ArrowBack to blog

Data Lakes vs. Data Warehouses

Explore the difference between the two storage paradigms of data lakes and data warehouses. Learn the pros and cons of each and which situations they are suited for.

Author's avatar
December 20th, 2022
Blog's image

There are many ways to store data, and each method has its own pros and cons. You need a strategy that balances cost, accessibility, and availability. Data warehouses and data lakes are widely used as storage platforms. Yes, they are rivals, but that does not mean that they cannot be used together. Many factors and features have to be considered when comparing the two storage platforms.

In this article, you will learn the difference between a data warehouse and a data lake by exploring their functions, benefits, and drawbacks. In addition, you will learn how to choose between a data lake and a data warehouse by looking at factors such as cost and performance.

What is a Data Lake?

A data lake is basically a storage repository that stores all raw unstructured, or semi-structured data from different sources in the same place. There are many benefits to creating a data lake instead of keeping your data in separate siloed locations.

Button's Icon
Data lake diagram
Data Lakes

The above diagram shows data flowing from the two sources, Twitter and an IoT sensor into the data lake. The data from the data lake is used for machine learning and data discovery.

Data lakes can be used to speed up data analytics and simplify data management. Data lakes add scalability to your data facilities because data can be received at different speeds, types, and from different locations whether on-premise or cloud. 

Data lakes are widely used in finance to store data that is used to build machine learning models. The media industry uses data lakes to create video recommendations for users. 

Data stored in the data lake can be used to improve research development and use customer data through CRMs.

SQL queries and big data analytics are used to fetch useful information from data lakes. The data can be processed using Python and third-party data analytics applications. 

You can save data as operational databases that will allow you to secure your data through encryption and other methods. Data lakes use object storage and flat architecture to store data. Metadata tags and unique identifiers are used to locate data. 

The flexibility of data lakes allows you to use the analytics tool and framework of your choice such as Presto or Apache Hadoop

More importantly, a data lake makes it possible to combine unstructured and semi-structured data stored in different sizes together. This is especially important because more than 80% of the world’s data is unstructured. A data lake can help you sift through this unstructured data and make sense of it in order to gain a deeper understanding of your users, product, and business. 

What Are the Benefits of Using a Data Lake?

Creating ML models: Data lakes enable you to run analytics such as machine learning and big data processing. The amount of data needed for these functions to be effective is massive, and a data lake performs a valuable role in providing a single source for the data that might be required.

Updating data: As no data structure is required, data lakes allow you to add new data easily which means that data lakes can be kept up to date. Also, data lakes accommodate both data streaming and batch processing. 

Choose your data sources: A data lake lets you decide where your data comes from. This means that you can choose the systems that you want to pull data from, whether that’s an internal CRM system or an external marketing automation platform. 

Data security: Since all of your data is stored in one centralized place, this means that it’s easier to ensure data security. You can use security tools to encrypt your data, and you can control who has access to it. 

Simplify your analysis: Data lakes also make it easier to analyze your data. This is because it’s all in one place and fully integrated, which makes it easier to perform analysis across different data sources. This can help you make better business decisions. 

Data governance: Data governance is a set of rules that determine how data is collected, stored, and used within your organization. Data lakes can help with data governance since everything is in one place.

Disadvantages of Using a Data Lake

Not everything can go in a data lake: While a data lake is a useful place to store all of your data, there are certain types of data that don’t belong there. For example, data that you need to query on a regular basis should be stored in a data warehouse rather than a data lake. This is because data warehouses make it possible to access data more quickly than data lakes. 

Lack of structure: A data lake is meant to store all of your data, including unstructured, semi-structured, and structured data. While a data lake can make it easier to integrate data across different sources, it can be difficult to make sense of data that comes from a variety of sources. This can be especially problematic if your data lake doesn’t have a clear schema that allows you to organize and understand the data.

Disorganized data: Since data stored in data lakes is raw and unstructured it can end up being hard to find the relevant data you need at the moment. It's important to set up the necessary catalogs that will help you to find the data you need. If you don't set up the right mechanisms to regulate your data mechanism. Data will get corrupt and lead to reliability issues. 

Slow performance: The bigger the data lake the slower the performance. Improper data partitioning makes things worse. 

What is a Data Warehouse?

In today’s digital world, businesses are striving to capture and analyze data in order to make informed decisions. Data Warehouses are a major component of most data analysis strategies. Unlike data lakes, data warehouses have structured and defined schema because the data sources are business applications. Data in a data warehouse is cleaner than data in a Data lake.

Button's Icon
DataWarehouse diagram
Data Warehouse

The above diagram shows data flowing from two sources: an operational database and an inventory management platform into the data warehouse. The data from the data warehouse is used for batch reporting and business intelligence

A data warehouse is a centralized information storage location that contains multiple tables of data from different sources, including relational databases, operational business intelligence systems, and other structured data sources. Data retrieved from a data warehouse is used for:

  • Analysis

  • Reporting 

  • Data mining

It’s a critical part of any business intelligence (BI) strategy, providing a centralized location where all information can be accessed, analyzed, and visualized – enabling organizations to make strategic decisions and run reports more efficiently. For this reason, it’s often described as the “central nervous system for BI”.

Since data is collected from various sources this gives you a better level of insight. Data scientists use SQL clients and business intelligence tools to access data warehouse data.

The data warehouse has 3 tiers:

1. The top tier that presents analytics results. 

2. The middle tier consists of the engine that analyzes data. 

3. The bottom tier contains the database server. SSD drives are used to store data that is accessed frequently. 

A data warehouse has multiple databases that have defined schemas and organized tables & columns. This means that you can also add integer or string descriptions.

Data warehouses are an effective means of storing and analyzing large quantities of data. They are also a great way for businesses to streamline their data processes, cut costs, and optimize ROI. You will learn more about the benefits of a data warehouse in the next section.

What Are the Benefits of Using a Data Warehouse?

Here are the benefits of using a data warehouse:

  • A data warehouse helps you to get data quickly which will help you to create business insights that aid decision-making. A data warehouse is a highly specialized storage platform that’s designed to store and organize data to be easily accessible, searchable, and usable across an organization. 

  • A data warehouse is well organized to help you compare and analyze relationships between different ideas and concepts. This helps you to plot and understand business trends. Data provides insights into your customers and their behaviors, your business and its operations, and your future goals – but for it to be truly effective, it needs to be accessible and organized in a central location.

  • Some modern data warehouses, such as the SAP data warehouse cloud supports both structured and unstructured data. 

  • Data stored in a data warehouse is stable and non-volatile and uses files and folders to store data. Previous data will not be erased when new data gets added.

Disadvantages of Using a Data Warehouse

Data warehouses are expensive and need more expertise to be maintained. This means that you will have to spend more time finding good talent and implementing cost-optimization measures. In addition, data warehouses collect data from various sources which can be a problem if the data has issues. For example, some fields may accept null values when they are not supposed to. 

Data Warehouse vs. Data Lake

Some companies use both data lakes and data warehouses. They store raw data in the data lake and then process it. In the end, the processed data will be moved to the data warehouse. This is typically where a company will require a data pipeline.

Data lakes and data warehouses serve different purposes and do not exist to eliminate each other. Your business use case will dictate which one should be your top priority but using them together is the best. Data warehouses are good for processes that are recurring. 

Here is a list of factors you should consider when choosing between a data warehouse and a data lake:

Data and Quality 

Data warehouses retrieve data from business applications and operational databases. Since the data found in data warehouses is not raw, its quality is better than that of a data lake. On the other side, data lakes take in raw and unstructured data that is retrieved from relational and non-relational IoT devices. If you want to store and retrieve quality data, then choose a data warehouse otherwise, choose a data lake.

Schema and data analytics 

Since data warehouses contain structured data and well-designed schemas they are used for Business Intelligence tasks and batch reporting. On the other side, data lakes have raw data that is used for predictive analytics and machine learning.

Performance and cost

Data warehouses are expensive when compared to data lakes because they need well-defined architecture, design, and the tools used to build data warehouses. Data lakes are built using tools such as Hadoop, which are built to be low-cost commodity hardware. 

Data warehouses have fast performance because their data can be retrieved easily since the data is structured. While data lakes are slower when it comes to retrieving data, and they tend to slow down as data gets bigger.


A data lake is a useful place to store all of your data, but it can be difficult to organize and understand all of this data if it’s not clearly structured. It can also be challenging to integrate data across different sources, especially if you want to combine unstructured and semi-structured data. That said, a data lake makes it possible to store all of your data in one place. This can make it easier to perform analytics across different data sources and gain a deeper understanding of your users, product, and business.

On the other side, a data warehouse enables you to retrieve data faster and fetches data from business applications to give you structured data. This structured data will help you to make quicker decisions. A data warehouse and data lake have their own pros and cons as we have learned previously in this article. Therefore, it is important to know what are your application’s and business needs and use cases. This will help you to know whether you should use a data lake or a data warehouse. But, it would be good if you used them together– a data lake stores unstructured data used for machine learning, while a data warehouse stores structured data that is used for business intelligence.

And if you want to use both? That's where you'll need to start thinking of how you will move data between them, as well as between the data sources and data sinks. That's where a technology like Apache Kafka comes in. Conduktor makes Apache Kafka easy, and you can start your free trial today.

We aim to accelerate Kafka projects delivery by making developers and organizations more efficient with Kafka.