Data Lake vs. Data Warehouse vs Data Lakehouse: Key Differences Explained

As the data gold rush grows in volume, businesses are seeking the most effective ways to store, manage, and analyze their data. As data leaders, it's crucial to understand the different data storage architectures available. 

Today, data warehouses (cloud or otherwise), data lakes, and the emerging data lakehouse are front and center. In this blog, we'll explore the distinct characteristics, advantages, and use cases of each to help you make informed decisions for your organization's data strategy.

What is a Data Warehouse?

A data warehouse is a centralized repository designed for storing large volumes of structured data from various sources. It is optimized for query and analysis, which provides businesses with valuable insights to support data-driven decisions.

History of the Data Warehouse

The concept of the data warehouse dates back to the late 1980s and early 1990s, when businesses recognized the need for a dedicated system to consolidate and analyze data from disparate sources. Bill Inmon, often referred to as the "father of the data warehouse," defined it as a subject-oriented, integrated, time-variant, and non-volatile collection of data that supports decision-making processes. This architecture has evolved significantly over the years, incorporating advancements in storage, processing, and analytical capabilities.

Who Uses the Data Warehouse?

Data warehouses are primarily used by business analysts, data analysts, and decision-makers. These professionals rely on the structured, cleaned, and curated data stored in warehouses to generate reports, dashboards, and insights that drive strategic business decisions. Data warehouses are favored for their ability to handle complex queries and provide fast, reliable access to historical data.

Industries such as finance, retail, healthcare, and telecommunications often prefer data warehouses because they require high-performance analytics on large datasets. 

Increasingly, the retail sector is integrating data analytics capabilities into its day-to-day operations in order to capitalize on the vast troves of customer data these businesses generate. For example, Kroger, home to the largest supermarket chain in the US by revenue, purchased the data science retail company, 84.51°, to drive its analytics strategy. More recently, Saks Fifth Avenue partnered with Amazon to purchase rival store Neiman Marcus, with the new CEO of the combined company Marc Metrick sharing that partnering with tech companies will help retail “future-proof” these brands.

What Problems Does the Data Warehouse Solve?

Data warehouses solve several critical problems, including:

• Integrating data from multiple sources for a unified view

• Ensuring data consistency and accuracy

• Supporting complex queries and reporting needs

According to a report from Brainy Insights, experts estimate that the data warehousing market, currently valued at USD $30.2 billion will reach USD $85.7 billion by 2032, highlighting the increasing reliance on data warehouses for business intelligence and analytics.

A data warehouse helps people address key questions like:

• How did our sales perform last quarter compared to the same period last year?

• What are the key trends in customer behavior over the past five years?

• Which products are driving the most revenue, and what are their profit margins?

Generating answers to such questions informs critical decision-making and business strategy.

What is a Data Lake?

A data lake is a centralized repository that allows organizations to store all their structured and unstructured data at any scale. Unlike data warehouses, data lakes can handle raw data in its native format, providing flexibility and scalability for various types of data and analytical workloads.

History of the Data Lake

The concept of the data lake emerged in the early 2010s as a response to the growing volume, variety, and velocity of data generated by modern businesses. James Dixon, then CTO of Pentaho, is credited with coining the term "data lake" to describe a storage repository that holds vast amounts of raw data in its native format until it is needed. This approach enables organizations to store diverse datasets, from structured transactional data to unstructured social media feeds, in a single repository.

Who Uses the Data Lake?

Data scientists, big data engineers, and analysts use data lakes to perform advanced analytics, machine learning, and data exploration. The flexibility of data lakes allows these professionals to store and process large volumes of diverse data, enabling them to uncover insights that were previously unattainable with traditional data storage solutions.

Industries such as technology, e-commerce, and manufacturing often prefer data lakes due to their need to process and analyze vast amounts of varied data. 

What Problems Does the Data Lake Solve?

Data lakes enable people to solve several key problems, including:

• Storing diverse data types in a single repository

• Enabling advanced analytics and machine learning

• Providing scalable storage solutions for growing data volumes

As the variety of data only increases, cloud-based data lake adoption is projected to rise by 50% by the end of 2024. This growth in volumes demands structure, which explains why about 55% of data leaders aim to combine data lakes with cataloging and governance tools by the end of 2024.

Data scientists and analysts can leverage the data lake to address sophisticated questions like:

• How can we leverage social media data to improve our marketing campaigns?

• What patterns can be detected in sensor data from our manufacturing processes?

• How can we use machine learning to predict customer churn?

What is a Data Lakehouse?

A data lakehouse is an emerging data management architecture that combines the best features of data lakes and data warehouses. It aims to provide the flexibility and scalability of a data lake with the structured data management and performance capabilities of a data warehouse.

History of the Data Lakehouse

The data lakehouse concept has emerged as organizations faced challenges with managing and analyzing data across separate data lakes and data warehouses. First launched by Databricks in 2019, the data lakehouse architecture seeks to unify data governance, storage, and analytics in a single platform. This approach addresses the limitations of both data lakes and data warehouses, offering a more versatile and efficient solution for modern data needs.

Many data and analytics programs use a data warehouse and a data lake together because they complement each other perfectly. The warehouse handles relational data for business reporting and tracking corporate performance, while the lake supports data science and advanced analytics with the flexibility to host any kind of data structure or file format.

According to Gartner, “the warehouse and lake are now converging into the data lakehouse, which is a single data architecture that combines and unifies the architectures and capabilities of lakes and warehouses.” This new architecture unifies the best of both worlds, providing greater agility for analytics with less data redundancy, a simpler setup, and a consistent view of all analytics data. The purpose of the data lakehouse is to make data management more efficient and innovative.

Who Uses the Data Lakehouse?

Data engineers, data scientists, and business analysts use data lakehouses to streamline their data workflows. According to Databricks, “​​The open data formats used by data lakehouses (like Parquet), make it very easy for data scientists and machine learning engineers to access the data in the lakehouse.”

While Databricks is widely considered the data lakehouse pioneer, providers like Snowflake, Azure, and AWS all offer variations of the lakehouse today, as well. While they offer distinct capabilities, they all share the advantage of offering 'mixed workloads' more efficiently than in traditional data warehouses, while adding better controls to the perceived chaos of the data lake.

By combining the strengths of both data lakes and data warehouses, data lakehouses allow these professionals to manage and analyze data more efficiently, providing faster insights and reducing the complexity of maintaining separate systems.

Industries such as finance, healthcare, and retail are increasingly adopting data lakehouses to leverage their comprehensive data management capabilities. 

What Problems Does the Data Lakehouse Solve?

According to The Forrester Wave: Data Lakehouses, Q2 2024:

Enterprises are leveraging lakehouses to accelerate new and emerging business cases such as business intelligence, data science, IoT insights, business 360, and real-time insights. Many organizations are now leveraging data lakehouses for multiple use cases. Forrester sees broad growth in data lakehouse initiatives across all industries, including financial services, retail, healthcare, manufacturing, and energy. Organizations are migrating their data lakes and data warehouses to lakehouses to reduce costs, improve data governance, and support real-time insights.

Data lakehouses solve several important problems, including:

• Integrating structured and unstructured data in a single platform

• Enhancing data governance and security

• Improving query performance and analytical capabilities

A data lakehouse addresses questions like:

• How can we efficiently manage and analyze both structured and unstructured data?

• What unified data platform can support our advanced analytics and BI needs?

• How can we enhance our data governance and security while maintaining flexibility?

Summing it up: What’s the difference?

How One Global Science Company Unlocked Value from Its Data Lakehouse

Curious to learn how a data catalog can help data leaders unlock new value from their data lakeouse? This case study shares those details. 

Challenge: Siloed Data Leads to Operational Inefficiencies

A global science company faced significant operational inefficiencies due to siloed data across numerous ERP systems following multiple mergers and acquisitions. This fragmented technology landscape hindered enterprise-level value extraction from their data.

To address this, the company established a Center of Excellence (CoE) to consolidate data from various ERP systems into a Databricks Lakehouse Platform on AWS. However, this revealed inconsistencies and a lack of context in the data, which was compounded by fragmented data source information and definitions. Users struggled to find and trust data from other divisions, perpetuating silos.

The organization aimed to make data searchable and discoverable, establish a common repository for terminology and rules, and guide users to trusted data sources and experts. CoE leadership identified the need for a robust data governance platform to achieve these objectives.

Implementation: Governed Data is Trusted Data

The CoE selected Alation to support their data governance efforts. Alation unified enterprise data standards with common policies and processes, facilitating seamless data sharing across divisions. It also provided metadata context, enhanced data discoverability, and established a common language for data definitions via Alation’s Business Glossary. Alation Analytics tracked data curation progress, and stewardship functions guided users to data owners.

Results: Deriving Value from Governed Data

With Alation, leaders could bridge gaps in the company’s growth, enabling greater enterprise value from previously siloed data. The platform improved search and discovery capabilities, helping employees leverage data across various systems. Alation’s user-friendly interface quickly delivered value, and the platform’s features, such as Trust Flags, ensured users accessed trustworthy data.

The company enhanced data literacy through over 4,000 Alation articles and guided users to data experts when needed. Integration with Databricks Unity Catalog allowed efficient data sampling, profiling, and querying. Alation's flexibility enabled the company to explore additional data quality and privacy tools, solidifying Alation as the cornerstone of their data governance strategy.

Conclusion

Understanding the differences between data warehouses, data lakes, and data lakehouses is crucial for making informed decisions about your data strategy. Each solution offers unique benefits and addresses specific challenges, making it essential to choose the one that aligns best with your business needs. Data warehouses excel in structured data and business intelligence, data lakes shine with unstructured data and advanced analytics, and data lakehouses provide a unified approach that combines the best of both worlds.

Choosing the right data storage solution can transform your organization, driving efficiency, enhancing insights, and providing a competitive edge. 

No matter the shape of your data architecture, there is always a place for a strong data management strategy that helps the people in your organization more easily find, understand, and trust your data. Today, leaders are increasingly turning to the data catalog to support superior usage of data lakes. In fact, a report from Ventana research found that satisfaction among data lake users who also had a data catalog was much higher than that of those without a data catalog.

Alation offers a path to realize this goal. Discover how our innovative data intelligence platform can support your data management needs and drive your business forward.

Ready to elevate your data strategy? Explore Alation now.