastructure

Frame SQLMesh as a transformation control layer for ODI, with emphasis on planning, lineage, environments, and engine portability.

207

dbt Core and Open Data Infrastructure

Explain where dbt Core fits in an ODI stack and where warehouse-centric assumptions need stronger open metadata and engine boundaries.

213

Data Modeling for Open Data Infrastructure

Explain how data modeling changes when tables, catalogs, transformations, and semantics have to survive many engines and AI workflows.

How Iceberg Metadata Enables Interoperability

Show how metadata files and snapshots help engines share tables.

Iceberg Snapshots Explained for Data Engineers

Explain snapshots, manifests, metadata, and time travel with practical examples.

Iceberg REST Catalogs Explained

Explain the purpose and architecture of the Iceberg REST catalog pattern.

Polaris vs. Hive Metastore: What Changes?

Compare legacy metastore assumptions with modern open catalog needs.

What a Catalog Actually Does in an Open Lakehouse

Explain catalog responsibilities without vendor marketing language.

How Table Formats, Catalogs, and Query Engines Work Together

Clarify the boundaries between storage metadata, catalog APIs, and compute engines.

Apache Arrow and the ODI Connectivity Layer

Explain Arrow as a common memory and transport layer for data interoperability.

Arrow Flight SQL and High-Performance Data Access

Explain where Flight SQL fits in an open data connectivity strategy.

143

Apache Gravitino and the Federated Metadata Catalog

Position Gravitino as multi-source catalog federation and contrast it with single-format catalogs.

144

Delta Lake as Open Data Infrastructure: An Honest Assessment

Give a fair assessment of Delta's openness, the UniForm bridge, and the governance caveats.

145

Apache Hudi as Open Data Infrastructure

Explain Hudi's upsert-first design and the workloads where it is the right open choice.

148

Parquet vs ORC: Choosing an Open Columnar Format

Compare the two open columnar formats on ecosystem fit, not just micro-benchmarks.

150

Substrait: Portable Query Plans for a Composable Stack

Explain why an engine-agnostic plan format matters for true interoperability.

151

PyIceberg: Working with Iceberg Without a JVM

Show Python-native Iceberg reads and writes and why a JVM-free path matters for adoption.

152

Iceberg v3 and Deletion Vectors Explained

Explain the v3 spec changes and what deletion vectors mean for merge-on-read performance.

153

Iceberg Branching, Tagging, and Write-Audit-Publish

Explain branching/tagging and the write-audit-publish pattern for safe data releases.

155

dbt and SQLMesh on the Open Lakehouse

Show how transformation frameworks change when the target is open, engine-agnostic tables.

215

Apache Flink to Iceberg: Streaming Patterns for ODI

Give practical patterns for writing streaming data into Iceberg while preserving governance, compaction, and table reliability.

216

dbt Core vs SQLMesh in the Open Lakehouse

Compare transformation workflow assumptions, state, lineage, environments, and engine portability without declaring one universal winner.

Iceberg Schema Evolution and Why It Matters

Explain field IDs, safe evolution, and why schema semantics matter.

Iceberg Partition Evolution: A Practical Guide

Show how partition evolution avoids historical rewrite traps.

How to Build an Iceberg-Based Lakehouse on Your Laptop

Provide a local tutorial that demonstrates ODI concepts hands-on.

DuckDB, Iceberg, and Local-First Analytics

Show why local-first analytics is a useful proving ground for open infrastructure.

Query Engines in ODI: Spark, Trino, DuckDB, DataFusion

Compare engine roles instead of crowning a single winner.

Apache DataFusion and the Future of Composable Query Engines

Explain why embeddable query engines matter for open data applications.

How to Benchmark Open Table Format Performance

Teach readers how to build fair table-format benchmarks and avoid misleading tests.

File Layout, Compaction, and Performance in Iceberg

Explain the operational mechanics that determine query performance.

How to Handle Deletes, Updates, and CDC in Open Table Formats

Explain patterns and tradeoffs for mutable data in open lakehouse tables.

Streaming Into Iceberg: Patterns and Tradeoffs

Compare streaming ingestion patterns and operational implications.

Zero-Copy Data Sharing: Promise, Limits, and Architecture

Explain when zero-copy sharing works, when it does not, and what infrastructure it needs.

146

Apache Paimon and the Streaming Lakehouse

Explain Paimon's streaming-first table design and how it complements batch open formats.

149

The Role of Apache Avro in Open Data Infrastructure

Explain where row-oriented Avro still belongs in an otherwise columnar open stack.

154

Materialized Views on the Open Lakehouse

Explain cross-engine and incremental materialized views and their open-format constraints.

222

DuckDB as an Edge Query Engine for ODI

Show where DuckDB belongs in local, embedded, and edge analytics without pretending it replaces distributed engines.

224

StarRocks on Open Lakehouse Tables

Place StarRocks in the ODI engine map for low-latency analytics over governed open tables.

225

Apache Doris on Open Lakehouse Tables

Place Apache Doris in the ODI engine map and separate engine acceleration from table ownership.

227

SQLMesh State and Data Contracts in the Open Lakehouse

Connect SQLMesh environments, planning, and state to ODI model governance and safe lakehouse change.

228

dbt Core in an Open Data Infrastructure Stack

Explain where dbt Core fits in ODI and which responsibilities remain outside transformation code.

233

Apache Flink CDC Into Iceberg and Paimon

Compare CDC landing patterns into Iceberg and Paimon and explain when each table contract fits.

Buyers and Comparisons

Buyers and Comparisons articles in the Open Data Infrastructure library.

How to Evaluate an Open Data Platform

Provide an evaluation checklist for vendors and internal platforms.

How to Ask Vendors About Open Data Infrastructure

Provide concrete procurement questions that expose openness, lock-in, and AI readiness.

25 Questions to Ask Before Buying a Data Platform

Create a practical question set for evaluating platform openness and control.

How to Tell If a Vendor Is Open or Just Open-Washing

Teach buyers to distinguish open standards from marketing language.

100

The Open Data Infrastructure Buyer's Guide

Create a comprehensive buyer guide that can become a cornerstone conversion asset.

187

Iceberg vs Parquet: They're Not the Same Thing

Fix a high-volume search confusion with a clear, citable comparison.

Iceberg vs. Delta Lake vs. Hudi Through an ODI Lens

Compare table formats by openness, interoperability, governance, and ecosystem fit.

Polaris vs. Unity Catalog: Open Catalog Tradeoffs

Compare catalog models with care around factual vendor claims.

Open Data Infrastructure vs. Data Mesh

Clarify organizational vs infrastructure patterns and where they reinforce each other.

Open Data Infrastructure vs. Data Fabric

Compare architecture claims and practical implementation differences.

Open Data Infrastructure vs. Lakehouse Architecture

Explain lakehouse as one ODI pattern, not the entire category.

185

Open Data Infrastructure vs Data Virtualization

Contrast federating access to captive data with actually owning portable data.

186

Open Data Infrastructure vs the Cloud Data Warehouse

Compare the warehouse model and the open model on control, cost, and AI readiness.

188

Table Format vs Catalog vs Query Engine: Who Does What

Draw clean responsibility boundaries among the three core lakehouse layers.

189

Data Lake vs Lakehouse vs Warehouse Through an ODI Lens

Run the classic comparison through the lens of who controls the data.

190

Polaris vs Nessie vs Gravitino: Open Catalog Options

Compare three open catalog approaches on versioning, federation, and governance.

191

Snowflake Open Catalog vs Apache Polaris

Compare the managed service with the upstream project on portability and control.

193

Managed vs Self-Hosted Open Catalog: How to Choose

Frame the operational-burden vs control tradeoff and when each is the right call.

214

DuckDB vs DataFusion vs StarRocks vs Doris for ODI

Compare engine roles by workload, embedding model, latency, governance, and fit in open data infrastructure.

Warehouse-Centric vs. Lakehouse-Centric ODI

Compare how each center of gravity affects openness and portability.

192

Trino vs Spark vs DuckDB for the Open Lakehouse

Match each engine to workloads instead of declaring one universal winner.

Industries

Industries articles in the Open Data Infrastructure library.

101

Open Data Infrastructure for Healthcare and Health Systems

Show why FHIR/HL7 mandates, PHI governance, and decade-long retention favor portable, governed data over EHR and warehouse lock-in.

102

Open Data Infrastructure for Financial Services and Banking

Connect risk reporting, audit lineage, and supervisory portability requirements to an open, governed data foundation.

106

Open Data Infrastructure for the Public Sector and Government

Argue that taxpayer-funded data and sovereignty requirements demand procurement neutrality and exit paths.

110

Open Data Infrastructure for Pharma and Life Sciences

Connect GxP, trial data lineage, and multi-decade retention to open formats and portable governance.

103

Open Data Infrastructure for Insurance

Explain how reusable claims and actuarial data products depend on open formats and portable governance.

104

Open Data Infrastructure for Retail and E-commerce

Show how customer 360 and real-time inventory across engines work better without a single-warehouse chokepoint.

105

Open Data Infrastructure for Manufacturing and Industrial IoT

Explain why sensor and time-series scale plus OT/IT convergence make open formats a longevity decision.

107

Open Data Infrastructure for Energy and Utilities

Tie long-lived grid and sensor assets plus regulatory reporting to durable open infrastructure.

108

Open Data Infrastructure for Telecommunications

Show how CDR-scale data and real-time AI analytics benefit from vendor-neutral, open table formats.

109

Open Data Infrastructure for Media and Entertainment

Explain why multi-cloud content and engagement data plus AI personalization need open foundations.

111

Open Data Infrastructure for Logistics and Supply Chain

Show how cross-partner data sharing without lock-in enables real supply chain visibility.

112

Open Data Infrastructure for B2B SaaS Companies

Explain why exposing customer-facing Iceberg tables turns data sharing into a product feature, not a liability.

232

Open Data Infrastructure for Customer 360

Show how ODI turns Customer 360 from a closed application promise into governed, portable customer context.

Roles

Roles articles in the Open Data Infrastructure library.

113

A CDO's Guide to Open Data Infrastructure

Give the CDO a board narrative, value framing, and a pragmatic place to start.

114

A CIO's Guide to Open Data Infrastructure

Frame ODI as portfolio risk management and vendor strategy, not a tooling choice.

115

A CTO's Guide to Open Data Infrastructure

Show the CTO how open foundations create architectural control and AI optionality.

116

The CFO's Case for Open Data Infrastructure

Translate openness into TCO, capex/opex, and lock-in framed as a balance-sheet risk.

117

A CISO's Guide to Open Data Infrastructure

Argue that control, auditability, and no black-box trust make openness a security posture, not a risk.

118

A Chief AI Officer's Guide to Open Data Infrastructure

Make explicit that every AI mandate is downstream of the data foundation the CAIO inherits.

119

Open Data Infrastructure for Data Engineering Leaders

Help eng leaders translate ODI into team structure, tooling decisions, and a credible roadmap.

120

Open Data Infrastructure for Analytics Engineers

Show analytics engineers how modeling and semantics change when tables are open and engine-agnostic.

Economics

Economics articles in the Open Data Infrastructure library.

121

The True Cost of Vendor Lock-In: A Quantified Model

Provide a model to estimate switching cost, rent extraction, and option value lost to lock-in.

122

Open vs Closed Data Platforms: A Total Cost of Ownership Model

Build a TCO framework with the cost categories vendors leave out of the comparison.

126

How to Build the Business Case for Open Data Infrastructure

Give a reusable ROI model and an executive deck outline that survives finance scrutiny.

123

Data Gravity and Cloud Egress Fees: The Hidden Lock-In

Explain how egress economics enforce captivity and which open patterns reduce it.

124

Storage Economics of the Open Lakehouse

Break down object-storage economics, compaction tradeoffs, and where money actually leaks.

125

Compute Cost Portability: Why You Should Be Able to Switch Engines

Argue that decoupling data from compute pricing is the main cost lever open infrastructure unlocks.

127

FinOps for the Open Lakehouse

Apply FinOps practice — allocation, accountability, optimization — to open lakehouse spend.

129

The Hidden Cost of Proprietary File Formats

Quantify the re-export, compatibility, and longevity tax of proprietary storage formats.

128

Consumption vs Capacity vs Open: Data Platform Pricing Models Compared

Compare pricing structures and the lock-in each one quietly creates.

237

Cost Allocation for Open Lakehouse Workloads

Show how to allocate storage, compute, catalog, and maintenance costs in an open lakehouse without hiding shared-platform spend.

Migration

Migration articles in the Open Data Infrastructure library.

130

Standards

Standards articles in the Open Data Infrastructure library.

167

Operations

Operations articles in the Open Data Infrastructure library.

194

ODI Article Library