introduction

1. What is a data lakehouse and why open-source?

In the modern data-driven landscape, organizations require robust solutions to manage, analyze, and extract insights from massive datasets. The data lakehouse is an emerging architectural paradigm that combines the scalability of data lakes with the performance and structure of data warehouses. It allows organizations to store vast amounts of raw data while enabling efficient analytics and query capabilities.

While commercial cloud-based data lakehouse platforms are highly functional, they often come with significant cost implications, vendor lock-in, and limited customizability. An open-source data lakehouse stack offers a compelling alternative. By leveraging open-source technologies, organizations gain flexibility, control over their architecture, and reduced costs. This approach empowers businesses to craft a solution tailored to their specific needs while staying aligned with open data standards.

architecture

2. Layers of an open data lakehouse

A robust open-source data lakehouse stack can be designed with five essential layers: persistence, open table format, data transformation, analytical querying, and visualization. Below, we explore each layer in detail.

Persistence: Object Storage for Scalability and Flexibility

At the foundation of the data lakehouse is a scalable object storage system. By using an S3-compatible object store such as MinIO or even native AWS S3, the architecture ensures a reliable and cost-effective solution for data persistence. Object stores are designed to handle large volumes of unstructured data, making them ideal for raw data storage.

Common file formats stored in this layer include CSV and Parquet. While CSV is widely supported and human-readable, Parquet is highly efficient for analytics due to its columnar storage format and compression capabilities. Choosing the right format depends on the use case, with Parquet being preferred for large-scale analytical workloads.

Open Table Format: Structuring Data for Efficient Consumption

The next layer introduces an open table format such as Apache Iceberg or DeltaLake. These table formats are essential for managing structured datasets in a data lakehouse, as they provide schema evolution, versioning, and transaction support.

Apache Iceberg, for example, organizes data into table partitions and enables snapshot isolation, ensuring consistent reads and writes. Delta Lake adds additional features such as time travel and ACID transactions. These open formats ensure that data stored in the object layer is organized and consumable by downstream systems without relying on proprietary solutions.

Data Transformation: Processing at Scale with Spark

To transform raw data into actionable insights, the stack includes a distributed data processing framework like Apache Spark. Spark excels at processing large-scale data efficiently, thanks to its in-memory computation capabilities and support for diverse data sources and formats.

With Spark, organizations can implement ETL (Extract, Transform, Load) workflows, aggregate data, and prepare it for analytical querying. Additionally, alternatives like Apache Flink or Dask can be considered for specific use cases, such as real-time stream processing or simpler workflows with parallel computation needs.

Analytical Database: Querying with DuckDB or ClickHouse

For querying and analyzing data, the stack integrates an analytical database such as DuckDB or ClickHouse. These databases provide high-performance SQL query capabilities tailored for analytical workloads.

DuckDB is a lightweight, in-process analytical database designed for single-node environments. Its seamless integration with programming languages like Python makes it ideal for interactive data exploration and prototyping. ClickHouse, on the other hand, is a distributed analytical database optimized for high-concurrency workloads and large-scale data. Both tools offer columnar storage and are well-suited for querying data stored in formats like Parquet or CSV.

Visualization: Insights with Apache Superset

The final layer of the stack is visualization, enabling stakeholders to derive actionable insights from the data. Apache Superset is a powerful, open-source business intelligence platform that integrates seamlessly with modern databases and supports rich visualizations.

With Superset, users can create dashboards, perform exploratory data analysis, and generate interactive reports. Its extensibility and ability to connect to a wide range of databases make it a versatile choice for organizations seeking to democratize data access and insights across teams.

conclusion

3. Benefits of an open solution

Building an open-source data lakehouse stack combines the best of flexibility, scalability, and cost-efficiency. By leveraging an object storage system, open table formats, powerful data processing frameworks, analytical databases, and visualization tools, organizations can construct a robust and customizable data platform.

The key advantages of this approach include:

1. Cost Savings: Open-source technologies eliminate licensing fees, significantly reducing operational costs.

2. Avoiding Vendor Lock-In: Organizations retain full control over their architecture and avoid dependency on proprietary platforms.

3. Flexibility and Customization: Each layer of the stack can be tailored to specific business needs, allowing organizations to evolve their platform over time.

4. Open Standards: Adopting open formats like Parquet, Iceberg, and Delta ensures interoperability and long-term data accessibility.

As data continues to grow in importance, open-source data lakehouse architectures offer an excellent pathway for organizations to manage and harness their data effectively. By adopting this approach, businesses can achieve a balance between innovation, efficiency, and cost control, paving the way for a data-driven future.