Tools Compared: Database, Data Warehouse, Data Mart, Data Lake

TLDR: Data lake vs data warehouse

• A data lake is a data storage repository the can store large quantities of both structured and unstructured data.
• A data warehouse is a central platform for data storage that helps businesses collect and integrate data from various operational sources.

Where do we store all of this new data?

Businesses across all industries are generating and storing more and more data with each passing day. Thanks to rapid advancements in connected devices, sensors, wearables, cloud storage, and IoT gadgets, the tidal wave of data is not expected to subside anytime soon. 

This makes perfect sense. In 2014, 2.8 billion people were internet users . Five years later, that number has climbed significantly. Today, 4.33 billion people use the internet every day . And people aren’t just accessing the internet from one device, either. For example, 81 percent of U.S. adults own smartphones . 

In other words, the bulk of adults have at least two gadgets that are producing data around the clock. This trend won’t slow down anytime soon, either. By 2030, it’s expected that the average person will own 15 connected devices !

Add it all up, and it comes as no surprise that experts predict that the entire digital landscape will grow to 44 zettabytes by 2020. What’s more, 463 exabytes of data will be created every day by 2025!

All of this data can help businesses tremendously by showing them the best path forward. But to do that, they need to first be able to make sense of the data .

To keep up with this data, technology companies are increasingly creating new and innovative ways to manage data storage and retrieval. In recent years, data lakes, data warehouses, and data marts have emerged as some of the primary methods of enterprise data storage. These solutions are scalable and flexible, and enable organizations to store tons of data.

Before we examine the differences between these three approaches to storage, let’s take a step back and take a look at how data has traditionally been stored: the database.

What Is a Database?

A database is a traditional method of storing data in tables, columns, and rows. This allows for easy data queries and processing. Databases are typically controlled by database management systems (DBMS), with relational database management systems (RDBMs) being the most common. 

Businesses typically use databases for when they need quick access to their data. For example, an airline might rely on a database to process customers’ online ticket purchases. And an e-commerce company like Amazon might use a database to track inventory levels and recommend products the customer might be interested in.

To ensure that transactions have integrity, databases need to have four components: atomicity, consistency, isolation, and durability. Databases that have these four features are said to be ACID-compliant .

Now that you have a good idea about how the bulk of data has been stored in the internet age, let’s take a look at some newer storage mechanisms that are taking on increasing importance.

Data Lake vs Data Warehouse

What Is a Data Warehouse?

A data warehouse is a central platform for data storage that helps businesses collect and integrate data from various operational sources. This data is put into reports, which are then used for data analytics purposes and business intelligence efforts. In this light, data warehouses serve as the backbone for mission-critical aspects of operations.

Many of today’s leading corporations in all sectors—including the airline, hospitality, healthcare, and retail industries—are using data warehouses to streamline their data intake, reduce waste, and increase efficiency. In most cases, data warehouses store structured data, typically from databases.

Here are some additional benefits of data warehouses.

1. Data Integration

A data warehouse enables businesses to collect data from various external sources and then integrate that data into one central storage platform. This makes it easier for data analytics teams to analyze all data. There aren’t any silos.

2. Data History

As the name suggests, data warehouses can store data in a way that lets analysts see how data has changed over time. For example, teams can determine who created a file, who modified it, and when. 

3. Better Data Quality

A data warehouse enables an organization to improve the quality of their data by shattering data silos. This enables organizations to unlock the full power of their structured data.

4. Better Data Insights

With more data on hand—and less data, if any, siloed away—analytics teams can make more sense of their data by collecting better and deeper insights. Armed with this information, they can then figure out the best path forward.

What Is a Data Mart?

A data mart is a mechanism through which business users access data that lives in a data warehouse. The needs of every employee and each team are different. As such, data marts typically help specific users or teams, not the entire workforce. 

Whereas a data warehouse typically includes an entire enterprise’s data, a data mart is a more user-focused function. To illustrate, an accountant might access financial information related to customer transactions from a data warehouse through a data mart.

Here’s a rundown of the three different types of data marts:

1. Independent Data Mart

An independent data mart functions without relying on an existing data warehouse. Independent data marts typically focus on one specific business objective. Data is stored from either internal or external sources and can be called upon when needed to perform data analysis and business intelligence. 

Because of their smaller scope, independent data marts are not compatible with data warehouses.

2. Dependent Data Mart

A dependent data mart lives on top of an existing data warehouse. In these arrangements, data lives in a centralized location. When it’s time to run analytics, only the relevant data is accessed.

3. Hybrid Data Mart

A hybrid data mart integrates data from external operational sources with an existing data warehouse. The main benefits here include speed, flexibility, and the hybrid data mart’s capacity to handle large storage structures.

What Is a Data Lake?

A data lake is a data storage repository the can store large quantities of both structured and unstructured data. A data lake functions similar to how its name might suggest. All data, regardless of format, is stored as-is. 

For instance, imagine that each bit of your business’ data is like a drop of water. These tiny drops of data flow freely from various streams and rivers until they reach their final destination: your data lake. 

Together, this data forms a large lake. A major benefit to data lakes is that they can store data without any prior processing. The data simply flows into the lake and then stays there, awaiting future requests from analysts and business users. This free-flowing process means more data can be collected, stored, and retrieved than ever before. What’s more, since data lakes themselves are unstructured, it’s much easier to access and modify the data within.

Here are some additional benefits that data lakes deliver to modern enterprises.

1. Unlimited Data Sources

Thanks to its free-flowing nature, data lakes can handle data from an unlimited amount of sources. 

2. Storage for Raw and Unstructured Data 

Thanks to a data lake’s flexible construction, it can take in both structured and unstructured data (as opposed to most traditional data warehouses). 

3. No More Data Silos

Since data silos are removed from the equation, data lakes help organizations maximize the potential of all of their data, including unstructured data.

4. Lower Costs

Data lakes can save an organization a considerable amount of money by eliminating the need for out-dated legacy methods of data storage. Data lakes are also much easier for analysts to use, which saves valuable work hours. 

Data Transformation is Key Regardless of Choice

Regardless of what you choose to use, data transformation is a critical element to faster analytics.  You may have solved the simple EL part of the process through data loader tools to get data into your Snowflake data cloud.  But that’s the simple part.  Transforming this large, diverse, and complex set of data into something consumable by your analytics  team is the difficult part.

Datameer SaaS Data Transformation is the industry’s first collaborative, multi-persona data transformation platform integrated into Snowflake.  The multi-persona UI, with no-code, low-code, and code (SQL) tools, brings together your entire team – data engineers, analytics engineers, analysts, and data scientists – on a single platform to collaboratively transform and model data.  Catalog-like data documentation and knowledge sharing facilitate trust in the data and crowd-sourced data governance.  Direct integration into Snowflake keeps data secure and lowers costs by leveraging Snowflake’s scalable compute and storage.

Learn more about our innovative SaaS data transformation solution, Sign up for your free trial today!