Data warehouses vs Data Lakes vs Databases – Which One Do You Need

By Reseun  McClendon

Today, your enterprise must effectively collect, store, and integrate data from disparate sources to both provide operational and analytical benefits. Whether its helping increase revenue by finding new customers or reducing costs, all of it starts with data.

Data analysts, data scientists, engineers, and managers all require a robust data storage solution for business intelligence (BI), SQL frameworks, research, and other analytics. The data storage solution you choose for your modern business must be more scalable, reliable, flexible, and secure than any traditional database. This is where modern databases, data warehouses, and data lakes come in.

What is a transactional database? 

A database is used to store, search, and report on data from a single source. The data contained in a database is often a collection of structured, semi-structured, or unstructured, items.  Structured data is highly specific and is stored in a predefined format. Unstructured and semi-structured data, however, are stored in varied formats. Databases are simple to create, and SQL may be used to query and report on the data. There are also a variety of open source and proprietary databases, meaning they are widely accessible to install and begin using on premium or on the cloud.

Cloud databases offer additional ease of access over traditional databases. Users can access the data from virtually anywhere using a vendor’s API or web interface. Cloud databases are also more scalable, as they can expand their storage capacities on run-time to accommodate changing business needs. Organizations can also opt to pay only for the scale of data they use, making cloud databases cost-effective. 

Databases (non-relational) typically represent their stored data in the form of unique identification key values. Every item in the collection may either represent a document, table, or anything else. Typically, distinct items in the same or different collections are not related, but a reference (or identifier) can be added to an item to refer to another. 

Common non-relational database engines are:

• MongoDB
• Apache Cassandra
• Redis
• Couchbase
• Apache Hbase

Relational databases are collections of items with pre-established relationships among them (Lo Duca, TDS, 2021). All items in a relational database are organized in tables, while columns represent item attributes. Each row of a table represents a single data item. To keep track of each item in a table uniquely, primary keys are defined for each table. However, multiple tables can also be related through different “foreign” keys. 

Relational databases require schemas and are not fit for unstructured or semi-structured data. This rigid schema makes them unsuitable as a centralized storage location for data from multiple sources because raw data may vary in format and structure. Relational databases, however, are popular for data analysis and self-contained applications. 

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Common SQL database engines are:

• MySQL
• Postgres
• Microsoft SQL Server
• SQLite 

Source: Fivetran

What is a data warehouse?    

Data warehouses are central repositories of information that can be analyzed to make business decisions. Data warehouses store both current and historical data, allowing data teams to explore how trends change over time. The data contained in a data warehouse is typically structured, meaning it is highly specific and stored in a predefined format. However, they can store unstructured and semi-structured data as well.

Source: CFI

A cloud data warehouse solution handles operations and hosting through a cloud services provider. Enterprises opt for usage-based or fixed payment schedules to gain the inherent flexibility of a cloud environment.

The up-front investment and lead times are typically much lower for cloud data solutions than for on-premises solutions. You don’t have to buy a storage hardware infrastructure, instead you can benefit from features such as automated scaling.

Companies are rapidly migrating from traditional data warehouses to the cloud. Managers are drawn to the cost savings and scalability that cloud-based services can provide. There is a long list of advantages cloud data warehousing can bring to your organization:

Efficient scaling 

Cloud data warehouses can seamlessly adapt to the scale of your business needs. When your company needs to expand, you save time and money by escaping hardware with a cloud ecosystem.

Outsourced storage operations 

Outsourced management and operations are included in the low upfront cost of your chosen cloud data warehouse provider. Your data team can focus on research, analytics, and growth initiatives.

Flexible pricing and cost efficiency 

Cloud data storage solutions typically offer a flexible pricing plan. You can opt for a flat-rate option or a usage-based option. If you are a smaller company and you opt for a throughput or per hour per node plan, you may save money over a flat-rate user. If you are a large company, a flat rate may be more efficient for your business.

Real-time analytics

Cloud data warehouses support streaming data, allowing you to query data in real time and drive fast, informed business decisions (Google Cloud, 2022).

Machine learning and AI initiatives 

You can quickly integrate machine learning (ML) and artificial intelligence (AI) into your analytics directly through your cloud provider. With AI and ML, your data team can automate routine processes and reveal new predictions so your business can thrive.

What is a data lake?

Data lakes commonly store semi-structured, and unstructured data, enabling you to store raw data from all sources without needing to process or transform it upfront (Confluent.io, 2022). 

When the data needs to be retrieved, however, you will have to apply some structure to the data stored in your data lake. This is ideal for data scientists and developers who can create new data models quickly, but it is not ideal for your business reporting personnel. Data lakes are much cheaper and more flexible than data warehouses and databases. These benefits have bolstered the popularity of data lakes in the modern data stack, but each storage solution has its ideal use cases.

Depending on the circumstance, a typical organization will require both a data warehouse and a data lake as they serve different needs. Data lakes are unique because they store relational data from line of business applications, and non-relational data from mobile apps, IoT devices, and social media (AWS, 2022). Because data does not need a definite schema when it is brought into your data lake, you can store all your data without careful design or future planning. You can write a schema later and then quickly begin various analytics on your data like SQL queries, big data analytics, full-text search, real-time analytics, and machine learning to uncover business insights. 

Which data storage method should you choose?

There are a few key differences to consider when selecting how to house all the data in your organization. As mentioned, databases perform best when there is a single source of structured data but have limitations at scale. These limitations make traditional databases ineffective for most organizations, leading managers to turn their focus to data warehouses and/or data lakes. Some characteristics of databases are that they include structured data, schema-on-write processing, free/low costs, variable speeds, weak security, an open user base, and use cases in reporting, analytics, and automation. 

When we compare data lakes and data warehouses, the strengths and weaknesses of both become clearer. The following table from Amazon Web Services (AWS) breaks down the characteristic distinctions between data warehouses and data lakes: 

Source: AWS

Note that many of these storage solutions can be used as both warehouses and/or data lakes. Databricks and Google Cloud BigLake, for instance, combine the functionality of a data warehouse with the flexibility of a data lake. Snowflake was built to be an all-in-one single platform for data lakes, data warehousing, data engineering, data science, and machine learning. 

Each platform is robust enough for scalable, reliable, and secure data storage. Choosing whether your organization needs a warehouse, a lake, or both can be a challenge. However, once you settle on a solid storage platform, you will empower your data teams and help you grow your business. 

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