Data Lake vs. Data Warehouse

Data storage methods are incredibly important to the functions of organizations.

In Yellowbrick’s 2021 ITDM Data Warehousing Survey, 76% of IT executives stated that their organization was investing more in analytics infrastructure such as data platforms, data warehouses, and more, indicating the growing importance of data storage infrastructure.

When deciding on the best option for your needs, it’s beneficial to understand the use cases for a data lake vs. a data warehouse. In this article, we’ll explain why this is so important as we delve deeper into what these terms mean, their benefits and uses, and the similarities and differences between the two.

What is a Data Lake?

A data lake, often found in cloud-based computing platforms, is a centralized storage repository that can store huge amounts of data in its raw state. This architecture can handle the massive volumes of data that most organizations produce without the need to structure it first.

Data lakes provide core data consistency across a variety of applications, powering actions like big data analytics, machine learning, and predictive analytics. Information stored in a data lake can be used to build data pipelines to help analytics tools find insights that inform key business decisions.

Data Lake Benefits

“A data lake typically is going to give you enough flexibility that you can continually add data-driven dimensions to it to serve the purpose,” said Van Howard, Evergreen’s Director of Data & AI Solutions.

The benefits of using a data lake include:

The ability to handle both structured and unstructured data
Massive volumes of data can be stored cost effectively
Data is available for use much faster by keeping it in a raw state
A broader range of data can be analyzed in new ways to gain unexpected and previously unavailable insights
Easily configuring queries, data models, and applications without the need to plan ahead
Performing real-time analytics and machine learning by importing data in its original format from multiple sources

Use Cases

Data lakes can be used for situations such as:

Streaming media: Streaming companies collect and process insights on customer behavior which can be used to improve recommendation algorithms
Healthcare: Healthcare organizations use data to streamline patient pathways and improve the quality of care
Internet of Things (IoT): Hardware sensors generate massive amounts of semi-structured and unstructured data on the surrounding physical world which is stored for future analysis
Finance: Investment firms collect and store market data to efficiently manage portfolio risks
Digital supply chain: Manufacturers use data lakes to consolidate disparate warehousing data
Sales and marketing: Data scientists and sales engineers often build predictive models to help determine customer behavior

What is a Data Warehouse?

A data warehouse aggregates large volumes of data from multiple sources into a single repository. This architecture is made up of a collection of database tables—typically under the categories of dimension tables (like reference data) and fact tables (like transactions)—that house structured files.

Data stored in data warehouses is used for the purposes of reporting and analysis. Since data warehouses store large amounts of information, users can easily access data to use for data mining, data visualization, and other forms of business intelligence reporting.

Data Warehouse Benefits

“Data warehouses enable you to economize on very repetitive data,” Van Howard says. The benefits of using a data warehouse include:

Better data quality since data has been cleansed, de-duplicated, and standardized
Consolidating data from multiple sources into one single source
Efficiently loading data without having to deal with the costs of deployment or infrastructure
Storing and analyzing long-term historical data spanning months and years
Securing data so that it’s private, protected, and safe
Preparing data for analysis through data mining, visualization tools, and other forms of advanced analytics

Use Cases

Data warehouses can be used for situations such as:

Finance and banking: Financial organizations can use data warehouses to generate secure and accurate reports and provide company-wide access to data
Food and beverage: Big conglomerates use data warehouses to run operations more efficiently by consolidating sales, marketing, inventory, and supply chain data in one easily accessible place
Sales and marketing: Companies can evaluate the effectiveness of campaigns by analyzing campaign data, customer interactions, and sales outcomes to identify areas for improvement and drive better ROI
Business insights: Businesses can analyze historical data to identify trends and patterns, such as sales growth, customer retention, or seasonal fluctuations, to inform business strategies and forecasts
Supply chain optimization: Manufacturers use data warehouses to identify bottlenecks, inefficiencies, or inventory issues, and develop strategies to optimize procurement, production, and distribution processes

Data Lake vs. Data Warehouse

With the foundation of what data lakes and data warehouses are, as well as the benefits and use cases of each, let’s get into where these concepts share similarities and where they differ.

The Differences

Where a data lake stores raw data of all types, a warehouse only stores highly structured and unified data that supports business intelligence and analytics needs.

Think of it like the items they’re named after—with a data warehouse the data must be structured, like on the shelves of an actual warehouse. With a data lake you can dump any kind of data in there, just like a lake is able to fit a wider range of items.

A data warehouse can only fit data that is somehow related to the data that has already been added. Meanwhile, a data lake can receive any form of data, no matter the structure or relation to other data already added.

Some other differences include:

Data stored in a data lake has no set purpose yet, while in a data warehouse the data is in use for analysis.
Data lakes use an Extract Load Transform (ELT) process—which means that they pull data, load it into storage, and then transform it into analyzable data when needed—and data warehouses use an Extract Transform Load (ETL) process, meaning the data must be transformed before it’s loaded in.
Data lakes utilize schema on read and data warehouses utilize schema on write.
Data lakes are typically accessed by data scientists and engineers, while data warehouses are usually accessed by managers and business analysts.

The Similarities

The biggest and most obvious similarity is that both data lakes and data warehouses store and process data. Another way in which they are similar is that both can be used for certain analysis purposes such as data visualization, business intelligence, and data analytics.

Data lakes and data warehouses also share the capability for cloud computing storage methods, such as Microsoft Azure. Another trait they have in common is that they are both scalable to accommodate growing data needs.

Using Data Lakes and Data Warehouses Together

Most organizations use both a data lake and a data warehouse to cover the range of their data storage needs. Both repositories work together to form a secure, end-to-end system for storage, processing, and faster time to insight.

When these repositories are used in tandem, it’s known as a data lakehouse. A data lakehouse is an open standards-based storage solution that addresses the needs of data scientists and data warehouse professionals, allowing for deep data analysis and reporting.

“A data lakehouse allows you to operate on top of the data lake without constantly having to duplicate and reprocess the data,” Van Howard says.

This ensures that everyone is working on the most up-to-date data while simultaneously reducing redundancies—allowing for a range of analytic activity without compromising core data consistency.

Make Data-Informed Actions for Your Business

Whether your organization’s needs call for a data lake vs. a data warehouse—or some combination of the two— utilizing these structures is an essential part of growing with the digital age.

Van Howard highlights the importance of creating a solid framework that allows for a smooth transition into expanded capabilities. “We want to get that core architecture right so that we can really power their journey into more and more and more expanded uses of that data without having to rebuild that core.”

If that sounds like a daunting task, Evergreen’s data experts can help. We specialize in delivering technical solutions that lay the foundation for data and cloud integration. Contact us today or fill out the form below to get started.