Companies are witnessing a data boom, which also calls for new infrastructure and data management capabilities. As it is, most enterprises spend over 30% of their IT budget on data storage, backup, and disaster recovery, as per research in 2022. And this spans both structured and unstructured datasets.

Two critical concepts related to data operations are lakes and warehouses. They have some things in common – for example, both are used for storage, and both are interoperable with the cloud. But knowing the difference between data lakes and data warehouses can help you optimize their use. For instance, data lakes are more suitable for unstructured (“big”) data than warehouses.

Before we discuss this and other differences between data lakes and data warehouses, let us briefly discuss each concept.

What is a Data Lake?

A data lake is a vast, massively scalable storage center that contains large amounts of unprocessed data until they are required for usage.

There is no restriction on the volume or size of accounts or a file, nor is there a specified use case. Therefore, it may include any kind of data. The data may be unprocessed, semi-structured, or structured, and it may come from a variety of sources. Whenever required, you may retrieve data from the data lake.

When you need to gather and store a huge amount of data sans processing or analyzing right at that time, you can use the data lake model. Data scientists or engineers are the end users of data lakes.

The centralization of multiple sources is the key benefit of data lakes; but, you should also remember a few disadvantages. Data security, as well as access management, represents the greatest risk to data lakes. Due to the possible requirement for privacy, data that is dumped into a lake without any oversight poses a threat.

Moreover, there may be issues with data quality. Without sufficient consideration and care, a data lake can degenerate into a swamp of unusable, unstructured data with no distinct identification or indexing.

What is a Data Warehouse?

Unlike data lakes, a data warehouse is a vast selection of enterprise data from both operational and external sources. The information has already been structured, filtered, and arranged for a specific purpose.

Data warehouses are often used to facilitate the exchange of information across department-specific databases in medium and large enterprises. They may hold information on products, orders, customers, inventories, and workers, among other elements. Entrepreneurs and business consumers are the end users of a data warehouse.

For useful business information, the majority of companies must aggregate data from many subsystems developed on different platforms. This problem is remedied by data warehousing, which consolidates all of an organization’s data into a centralized repository and allows for access from a single site.

There are a few disadvantages to consider while using data warehouses. It requires continuous data cleaning, transformation, and integration. Owing to the many (sometimes contradictory) goals that a company seeks to accomplish, the implementation may be fraught with difficulty.

In addition, data warehouses may need the reconfiguration of your IT and operational systems.

As you can see, a data lake and a data warehouse has their own set of pros and cons. It is important to know the difference between the two to employ each system appropriately.

Data Lakes Support Unstructured Data but Warehouses Do Not

This is possibly the biggest difference between data lakes and data warehouses.

In data lakes, raw data is stored in its original format. In addition to semi-structured and unstructured data such as Internet of Things (IoT) device logs (text), photos (.png,.jpg), videos (.mp4,.wav, etc.), and other structured formats, transactional information received via a customer relationship management (CRM) and enterprise resource planning (ERP) system can also be incorporated, as well as big data like social media chatter.

In contrast, a data warehouse may store text, numerical, and other forms of data accessible using structured query language (SQL) queries. This indicates that the categories of data stored in a warehouse are equivalent to those found in relational databases.

Data lakes allow the storing of unorganized, semi-structured, and structured information, while the majority of data saved in data warehouses is structured. Yet, certain datasets, like Snowflake (which features a variant and object data type), can also store semi-structured data.

Data warehouses may store information from both unstructured and semi-structured resources, but only after it has been transformed.

(Also Read: Data Privacy vs. Data Security)

Data Lakes Use Schema-on-Read, while Data Warehouses Use Schema-on-Write

The schema describes the formalized organization of data. Data lakes benefit from schema-on-read. As such, each time we receive data, the format and structure are specified, but there’s no big-O (order of the function) rule set up before querying the data lake.

In contrast to warehouses, lakes don’t employ schema-on-write, meaning that the data’s structure and organizing must be specified before its transfer to the data warehouse.

In contrast, data architects or operators must invest a great deal of effort in the data framework for data warehouses. This is due to the fact that the data structure must be simple to utilize and report on, for data analysts. This covers both normalized or denormalized tables, as well as the star and snowflake schemas. Since the data model must be prepared for research and business intelligence, schema-on-write is used.

This difference between data lakes and data warehouses stems from one central fact: Lakes hold all the data that an enterprise needs, might employ later, and may never use. A data warehouse, on the contrary hand, selects the material it will ultimately store with great care before absorbing it, since it must be better prepared for usage.

Data Warehouses Use ETL Workflows and are Usually More Expensive

The extract, transform, and load (ETL) method is used to transfer data into warehouses. These are the actions taken:

• Gaining information from raw data sources
• Decontaminate and interpret the data
• Adding material into operational data repositories

In contrast, data lakes use the ELT approach. If necessary, a data analyst or architect modifies the data after analysis. This difference between data lakes and data warehouses contributes to another important factor: data lakes can get away with using scalable, inexpensive commodity servers as well as cloud-led object storage with low-cost specialized tiers. This decreases the price per gigabyte of data stored.

In contrast, data warehouses are much more expensive due to the additional processing resources needed for running analytical queries, along with their storing expenses. Its use of ETL instead of ELT also runs up added expenses.

Data Lakes Are Easier to Use, but Data in Warehouses Are More Usage Ready

The word “ease of use” refers to the overall usability of a data repository, not the data stored within it. As the architecture of a data lake does not have a definite structure, it is simple to access and change. Furthermore, since data lakes have no limitations, users may alter data quickly. By definition, data warehouses are much more structured.

The processing and organizing of the data in a data warehouse makes the data simpler to interpret and utilize. Each piece of information saved in a warehouse has been done so for a specific purpose, as only filtered and processed data is stored there. In other words, space is not wasted on information that may never be used, and the data is all ready for use.

Yet, structural limitations make it difficult and expensive to modify data warehouses.

As you can see, both data lakes and data warehouses offer important benefits for your business. If you regularly deal with big data, lakes are a must-have; in comparison, warehouses are essential to power BI and analysis, and often the two are used side by side for best results.