Building an Open Data Platform On-Premise with Open-Source Tools
- Luke
- Feb 28
- 11 min read
In an era where data fuels decision-making, enterprises need robust platforms to ingest, store, process, transform, and analyze data efficiently. For organizations constrained by compliance requirements, data sovereignty, or cost considerations, on-premise solutions remain a compelling choice. In this blog post, I’ll walk you through a fully open-source Open Data Platform deployed on an on-premise Kubernetes cluster. This solution embraces modern data engineering practices, offering scalability, security, and governance without reliance on proprietary software.
Here’s the detailed stack we’ll cover:
Data Ingestion: Airbyte OSS
Open Lakehouse Architecture:
Data Lake: MinIO
Data Processing: Trino
Data Catalog: Apache Polaris
Table Format: Apache Iceberg
Data RBAC/Security: Apache Ranger
Data Transformation Framework:
Data Transformation: DBT + Elementary/Great Expectations
Data Orchestration: Apache Airflow
BI Tools: Superset or Metabase
Data Governance: OpenMetadata
Infrastructure: On-Premise Kubernetes Cluster
Let’s explore each component in depth, discussing its role, features, and why it’s a stellar fit for this platform.
1. Data Ingestion: Airbyte OSS
What is Airbyte OSS?
Airbyte Open Source Software (OSS) is a modern data integration platform designed to streamline the extraction and loading of data from a wide variety of sources—databases (e.g., PostgreSQL, MongoDB), APIs (e.g., Salesforce, Stripe), file systems, and more—into centralized destinations like data lakes or warehouses. It’s built with extensibility in mind, allowing users to customize it for unique ingestion needs.
Detailed Features and Functionality
Airbyte operates on a connector-based architecture, where each connector is a modular component responsible for interfacing with a specific data source or destination. The platform offers over 200 pre-built connectors, covering popular systems like MySQL, Google Sheets, and REST APIs. For niche or proprietary systems, its Connector Development Kit (CDK) enables engineers to create custom connectors in languages like Python or Java. Airbyte supports both batch and incremental syncs, with options for real-time streaming via webhook integrations where applicable.
The OSS version is fully deployable on-premise, managed via a web-based UI or CLI, and integrates seamlessly with Kubernetes for scalability. It also includes logging and monitoring features to track sync performance and troubleshoot issues.
Why Airbyte OSS?
Broad Connectivity: With its extensive library of connectors, Airbyte eliminates the need to write custom scripts for common sources, saving engineering time. Its CDK ensures no source is left behind.
User-Friendly Design: The intuitive UI allows data engineers and even business users to configure pipelines without deep technical expertise, while still offering programmatic control for advanced users.
On-Premise Fit: Unlike many cloud-first ingestion tools, Airbyte OSS runs locally, aligning with data residency requirements and integrating with MinIO as a landing zone.
Community Momentum: As an open-source project, it benefits from rapid contributions, ensuring new connectors and features are added frequently.
Advantages
Eliminates licensing costs and vendor lock-in, making it budget-friendly for enterprises.
Scales horizontally on Kubernetes, handling high-volume ingestion from multiple sources concurrently.
Supports diverse use cases, from batch ETL for historical data to near-real-time syncs for operational analytics.
In this platform, Airbyte OSS will act as the ingestion engine, pulling raw data from operational systems and depositing it into the MinIO data lake for further processing. 2. Open Lakehouse Architecture
The lakehouse architecture merges the scalability and flexibility of data lakes with the structure and performance of data warehouses. Our open lakehouse is built with these components:
2.1. Data Lake: MinIO
What is MinIO?
MinIO is a high-performance, distributed object storage system that adheres to the Amazon S3 API standard. It’s designed to manage unstructured and semi-structured data at scale, making it an ideal foundation for a data lake.
Detailed Features and Functionality
MinIO stores data as objects in buckets, supporting petabyte-scale deployments with a lightweight footprint. It offers features like erasure coding for data durability (protecting against hardware failures), server-side encryption for security, and multi-tenancy for isolating workloads. Its S3 compatibility ensures it works with a vast ecosystem of tools, from ingestion platforms like Airbyte to query engines like Trino. MinIO also provides a web-based console for managing buckets and monitoring storage health.
Deployed on Kubernetes, MinIO can scale out by adding nodes, distributing data across the cluster for performance and resilience. It supports high-throughput workloads, such as machine learning training or real-time analytics, with low-latency access.
Why MinIO?
Universal Compatibility: Its S3 API support makes it a drop-in replacement for AWS S3, enabling integration with virtually any S3-compatible tool or library.
Performance Optimization: MinIO is written in Go, delivering exceptional speed for read/write operations, crucial for large-scale analytics workloads.
On-Premise Control: Keeps data within organizational boundaries, addressing compliance needs like GDPR or HIPAA.
Advantages
Resource-efficient, running on commodity hardware to minimize infrastructure costs.
Provides enterprise-grade features (e.g., encryption, versioning) without proprietary overhead.
Scales seamlessly with Kubernetes, adapting to growing data volumes.
MinIO will serve as the storage backbone, hosting raw, processed, and curated datasets in our lakehouse.
2.2. Data Processing: Trino
What is Trino?
Trino is a distributed SQL query engine built for high-speed analytics across large, heterogeneous datasets. Originally forked from PrestoSQL, it excels at querying data in place - whether in a data lake, database, or external system - without requiring data movement.
Detailed Features and Functionality
Trino uses a coordinator-worker architecture, where the coordinator parses queries and distributes tasks to worker nodes for parallel execution. It supports standard ANSI SQL, making it accessible to analysts familiar with traditional BI tools. Trino’s connector framework allows it to query MinIO (via the Iceberg connector), relational databases (e.g., PostgreSQL), and even NoSQL systems (e.g., Elasticsearch) in a federated manner.
Key features include dynamic filtering for optimizing joins, cost-based query optimization, and support for complex operations like window functions and geospatial queries. On Kubernetes, Trino scales effortlessly by adding worker nodes to handle increased query loads.
Why Trino?
Federated Querying: Analysts can join data across MinIO, operational databases, and external sources without ETL overhead, reducing latency and complexity.
High Performance: Its in-memory processing and parallel execution deliver fast results, even on petabyte-scale datasets.
Open Ecosystem: As an open-source tool, Trino integrates natively with Iceberg and other components, avoiding proprietary silos.
Advantages
Eliminates the need for a separate data warehouse by querying the lakehouse directly.
Scales dynamically on Kubernetes, balancing cost and performance.
Community-driven enhancements ensure it stays cutting-edge.
Trino will be the processing powerhouse, enabling ad-hoc analytics and batch queries on our lakehouse.
2.3. Data Catalog: Apache Polaris
What is Apache Polaris?
Apache Polaris is an emerging open-source data catalog tailored for modern data platforms. It centralizes metadata management, providing discovery, lineage, and governance capabilities for datasets.
Detailed Features and Functionality
Polaris collects and stores metadata about datasets—such as schema, ownership, and storage location—in a searchable repository. It integrates with Iceberg for table metadata, Trino for query history, and Ranger for access policies. Users can browse datasets via a web interface, filter by tags or attributes, and view documentation. Polaris also supports integration with governance tools like OpenMetadata for a holistic metadata strategy.
As a newer project, it’s designed with cloud-native and lakehouse architectures in mind, offering REST APIs for programmatic access and extensibility.
Why Apache Polaris?
Centralized Metadata: Provides a single pane of glass for discovering and understanding datasets across the lakehouse.
Native Integration: Works seamlessly with Iceberg and Trino, ensuring metadata reflects the actual state of data.
Open-Source Flexibility: Avoids the complexity and cost of proprietary catalogs while offering customization potential.
Advantages
Simplifies data discovery, reducing time-to-insight for analysts.
Lightweight and scalable on Kubernetes, fitting on-premise constraints.
Future-proofs the platform as lakehouse adoption grows.
Polaris will catalog our datasets, making them accessible and understandable to all stakeholders.
2.4. Table Format: Apache Iceberg
What is Apache Iceberg?
Apache Iceberg is an open table format that brings database-like features—such as ACID transactions and schema evolution—to data lakes. It’s designed for large-scale analytics and integrates with engines like Trino.
Detailed Features and Functionality
Iceberg organizes data into tables with metadata layers that track file locations, partitions, and snapshots. This abstraction enables features like:
ACID Compliance: Atomic updates and deletes ensure data consistency, even during concurrent writes.
Schema Evolution: Add, drop, or rename columns without rewriting the entire dataset.
Time Travel: Query historical snapshots for auditing or rollback.
Partition Evolution: Change partitioning strategies (e.g., from daily to hourly) without downtime.
Stored in MinIO, Iceberg tables are accessed via Trino, leveraging its metadata for efficient query planning.
Why Apache Iceberg?
Reliability: ACID support makes it suitable for critical workloads like financial reporting.
Flexibility: Schema and partition evolution adapt to changing business needs without disruption.
Performance: Metadata optimizations reduce query overhead on large datasets.
Advantages
Seamless integration with MinIO and Trino streamlines the lakehouse.
Reduces operational complexity compared to traditional file-based lakes.
Growing adoption ensures long-term support and compatibility.
Iceberg will provide structure and reliability to our data lake, enabling warehouse-like functionality.
2.5. Data RBAC/Security: Apache Ranger
What is Apache Ranger?
Apache Ranger is an open-source security framework for managing access control and auditing across data platforms. It provides centralized policy management for tools like Trino and Iceberg.
Detailed Features and Functionality
Ranger defines policies at granular levels—tables, columns, rows, or even masked data—using RBAC or attribute-based access control (ABAC). It integrates with LDAP/AD for user authentication and logs all access attempts for compliance audits. For our stack, Ranger enforces security on Trino queries and Iceberg tables, ensuring sensitive data (e.g., PII) is protected.
Its web UI simplifies policy creation, while plugins extend its reach to various components. Deployed on Kubernetes, Ranger scales to handle enterprise-wide security needs.
Why Apache Ranger?
Granular Control: Protects data at multiple levels, aligning with strict compliance requirements.
Centralized Management: Manages policies across the platform from one interface.
Audit Trails: Tracks who accessed what and when, critical for regulated industries.
Advantages
Enhances security without sacrificing performance.
Open-source nature avoids costly proprietary alternatives.
Integrates natively with Trino and Iceberg, ensuring a cohesive security layer.
Ranger will safeguard our data, ensuring compliance and trust in the platform.
3. Data Transformation Framework
3.1. Data Transformation: DBT + Elementary/Great Expectations
What is DBT?
DBT (Data Build Tool) is an open-source framework that enables data engineers to transform data using SQL. It compiles SQL models into executable queries, optimized for engines like Trino.
What are Elementary and Great Expectations?
Elementary: A data observability tool that integrates with DBT to monitor data quality, detect anomalies, and generate reports.
Great Expectations: A data validation framework that defines expectations (e.g., “no nulls in this column”) and tests them against datasets.
Detailed Features and Functionality
DBT organizes transformations into modular SQL files, defining raw-to-curated data pipelines (e.g., staging, intermediate, and final models). It supports Jinja templating for dynamic logic and generates DAGs for dependency management. Running on Trino, DBT leverages the lakehouse’s compute power to transform Iceberg tables efficiently.
Elementary adds observability by tracking metrics (e.g., row counts, freshness) and flagging issues, while Great Expectations enforces strict validation rules (e.g., data type checks, range constraints). Both integrate with DBT’s workflow, running tests post-transformation to ensure quality.
Why DBT + Elementary/Great Expectations?
SQL-Centric: DBT’s SQL-first approach leverages existing skills, reducing the need for complex coding.
Modularity: Reusable models improve maintainability and collaboration across teams.
Quality Assurance: Elementary provides visibility into pipeline health, while Great Expectations ensures data meets standards before downstream use.
Advantages
DBT’s Trino integration optimizes transformations for performance.
Elementary/Great Expectations catch issues early, reducing debugging time.
Open-source tools keep costs low while delivering enterprise-grade functionality.
DBT will transform raw data into curated datasets, with Elementary or Great Expectations validating quality.
3.2. Data Orchestration: Apache Airflow
What is Apache Airflow?
Apache Airflow is an open-source workflow orchestration platform that schedules and manages data pipelines as Directed Acyclic Graphs (DAGs).
Detailed Features and Functionality
Airflow defines pipelines as Python scripts, where tasks (e.g., run Airbyte sync, execute DBT model, query Trino) are chained with dependencies. Its scheduler ensures tasks run on time or retry on failure, while the web UI provides visibility into pipeline status, logs, and execution history. Airflow’s operator ecosystem includes pre-built integrations for Airbyte, DBT, and Kubernetes, with custom operators extensible via Python.
On Kubernetes, Airflow uses the KubernetesExecutor to run tasks as pods, scaling dynamically based on workload. It also supports advanced features like XCom for passing data between tasks and dynamic DAG generation.
Why Airflow?
Workflow Clarity: DAGs make dependencies explicit, reducing pipeline errors.
Extensive Integrations: Native support for Airbyte, DBT, and Trino simplifies orchestration.
Robust Monitoring: The UI and logging help engineers track and troubleshoot workflows.
Advantages
Scales efficiently on Kubernetes, handling complex, multi-step pipelines.
Open-source community provides plugins and updates, minimizing development effort.
Flexible scheduling supports batch, real-time, or event-driven workflows.
Airflow will orchestrate the entire data lifecycle, from ingestion to transformation to delivery.
4. BI Tools: Superset or Metabase
What are Superset and Metabase?
Apache Superset: An open-source BI platform for creating dashboards, charts, and running SQL queries.
Metabase: A lightweight, open-source BI tool focused on ease of use and rapid dashboard creation.
Detailed Features and Functionality
Superset: Offers a rich visualization library (bar charts, heatmaps, geospatial maps), a SQL editor for custom queries, and role-based permissions. It connects to Trino via JDBC, enabling analysts to explore Iceberg tables directly. Superset’s caching (via Redis) and async query execution optimize performance for large datasets.
Metabase: Provides a no-code interface for building dashboards, with drag-and-drop filters and automatic visualizations. It supports Trino and excels at quick setup, with features like question history and sharing for collaboration.
Both tools run on Kubernetes, scaling to support multiple users and concurrent queries.
Why Superset or Metabase?
Superset: Ideal for advanced analytics, offering customization and scalability for large teams.
Metabase: Perfect for smaller teams or rapid prototyping, with a shallow learning curve.
Trino Integration: Both leverage the lakehouse’s querying power, delivering insights without data movement.
Advantages
Superset’s depth supports complex use cases like financial forecasting.
Metabase’s simplicity accelerates time-to-value for non-technical users.
Open-source licensing keeps costs down while providing flexibility.
Choose Superset for power users or Metabase for simplicity—both empower stakeholders with data-driven insights.
5. Data Governance: OpenMetadata
What is OpenMetadata?
OpenMetadata is an open-source platform for metadata management, offering data lineage, documentation, and governance features.
Detailed Features and Functionality
OpenMetadata centralizes metadata from Airbyte (ingestion), Trino (queries), DBT (transformations), and Iceberg (tables) into a unified repository. Its lineage tracking visualizes data flow from source to destination, while the documentation feature lets teams add descriptions, tags, and ownership details. It supports APIs for automation and integrates with Polaris and Ranger for cataloging and security.
The web UI provides search, profiling (e.g., column statistics), and collaboration tools, making it a one-stop shop for governance. Deployed on Kubernetes, it scales to handle metadata for thousands of datasets.
Why OpenMetadata?
End-to-End Visibility: Tracks data provenance, aiding compliance and troubleshooting.
Rich Documentation: Ensures datasets are well-understood and reusable.
Open Ecosystem: Integrates with the stack, enhancing Polaris and Ranger.
Advantages
Meets on-premise governance needs without proprietary tools.
Open-source flexibility allows customization for unique requirements.
Simplifies audits with comprehensive lineage and metadata.
OpenMetadata will govern our platform, ensuring transparency and accountability.
6. Infrastructure: On-Premise Kubernetes Cluster
Why Kubernetes?
Kubernetes is an open-source container orchestration platform that automates deployment, scaling, and management of applications like our data stack.
Detailed Features and Functionality
Kubernetes runs each component - Airbyte, MinIO, Trino, Airflow, etc. - as containers in pods, managed via Helm charts for consistency. It provides:
Auto-Scaling: Adds resources (e.g., Trino workers) based on demand.
High Availability: Replicates pods across nodes, ensuring uptime.
Resource Management: Allocates CPU/memory efficiently across the cluster.
On-premise, Kubernetes runs on bare metal or virtualized hardware, with storage backed by local disks or SANs. Tools like Kube Prometheus monitor cluster health, while Ingress controllers expose UIs (e.g., Airflow, Superset).
Advantages
Unifies deployment across the stack, reducing operational overhead.
Scales horizontally to match data growth and query loads.
Open-source nature aligns with the platform’s cost-saving ethos.
Kubernetes will host our entire solution, providing a resilient and scalable foundation.
Summary
This Open Data Platform delivers a modern, on-premise data ecosystem using open-source tools:
Airbyte OSS ingests data from myriad sources with ease and scalability.
The Open Lakehouse (MinIO, Trino, Iceberg, Polaris, Ranger) offers a secure, performant, and structured data foundation.
DBT and Airflow, with Elementary/Great Expectations, transform and orchestrate data reliably.
Superset/Metabase provide flexible BI capabilities for all users.
OpenMetadata ensures governance and visibility.
Kubernetes ties it together with enterprise-grade infrastructure.
This architecture balances cost, control, and capability, making it ideal for organizations seeking a future-proof, on-premise data solution.
Integrating open-source tools into your company’s data strategy offers immense benefits - cost efficiency, flexibility, and freedom from vendor lock-in. However, the journey of designing, deploying, and operating such a platform comes with its share of challenges, from ensuring seamless integration to optimizing performance at scale.
With over 10 years of experience as data engineers and architects, our team specializes in consulting, designing, implementing, and managing open-source data platforms tailored to your needs. We’ve navigated the complexities of on-premise deployments, delivering efficient and reliable solutions for organizations worldwide. Don’t hesitate to reach out to us for a detailed consultation - let’s unlock the full potential of your data together!
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