Database vs Data Warehouse vs Data Lake: A Guide to Choosing the Right Solution

Whether you’re building an application or need BI to empower your stakeholders, there are multiple ways to approach data storage and management. Knowing the differences between a database, data warehouse, and data lake will help you choose the right solution.

In a nutshell, a database is used for transactions, while warehouses are used for analytics and reporting. Data lakes are also designed for analytics, but they’re best for collecting raw, unfiltered data (because you can store any type of data without applying a schema).

In this article, we’ll explore these differences in more detail. You’ll discover the main strengths and weaknesses of each solution, best use cases, and a few examples of software to use. Let’s go.

Empower your end-user to make better business decisions with remarkable embedded analytics experiences. Learn more. 

Contents

Key differences at a glance

Before we get into the weeds, let’s take a look at key differences between databases, data warehouses, and data lakes.

What is a database?

A database is a collection of real-time information that has been stored in an organized way. Typically, the term “database” is used to describe a relational or SQL database. But you can get both relational and non-relational (noSQL) databases. 

Here are some key characteristics of SQL databases:

• Data is stored in rows and columns (like a spreadsheet)
• There’s a well-defined relationship between tables
• Data is highly-detailed and searchable
• They’re well suited to Online Transaction Processing (OLTP) thanks to high levels of data integrity 
• They usually store live, real-time data
• But they don’t typically store historical data 

So, if your database is a point-of-sale (POS) system for processing sales transactions, you’ll store all the latest details relating to each transaction. However, say a customer updates their delivery address—the database may not store the last address, only the new one.

“There’s a big difference between database types,” says Tom Gardiner, CEO at Embeddable. “Their individual qualities should be carefully considered when planning for different applications—to ensure you can deliver the desired result.” NoSQL databases are even more flexible than SQL databases. They can store semi-structured data like JSON or XML documents, and can store data using key-value pairs rather than rows and columns.

What is a database typically used for?

SQL databases are ideal for reliable, accurate data processing such as record-keeping, transaction processing, and inventory management. That’s because they have high levels of data integrity with well-defined relationships across tables.

From Netflix to Amazon, a lot of the apps we use every day are built on databases. Facebook uses multiple database systems including MySQL and Apache Cassandra (a NoSQL database) to manage user data and content.

Here are some more examples of what you could use an SQL database for:

• E-commerce platforms, e.g. order management, inventory tracking, product catalogs and customer data 
• Booking systems, e.g. event ticketing, appointment scheduling, hotel reservations, travel reservations 
• Financial systems, e.g. banking applications, accounting systems, financial transaction processing 
• Content management systems (CMS), storing website content, user data, user comments 

SQL databases are powerful, but they’re not typically used for complex querying and analytics. Firstly, they don’t usually store historical data so this can limit analysis. But they’re also quite slow to run large queries and run the risk of interrupting or slowing down live transactions.

Examples of databases

MySQL is probably the most widely used relational database, as it’s open source and easy to use. Other examples include:

PostgreSQL

Oracle Database

Microsoft SQL Server

IBM DB2

Considering embedding analytics into your app? Get the recommended databases for user-facing analytics that support sub-second query responses and concurrent queries-per-second (QPS).

What is a data warehouse?

A data warehouse is a central repository for storing large amounts of historical data. It uses Online Analytical Processing (OLAP) to allow pre-aggregation and multidimensional data analysis. (In simple English, that just means a data warehouse collects and summarizes data from multiple sources.)

Key characteristics of a data warehouse include:

• Data is typically organized around specific business areas, such as sales, finance, or marketing
• It can store large volumes of data, integrated from multiple sources
• It’s updated on a regular basis but is rarely ‘live’
• This usually involves a extract, load, transform (ETL) process to store the data in a consistent way
• Otherwise, data is read-only and cannot be updated or modified by users

So, you could combine information from your Customer Relationship Management (CRM), POS, and supply chain management systems all within a data warehouse. That way, you could run analytics using data from each source to inform decision-making about the business. Just remember that, to make data digestible for different stakeholders, you’ll need a data professional or engineer to help with designing dashboards and charts.

Pro tip: Data warehouses typically have a more rigid schema than databases, as they have to apply some consistency to data from different sources. It’s not exactly a downside though. This inflexibility allows for more efficient and powerful analytical querying.

“If we’re talking about curating data, that’s a data warehouse. We’re going to be applying logic, organization, structure, governance, order to the data to present it as information. It’s strict.”
— Tony Dahlager, VP of Account Management at Analytics8

What is a data warehouse typically used for?

Data warehouses can store and manage large amounts of historical data from multiple sources, which makes them ideal for business intelligence (BI). This level of data analysis and reporting is handy for businesses wanting to make more informed decisions, backed by data.

You could use a data warehouse for:

• Financial reporting and budgeting, e.g. generating reports, analyzing profitability, forecasting, and budgeting
• Supply chain optimization, e.g. analyzing logistics data, inventory levels, and supplier performance
• Customer analytics, e.g. analyzing customer data and interactions to improve retention and create target marketing campaigns 
• Sales forecasting, e.g. analyzing historical sales data and market trends to identify opportunities and optimize the sales pipeline

Note: In a warehouse, data is often summarized rather than detailed. Consolidating data into higher-level aggregates (e.g. daily, monthly, or yearly totals) makes it more efficient for querying and analysis, but not so good for getting highly-detailed, real time data.

Examples of data warehouses

• Amazon Redshift
• Snowflake
• Google BigQuery
• Teradata

What is a data lake?

A data lake is a repository for any kind of raw, unfiltered data. Unlike a data warehouse, which is hierarchical, a data lake tends to have a flat architecture. Thanks to its vast size and flexible nature, a data lake is ideal for the collection of data in any format—as well as advanced analytics.

Take a look at the key characteristics of a data lake:

• It can store large amounts of current and historical data
• Data can be structured (in rows and columns), semi-structured (JSON or CSV logs), or unstructured (videos, images, documents)
• Data does not need to be processed or transformed to be stored 
•  It’s typically inexpensive to run using cloud storage services

Pro tip: Data lakes are very flexible with a schema-on-read approach. That means an analyst can apply schema according to their needs—and access data without structuring it first.

“If we’re talking about collecting data rather than curating data, we’re talking about a data lake. We’re not monitoring quality, we’re not integrating it, we’re not governing it. It’s mostly raw, there’s no business logic being applied to it.”
— Tony Dahlager, VP of Account Management at Analytics8

What is a data lake typically used for?

Data lakes can store and analyze massive amounts of data in its raw form, meaning you can get insights from data that might be too complex or varied for a traditional data warehouse.

Data lakes are commonly used for:

• Internet of Things (IoT) analytics, storing data from IoT devices, real-time monitoring, and predictive maintenance
• Machine learning and AI, storing and retrieving large amounts of data for training language models and AI applications
• Log and Clickstream analysis, storing web logs, application logs, and clickstream data to analyze user behavior, website performance, and security monitoring
• Cybersecruity and threat intelligence, analyzing security logs, network data, and threat intelligence data to optimize cybersecurity and incident response

Note: With total flexibility comes some risk. Data lakes can become unusable data swamps without proper governance. They could also end up ingesting sensitive data, which is a privacy concern if lots of teams have access.

Examples of data lakes

• Databricks Delta Lake
• Azure Data Lake
• Cloudera
• Amazon S3 or AWS Lake Formation
• Google BigLake

Database vs data warehouse vs data lake: Which one is right for your needs?

If you’re mainly running transactions in the backend of an app, go for a database. That said, many businesses use a combination of one or more databases alongside a data warehouse. If you have vast amounts of varied raw, unfiltered data, a data lake could be the way to go. 

Here are the main differences:

• A database is designed for OLTP and stores structured data. Data is live, highly detailed, and reliable. Use it for storing, organizing, and processing data but not for BI. It can work slowly when querying large amounts of data.

• A data warehouse is designed for OLAP and stores structured data with a more rigid schema. Data requires ETL processing before storage, and is usually summarized rather than highly detailed. Data warehouses are generally faster and won’t slow down transactions. Use it for managing and analyzing data from multiple sources.

• A data lake is designed for storing vast amounts of raw data, including structured, semi-structured, and unstructured data, with a schema-on-read approach. Data can be stored cheaply without any processing. Use it for storing and analyzing highly varied data or machine learning.

It’s worth noting that these solutions aren’t always found on separate platforms. For example, Oracle Autonomous Data Warehouse, combines the features of databases and data warehouses. Snowflake also offers both data warehousing and data lake capabilities. It’s worth speaking to the sales teams from a few providers to see if they can handle your use case. 

Finally, if you’re interested in embedding analytics into your app for a remarkable user experience, consider Embeddable, a software developer toolkit for building analytics experiences. Embeddable integrates with popular databases to provide lightning fast analytics experiences that feel native to the rest of your app.

Empower your end-user to make better business decisions with remarkable embedded analytics experiences. Learn more. 

Frequently asked questions about databases vs data warehouses vs data lakes

What’s the difference between a database, a data warehouse and a data lake?

There are a few differences between a database, data warehouse, and data lake, even though they’re all used for data storage and management. Here’s how they compare:

• A database is designed to store and manage live, structured data. It’s best for operational use cases like online transaction processing (OLTP).

• A data warehouse is designed to store historical, structured data from multiple sources. It’s best for online analytical processing (OLAP), business intelligence, and reporting.

• A data lake is designed to store large volumes of raw data in its native format. It can store structured, semi-structured, and unstructured data with a schema-on-read approach, making it more flexible but potentially less organized. It’s best for advanced analytics like machine learning.

Is a data warehouse bigger than a database?

A data warehouse isn’t necessarily bigger than a database. It just depends on your use case. Generally, data warehouses are designed to store large volumes of historical data. That data usually accumulates over time from multiple sources, so it could be bigger. But some operational databases can grow to massive sizes, especially if they support large-scale applications.

When choosing between a data warehouse and a database, it’s more important to consider what you’re using it for than the size of the dataset. Data warehouses are for analytics and historical data. Databases are for transactional workloads and real-time data.

Why do companies need both databases and data warehouses?

Since databases and data warehouses serve different purposes, you might choose to use both. Databases typically handle day-to-day transactions, while data warehouses enable in-depth analysis of historical trends and patterns.

Say you run a project management SaaS. A transactional database will store real-time data on user accounts, project details, task assignments, and file uploads. This supports multi-user access and keeps activity feeds and notifications up to date. 

But you might also want to run in-depth analysis to understand your users and improve the product. A data warehouse could integrate historical usage data from the app, customer data from your CRM, and marketing insights to identify opportunities for the business.