A data lake stores raw, unstructured, and semi-structured data in its native format, making it ideal for big data analytics and real-time processing.

In contrast, a data warehouse stores processed, structured data organised into schemas, ensuring consistency and high performance for business intelligence and historical reporting.

Did you know bad data cost enterprises millions of dollars on average? When data is unstructured, poorly organised, or improperly stored, your businesses lose out on the key ingredient to success. Learning the differences between a data lake vs data warehouse is useful in effectively equipping your data scientists and business analysts with what they need to succeed.

This guide will help you understand the differences between these two tools and which ones you should be using going forward.

A data lake is the simpler of the two storage types, storing a wide variety of raw data for easy retrieval. This data runs the gamut from text-based documents, such as Excel sheets or notes, to visual assets like photo and video.

Data lakes are widely credited as the foundation for AI and machine learning according to a Grand View Research study. They're also incredibly helpful for businesses who want to better tailor their strategy to shifting business environments.

One of the key selling points of data lakes is the ability to handle unstructured data. That is, any information that does not have a predefined data model or schema. Below are a few specific benefits to using and maintaining data lakes in your business operations.

Data lakes are a fundamentally scalable operation, allowing businesses to shrink or expand their data whenever they need to. A data lake can easily handle several petabytes of data, which is essential when you're storing data like sensor data, IoT data, and user interaction records that can easily be several gigabytes a piece.

Raw data is highly appealing to businesses who are rapidly expanding since it doesn't need to be converted into another format. As such, data lakes are highly flexible and allow businesses to simply store whatever is most useful to them at the time.

Customer and business demand is always changing, so the ability to file away any useful data on-the-fly is key to adapting successfully.

The data lake is a budget-friendly solution since there's no need to convert or clean the stored data. Even very large amounts of data can easily take residence on a cloud computing platform with scalability built in.

If a business needs more room to store data, there are cost-effective upgrades that are within even limited budgets.

The data lake is one of the most powerful assets for a business who requires a high level of detail for their operations. With easy access to a variety of data sources, getting deeper insight into customer behaviour or industry trends is only a click away.

Just a few of the most ideal use cases for data lakes include (but aren't limited to):

• Advanced analytics for data scientists
• Historical data storage
• IoT data
• Machine learning

While the data lake is a highly flexible and cost-effective solution for storing business or industry data, it may not be the best tool for your needs. The data warehouse offers a finer level of control over how data is stored, accessed, and utilized. That said, it comes with a few downsides, too. Let's look at the bigger picture of the data warehouse and what that means for your data operations.

If you've ever lost time trying to find specific documents or information, data warehouses offer a solution. This highly structured storage operation is ideal for larger businesses who need to save time and effort analysing different types of data.

When it comes to the data lake vs data warehouse comparison, the latter is much more structured. You can think of the former like a basket filled with different objects, while the latter is like a neatly organised bookshelf.

The data warehouse converts different forms of data into a tidy framework, making sure similar information, sources, or file types are organised and properly aggregated. For example, a data warehousing operation may take valuable data from multiple social media accounts and separate them based on highly specific details such as posting time, content length, and content type.

What happens when data scientists need to find a specific set of data by its publishing date or industry? Data warehouses come with useful querying features that allow employees to find the information they need in a fraction of the time.

For example, one employee could use an OLAP query (or online analytical processing) to find several perspectives on the same set of data. They could analyse several studies by their customer retention rates or region.

Unlike data lakes with raw data stored on-the-go, data warehouses offer more speed and efficiency at query time.

One of the most instantly appealing elements of data warehouses is how consistently data is stored. While data lakes give you the flexibility to upload all kinds of data on a rolling basis, data warehousing ensures this information is organised down to the letter.

There are many ways to properly store data in a data warehouse so it can be quickly retrieved by the right people at the right time. A few ways to do this include (but aren't limited to):

• Correcting outdated data, such as swapping out an old study for a new one
• Deleting duplicate records to avoid confusion
• Putting data into standardised formats

While some people will use both data lakes and data warehouses, the higher level of organisation in a data warehouse makes it better suited to a broad variety of tasks. A few of the tasks that rely on the functionality of a data warehouse include:

• Business intelligence
• Operational reporting
• Regulatory compliance

Now that you have a better idea on the benefits of a data lake and a data warehouse, it's time to learn even more about how they can be used. You may find one suits your business needs more than the other.

Have you ever wondered how much faster your company could grow if you weren't constantly trying to organised your existing data? On the other hand, maybe organising data is your strength, and you simply need more capacity.

Let's take a look at the purpose and use cases of both storage types.

Since data lakes offer unparalleled data storage and scalability, they're incredibly useful for the following tasks and roles:

• Best for data scientists
• Suitable for exploratory data analysis and machine learning processes
• Generally useful for storing raw, unprocessed data on-the-go

Since the data warehouse is more well-organised, its ability to offer fast retrieval of complex data sets makes it vital for higher business operations such as:

• Business analysts and decision-makers
• Best for generating structured reports and dashboards
• Suitable for storing processed and cleaned data

The structure of data refers to the process of labelling, organising, and storing data. It can also refer to the methods you use to retrieve it, such as querying.

Below is a brief overview of the data structure of a data lake and a data warehouse.

Since the data lake is a more flexible data storage solution, there is little organisation or labelling at the start. A few of the ways businesses prefer to structure their data lake include:

• A schema-on-read approach (schema applied when data is read)
• The ability to store raw, semi-structured, and unstructured data
• Supports varied data types (text, images, videos, etc.)

Since the data warehouse requires highly structured data, there are a few go-to processes when using it. These processes include:

• A schema-on-write approach (schema defined before data is written)
• The storage of structured data in tables with fixed schema
• The ability to use ETL (Extract, Transform, Load) processes to ensure data quality

Since data lakes are essentially massive storage solutions guided by a business philosophy, they generally require lower costs. Cloud storage options for businesses offer flexible pricing models that make it easy to scale without a dramatic budget requirement.

That said, a data lake will also provide the following performance issues such as:

• Generally slower query performance due to unstructured data
• Significant processing power for both analysis

Data warehouses regularly command higher costs since they require more upkeep to make sure data is consistently cleaned and organised.

The flip side to this higher cost is a more efficient data retrieval process for data scientists and analysts. You can expect benefits such as:

• Faster query performance for structured data
• Efficient for predefined queries and regular reporting

Now that you know a little more about the data lake and data warehouse for your business, let's look at how they can integrate with other tools and processes.

Since the data lake is a more unrefined way to store data, you don't need much in the way of complex software or hardware. A cloud-based solution with a communication platform is a simple foundation you can start with.

The data lake offers the following integration capabilities such as:

• The ability to integrate with a wide range of data sources (such as real-time streams, IoT devices, and social media platforms)
• Benefiting from specialised skills and tools for data retrieval and analysis (such as Hadoop or Spark)

On the downside, data governance can be a little complex due to its unstructured nature. This means duplicate files, corrupted files, and incompatible files are a few issues you can face while filling up your data lake with fresh data.

A 2021 study found over half of all IT executives stating monetising their data warehouses was a high priority. The data warehouse requires a little more manpower when it comes to integrating with established systems, but it's well worth the effort.

The benefits you can expect to enjoy with a data warehouse include:

• The ability to integrate well with traditional data sources (such as ERP systems and CRM systems)
• Accessibility through standard SQL and BI tools, making it much easier for business users
• Generally strong data governance and security mechanisms in place due to structured data

The data lake and the data warehouse are some of the most invaluable tools for maintaining a successful business. Simply having a lot of data isn't enough: you need to make sure employees are able to use it in the right place and right time.

To summarise:

• Data lakes offer both flexibility and scalability for handling diverse data types such as text, video, sensor data, and spreadsheets
• Data warehouses provide structured, consistent, and high-performance data storage for BI needs

InterSystems IRIS® provides businesses like yours a comprehensive solution so you can enjoy the following benefits:

• The best features of both data lakes and data warehouses
• Scalability, flexibility, and high performance across both types
• Maintain data integrity, security, and accessibility standards
  

InterSystems IRIS provides robust and versatile solutions. Our cloud-first data platform has made it easier than ever to bring your data and application silos together. InterSystems utilises cutting-edge tools like generative AI, machine learning, and interoperability, to help your business outpace the competition at an unprecedented rate.