Comparing the benefits of a data warehouse vs data lake vs data lakehouse

Data lake benefits: Modern, versatile, inexpensive

A data lake is a more modern technology than a data warehouse. In fact, Data lakes offer an alternative approach to data storage that is less structured, less expensive, and more versatile. When they were first introduced, these changes revolutionized data science and kick-started big data as we know it today. In this sense, the movement towards data lakehouses is just the continuation of a longstanding shift away from traditional data warehouses towards data lakes based on cloud object storage.  

Read through the list of benefits below to learn more about why organizations deploy data lakes. Data lake houses inherit these benefits and build additional functionality and value on top of them.

Data lakes separate storage and compute

In the past, compute and storage resources were combined on the same machines. This was due to the prevalence of on-premises warehouse systems, and the practice was continued with early data lakes based on the Hadoop Distributed File System (HDFS). 

In contrast, modern data lakes based on cloud object storage allow for the separation of compute and storage, ensuring that each resource can be scaled as needed. This is often one of the main ways that data lakes reduce cost using the cloud.

Data lakes allow for storage of data in multiple structures

Unlike data warehouses, data lakes store data in many structures. This includes structured, semi-structured, and unstructured data. Additionally, data entering the lake does not necessarily need to be schematized in advance. Instead, it can be left in a raw format until needed, using a process known as schema on read. This advantage allows data lakes to house a wide variety of data structures more easily and more cost-effectively than the data warehouses of the past.

Data lakes save money by reducing costs

Many data lakes make use of cloud object storage, including AWS S3, Azure ADLS, or GCP Google Cloud Storage. Compared to traditional data warehouses, cloud object storage is very inexpensive, owing to the massive economies of scale involved in cloud operation and due to the nature of object storage itself. For this reason, data lakes are often by far the most economical options for businesses, especially when compared to costly data warehouses.

Data lakes allow businesses to scale their storage capacity as needed

Data lakes are highly scalable, especially when using cloud object storage. This is true of both storage and the compute resources needed to query them. 

For example, a business’s data needs change over time. As storage requirements increase, more cloud object storage can be added. At the same time, if querying increases and more compute resources are needed, these can be scaled independently to meet demand. 

This agility—being able to tailor storage for storage needs and compute for compute needs—contrasts with previous systems that required significant data architecture and planning to scale effectively. Data lakes’ comparative agility is one of their primary advantages.

Cloud object storage or HDFS

Today, data lakes often use cloud object storage, which is the most versatile and least expensive option for most businesses. However, data lakes may also use Hadoop HDFS, which can be an advantage for some legacy systems, especially those on-premises. One of their key advantages is the ability to create data lakes using either technology.

Machine learning workflow integration

The ability of data lakes to record large amounts of raw data in a semi-structured or unstructured form makes them especially useful for machine learning. The data in the lake can be used to feed data science models or queried using Python, Scala, or R. The recent abundance of unstructured data, coupled with the desire to create insights from it, has made data lakes especially valued for these purposes.

This ability to harness unstructured data also makes data lakes an ideal technology for Artificial Intelligence (AI) modeling. In fact, AI and large language models (LLMs) are growing rapidly as an evolving use case of data lakes. 

Data lakehouse benefits

By combining the best of data lakes and the best of data warehouses, data lakehouses offer many benefits. Their emergence also represents the next stage in the evolution of the data lake, adding additional features and functionality to address a variety of business needs better. 

Data lakes and data lakehouses are similar

The first thing to understand is that a data lake and a data lakehouse are not entirely different technologies. In fact, the underlying storage technology used in a data lakehouse is very similar to a data lake in many key ways. Both are built on the same cloud object storage, and both allow for inexpensive data storage in multiple structures. 

However, the data lakehouse collects and stores more metadata using a modern table format like Iceberg. Because of this, it performs better than the traditional data lake in certain key areas. This key architectural difference allows organizations to gain additional functionality compared to a traditional lake while sacrificing nothing.

Data lakehouses democratize access to data

Data lakehouses make the data lake more accessible to different people in the organization who would not otherwise have benefited, and might have been forced to use a costly data warehouse instead. Data is no longer gated and is only suitable for data engineers. At the same time, this shift comes without sacrificing the original use cases that made data lakes popular in the first place. Overall, organizations that adopt a lakehouse architecture enjoy:

• Open architecture
• Increased functionality
• Better scale
• Better economic impacts
• Fewer complexities

Data lakehouses reduce the complexity of managing a data lake

Data lakehouses create an improved governance layer between raw data and consumable data. This allows for a management style that is more in line with a data warehouse or database but achievable using data lake technology, with all of its inherent cost benefits.

ACID compliance and transactional support

Data lakehouses introduce ACID compliance and greater support for transactional data than traditional data lakes. This is particularly important in some industries and some use cases. It means that organizations that may have had to employ separate systems before can now consider using a single system.

Improved updates and schema evolution

Modern table formats like Iceberg allow for schema evolution and enhanced functionality when updating or deleting data from a table. Data lakes based around cloud object storage typically include immutable storage, causing problems when the data needs to be updated or deleted. Schema evolution, partition evolution, and time travel are some of the key features that draw people to data lakehouses, particularly those constructed using the Iceberg table format and other modern table formats like Delta Lake and Apache Hudi. 

Departing from Hive

Lakehouses use modern, open table formats, including Iceberg, which involve fewer required operations compared to Hive. In the past, Hive was innovative, but Iceberg, Delta Lake, and Hudi are far superior and represent one of the main architectural reasons for the increased features and performance found in lakehouses.

Lakehouses also reduce reliance on the Hive Metastore (HMS) compared to traditional data lakes. This is particularly true when using Starburst Galaxy, which includes its metastore optimized for lakehouse table formats. AWS Glue is also supported, offering further customization. 

6 Business benefits of a data lakehouse

The move to a data lakehouse comes with many organizational advantages. When businesses move to a modern lakehouse format, they are able to achieve the following benefits. 

1. Operationalize the lake

Data lakehouses offer much better support for transactional systems compared to traditional data lakes. This is achieved by the unique way that data lakehouses handle metadata. 

This allows organizations that adopt a data lakehouse to take a more active approach to building business insights based on real time, updated data, which improves data reliability and the value of the insights derived.

2. Reduce the need for data warehouses

In the past, the limitations of a data lake meant that organizations needed to run a costly data warehouse alongside it. Now, with its increased functionality, the data lakehouse either reduces or eliminates the need for a warehouse.

3. Reduce costs

Lakehouses help to reduce costs by transitioning data from costly data warehouses to more efficient cloud object storage. Cloud object storage is by far the least expensive storage medium and helps drive efficiencies by separating compute and storage costs.

4. Improve performance

Lakehouses based around modern table formats like Iceberg are more performant than traditional data lakes. In fact, their performance is more comparable to that of data warehouses. Because of this, adopting a data lakehouse saves time and effort, reducing costs.

5. Avoid vendor lock-in

Data lakehouses are built on top of cloud object storage. These services are available from multiple cloud vendors, and the data stored inside them uses a standard, open table format, typically Iceberg. This means that it is easy to copy files from one vendor to another if needed. In this way, organizations can avoid licensing software from a single company, reducing vendor lock-in.

6. Low-cost starting point

Adopting a data lakehouse is not an all-or-nothing proposition. Often, businesses do not fully replace their current solutions; instead, they augment them with the addition of a lakehouse at first. This allows them to test and optimize the solution to fit their needs.

Data lakehouses include hybrid benefits from both data warehouses and data lakes

Read through the table below to learn more.

Data warehouses vs. data lakehouses
High performance	Lakehouses are highly performant, approaching the performance found in data warehouses.
Reliability	Data lakehouses include an improved transactional layer, increasing reliability. This makes their performance similar to a database or data warehouse.
Full CRUD	Lakehouses support full CRUD, including: Record-level updates and deletes Improved data governance  Version control

Data lakes vs data lakehouses
Low cost	Like data lakes, lakehouses make use of low-cost cloud object storage.  Organizations can use a variety of solutions, including AWS, Azure and GCP.
Multiple types of data	In common with data lakes, lakehouses can ingest:  Structured data semi-structured data unstructured data
Adaptability	Lakehouses are just as adaptable as data lakes and include:  Separation of storage and compute Scaling Elasticity