Data Warehouse or Data Lakehouse - Which One is Right for You?
As businesses generate more data than ever, choosing the right data architecture is critical. Two of the most prominent architectures today are data warehouses and data lakehouses - each designed to serve different needs.
For many years, a data warehouse is the preferred choice for structured analytics and business intelligence (BI). It is optimized for high-speed querying and reporting. A data lakehouse on the other hand is a newer hybrid approach that combines the flexibility of a data lake with the performance benefits of a warehouse. This makes it ideal for machine learning, AI, and unstructured data processing.
But which architecture is right for your business? Should you stick with a structured, performance-driven warehouse, or embrace the flexibility of a lakehouse? The answer depends on your data, use cases, and analytics needs.
In this article, we’ll break down the key differences between data warehouses and data lakehouses, explore when to use each, and help you make an informed decision.
What is a Data Warehouse?
A data warehouse is a structured, high-performance system which is designed for analytical workloads and business intelligence (BI). Its architecture consists of multiple layers that facilitate data ingestion, storage, transformation, and querying to ensure optimized performance and governance.
Data Ingestion
Data enters the data warehouse from operational databases, APIs, flat files, or a data lake. The ingestion process follows one of two approaches:
ETL (Extract, Transform, Load) - Traditionally, data is extracted from source systems, transformed before loading, and stored in a structured format. This approach ensures clean, high-quality data but requires extensive processing before analysis.
ELT (Extract, Load, Transform) - With modern cloud-based warehouses and data lakes as staging areas, raw data is first loaded into the warehouse without transformation. Transformations are then performed inside the warehouse using SQL-based tools like dbt. This approach is more flexible and allows for incremental processing.
Storage & Data Modeling
Once data is ingested, it is stored in an optimized columnar format for fast analytical queries. Data modeling follows relational data modeling approaches like the Star- or Snowflake Schema.
These schemas are designed to support efficient aggregations, filtering, and drill-down analyses which is ideal for analytical workloads.
Query Processing & Performance Optimization
Modern data warehouses leverage various optimization techniques to ensure high-speed queries, including:
✅ Columnar Storage - Reduces I/O and improves scan efficiency
✅ Indexing & Partitioning - Speeds up lookups and enables parallel processing
✅ Materialized Views & Caching - Precomputes and stores frequently queried results
✅ Distributed Query Engines - MPP (Massively Parallel Processing) architecture scales queries across multiple nodes (e.g., Redshift, Snowflake, BigQuery)
Consumption & BI Layer
Data in the warehouse is accessed via SQL-based queries, dashboards, and reporting tools such as Tableau, Power BI, and Looker. This layer enables business users to analyze trends, generate reports, and derive actionable insights.
Key Takeaways
✔ Data warehouses use structured data and are optimized query performance
✔ The data is loaded into the data warehouse using ETL or ELT pipelines
✔ Star and Snowflake schemas structure data for efficient analytical workloads
✔ Cloud-based warehouses leverage distributed compute, caching, and columnar storage to deliver scalable, high-speed analytics
What is a Data Lakehouse?
A data lakehouse is a modern hybrid architecture that combines the structured query performance of a data warehouse with the flexibility and scalability of a data lake [1]. It enables organizations to store structured, semi-structured, and unstructured data in a unified platform while supporting business intelligence (BI), machine learning (ML), and advanced analytics.
[1] data lake: A data lake is a system or repository of data stored in its natural/raw format, usually object blobs or files.
Data Ingestion
Data is ingested into a data lakehouse from various sources, including operational databases, IoT devices, logs, APIs, and streaming platforms.
Raw Data Storage: Data is loaded into the lakehouse in its original format (e.g., JSON, Avro, Parquet, CSV, images, videos)
Schema-on-Read: Instead of enforcing a schema before data is written (schema-on-write), the schema is applied at query time, allowing for greater flexibility
Multi-Format Support: A lakehouse can store structured (tables), semi-structured (JSON, XML), and unstructured (images, text) data in a unified environment
If you already have a data lake, you don’t necessarily need to move data into a separate storage system for a data lakehouse. Instead, it enhances your existing data lake by enabling structured querying, transactions, and performance optimizations. You can either:
Query the data lake directly using a metadata layer
Optimize and structure specific datasets for faster BI queries
Storage & Data Management
Unlike a data warehouse, which stores data in optimized relational tables, a lakehouse uses an open data lake storage layer (e.g., AWS S3, Azure Data Lake Storage, Google Cloud Storage) combined with a transactional storage format (open table format), such as Delta Lake, Apache Iceberg or Apache Hudi.
The separation of compute and storage makes a lakehouse more cost-effective than traditional data warehouses, as raw data can be stored cheaply while compute resources scale on demand.
Data models in a lakehouse often follow the medallion model, with a bronze(raw data), silver (filtered and cleaned) and gold layer (business-level aggregations).
Query Processing & Performance Optimization
A key challenge in data lakes is query performance, as raw files are not optimized for analytics. A lakehouse overcomes this with:
✅ Data Skipping & Metadata Caching - Improves query performance by organizing data files efficiently
✅ Vectorized Query Execution - Uses columnar storage (Parquet, ORC) for faster scans
✅ Data Compaction - Merges small files into larger, optimized data structures
✅ Metadata Layer - Tools like Delta Lake, Iceberg, and Hudi track schema changes and transaction history
✅ Distributed Compute Engines - Supports processing with Apache Spark, Trino, Presto, and Dremio for large-scale analytics
Unlike a warehouse, a lakehouse allows both SQL-based BI queries and AI/ML processing on the same dataset without duplicating data.
Consumption & BI/AI Layer
A data lakehouse enables multiple analytics use cases from a single platform:
Business Intelligence (BI): Supports SQL-based analytics for dashboards and reporting
Data Science & Machine Learning: Allows Python, R, and Spark ML workloads on raw data
Real-Time Analytics: Enables streaming and low-latency query execution
Multi-Tool Compatibility: Works with Power BI, Tableau, Looker, Jupyter Notebooks, and AI frameworks like TensorFlow & PyTorch
Key Takeaways
✔ Flexible ingestion enables flexible schema handling and raw data storage
✔ Open table formats (Delta, Iceberg, Hudi) add ACID transactions and indexing
✔ Distributed compute engines (Spark, Trino, Presto) allow scalable analytics
✔ Supports both BI and AI/ML workloads from a single data repository
✔ Cost-efficient and scalable due to separation of storage & compute
How to select the right for your Business?
Now that we understand the key differences between a data warehouse and a data lakehouse, the next question is: Which one is the right fit for your organization? The answer depends on factors such as the types of data you handle, your analytical needs, budget constraints, and available technical expertise.
When to use a Data Warehouse?
A data warehouse is best for organizations that need structured, high-performance analytics and business intelligence (BI).
Use a data warehouse if:
Your main use case is BI and reporting - You rely on SQL-based dashboards, predefined reports, and ad hoc queries
You work with structured, transactional data - Your data comes from ERP, CRM, finance, or sales systems, and follows a well-defined schema.
You need fast, optimized query performance - Your analysts require low-latency, high-speed queries for decision-making.
You require strong data governance and compliance - You work in a regulated industry (finance, healthcare, etc.) and need strict security controls, role-based access, and auditing.
You want an easy-to-maintain solution - Cloud-based data warehouses like Snowflake, BigQuery, and Redshift manage scaling, optimization, and performance tuning automatically.
Example Use Cases:
Financial reporting - Structured data from accounting and sales systems
Retail analytics - Sales dashboards, customer segmentation
Healthcare data analysis - Compliance-driven structured reporting
Operational efficiency tracking - Performance metrics for supply chain and HR
When to use a Data Lakehouse?
A data lakehouse is best for organizations that need both structured and unstructured data processing for AI/ML, real-time analytics, and advanced workloads.
Use a data lakehouse if:
You need to store and analyze large, diverse datasets - Your organization works with semi-structured and unstructured data (logs, IoT, social media, images, videos, machine learning features, etc.).
You want to support multiple analytics use cases - Your team runs SQL queries, machine learning, and real-time analytics on the same platform.
You want cost-efficient storage and compute - A lakehouse separates storage from compute, allowing you to store large amounts of data cost-effectively while scaling compute as needed.
You prefer open data formats and flexible access - Your organization values Apache Parquet, Delta Lake, Iceberg, or Hudi to avoid vendor lock-in and allow multiple tools to access the data.
Your data scientists and analysts need access to the same data - Data engineers, analysts, and ML teams can use Spark, Python, SQL, and BI tools without moving data between systems.
Example Use Cases:
Machine Learning & AI - Training models on raw images, sensor data, text, and structured features.
IoT and Streaming Analytics - Real-time analysis of sensor readings, clickstream data, logs.
Big Data Analytics - Large-scale data processing across structured and unstructured data.
Media & Entertainment - Storing and analyzing videos, audio, and metadata.
Hybrid Architectures
Many organizations find that a single architecture doesn’t fully meet their needs. Instead, they implement a hybrid approach, using a data lakehouse for raw data storage and exploratory analysis while maintaining a data warehouse for high-performance BI and structured reporting.
How does the hybrid architecture work?
In a hybrid architecture, data is first ingested into a data lake (or a data lakehouse layer), providing a cost-effective and scalable storage solution. This allows data scientists and analysts to work with structured, semi-structured, and unstructured data directly from the lakehouse for advanced analytics and AI use cases.
Frequently accessed structured data is then loaded into the data warehouse, where it is transformed into curated and optimized tables. This business-critical data is essential for business intelligence and reporting use cases, ensuring high performance and reliability. In addition a lot of warehouses are supporting “external tables” for querying raw data in the lake storage without ingestion.
When to use the hybrid approach?
Your organization relies heavily on BI and reporting but also wants to invest in AI and machine learning. With a hybrid approach, each team gets the right tool for the job.
Your enterprise handles large volumes of semi-structured and unstructured data while still requiring BI capabilities. The hybrid approach supports BI while keeping semi-structured and unstructured data readily available.
If you're looking for a cost-effective solution, the hybrid approach may be the right fit. It allows you to load only the most valuable data into the data warehouse while storing raw data in the more affordable lakehouse.
Conclusion
As data-driven decision-making becomes essential, choosing the right data architecture is crucial for success. The best approach depends on the unique needs and goals of your organization.
A data warehouse is best suited for businesses that rely on structured reporting and require fast, optimized performance for business intelligence and analytics. On the other hand, a data lakehouse provides the flexibility to store and analyze structured, semi-structured, and unstructured data, making it an ideal choice for organizations that need to support AI, machine learning, and large-scale analytics.
For many companies, a hybrid approach offers the best of both worlds by combining the high-performance querying of a data warehouse with the scalable, cost-efficient storage of a data lakehouse.
By understanding your data types, analytical needs, compliance requirements, and budget constraints, you can design a data architecture that not only supports your current operations but also scales with future demands. Regardless of the approach you choose, aligning your data strategy with your business goals will ensure that your organization remains competitive in an increasingly data-driven world.