Introduction Introduction In my most recent installments, I’ve been looking at Apache Iceberg, Apache Kafka, and Apache Flink. This led me to think about what might be a helpful extension in that space to look at, which led me to think about navigating the complexities of modern data ecosystems. Managing metadata effectively ensures data discoverability, governance, and collaboration. Enter OpenMetadata, an open-source platform designed to streamline metadata management, offering a robust data discovery, observability, and governance solution. In this blog post, we’ll dive into the technical underpinnings of OpenMetadata, explore its architecture, key features, and use cases, and provide visual aids to help you understand why it’s gaining traction in the data engineering community. With that preamble, let’s dive in! Apache Iceberg Apache Iceberg Apache Kafka Apache Kafka Apache Flink Apache Flink OpenMetadata OpenMetadata What is OpenMetadata? What is OpenMetadata? OpenMetadata is a unified, open-source metadata platform that empowers organizations to manage their data assets efficiently. Launched in 2021 and inspired by lessons from Uber’s metadata infrastructure, it provides a centralized repository for metadata, enabling data discovery, lineage tracking, quality monitoring, and team collaboration. With over 300 contributors and adoption across diverse industries, OpenMetadata stands out for its simplicity, extensibility, and vibrant community. It’s built to address the challenges of fragmented data ecosystems, where metadata often becomes a bottleneck for scalability and governance. Unlike traditional metadata tools that rely on complex graph databases or proprietary systems, OpenMetadata adopts a streamlined architecture with a schema-first approach. It supports over 90 connectors for ingesting metadata from databases, data warehouses, pipelines, and dashboards, making it a versatile choice for modern data stacks. Its user-friendly interface caters to technical and non-technical users, fostering a data-driven culture. Why OpenMetadata Matters Why OpenMetadata Matters Reading a database and producing a report was pretty straightforward in the olden days. You did some joins, some filtering, some formatting, and bang, you were done. Now you have complex pipelines that grab data from various sources and types. When, not if, something goes south with the results, it’s not easy to trace where it went wrong, and this is where OpenMetadata comes in. It’s a critical asset for understanding data lineage, ensuring quality, and enabling collaboration, addressing several pain points: Fragmented Data Sources: Organizations often use multiple tools (e.g., Snowflake, dbt, Metabase), leading to siloed metadata. OpenMetadata centralizes this metadata into a unified graph. Data Discoverability: Finding relevant data assets can be time-consuming. OpenMetadata’s search capabilities and metadata enrichment make discovery intuitive. Governance and Compliance: OpenMetadata supports robust governance without excessive manual effort through features like metadata versioning and automated workflows. Scalability: Its lightweight architecture and extensive connector support suit enterprises of all sizes. Fragmented Data Sources: Organizations often use multiple tools (e.g., Snowflake, dbt, Metabase), leading to siloed metadata. OpenMetadata centralizes this metadata into a unified graph. Fragmented Data Sources Data Discoverability: Finding relevant data assets can be time-consuming. OpenMetadata’s search capabilities and metadata enrichment make discovery intuitive. Data Discoverability Governance and Compliance: OpenMetadata supports robust governance without excessive manual effort through features like metadata versioning and automated workflows. Governance and Compliance Scalability: Its lightweight architecture and extensive connector support suit enterprises of all sizes. Scalability Architecture of OpenMetadata Architecture of OpenMetadata Based on the time I spent, it appears that OpenMetadata is comprised of four core components: Metadata Store: A central repository that stores the metadata graph, connecting data assets, users, and tool-generated metadata. It uses a relational database (e.g., MySQL, Postgres) for storage, avoiding the complexity of graph databases like Neo4j. Ingestion Framework: A pluggable framework that ingests metadata from over 90 sources, including databases (e.g., BigQuery, Snowflake), data lakes (e.g., S3, Iceberg), and BI tools (e.g., Power BI). Connectors are written in Python and support custom extensions. Metadata Schemas: JSON-based schemas define metadata entities (e.g., tables, dashboards) and relationships. These schemas are extensible, allowing organizations to tailor metadata to their needs. User Interface: A web-based UI built with React, offering search, lineage visualization, and collaboration tools. It integrates with Elasticsearch for full-text search and supports CMD + K shortcuts for quick navigation. Metadata Store: A central repository that stores the metadata graph, connecting data assets, users, and tool-generated metadata. It uses a relational database (e.g., MySQL, Postgres) for storage, avoiding the complexity of graph databases like Neo4j. Metadata Store Ingestion Framework: A pluggable framework that ingests metadata from over 90 sources, including databases (e.g., BigQuery, Snowflake), data lakes (e.g., S3, Iceberg), and BI tools (e.g., Power BI). Connectors are written in Python and support custom extensions. Ingestion Framework Metadata Schemas: JSON-based schemas define metadata entities (e.g., tables, dashboards) and relationships. These schemas are extensible, allowing organizations to tailor metadata to their needs. Metadata Schemas User Interface: A web-based UI built with React, offering search, lineage visualization, and collaboration tools. It integrates with Elasticsearch for full-text search and supports CMD + K shortcuts for quick navigation. User Interface The simplicity of the setup reduces deployment overhead. For example, setting up a local environment takes minutes, and the platform supports cloud deployments on AWS, Azure, and Google Cloud. Key Features OpenMetadata offers a rich set of features that, based on my experience in the space, really cover what people need/want to do. Here’s a breakdown of the most impactful ones that I gleaned from the documentation: Data Discovery The full-text search engine, powered by Elasticsearch, indexes entity names, descriptions, tags, and even conversation threads. Users can refine searches with filters or use advanced queries to explore tables, dashboards, pipelines, and more. Data Lineage Lineage tracking provides column-level visibility into data flows across pipelines and tools. For example, you can trace how data moves from a PostgreSQL table through a dbt transformation to a Power BI dashboard. Lineage can be exported as PNG or PDF for documentation. Data Quality and Profiling Includes no-code data quality tests and profiling tools. Users can define test suites, monitor data health, and view results in an interactive dashboard. AutoPilot, an AI-driven feature, automates metadata extraction and profiling for new services, reducing onboarding time. Collaboration The platform fosters collaboration through conversation threads, task creation, and event notifications. Data producers and consumers can communicate directly on data assets, reducing silos. Governance Supports metadata versioning, tagging, and ownership assignment, enabling compliance with data governance policies. Its two-way metadata synchronization pushes enriched metadata (e.g., tags) back to source systems like Snowflake, ensuring consistency. Extensibility The schema-first approach and REST APIs allow developers to extend metadata entities and integrate with custom tools. The ingestion framework supports community-contributed connectors, ensuring flexibility. Data Discovery The full-text search engine, powered by Elasticsearch, indexes entity names, descriptions, tags, and even conversation threads. Users can refine searches with filters or use advanced queries to explore tables, dashboards, pipelines, and more. Data Discovery Data Discovery The full-text search engine, powered by Elasticsearch, indexes entity names, descriptions, tags, and even conversation threads. Users can refine searches with filters or use advanced queries to explore tables, dashboards, pipelines, and more. Data Lineage Lineage tracking provides column-level visibility into data flows across pipelines and tools. For example, you can trace how data moves from a PostgreSQL table through a dbt transformation to a Power BI dashboard. Lineage can be exported as PNG or PDF for documentation. Data Lineage Data Lineage Lineage tracking provides column-level visibility into data flows across pipelines and tools. For example, you can trace how data moves from a PostgreSQL table through a dbt transformation to a Power BI dashboard. Lineage can be exported as PNG or PDF for documentation. Data Quality and Profiling Includes no-code data quality tests and profiling tools. Users can define test suites, monitor data health, and view results in an interactive dashboard. AutoPilot, an AI-driven feature, automates metadata extraction and profiling for new services, reducing onboarding time. Data Quality and Profiling Data Quality and Profiling Includes no-code data quality tests and profiling tools. Users can define test suites, monitor data health, and view results in an interactive dashboard. AutoPilot, an AI-driven feature, automates metadata extraction and profiling for new services, reducing onboarding time. Collaboration The platform fosters collaboration through conversation threads, task creation, and event notifications. Data producers and consumers can communicate directly on data assets, reducing silos. Collaboration Collaboration The platform fosters collaboration through conversation threads, task creation, and event notifications. Data producers and consumers can communicate directly on data assets, reducing silos. Governance Supports metadata versioning, tagging, and ownership assignment, enabling compliance with data governance policies. Its two-way metadata synchronization pushes enriched metadata (e.g., tags) back to source systems like Snowflake, ensuring consistency. Governance Governance Supports metadata versioning, tagging, and ownership assignment, enabling compliance with data governance policies. Its two-way metadata synchronization pushes enriched metadata (e.g., tags) back to source systems like Snowflake, ensuring consistency. Extensibility The schema-first approach and REST APIs allow developers to extend metadata entities and integrate with custom tools. The ingestion framework supports community-contributed connectors, ensuring flexibility. Extensibility The schema-first approach and REST APIs allow developers to extend metadata entities and integrate with custom tools. The ingestion framework supports community-contributed connectors, ensuring flexibility. Extensibility Use Cases Use Cases The flexible design makes it applicable across many industries. Here are a few scenarios to consider:: Data Centralization: A retail company uses OpenMetadata to consolidate metadata from Snowflake, dbt, and Metabase, providing a single source of truth for analytics teams. Governance Automation: A financial institution leverages AutoPilot to automate metadata tagging and enforce data masking policies in BigQuery, ensuring compliance with GDPR. Data Discovery for AI: A SaaS provider uses OpenMetadata to standardize metadata for diverse customer datasets, enabling seamless integration into AI model pipelines. Data Centralization: A retail company uses OpenMetadata to consolidate metadata from Snowflake, dbt, and Metabase, providing a single source of truth for analytics teams. Data Centralization Governance Automation: A financial institution leverages AutoPilot to automate metadata tagging and enforce data masking policies in BigQuery, ensuring compliance with GDPR. Governance Automation Data Discovery for AI: A SaaS provider uses OpenMetadata to standardize metadata for diverse customer datasets, enabling seamless integration into AI model pipelines. Data Discovery for AI Collaboration Across Teams: An e-commerce platform uses OpenMetadata’s collaboration tools to bridge gaps between business analysts and data engineers, improving dashboard creation efficiency. Getting Started Getting Started There are multiple ways to get started with OpenMetadata, and the options are clearly described here. I don’t want to replicate them in this blog, because they can change over time. What shouldn’t change, though, is the UI address, so: here here Access the UI: Navigate to http://localhost:8585 to access the web interface. Configure Connectors: Connect to your data sources using the ingestion framework. As I've said a few times, there are over 90 to choose from at the time of this writing. The documentation provides step-by-step guides for popular tools. Access the UI: Navigate to http://localhost:8585 to access the web interface. Access the UI Configure Connectors: Connect to your data sources using the ingestion framework. As I've said a few times, there are over 90 to choose from at the time of this writing. The documentation provides step-by-step guides for popular tools. Configure Connectors You can avoid the installation step and try the OpenMetadata Sandbox at sandbox.open-metadata.org. The community Slack channel is also an excellent resource for support and feature discussions. sandbox.open-metadata.org Slack Slack Challenges and Considerations Challenges and Considerations While OpenMetadata is powerful, it has limitations. For modern data lakehouse architectures (e.g., Delta Lake), connector functionality may be limited, and ingestion processes (e.g., Athena) can incur costs if not optimized, so don’t just blindly point it at a massive data lake and fire it off. Check connector compatibility and test the platform in a proof-of-concept before full deployment. Summary Summary What the heck is OpenMetadata, then? It is an open-source project that provides a unified metadata management solution. Its lightweight architecture, extensive connector support, and focus on collaboration make it a compelling choice for modern data stacks. By centralizing metadata, enhancing discoverability, and automating governance, OpenMetadata empowers organizations to unlock the full potential of their data assets. Whether you’re building a data-driven culture or tackling compliance challenges, OpenMetadata is worth exploring. Want to read more in my “What the Heck is???” series? A handy list is below: What The Heck Is DuckDB? What the Heck Is Malloy? What the Heck is PRQL? What the Heck is GlareDB? What the Heck is SeaTunnel? What the Heck is LanceDB? What the heck is SDF? What the Heck is Paimon? What the Heck is Proton? What the Heck is PuppyGraph? What the Heck is GPTScript? What the Heck is WarpStream? What the Heck is DeltaStream? What The Heck Is DuckDB? What The Heck Is DuckDB? What The Heck Is DuckDB? What The Heck Is DuckDB? What the Heck Is Malloy? What the Heck Is Malloy? What the Heck Is Malloy? What the Heck Is Malloy? What the Heck is PRQL? What the Heck is PRQL? What the Heck is PRQL? What the Heck is PRQL? What the Heck is GlareDB? What the Heck is GlareDB? What the Heck is GlareDB? What the Heck is GlareDB? What the Heck is SeaTunnel? What the Heck is SeaTunnel? What the Heck is SeaTunnel? What the Heck is SeaTunnel? What the Heck is LanceDB? What the Heck is LanceDB? What the Heck is LanceDB? What the Heck is LanceDB? What the heck is SDF? What the heck is SDF? What the heck is SDF? What the heck is SDF? What the Heck is Paimon? What the Heck is Paimon? What the Heck is Paimon? What the Heck is Paimon? What the Heck is Proton? What the Heck is Proton? What the Heck is Proton? What the Heck is Proton? What the Heck is PuppyGraph? What the Heck is PuppyGraph? What the Heck is PuppyGraph? What the Heck is PuppyGraph? What the Heck is GPTScript? What the Heck is GPTScript? What the Heck is GPTScript? What the Heck is GPTScript? What the Heck is WarpStream? What the Heck is WarpStream? What the Heck is WarpStream? What the Heck is WarpStream? What the Heck is DeltaStream? What the Heck is DeltaStream? What the Heck is DeltaStream?
