Database vs. Data Warehouse vs. Data Lake: Choosing the Right Solution

From customer interactions and sales transactions, to website traffic and social media activity, the types of data that you'll need to store vary greatly. Not to mention, how you store and manage it significantly impacts your ability to extract data-driven insights for business and operational purposes. That's why it's critical to choose the right data storage solution for your use case.

Three of the most prominent data storage solutions are databases, data warehouses and data lakes. Although these terms are often used loosely and interchangeably, this post will look at their unique characteristics, discuss their relative strengths and weaknesses and guide you in selecting the best option for your needs. Let's get started by looking at these three different types of data storage at the fundamental level.

What is a database?

Most of us intuitively understand what a database is. At its core, it's a structured set of data. With the ability to contain data for everything from customer details and product inventories to financial records and more, a database stores data so that users can use it to perform tasks like making simple queries or generating reports.

There are two primary types of databases:

• Relational databases (SQL). The most widely known type of database, a relational database relies on a predefined schema that outlines how data is organized and relationships are established between points of data. This rigid structure makes relational databases ideal for handling structured data like financial transactions and customer information, where consistency and integrity are critical. Popular examples include MySQL, PostgreSQL and Microsoft SQL Server.

• Non-relational databases (NoSQL). Unlike their relational counterparts, NoSQL databases are designed to handle unstructured or semi-structured data. Data stored in this type of database may include social media posts, sensor data or multimedia files. These databases offer greater flexibility and scalability, making them extremely popular in modern application architectures where flexibility is important. MongoDB®, Cassandra and Redis are some of the most widely used examples of NoSQL databases.

Compared to other types of data storage, databases are relatively simple to create and manage, with options ranging from open-source solutions to proprietary systems. There are very few apps in existence that don't have a database as part of their core application architecture.

What is a data warehouse?

A data warehouse is a centralized repository that stores structured data from multiple sources, including data that's moved over from other application and operational databases. Unlike a database, which focuses on the most current data, data warehousing focuses on historical data for analysis and reporting. The warehouse acts as a massive data vault, where data from various operational systems is consolidated into a unified view, giving organizations a complete historical picture beyond the bounds of a single database or data source.

Key benefits of a data warehouse include:

• Business intelligence. By consolidating data from different sources, data warehouses enable organizations to gain valuable insights into their business performance.

• Data analysis. The structured and organized nature of a data warehouse makes it easier to conduct in-depth data analysis, identify trends and uncover patterns. Query engines within these platforms are also highly optimized for these more in-depth queries.

• Reporting. Data warehouses power comprehensive reports and dashboards, helping businesses make data-driven decisions.

Popular data warehouse solutions include some big names you've likely heard of, including Amazon Redshift and Google BigQuery. However, creating a data warehouse is generally a bit more complex than standing up a database, so more planning and expertise is usually involved.

What is a data lake?

Unlike the structured environment of a database or data warehouse, a data lake is designed to accommodate a diverse mix of data, including structured, semi-structured and unstructured. Organizations often dump everything into a data lake in a raw format and process it on the platform itself as required. Think of it as a central repository where you can store everything from customer transactions and sensor readings to social media feeds and video files.

Key characteristics of a data lake include:

• Variety. Data lakes can handle a wide range of data types, including logs, images, videos and social media content, all in their raw format.

• Flexibility. There's no need to preprocess or transform data before storing it in a data lake. This allows for agile analysis and exploration as new questions arise. However, the lack of upfront data structuring can make data governance and security more challenging compared to databases or data warehouses.

• Scalability. Data lakes are built to handle massive volumes of data, making them ideal for organizations dealing with big data challenges.

• Cost-effectiveness. Storing data in a data lake is generally cheaper than storing it in a data warehouse, as there's no need for upfront transformation and structuring.

Data lakes provide a powerful platform for organizations to leverage the full potential of their data, enabling advanced analytics, machine learning and data discovery. So, can you ditch the data warehouse if you dump everything into a data lake? Let's look at the key differences between each platform to see if this is true.

Key differences between data storage solutions

While databases, data warehouses and data lakes serve as repositories for data, they cater to distinct purposes and have unique capabilities. You can't necessarily use each of these technologies to replace the other. For instance, you wouldn't typically use a data warehouse instead of a database for application-level transactions. However, you may not need all three technologies in your stack. Some platforms can cover two to three of these capabilities in a single solution, like SingleStore, which can manage all three use cases.

When it comes to use cases, databases are your go-to solution for managing structured, transactional data, powering applications and ensuring efficient data retrieval. Data warehouses excel at handling large volumes of structured data from multiple sources, transforming it into a goldmine for analytics and reporting. On the other hand, data lakes provide the flexibility to handle a diverse mix of raw data, from structured and semi-structured to unstructured, making them ideal for advanced analytics and big data applications.

So which ones do you need? To help you navigate this relatively complex topic, let's take a look at the differences in the following table:

*Schema-on-read refers to the ability to define the data structure only when reading the data, allowing for more flexibility compared to schema-on-write systems like databases or data warehouses.

Choosing the right data storage solution

With a clearer understanding of the distinct characteristics of databases, data warehouses and data lakes, it's time to tackle the crucial question: which solution is the right fit for your organization? The answer completely depends on what kind of data you are working with, and what you want to do with it. There are three main things to consider when figuring out which solution you should be using. Let's take them one by one.

Consider your data needs

The first step in choosing which data storage solution to use is to identify the nature and purpose of your data. Here are the areas where each platform excels:

• Transactional data. A database is your best bet if your primary focus is on handling real-time transactions, like processing orders or managing user accounts within an application.

• Raw, unfiltered data. For organizations dealing with vast amounts of raw, unfiltered data from various sources, a data lake provides the flexibility and scalability to store and analyze this information effectively.

• Historical data for analysis. If your goal is to analyze historical data for business intelligence and reporting, a data warehouse offers the structure and tools to extract valuable insights.

Consider your data processing requirements

How you intend to process and analyze your data also plays a crucial role in your decision. A few things to be aware of include:

• Schema-on-read. Data lakes allow you to store raw data with all its metadata, applying a schema only when extracting data for analysis. This flexibility is ideal for exploratory analysis and data discovery.

• ETL processes. Traditionally, data warehouses use Extract, Transform, Load (ETL) workflows to transform raw data before storage. However, many modern data warehouses now support ELT (Extract, Load, Transform), where raw data is ingested first and transformations happen during queries, offering greater flexibility.

• Agile data modeling. Data lakes are particularly well-suited for data scientists and developers who need to create and test new data models quickly.

Consider your data storage and budget constraints

Finally, it's essential to consider your data storage capacity and budget limitations. When looking at each solution, here are a few areas to double-click on:

• Cost-efficiency. Data lakes offer a cost-effective solution for storing raw data, as they eliminate the need for upfront processing and transformation.

• Storage costs. Data warehouses, with their focus on structured data and analytical processing, can incur higher storage costs.

• Scalability and cost. Databases offer the flexibility to scale up or down based on your needs, allowing you to balance cost and performance effectively.

Data warehouses for analytical data

Data warehouses excel at providing a holistic view of your business by consolidating data from various departments and systems. This aggregated data allows analysts to answer complex business questions, track key performance indicators (KPIs) and uncover hidden opportunities. Data warehouses excel in analytics use cases compared to running similar workloads on a database.

Key advantages of data warehouses for analytical data include:

• Centralized data. Consolidates data from various sources into a single repository

• Historical analysis. Stores historical data to track trends and patterns over time

• Optimized performance. Designed for efficient analytical queries and reporting

• Business intelligence. Provides the foundation for data-driven decision-making through dashboards and reports

If your organization is mainly looking to leverage data analysis, a data warehouse is a better option than a database for running this type of workload. While data lakes can also handle analytical workloads, data warehouses often outperform data lakes for traditional BI queries due to their structured nature and optimized query engines.

Data lakes for big data analytics

When it comes to big data-type problems, data lakes are the ideal solution over data warehouses. These solutions are designed to accommodate all types of data, from structured and semi-structured to unstructured, making them perfect for organizations dealing with massive datasets from many data sources.

Unlike data warehouses, which require data to be structured before storage, data lakes allow you to store raw data in its native format. This "schema-on-read" approach provides incredible flexibility, allowing you to adapt your analysis as your needs evolve.

Key benefits of data lakes for big data analytics include:

• Scalability. Handle petabytes of data from various sources including social media feeds, sensor data and log files

• Flexibility. Store any type of data without needing upfront transformation

• Cost-effectiveness. Store raw data cost-effectively, reducing storage expenses

• Data exploration. Enables data scientists and analysts to explore data freely and discover new insights

• Machine learning. Provides a rich dataset for training machine learning models and developing AI applications

Data lakes provide the scalability, flexibility and cost-effectiveness to unlock the full potential of big data use cases, going beyond the bounds of many data warehouse solutions and avoiding their drawbacks. They can generally also handle data warehouse workloads, and in the age of AI and machine learning, are a must-have technology.

Databases for transactional data

When it comes to handling transactional operations, where data integrity and consistency are most critical, databases are always going to be the top pick. Think of online shopping carts, banking transactions or social media interactions — these all rely on databases to ensure data is processed accurately and reliably for use within applications and other services.

Unlike data warehouses and lakes, which focus on analytical processing and large-scale data storage, databases are optimized for quick data retrieval and updates. They are designed to handle frequent read and write operations, ensuring that applications can access and modify data with minimal latency.

Key advantages of databases for transactional data include:

• Data integrity. Ensures data accuracy and consistency through ACID properties (atomicity, consistency, isolation, durability)

• Real-time access. Provides low-latency access to data for real-time applications

• Concurrency control. Manages concurrent access to data, preventing conflicts and ensuring data integrity

• Data security. Offers extensive security features to protect sensitive information

Even though your application may still use a data lake or warehouse for some aspects of functionality, if your application requires transaction processing, a database will still sit as the central hub for the application's data needs.

Unlocking the best of all worlds with SingleStore

So far, we've explored the strengths and weaknesses of databases, data warehouses and data lakes. As we discussed, the likelihood of using more than one of these technologies is quite high. But what if you could combine the best features of all three into a single, unified platform? That's exactly the goal of the SingleStore platform.

SingleStore is a real-time database that breaks down the traditional barriers between transactional and analytical workloads. It allows you to transact, analyze and search diverse data types while delivering next-level speed and scalability. The result is a data storage solution that eliminates the need for separate systems and complex ETL pipelines generally required to move data between platforms like an application database and data lake.

Here's how SingleStore bridges the gap:

• Transactional power. SingleStore supports high-volume transactional workloads and excels in hybrid transactional/analytical processing (HTAP), allowing both transactional and analytical queries on the same data.

• Analytical prowess. Perform complex analytical queries and generate real-time insights without needing a separate data warehouse.

• Data lake flexibility. Store and analyze structured, semi-structured and unstructured data within a single platform.

• Scalability and performance. Scale your data infrastructure effortlessly to accommodate growing data volumes and demanding workloads.

When teams bring SingleStore into their stack, they:

• Eliminate data silos. Consolidate your data into a single source of truth, improving data accessibility and reducing complexity

• Accelerate time to insight. Perform real-time analytics on transactional data, enabling faster decision-making.

• Reduce infrastructure costs. Simplify your data architecture and reduce the overhead associated with managing multiple systems.

• Increase agility. Adapt to changing business requirements with a flexible and scalable platform.

By bridging the gap between transactional and analytical workloads, SingleStore sidesteps the question of "Which types of data storage do I need?" by providing the best of all worlds in a single platform.

Get started with SingleStore today

Whether you need the transactional power of a database, the analytical capabilities of a data warehouse or the flexibility of a data lake, selecting the right data storage solution is critical. And with innovative solutions like SingleStore emerging, you can even break down traditional barriers and combine the best of all worlds into a single, unified platform.

As you embark on your data journey, remember that the optimal solution is not a one-size-fits-all answer. It's about finding the perfect fit for your unique needs and harnessing the power of data to drive innovation, improve decision-making and achieve your business goals. Instead of creating a complex data stack, leverage SingleStore to get everything under one roof. Get best-in-class performance and scalability while covering all the bases.

Ready to try SingleStore for your database, data warehouse and data lake needs? Start free with SingleStore today.