Data warehouses and data lakehouses are powerful tools for businesses looking to store, organize, and analyze large amounts of data. They provide secure storage environments for businesses to access large amounts of data and make informed decisions. Organizations must decide which option is best suited for their specific needs based on cost, scalability, security, speed, ease of use, and other criteria.

Here are the 5 key takeaways from this article:

• A data lakehouse and a data warehouse are similar in many respects, but there are key differences.

• The primary difference between the two is that the data warehouse primarily supports structured data while the data lakehouse supports structured data, text, and machine generated data.

• Data warehouses have many options for integration while the data lakehouse has far fewer options.

• Common connectors in data warehouses include geography, time, cost and other identifiers such as sex, age, and ethnicity.

• Integrate.io is a powerful solution for data integration that can help businesses transition their data into a data lakehouse or warehouse quickly and easily

In this article, we will go over the key differences between the two, and the advantages of each. By understanding the differences between these two solutions, you can make an informed decision on which architecture will work best for your business. 

Table of Contents

Introduction

This is a guest post by Bill Inmon, known as the father of the data warehouse. In this article, he will discuss the data lakehouse and data warehouse, how they are similar, how they are different, and the potential advantages of each.

Data warehouses have been around for decades and have become increasingly popular due to their ability to aggregate massive amounts of structured and unstructured data from multiple sources. Data lakehouses are the newest addition to the family of enterprise-level big data solutions, offering an alternative way of storing vast amounts of information with more flexibility than traditional warehouses.

With both options available, organizations must decide which is best suited for their needs based on factors such as cost, scalability, security, speed, ease of use, and more.

What is a data lakehouse?

A data lakehouse is a data storage repository designed to store both structured data and data from unstructured sources. It allows users to access data stored in different forms, such as text files, CSV or JSON files. Data stored in a data lakehouse can be used for analysis and reporting purposes.

What is a data warehouse?

A data warehouse is a data storage repository designed to store structured data. It allows users to access data stored in relational databases, such as Oracle or SQL Server. Data stored in a data warehouse can be used for analysis and reporting purposes.

What are the Key Differences Between a Data Lakehouse and a Data Warehouse?

Here's a comparison table highlighting the key differences between a data lakehouse and a data warehouse:

Criteria

Data Lakehouse

Data Warehouse

Data Storage

Stores data in raw and unstructured formats like files, images, audio, and video.

Stores data in a structured and predefined format like tables and columns.

Data Integration

Supports real-time data ingestion from various sources.

Integrates batch data from a limited set of structured sources.

Data Processing

Offers both batch and real-time processing capabilities.

Mainly supports batch processing.

Data Schema

Supports both structured and unstructured data without a fixed schema.

Stores structured data with a predefined schema.

Data Quality

Data quality is low, requiring significant processing and cleansing before use.

Data quality is typically high and well-maintained.

Querying and Analysis

Supports ad-hoc queries, exploratory analysis, and machine learning.

Supports standardized SQL queries and predefined reports.

Cost

Lower cost due to the use of open-source technologies and cloud storage.

Higher cost due to the need for structured data storage and proprietary technologies.

Flexibility

Highly flexible and can adapt to changing business needs.

Less flexible and requires significant effort to make changes.

Agility

Provides fast access to data, enabling quick decision-making.

Slower access to data due to the need for predefined schemas.

Security and Governance

Requires a robust governance framework to ensure data security and compliance.

Offers strong governance and security features.

Overall, a data lakehouse offers a more flexible and agile approach to data management, while a data warehouse provides a more structured and secure environment for storing and processing data. The choice between the two depends on the specific business needs and the type of data being managed.

Isn’t a Data Lakehouse the Same Thing as a Data Warehouse?

Just today Mike Renwick asked a question on Linkedin. The question was – “isn’t a data lakehouse just the same thing as a data warehouse?” This turns out to be a really interesting question. So rather than answer Mike, I thought I would convey the answer to a lot of people. There indeed are a lot of similarities between a data lakehouse and a data warehouse.

Yes, they are basically the same type of structure. But there are some significant differences as well.

The Golden Gate Bridge and The St Louis bridge

In order to explain, let me make an analogy. Let's examine the question – are the Golden Gate bridge and the St Louis bridge (Eads bridge) across the Mississippi the same type of bridge? There are many similarities between the two bridges.

Both bridges:

  • Are large and long

  • Cross significant bodies of water

  • Accommodate cars

  • Connect significant populations that without the bridges would be significantly inconvenienced

In these regards, the Golden Gate Bridge and the St Louis bridges are very similar.

But in other regards, the bridges are quite different. The Eads bridge accommodates a railway, but the Golden Gate bridge does not. The Golden Gate bridge has to take into account tides, the Eads bridge does not. The Golden Gate bridge has to account for the corrosive effects of salt water, the Eads bridge does not. The Golden Gate bridge had great difficulty finding bedrock, the Eads bridge had less difficulty. The Eads bridge has to account for periodic floods, the Golden Gate bridge does not. The Golden Gate bridge has to account for the fact that there are occasional movements of the earth. The Eads bridge sits on relatively stable ground.

And this is just the shortlist. There are MANY differences between the Eads bridge and the Golden Gate bridge.

The Data Lakehouse and The Data Warehouse

Now let’s take a look at the data lakehouse and the data warehouse. Both the data warehouse and the data lakehouse:

  • Are architectures for data

  • Require the integration of data

  • Support enhanced business value

  • Support the storage of data over time.

In these regards, the data warehouse and the data lakehouse indeed are the same sort of structure. But there are many differences between the two types of structures.

The data warehouse primarily supports structured data. The data lakehouse supports structured data, text, and machine-generated data. The data warehouse typically supports a significant amount of data. The data lakehouse supports a colossal amount of data. The data warehouse has many options for the integration of data – social security number, part number, order number, passport number, prescription number, telephone number, etc. The data lakehouse – when integrating data across the many types of data found in the data lakehouse – has far fewer options when it comes to the commonality of data across different platforms. Integration in the data lakehouse makes use of common connectors.

The problem is that there are not many common connectors Some of the common connectors are:

  • Geography – every occurrence of data has an associated physical location

  • Time – every occurrence of data has an associated moment in time

  • Cost – most data has some cost associated with it

When it comes to integrating data about humans there are common connectors:

  • Sex

  • Age

  • Ethnicity

  • Date of birth, etc.

There are far fewer common connectors in the data lakehouse than there are connectors found in structured data.

So that leaves us with the question – isn’t the data warehouse just a different version of the data lakehouse?

The answer is a big fat – “sort of”. Just like the Golden Gate bridge is the same thing as the Mississippi bridge.

Related Reading: From Data Warehouse to Lakehouse

Which is better The Data Lakehouse or Data Warehouse?

That depends on the data, data applications, and data usage. Both serve a purpose in data storage and analysis and the choice of which one to use should be based on specific needs for data integration, data security, data usage, and data storage capacity.

Data lakehouses have grown in popularity over time due to their ability to store vast amounts of unstructured data, while data warehouses have been the go-to data storage choice for many years due to their data integration capabilities and secure data storage.

Ultimately, the right data architecture should be chosen depending on the data needs of an organization. There is no one size fits all solution when it comes to data architectures – different organizations need different data architectures to fit their data needs.

Bill Inmon, the father of data warehousing, often said “there’s more than one way to skin a cat” – and that same rule applies here. Depending on your data needs, there is a data architecture out there that can help you get the most out of your data.

So data lakehouse vs data warehouse? It’s up to you. Choose the data architecture that best suits your data needs and can help you make the most of your data. With all the advances in data technology, there’s no need to settle for a one-size-fits-all data architecture anymore.

Related Reading: Data Lakes: The Achilles Heel of the Big Data Movement

How Integrate.io Can Help

Integrate.io is a powerful solution for data integration. Integrate.io lets you quickly and easily move data between various sources to any supported destination, including a data warehouse or data lakehouse. It provides powerful analytics capabilities that help you gain insights from your data in real-time, enabling faster and more effective decision-making. With Integrate.io, you can rest assured knowing that all of your business’s data is securely stored and fully integrated with your systems. Whether you are looking to move data into a data lakehouse or data warehouse, Integrate.io can help you make the transition quickly and easily. With its advanced features and powerful analytics capabilities, Integrate.io is the perfect solution for any business looking to optimize their data integration process. Try Integrate.io today and get the most out of your data!