As the data industry grows and evolves, a lot of terms get mixed up and combined, and thrown around. If you aren’t a data scientist, it’s easy to get lost—so let’s start by defining what data lakes and data warehouses actually are:
Both data lakes and data warehouses store data from multiple sources, consolidating it into one central repository. They create a go-to place to store and retrieve all your business data.
Now that we’re on the same page, let’s dig into how they differ.
Data Lake vs. Data Warehouse: What Are They?
A data lake holds data in its “native, raw format.” In other words, data lakes store unprocessed data from all sources and store it in that same state—unprocessed and unstructured—using “flat architecture and object storage.”
The data lake basically serves as a dumping ground for data. Then transformation and cleaning happen downstream.
A data warehouse also holds data but in a structured way. With a data warehouse, processing and transformation of data happens first, before you put data into the warehouse. That makes it quicker to query and analyze data as needed.
For practical purposes, you can think about the difference between data lakes and data warehouses like this:
- What technology are you using to store data? Tools like Snowflake, Google BigQuery, and Amazon Redshift are all data warehouses, for example. AWS S3, Google Cloud Storage, Microsoft Azure Datalake, Databricks Deltalake are examples of data lakes.
- How are you storing and using data? Are you storing all data? That’s a data lake. Only storing data that’s been transformed and cleaned? That’s a data warehouse.
Data Types: Raw Data vs. Structured Data
Here’s the TL;DR from above: The main difference between data lakes and data warehouses comes down to the type of data stored in each.
In a data warehouse, the data there is relational and has already been ‘cleaned’. Because of that, data warehouses are often used to store business data previously cleaned via ETL (like Fivetran) or behavioral data platforms (like Snowplow).
Data lakes are much less structured. Data is dumped into a data lake in its raw form, with no cleaning or processing done.
Despite the differences, data lakes and warehouses can be used together—they can use one single technology or a combination of multiple. Often, a company may use a data lake as a dumping ground for data—cleaning it up via ETL later on and moving the cleaned data into a data warehouse.
What Are Some Examples of Each?
Let’s look at some specific examples of data lakes and data warehouses in the market—to help put some logos to all that information.
Indicative is the only Product Analytics platform that integrates directly with your data warehouse or data lake, so we’re pretty familiar with the space. Here are a few of the major data warehouse players:
- Google BigQuery
- Amazon Redshift
On the data lake side, here are a few names you may be familiar with:
- Amazon Web Services S3
- Google Cloud Storage
- Microsoft Azure Datalake
- Databricks Deltalake
View an infographic of the modern data ecosystem to visualize how these technologies fit. (infographic blog post)
Pros and Cons of Data Lakes and Data Warehouses
While the raw vs. structured data difference seems simple enough, it can have a big impact downstream. So if you’re working to figure out whether your company needs a data lake or a data warehouse, it’s helpful to understand the pros and cons that comes with each approach to data storage.
When you’re storing data in its raw form, that impacts several things that may or may not be important for your company.
Data lake pros:
- Data lakes prioritize speed of data entry, so loading data into the lake is quick.
- Traditionally, data lakes cost less than warehouses, making them more affordable to scale as needed.
- Because they store data in its raw form, data lakes can accommodate more flexibility in how the data is ultimately used.
Data lake cons:
- Data lakes are difficult and slow to query in real-time.
- Can be prone to reliability issues thanks to data duplication, and inconsistency, making it harder to reason with and query the data.
- Because data has to be cleaned and transformed before it can be retrieved and analyzed for business use, data lakes can slow down analysis.
Data warehouses store data in a highly-structured way, using ETL and strong schemas. There are several implications, advantages, and disadvantages to that approach, too.
Data warehouse pros:
- Data warehouses prioritize speed of data retrieval and analysis—once the data is loaded, it’s ready to query and analyze much more quickly.
- Because the data in your data warehouse is already cleaned and structured, you can trust analyses are based on consistent and accurate data.
- Structured data is easy to connect with Business Intelligence (BI) and other analytics tools, making your data more accessible and digestible across the business. Most of the time, you can query the data using SQL, which is widely known and used.
Data warehouse cons:
- Historically, data warehouses were costly, on-premise solutions. Cloud data warehouses are changing that, but can still come with potentially higher costs as you scale.
- With data cleaning and synthesizing front-loaded, it takes more effort to load and enter data into a data warehouse.
Will Data Lakes Replace Data Warehouses?
Historically, data warehouses were expensive, on-premise, and physical solutions for data storage. The high cost kept many companies from being able to afford a data warehouse.
As the space has evolved, the traditional type of data warehouse has fallen out of favor. That’s led some to speculate whether data lakes—a lower cost, cloud-based alternative—would replace the data warehouse completely.
In our view, that’s the wrong question to ask. We believe data lakes and warehouses will converge, adopting similar features and capabilities.
Old school data warehouses aren’t the same data warehouses that are popular today. The data ecosystem is massively in flux, and new data warehouses have already evolved far beyond the expensive, on-premise solutions before them.
Today’s data warehouses are cloud-based, scalable, and much more affordable than their predecessors. Modern cloud data warehouses (or CDWs) like Google BigQuery, Amazon Redshift, and Snowflake are tools that democratize data storage and make it easier and more affordable than ever for companies to own, control, and operationalize their data.
Modern organizations will use multiple technologies and multiple approaches to data storage and transformation. So the data warehouse of the future will likely become a component of an organization’s data infrastructure.
To make the most of your data, then, you need to be able to be nimble with that data. Organizations that figure out how to be nimble with data aren’t concerned about the semantics or technical specs of how it gets done—whether using a data warehouse, data lake, or something else.
Instead, they’ll ask, “How can I most easily, effectively, and cheaply get value from data?”
That’s the big question.
When to Use a Data Lake vs. Data Warehouse
If you’re choosing between a data lake and a data warehouse for your company, there are several factors to consider.
Ultimately, it all comes down to how you use your data. Depending on the destination platform, business unit, and use case your data infrastructure needs to support, you’ll have different needs for your storage solution.
For example, how adaptable does your data storage need to be? How quickly do you need to be able to derive insights?
The answers to all those questions will help inform which storage solution will work best for you.
If you need to be able to quickly query your behavioral data and uncover your customer journey, a data warehouse may be the best option.
For example, a data warehouse may be the best option if you need to understand:
- The various paths customers take in your product before they convert or churn
- The highest value marketing channels to optimize acquisition and campaign budgets
- Customer behavior or specific customer segments in order to personalize experiences
A data lake, on the other hand, might be the better option if:
- Your data set is large and diverse
- You want to collect data that allows for a variety of types of analysis not yet defined
- You’re running machine learning or artificial intelligence off your data
Let’s break that down a little more and look at 6 of the biggest considerations:
Your current cloud platform
Depending on the cloud system your business already uses, you may be better off going with the data solution they offer.
For example, if you already use Google Cloud Platform, it’s super easy to set up a BigQuery data warehouse. If you use Amazon Web Services (AWS), you have your pick of both—a data warehouse using Redshift or a data lake using S3 (or both!) You can easily query data using Amazon Athena, too.
Traditionally, data warehouses have been the more expensive option. But cloud data warehouses are changing that, bringing costs within reach for more companies and making the data warehouse option more competitive with data lakes from a price standpoint.
That said, pricing structures and costs vary even within each storage category, so it’s important to keep your budget in mind and do plenty of research on both upfront and ongoing costs of each tool you consider.
If you want to be able to run and analyze queries quickly, a data warehouse will get you there faster—because the data stored there is already cleaned, transformed, and structured.
If you’re more concerned with the speed of data entry and loading, a data lake will make quicker work of the front end and allow for more flexible workloads.
Ease of use
Ease of use is an important consideration, but first, you need to define who the tool needs to be easy to use for.
If the people on your team who need access to data are non-technical business users, a data warehouse is likely the better option. That way, you can easily pipe data from the warehouse into BI tools—where it can be queried using SQL—analytics tools (like Indicative), or reverse ETL tools (like Census and Hightouch).
Do You Need Both?
By now, you might be wondering whether your company needs both—a data lake and a data warehouse. The answer is: maybe.
While many companies opt to use data lakes and data warehouses together as part of a broader data infrastructure, the decision largely comes down to a handful of factors:
- Your data and analytics stack: You’ll want a solution that works seamlessly with the other tools you already use on a regular basis.
- Who needs access to your data: If only technical team members need access to your data, a data lake might make sense. If business users need to be able to analyze and query data, a data warehouse that connects with tools built for business users is probably a better option.
- How you use data (and how you anticipate using it in the future): Data lakes tend to be more flexible for multiple use cases because the data stored there isn’t processed into a particular format yet.
- How secure you need to keep your data: Cloud data warehouses ensure you fully own and control your data and they’re often easier to secure up to regulatory standards.