Data Lake vs Data Warehouse: How to choose?

Currently, data management is a continually developing field that requires careful consideration when deciding which solution should be implemented to store, process, and analyze data effectively. There are two forms that are frequently selected: data warehouse vs data lake. To make this clearer, one may ask: so what exactly does it set apart one level from another? In this blog post, we will look at data lake vs data warehouse – in terms of its structure, advantages, and applications.

What is a Data Lake?

A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. It can include data in its raw form, such as documents, images, videos, and other unstructured content, as well as structured data like rows and columns from databases.
To get a deeper understanding of how they work, take a look at their architecture.

How would you benefit from a Data Lake, and do you need it?

Data lakes are most appropriate if your organization works with massive amounts of undefined complex data. Some of its advantages include:

• Scalability: Data lakes are elastic architectures that facilitate application scaling, making them ideal for organizations that handle massive amounts of data.
• Flexibility: Since data lakes contain raw data, analysts can handle the results in any form at any time in the future. This is particularly useful for data scientists who need access to a variety of data types.
• Cost-Effectiveness: It is also always cheaper to store the data in its raw form rather than first transforming it into a structured format, which can also lead to relatively low costs.
• Support for Advanced Analytics: Data lakes are useful for big data, machine learning, and real-time data processing.

But do you really need a data lake? If your enterprise is data-driven and mostly handles structured data or if you require fast SQL-type querying, a data warehouse could be more suitable. On the other hand, if the data requirements are heterogeneous and the analytics required are sophisticated, a data lake might be the best solution.

Data Warehouse Overview

A data warehouse is a more organized type of repository. The data in the data warehouse are more structured than those in the data lake and are related to one another. Data warehouses are usually applied to reporting and analysis, providing high efficiency for SQL requests.
To implement ETL in your Data Warehouse, read our blog ETL Data Warehouses: The Ultimate Guide.

Benefits of Data Warehouse

• High Performance: Business intelligence typically requires fast query responses, and data warehouses are built around this need.
• Structured Data: Pre-processing and organizing of data make it easier to run through queries beyond simple ones that could facilitate in decision-making.
• Consistency: Data warehouses are committed to data consistency, where data is normalized before storing it in the data warehouse.
• Security: Data warehouses often include robust security features, making them ideal for handling sensitive business data.

Difference between Data Lake vs. Data Warehouse

When choosing between a data warehouse vs data lake, there are various considerations to make. Below is a detailed comparison to help you understand the key differences:

Feature	Data Lake	Data Warehouse
Data Type	Structured, Semi-Structured, Unstructured	Structured
Schema	Schema-on-read	Schema-on-write or Schema-on-read
Data Formats	Raw unfiltered Data	Processed data
Type	Non Relational and Relational	Only Relational
Users	Data Scientists, Data Engineers	Business Analysts
Costs	Generally Cheaper	Can be Expensive
Use Cases	Machine learning, exploratory analytics, operational analytics, big data, and profiling	Batch reporting, BI, and visualizations
Data Sources	Big data, IoT, social media, streaming data	Application, business, transactional data, batch reporting
Design	Flat Architecture	Hierarchical
Price/Performance	Query results getting faster using low-cost storage and decoupling of compute and storage.	Fastest query results using local storage

Future Trends

While data management is progressing further, certain trends are emerging that aim to bridge the gap between data warehouse vs data lake. Two emerging patterns deserve attention: Data Marts and Data Lakehouses.

Data Marts

Data marts are a subset of data warehouses designed for a specific business line or department. They offer a more focused approach to data storage, often aligning with the needs of individual teams within an organization.

Feature	Data Lake	Data Warehouse	Data Mart
Data Type	Structured, Semi-Structured, Unstructured	Primarily Structured	Highly Structured
Type	Non-Relational and Relational	Relational	Relational
Schema	Schema-on-Read	Schema-on-write or Schema-on-read	Schema-on-Write
Sources	Multiple Sources	Limited, Pre-defined Sources	Specific to a Business Line
Scalability	Highly Scalable	Limited by Design	Scalable within a Business Line
Users	Data Scientists, Analysts	Business Analysts, Executives	Specific Business Users
Use Cases	Advanced Analytics, ML, Big Data	Reporting, BI, SQL Queries	Departmental Reporting
Cost	Generally Cheaper	Can be Expensive	Cost-effective for Departments

Data Lakehouses

Data lakehouses combine the best of both worlds: the scalability of data lakes and the structure and performance of data warehouses. They allow you to store raw data while also providing structured data for reporting and analysis.

Feature	Data Lake	Data Warehouse	Data Lakehouse
Data Type	Structured, Unstructured	Primarily Structured	Both
Type	Non-Relational	Relational	Hybrid
Schema	Schema-on-Read	Schema-on-Write	Schema-on-Read and Write
Sources	Multiple Sources	Limited, Pre-defined Sources	Multiple Sources
Scalability	Highly Scalable	Limited by Design	Highly Scalable
Users	Data Scientists, Analysts	Business Analysts, Executives	Both
Use Cases	Advanced Analytics, ML, Big Data	Reporting, BI, SQL Queries	All of the Above
Cost	Generally Cheaper	Can be Expensive	Variable

Conclusion

Now, the question is what to choose: data lake vs warehouse. The answer depends on your needs; a data lake suits specific circumstances. If you need a solution that can accommodate the needs of various data formats and high levels of analysis, a Data lake is for you. If, on the other hand, your needs are more analytical on structured data and faster SQL queries, then a data warehouse is more suited for such an application. However, new architectures such as data lakehouses and data marts are more integrated, breaking the conventional barriers between them.

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