Data warehousing, data lakes, and data lakehouses are three common data architecture options used by organizations today. A data architecture refers to the overall strategy and design for data storage, integration, and access within an organization. As companies gather increasing amounts of data from various sources, having the right data architecture is crucial for efficiently managing and deriving value from data.
Understanding the key differences between a data warehouse, a data lake, and a data lakehouse enables organizations to choose the optimal architecture based on their specific data needs and use cases. With the right data architecture in place, companies can enable faster insights, better decision making, and strategic advantage through data.
Definitions
A data warehouse is a centralized repository of integrated data from one or more sources, structured specifically for query and analysis. IBM Data warehouses aggregate structured data from multiple sources into one place to run analytic queries and generate reports.
A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. Google Cloud Data lakes can store data in its native format until it is needed.
A data lakehouse combines the capabilities of data warehouses and data lakes into one system. Databricks Data lakehouses provide the performance of a warehouse with the flexibility of a lake in an open format.
History
As data storage and analysis needs evolved over the years, different architectures emerged to meet those needs.
Data warehouses first appeared in the 1980s as a solution for bringing together data from across an organization into a centralized repository for reporting and analysis (https://www.databricks.com/discover/data-lakes/history). The data warehouse model involved carefully structuring the data into schemas optimized for relational database querying. This required significant upfront investment in modeling and ETL (extract, transform, load) processes.
Data lakes emerged in the early 2010s as a way to cheaply store vast amounts of unstructured and semi-structured data in its native formats, rather than forcing it into rigid schemas (https://www.databricks.com/discover/data-lakes/history). This enabled organizations to collect and store data now and figure out how to use it later. However, the lack of structure and governance in early data lakes made it challenging to query and analyze the data at scale.
The data lakehouse concept combines elements of both the data warehouse and data lake in an effort to get the best of both worlds – the governance, performance and ease of use of a warehouse with the agility, scalability and cost-effectiveness of a lake (https://medium.com/quantumblack/lakes-warehouses-lakehouses-a-short-history-of-data-architecture-bc942b0ed463). The lakehouse emerged around 2020 as a logical evolution in data architecture.
Structure
Data warehouses, data lakes, and data lakehouses have different architectures and structures. Data warehouses use a schema-on-write approach where data is structured and modeled before it is loaded into the warehouse (https://www.snowflake.com/guides/what-data-lakehouse/). This makes querying fast but adding unstructured or new data difficult.
Data lakes use a schema-on-read approach where data is loaded in its raw form into object or file storage (https://www.linkedin.com/pulse/data-warehouse-lake-lakehouse-what-best-solution-your-strategy). This makes it easy to store unstructured and new data but querying requires applying structure at read time which can be slow.
Data lakehouses combine elements of warehouses and lakes. They store raw data like a lake but also prepare some data for querying like a warehouse (https://www.snowflake.com/guides/what-data-lakehouse/). This provides flexibility for both unstructured and structured data while optimizing performance.
Data Storage
A key difference between data warehouses, data lakes, and data lakehouses is how data is stored. Data warehouses store data in a structured schema with carefully defined columns and data types. This makes querying the data faster, but requires more upfront effort to transform and cleanse the data before loading it. In contrast, data lakes store raw, unstructured data in its native format. This provides more flexibility, but can make querying slower since the data is not optimized. Data lakehouses aim to get the best of both worlds by storing both raw data like a data lake, as well as structured, optimized data like a warehouse.
According to Databricks, data lakes hold data in an unstructured way, often as blobs or files. There is no data model or schema enforced at ingestion time. In data warehouses, data is structured and modeled before being loaded. This requires upfront data wrangling but enables faster querying later. Data lakehouses allow storing data both structured and unstructured ways.
Overall, data lakes offer more flexibility on data storage, while data warehouses offer more performance. Data lakehouses aim to balance both.
Schema
Schema on write and schema on read are two different approaches for defining data structure in databases and data lakes. Schema on write requires the schema or structure to be predefined before the data is stored. With schema on write, all the data must conform to the predefined schema before being written to the database or data warehouse (1). Schema on read, on the other hand, allows data to be stored without first defining the structure or schema. The structure is applied when the data is read and queried instead (2).
Schema on write is traditionally used in data warehouses and relational databases, ensuring consistent and clean data that matches the predefined structure. However, it lacks flexibility for handling varied and changing data. Schema on read is common in big data systems like data lakes, allowing flexible storage of raw data of different shapes and sizes. But data lack structure and consistency until read.
Use Cases
Data warehouses, data lakes, and data lakehouses each have ideal use cases based on the type of data analytics needed.
Data warehouses work well for standard reporting and BI analytics on clean, structured data. Common use cases include sales reporting, financial reporting, and operational analytics.
Data lakes excel at handling unstructured and semi-structured data from multiple sources. Typical use cases are analyzing machine logs, social media data, IOT sensor data, and other raw data feeds.
Data lakehouses combine the capabilities of data warehouses and data lakes. They work well for real-time analytics on both structured and unstructured data. Example use cases are fraud detection, personalized recommendations, predictive maintenance, and other operational decisions that require fresh data.
Query Performance
One of the key differentiators between a data warehouse and a data lake is query performance. Data warehouses are optimized for fast, complex analytical queries across large datasets (Source). The data is processed, structured, and indexed to enable users to efficiently slice and dice the data, generate reports, and get quick answers to business questions. Query performance is a top priority in data warehouse design.
In contrast, data lakes can have slower query performance due to less optimization for analytics and the storage of multi-structured data (Source). Data lakes are optimized more for capturing and storing vast amounts of raw data for future potential analysis. While technologies like Apache Spark allow for faster queries on data lakes, overall query speeds may be slower compared to finely tuned data warehouses.
When fast query performance is critical, data warehouses have a clear advantage. But data lakes offer more flexibility for ad-hoc analytics at lower cost. The best solution depends on an organization’s specific needs and priorities around reporting, analysis, and decision making.
Cost
When comparing system costs between data warehouses, data lakes, and data lakehouses, there are some key differences to consider. Data warehouses built on proprietary hardware and software can be quite expensive, often costing millions of dollars. Data lakes built on open source technology like Hadoop and object storage are designed to run on low-cost commodity hardware, reducing costs substantially compared to data warehouses. According to Striim, data lakes can offer 70-90% cost savings over data warehouses.
Data lakehouses aim to provide the best of both worlds – leveraging low-cost object storage like data lakes while also providing performance optimizations like data warehouses. According to Databricks, data lakehouses built on Delta Lake can offer up to 90% cost savings compared to data warehouses. By building on open source technology and running on cloud infrastructure, data lakehouse solutions can minimize infrastructure costs.
In summary, data lakes and data lakehouses typically have much lower infrastructure, storage, and maintenance costs compared to traditional on-premises data warehouses. Data lakehouses aim to approach the savings of data lakes while also improving performance for analytics queries. The extreme cost savings of data lakes and lakehouses make big data analytics affordable for more organizations.
Conclusion
In summary, data warehouses are best suited for structured, cleaned, and transformed data that needs to be analyzed through SQL queries. They enable fast query performance through their schema-on-write optimization. Data lakes can store vast amounts of raw, unstructured data affordably, making them useful for exploratory analytics. Data lakehouses combine aspects of warehouses and lakes, storing data using schemas while still working with unstructured data sources. They aim to provide the best of both worlds.
When choosing between these systems, consider the use case and data profile. If you need to run heavy structured SQL-based analytics, a data warehouse is likely the best fit. For digging into diverse raw data, a data lake has the flexibility. And for organizations that want to analyze raw data using warehouse-style optimizations, a lakehouse bridges the gap. The right solution depends on an organization’s analytics needs, budget, and existing infrastructure.
