Data Lake vs. Data Warehouse: What’s the Difference?
Data lakes and data warehouses are two of the most popular forms of data storage and processing platforms, both of which can be employed to improve a business’s use of information.
However, these tools are designed to accomplish different tasks, so their functions are not exactly the same. We’ll go over those differences here, so you have a clear idea of what each one entails and choose which would suit your business needs.
See the Top Data Lake Solutions and Top Data Warehouses
What is a data lake?
A data lake is a storage repository that holds vast raw data in its native format until it is needed. It uses a flat architecture to store data, which makes it easier and faster to query data.
Data lakes are usually used for storing big datasets. They’re ideal for large files and great at integrating diverse datasets from different sources because they have no schema or structure to bind them together.
How does a data lake work?
A data lake is a central repository where all types of data can be stored in their native format. Any application or analysis can then access the data without the need for transformation.
The data in a data lake can be from multiple sources and structured, semi-structured, or unstructured. This makes data lakes very flexible, as they can accommodate any data. In addition, data lakes are scalable, so they can grow as a company’s needs change. And because data lakes store files in their original formats, there’s no need to worry about conversions when accessing that information.
Moreover, most companies using a data lake have found they can use more sophisticated tools and processing techniques on their data than traditional databases. A data lake makes accessing enterprise information easier by enabling the storage of less frequently accessed information close to where it will be accessed. It also eliminates the need to perform additional steps to prepare the data before analyzing it. This adds up to much faster query response times and better analytical performance.
Also read: Snowflake vs. Databricks: Big Data Platform Comparison
What is a data warehouse?
A data warehouse is designed to store structured data that has been processed, cleansed, integrated, and transformed into a consistent format that supports historical reporting and analysis. It is a database used for reporting and data analysis and acts as a central repository of integrated data from one or more disparate sources that can be accessed by multiple users.
A data warehouse typically contains historical data that can be used to generate reports and analyze trends over time and is usually built with large amounts of data taken from various sources. The goal is to give decision-makers an at-a-glance view of the company’s overall performance.
How does a data warehouse work?
A data warehouse is a system that stores and analyzes data from multiple sources. It helps organizations make better decisions by providing a centralized view of their data. Data warehouses are typically used for reporting, analysis, predictive modeling, and machine learning.
To build a data warehouse, data must first be extracted and transformed from an organization’s various sources. Then, the data must be loaded into the database in a structured format. Finally, an ETL tool (extract, transform, load) will be needed to put all the pieces together and prepare them for use in analytics tools. Once it’s ready, a software program runs reports or analyses on this data.
Data warehouses may also include dashboards, which are interactive displays with graphical representations of information collected over time. These displays give people working in the company real-time insights into business operations, so they can take action quickly when necessary.
Also read: Top Big Data Storage Products
Differences between data lake and data warehouse
When storing big data, data lakes and data warehouses have different features. Data warehouses store traditional transactional databases and store data in one table with structured columns. Comparatively, a data lake is used for big data analytics. It stores raw unstructured data that can be analyzed later for insights.
| Parameters | Data lake | Data warehouse |
| Data type | Unstructured data | Processed data |
| Storage | Data are stored in their raw form regardless of the source | Data is analyzed and transformed |
| Purpose | Big data analytics | Structured data analysis |
| Database schema | Schema-on-read | Schema-on-write |
| Target user group | Data scientist | Business or data analysts |
| Size | Stores all data | Only structured data |
Data type: Unstructured data vs. processed data
The main difference between the two is that in a data lake, the data is not processed before it is stored, while in a data warehouse it is. A data lake is a place to store all structured and unstructured data, and a data warehouse is a place to store only structured data. This means that a data lake can be used for big data analytics and machine learning, while a data warehouse can only be used for more limited data analysis and reporting.
Storage: Stored raw vs. clean and transformed
The data storage method is another important difference between a data lake and a data warehouse. A data lake stores raw information to make it easier to search through or analyze. On the other hand, a data warehouse stores clean, processed information, making it easier to find what is needed and make changes as necessary. Some companies use a hybrid approach, in which they have a data lake and an analytical database that complement each other.
Purpose: Undetermined vs. determined
The purposes of a data lake’s data are undetermined. Businesses can use the data for any purpose, whereas data warehouse data is already determined and in use. Hence why data lakes have more flexible data structures compared to data warehouses.
Where data lakes are flexible, data warehouses have more structured data. In a warehouse, data is pre-structured to fit a specific purpose. The nature of these structures depends on business operations. Moreover, a warehouse may contain structured data from an existing application, such as an enterprise resource planning (ERP) system, or it may be structured by hand based on user needs.
Database schema: Schema-on-read vs schema-on-write
A data warehouse follows a schema-on-write approach, whereas a data lake follows a schema-on-read approach. In the schema-on-write model, tables are created ahead of time to store data. If how the table is organized has to be changed or if columns need to be added later on, it’s difficult because all of the queries using that table will need to be updated.
On the other hand, schema changes are expensive and take a lot of time to complete. The schema-on-read model of a data lake allows a database to store any information in any column it wants. New data types can be addcolumns, and existing columns can be changed at any time without affecting the running systemed as new . However, if specific rows need to be found quickly, this could become more difficult than schema-on-write systems.
Users: Data scientist vs. business or data analysts
A data warehouse is designed to answer specific business questions, whereas a data lake is designed to be a storage repository for all of an organization’s data with no particular purpose. In a data warehouse, business users or analysts can interact with the data in a way that helps them find the answers they need to gain valuable insight into their operation.
On the other hand, there are no restrictions on how information can be used in a data lake because it is not intended to serve one single use case. Users must take responsibility for curating the data themselves before any analysis takes place and ensuring it’s of good quality before storing it in this format.
Size: All data up to petabytes of space vs. only structured data
The size difference is due to the data warehouse storing only structured data instead of all data. The two types of storage differ in many ways, but they are the most prevalent. The first way they differ is in their purpose: Data lakes store all data, while warehouses store only structured data.
Awareness of what type of storage is needed can help determine if a company should start with a data lake or a warehouse. A company may start with an enterprise-wide information hub for raw data and then use a more focused solution for datasets that have undergone additional processing steps.
Data lake vs. data warehouse: Which is right for me?
A data lake is a centralized repository that allows companies to store all of its structured and unstructured data at any scale, whereas a data warehouse is a relational database designed for query and analysis.
Determining which is the most suitable will depend on a company’s needs. If large amounts of data needs to be stored quickly, then a data lake is the way. However, a data warehouse is more appropriate if there is a need for analytics or insights into specific application data.
A successful strategy will likely involve implementing both models. A data lake can be used for storing big volumes of unstructured and high-volume data while a data warehouse can be used to analyze specific structured data.
Read next: Snowflake vs. Databricks: Big Data Platform Comparison
The post Data Lake vs. Data Warehouse: What’s the Difference? appeared first on IT Business Edge.