Data lake x data warehouse: which is better for my company?

Successful companies continue to derive commercial value from their data. The sheer amount of data that organizations collect from various sources goes beyond what traditional relational databases can handle, creating the need for additional systems and tools to manage it, such as data lakes and data warehouses.

Data warehouses and data lakes represent two of the main solutions for managing corporate data today. While they may share some overlapping features and use cases, there are fundamental differences in the management philosophies, design characteristics, and ideal usage conditions for each of these technologies.

In this article, we share the main differences between data lake x data warehouse and explain in a practical way which is the best solution for your strategy.

Data lakes: a broad repository for data

A data lake is a centralized repository for hosting raw, unprocessed enterprise data. Data lakes can span hundreds of terabytes or even petabytes, storing replicated data from operational sources including databases and SaaS platforms.

They make unedited and summarized data available to any authorized stakeholder. Thanks to their potentially large (and growing) size and need for global accessibility, they are often implemented on distributed storage cloud-based.

Data warehouses: essential for complete big data projects

A data warehouse is a decision support system that stores historical data from across an organization, processes it, and makes it possible to use the data for critical business analysis, reports, and dashboards.

A data warehouse system stores data from multiple sources, typically structured, Online Transaction Processing (OLTP) data such as invoices and financial transactions, Enterprise Resource Planning (ERP) data, and Customer Relationship Management (CRM) data. The data warehouse focuses on data relevant for business analysis, organizes and optimizes it to enable efficient analysis.

Data lake x data warehouse: main differences

Let's now take a deep dive and compare the properties of a data lake and a data warehouse.

Operation type

Warehouses are used for online analytical processing (OLAP). This includes running reports, aggregating queries, performing analysis, and creating models such as the OLAP model based on what you want to do. These operations are normally performed after transactions are completed.

For example, you want to check all transactions made by a particular customer. Since the data is stored in a denormalized format, you can easily fetch data from a single table and show the required report.

A data lake is typically used to perform raw data analysis. All raw data i.e. XML files, images, pdf etc. are just gathered for further analysis. During data capture, you don't need to define the schema. You may not know how this data can be used in the future. You are free to perform different types of analysis to discover valuable insights.


Warehouses use schema on write. Before storing data, it must be transformed and provided for application in analysis and reporting. You need to know what you will use the data for before importing it into the data warehouse. As new requirements emerge, it may be necessary to reevaluate the models that were previously defined.

On the other hand, data lakes employ schema-on-read. Without the need for a single schema, users can store any type of data in the data lake. They can discover the schema later while reading the data. This means different teams can store their data in the same place, without relying on IT departments to write ETL jobs and query the data.


Warehouses tend to store extremely sensitive data for reporting purposes. This could be compensation data, credit card information, health data, and so on. Data security for data warehouses is mature and robust, as this technology has been around for a long time. Only authorized personnel can access them.

Data lake is a relatively new technology and therefore data security is still evolving. As mentioned, a data lake is created using open source technologies. Therefore, its data security is not as great as that of a data warehouse.


Data warehouse applications use relational database technologies. This is because relational database technologies support fast queries on structured data. The data lake can easily scale to large volumes and can handle any data structure.

Applicability: when to adopt each of them

Let's quickly recap the differences between data warehouses and data lakes to make sure we're on the same page.

Data warehouses store structured data, operate with a schema-on-write process model, have tightly coupled computing and storage requirements, and are most effective for managing data with predefined analytics use cases.

Data lakes store all types of data (structured, unstructured, and semi-structured), operate with a schema-on-read process model, have loosely coupled storage and compute requirements, and work well for managing data with undefined use cases.

But they often require expertise from data engineers or data scientists to figure out how to sift through all the multi-structured datasets, and they require integration with other systems or analytics APIs to support BI. With all that said, which option is best for you?

The first point to note in the data lake vs. data warehouse decision process is that these solutions are not mutually exclusive. Neither a data lake nor a data warehouse by itself comprises a data and analytics strategy — but both solutions can be part of one.

The warehouse model is all about functionality and performance — the ability to ingest data from RDBMS, transform it into something useful, and then send the transformed data to downstream BI and analytics applications.

These functions are all essential, but the data warehouse paradigm of schema-on-write, tightly coupled storage/computation, and dependence on predefined use cases makes data warehouses a suboptimal choice for large, multistructured data or multi-model resources.

Data lakes provide a less restrictive philosophy that is better suited to meet the demands of a world of big data: schema-on-read, loosely coupled storage/computation, and flexible use cases that combine to drive innovation by reducing the time, cost, and complexity of data management. But without data warehouse functionality, a data lake can become a data swamp — a quagmire of data that is impossible to sift through.

To avoid creating data swamps, IT managers need to combine the data storage capabilities and design philosophy of data lakes with data warehouse capabilities such as indexing, querying, and analysis. When this happens, business organizations will be able to make the most of their data while minimizing the time, cost and complexity of business intelligence and analytics.

Crafting a complete, future-proof strategy for enterprise data management

Enterprises continue to rely on a variety of data storage and analytics solutions to meet their needs, including RDBMS, operational data stores, data warehouses, clusters Hadoop and data lakes.

While most of these solutions have been around long enough for their shortcomings to be known (cost, complexity, scalability, etc.), newer alternatives like data lakes are still reaching maturity and showing their potential for future scalability, flexibility, and manageability. resilient data management in the cloud.

But that doesn't mean you should replace your entire data and analytics strategy with a single data lake implementation. Instead, think of data lakes as one of many possible solutions in your D&A toolbox — one that you can leverage when it makes sense to enable key analytics use cases. An effective data lake must be cloud-native, simple to manage, and interconnected with familiar analytical tools so it can deliver value.

In this article, we take a practical look at the differences between data lake vs. data warehouse, and we hope this analysis helps you determine the ideal approach for your company. To choose the right, future-proof solution for your business, count on the support of a specialized consultancy, such as Integrity, to assess your environment and indicate the necessary improvements to it.


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