Expert Insight
Data Platform Democratization:
Why the Lakehouse Is Key
As corporate data volumes skyrocket, companies are increasingly forced to optimize IT budgets without sacrificing the agility of their analytical processes. In this article, Databorn — an IT integrator with extensive experience in delivering data projects of all sizes — explores the different approaches to organizing corporate data analytics and explains how the lakehouse architecture addresses the core challenges of data democratization.
Today, corporate success depends on how effectively business users—marketers, financial and product analysts, product managers, and HR specialists—can leverage data within an analytical platform for daily decision-making. Data management is no longer the exclusive domain of IT departments.
Today, corporate success depends on how effectively business users—marketers, financial and product analysts, product managers, and HR specialists—can leverage data within an analytical platform for daily decision-making. Data management is no longer the exclusive domain of IT departments.
Data democratization reshapes traditional workflows but also creates new hurdles. Some users require real-time data, finding daily batch updates insufficient. Others need to ingest logs from diverse hardware vendors. Companies must connect more source systems, store deeper historical data, and expand storage capacity. As the user base grows and the range of tasks expands, the Total Cost of Ownership (TCO) rises—and this growth must be controlled to keep budgets within planned limits.
The Evolutionary Path: From Data Warehouse and Data Lake to Lakehouse
The lakehouse concept is straightforward: combine the best features of data warehouses and data lakes while eliminating their primary drawbacks.
Data Warehouse (DWH)
Typically built on RDBMS with ACID transaction support, these systems are designed for structured (tabular) data queried via SQL.
The Drawbacks: High TCO; storage and compute are tightly coupled. While some databases support linear scaling, it is usually limited to symmetric nodes. In practice, there is a practical ceiling on the number of concurrent users and cluster scaling, after which it becomes more efficient to spin up an entirely new cluster. Furthermore, DWHs struggle with unstructured data (logs, images, video, audio) and real-time streaming analytics.
Data Lake
Data lakes emerged to solve the high cost of DWH storage and processing, allowing companies to store massive datasets in any format at a significantly lower cost.
Initially, compute engineers had to write complex distributed code using various frameworks. This approach was too technical for analysts and business users. The need to lower the barrier to entry and accelerate processing led to the development of alternative engines, including SQL-on-Hadoop. Thanks to the maturity of the data lake ecosystem, we now have a choice of powerful engines like Spark, Trino, and Impala—the compute backbone of the modern lakehouse.
In addition to traditional batch processing, demand grew for tools capable of handling continuous data streams with low latency and delivery guarantees. This led to the rise of Spark Streaming, Flink, and other stream-processing tools.
The Drawbacks: A major issue was table updates. Consumers often received inconsistent data if an update process was ongoing or failed mid-way.
Lakehouse
Open Table Formats (OTF) solved the update problem by introducing ACID guarantees at the table level. These open formats ensure compatibility across various tools. Today, the Apache Iceberg format is supported by most compute engines for reading, with many also supporting writes and updates.
The separation of storage and compute is now an architectural cornerstone rather than an optional feature. This provides several strategic advantages:
Data Warehouse (DWH)
Typically built on RDBMS with ACID transaction support, these systems are designed for structured (tabular) data queried via SQL.
The Drawbacks: High TCO; storage and compute are tightly coupled. While some databases support linear scaling, it is usually limited to symmetric nodes. In practice, there is a practical ceiling on the number of concurrent users and cluster scaling, after which it becomes more efficient to spin up an entirely new cluster. Furthermore, DWHs struggle with unstructured data (logs, images, video, audio) and real-time streaming analytics.
Data Lake
Data lakes emerged to solve the high cost of DWH storage and processing, allowing companies to store massive datasets in any format at a significantly lower cost.
Initially, compute engineers had to write complex distributed code using various frameworks. This approach was too technical for analysts and business users. The need to lower the barrier to entry and accelerate processing led to the development of alternative engines, including SQL-on-Hadoop. Thanks to the maturity of the data lake ecosystem, we now have a choice of powerful engines like Spark, Trino, and Impala—the compute backbone of the modern lakehouse.
In addition to traditional batch processing, demand grew for tools capable of handling continuous data streams with low latency and delivery guarantees. This led to the rise of Spark Streaming, Flink, and other stream-processing tools.
The Drawbacks: A major issue was table updates. Consumers often received inconsistent data if an update process was ongoing or failed mid-way.
Lakehouse
Open Table Formats (OTF) solved the update problem by introducing ACID guarantees at the table level. These open formats ensure compatibility across various tools. Today, the Apache Iceberg format is supported by most compute engines for reading, with many also supporting writes and updates.
The separation of storage and compute is now an architectural cornerstone rather than an optional feature. This provides several strategic advantages:
- Independent Scaling: Scale storage or compute independently based on specific needs.
- Storage Flexibility: Choose the storage option that best fits your requirements, whether cloud-native (S3/ADLS/GCS), on-premises, or hybrid.
- Task-Specific Compute: Select the engine best suited for the job (batch/streaming, ad-hoc/ELT).
- Multi-Engine Concurrency: Run multiple compute engines simultaneously.
- Resource Efficiency: Spin up, scale, or shut down compute clusters as needed.
These capabilities offer immense opportunities for TCO optimization, particularly in cloud environments, though significant savings are also achievable on-premises.
Many engines originally designed for data lakes are evolving into lakehouse platforms by adding support for S3-compatible object storage, OTFs, and Kubernetes deployment.
Data Democratization and Lakehouse
The lakehouse architecture is perfectly aligned with data democratization, offering efficient solutions for growing workloads and query complexity.
User Scalability: A growing user base no longer has to be a bottleneck. By decoupling compute from storage, companies can deploy separate compute engines for different user groups or tasks against a single storage layer. This might involve different engines entirely or multiple independent tenants of the same engine (e.g., two separate Trino clusters).
User Scalability: A growing user base no longer has to be a bottleneck. By decoupling compute from storage, companies can deploy separate compute engines for different user groups or tasks against a single storage layer. This might involve different engines entirely or multiple independent tenants of the same engine (e.g., two separate Trino clusters).
Some engines allow for multiple coordinators to handle large numbers of concurrent sessions. Databorn has successfully implemented lakehouses supporting over 500 parallel users using a domain-based approach to resource sharing.
Data Growth: Storage can be expanded independently of compute. Companies can organize multi-tier storage (hot/cold) using different disk types, apply various compression algorithms, and utilize multiple repositories with varying redundancy levels.
Versatility: The architecture can accommodate a wide range of data types and workloads. A single repository can ingest any file format, with the optimal engine chosen for each specific task:
Security: In addition to encryption in transit and at rest, lakehouse platforms now support robust Role-Based Access Control (RBAC) with data filtering and masking.
When selecting a solution, it is vital to ensure that:
1. Security policies apply consistently across all compute engines available to users.
2. Users cannot bypass controls, such as masking policies by accessing the underlying storage files directly. Security must be enforced at both the compute and storage layers.
Data Growth: Storage can be expanded independently of compute. Companies can organize multi-tier storage (hot/cold) using different disk types, apply various compression algorithms, and utilize multiple repositories with varying redundancy levels.
Versatility: The architecture can accommodate a wide range of data types and workloads. A single repository can ingest any file format, with the optimal engine chosen for each specific task:
- Spark for ETL: Handles complex integrations (JDBC, REST API, file processing).
- Impala or StarRocks: High-performance SQL engines for rapid tabular data analysis with minimal overhead.
- Trino: Ideal for federated SQL queries when ad-hoc analysis requires data from external systems.
- Flink: Enables stream analytics with minimal latency.
Security: In addition to encryption in transit and at rest, lakehouse platforms now support robust Role-Based Access Control (RBAC) with data filtering and masking.
When selecting a solution, it is vital to ensure that:
1. Security policies apply consistently across all compute engines available to users.
2. Users cannot bypass controls, such as masking policies by accessing the underlying storage files directly. Security must be enforced at both the compute and storage layers.
Replacement or Supplement?
A common question is what to do with legacy data warehouses. The best approach is an audit to identify strengths, bottlenecks, and projected growth. External expertise is often valuable here to provide an unbiased perspective across multiple platforms.
If a DWH meets core business needs but specific tasks are better suited for a lakehouse, the two can coexist. Common hybrid scenarios include:
If a DWH meets core business needs but specific tasks are better suited for a lakehouse, the two can coexist. Common hybrid scenarios include:
- Analytical Sandboxes: Replicating DWH data into a lakehouse for analysts and data scientists to use a wider range of tools, including Machine Learning.
- Storage Offloading: Moving historical data to a lakehouse while keeping only the last 1–2 years in the "expensive" DWH.
- Compute Offloading: Moving heavy report calculations and data mart builds from the DWH to the lakehouse.
- New Capabilities: Using the lakehouse for unstructured data or streaming analytics while maintaining existing DWH workflows.
However, if the DWH is failing to handle the load or presents critical architectural limitations, a full migration to a lakehouse may be the most cost-effective long-term choice.
Conclusion
The lakehouse architecture merges the reliability of the data warehouse with the flexibility and cost-efficiency of the data lake. It has evolved from a series of experimental pilots into a mature, production-ready solution. Whether serving as a standalone platform or complementing an existing DWH, the lakehouse provides a proven path for organizations looking to scale their data capabilities and empower their workforce.
From the StarRocks community perspective, the value of a lakehouse is not only in where data is stored, but in how easily and efficiently that data can be used. Open table formats, separated storage and compute, and multi-engine interoperability create the foundation. But data democratization becomes real only when analysts, engineers, and business teams can query trusted data with familiar SQL, interactive performance, and predictable cost.
This is where a high-performance analytical engine plays an important role in the modern lakehouse stack. StarRocks is designed to serve as a fast SQL query layer across both internal data and open data lake formats, helping organizations deliver warehouse-like analytics performance while preserving the openness and flexibility of the lakehouse architecture.
As more enterprises modernize their data platforms, we believe the future will not be defined by a single engine or a single storage system, but by open, interoperable architectures that allow each workload to use the right tool.
From the StarRocks community perspective, the value of a lakehouse is not only in where data is stored, but in how easily and efficiently that data can be used. Open table formats, separated storage and compute, and multi-engine interoperability create the foundation. But data democratization becomes real only when analysts, engineers, and business teams can query trusted data with familiar SQL, interactive performance, and predictable cost.
This is where a high-performance analytical engine plays an important role in the modern lakehouse stack. StarRocks is designed to serve as a fast SQL query layer across both internal data and open data lake formats, helping organizations deliver warehouse-like analytics performance while preserving the openness and flexibility of the lakehouse architecture.
As more enterprises modernize their data platforms, we believe the future will not be defined by a single engine or a single storage system, but by open, interoperable architectures that allow each workload to use the right tool.