Data Lake vs. Data Warehouse: Understanding Key Differences

Data lakes are massive storage repositories for unstructured data, while data warehouses are organized and user-facing.

August 22, 2022

Data lakes are massive, free-flowing storage repositories for structured and unstructured data, whereas data warehouses include organizational information for processing and analysis. This article explains the pros and cons of data lakes and data warehouses, their fundamental differences, and their similarities. 

Data Lake vs. Data Warehouse

Big data describes businesses’ organized, semi-structured, and unstructured data collection. This data may be mined for information and utilized in advanced analytics applications such as machine learning, predictive modeling, and other types of advanced analytics. Organizational data management designs now frequently include systems for processing and storing large amounts of data and tools for supporting big data analytics applications.

Data lakes and warehouses are the two most popular storage solutions when permanently storing massive volumes of data. While both are often used for big data storage, they differ significantly from structure and processing to who uses them and why.

What is a data lake?

A data lake is a large, highly scalable data storage facility that keeps significant volumes of raw data until it is required for use. A data lake may contain any form of data because there is no set limit on the size of an account or a file, and there is no established use yet. The data is unstructured, semi-structured, or organized and originates from many sources. You can query data from the data lake as needed.

Businesses utilize the data lake idea for rapid storage without transformation when they need to collect and store a large volume of data without processing or analyzing it all at once. Data scientists and engineers are the data lakes’ end consumers. It offers the following advantages:

  • Providing business users with instant access to all data
  • Eliminating the need to transport data
  • Accelerating delivery by allowing business units to put up apps swiftly
  • Making flexible access to the data possible for users from different departments who may be dispersed around the world

The fundamental advantage of data lakes is the centralization of various content sources. However, one should also remember a few cons. Data security and access control pose the most significant threat to data lakes. Due to some of the data’s potential need for privacy and regulation, specific data can be deposited into a lake without any control. Ungoverned and unusable data and disparate and complex tools are all possible outcomes of unstructured data.

Further, there may be problems with data quality. Sorting through data lakes takes a lot of time. To manage and uphold data integrity, data lakes need frequent data governance. Without the proper care and attention, a data lake may become a swamp of worthless, disorganized data with no clear identification or metadata.

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What is a data warehouse?

A data warehouse is a sizable collection of organizational data from several operational and external sources. The data has already been processed for a particular purpose and is formatted, filtered, and organized. For sophisticated querying and analytics, data warehouses regularly gather processed data from a variety of internal applications and systems of external partners.

Data sharing between department-specific databases is common among medium-sized and large businesses. A data warehouse can help store data about products, orders, customers, inventory, employees, etc. Entrepreneurs and business users are the data warehouse’s end users. It offers the following advantages:

  • Boosting the operational value of business systems, particularly customer relationship management
  • Supporting storing analyses and past search queries.
  • Offering a significant amount of information processing capability.
  • Enabling greater flexibility and speed 

Most enterprises must combine data from several subsystems developed on various platforms to execute valuable business intelligence. This issue is resolved by data warehousing, which compiles all of the organization’s data into a single repository and makes it accessible from one central location. Another benefit is more straightforward audits – the purpose of an auditing process is to guarantee that data is correct, current, and accessible, which is also the aim of a data warehouse.

There are also a few cons to consider when leveraging data warehouses. It necessitates ongoing cleansing, transformation, and data integration. Difficulties may arise throughout the implementation phase due to the various objectives that an organization seeks to pursue. 

Finally, they necessitate a study of the data model, objects, transactions, and storage, owing to their complicated and diverse design. Data warehouses may also need the reorganization of operational systems.

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13 Key Comparisons Between Data Lake and Data Warehouse

The most critical points of differentiation between a data lake and a warehouse are the data structure, desired consumers, processing techniques, and the overall goal of the data. These principal variations are shown below.

1. Data structure

Data lakes provide convenient storage for unstructured, semi-structured, and structured data. Most of the data stored in data warehouses is organized in a structured fashion; however, some data warehouses, such as Snowflake (which has a variation and object data type), also have the capacity to hold semi-structured data. Data warehouses can store information from unstructured and semi-structured sources, but they must first convert it by calculating metrics.

2. The data types stored 

Raw data is kept in its original format in data lakes. In addition to semi-structured and unstructured data like the Internet of Things (IoT) device logs (text), as well as photos (.png, .jpg), videos (.mp3, wave, etc.), and other complicated data formats, this can also incorporate transactional data from customer relationship management (CRM) and enterprise resource planning (ERP) systems. 

Text, numerical, and other types of data accessible via structured query language (SQL) queries are examples of data that can be kept in a data warehouse. This means that the data types held in a warehouse are identical to those observed in relational databases. 

3. Data curation

Data lakes store all the information that an organization needs, may use in the future, and even information that analysts may never use. This information includes both current and potential future requirements. On the other hand, a data warehouse puts great effort into selecting the data it will eventually store before putting it into the data warehouse. 

4. The schema of organization

The schema explains how data is formally organized. Data lakes take advantage of schema-on-read. So, every time we read data, the format and structure are given, and there is no big-O (order of the function) rule in place before we query the data in the data lake. They use schema-on-write, meaning one must set the data’s structure and organization before moving it to the data warehouse.

The data model for data warehouses takes a lot of work from data architects and operators. This is because the data structure needs to be easy for data analysts to use and report on. This includes normalized and denormalized tables, the star schema, and the snowflake schema. Schema-on-write is used because the data model needs to stay true to itself.

5. Processes in use 

Data is brought into data warehouses through the extract, transform, load (ETL) procedure. They:

  • Obtain data from the sources of their raw data.
  • Clean up and model the data.
  • Fill operational data repositories with data.

Data lakes, on the other hand, employ the ELT method. After analysis, a data analyst or architect transforms the data if required. 

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6. Volume-handling capacity

Large volumes of data are kept in both data lakes and warehouses. However, the size of the enormous amounts of data that each solution can retain varies by order of magnitude. Data warehouses work with terabytes, but a data lake often holds petabytes. However, data lakes are still in their infancy compared to data warehousing technology, which has been well tested and is reasonably mature.

7. Costs incurred

Data lakes often use scalable, low-cost commodity servers or cloud-first object storage with specialized low-cost layers, resulting in a lower cost for every gigabyte of data saved. On the other hand, data warehouses are substantially more costly since they need increased computational resources to run analytical queries in addition to their storage costs. 

8. Purpose: indeterminate vs. in-use

In a data lake, a particular piece of data may serve various purposes. A data lake receives raw data, sometimes intending to use it for a specific purpose later on and sometimes merely for storage. Accordingly, data lakes are less organized and have less filtering of the data than their counterparts.

Data altered for a specific purpose is referred to as processed data. Because only processed data is stored in data warehouses, each piece of information there has been used by the organization for a particular objective. In other words, material that one might never need is not wasting storage space. 

9. Flexibility vs. security in access

The term “accessibility and simplicity of use” relates to the utilization of a data repository as a whole, not the data contained inside it. Since data lake design lacks structure, it is easy to access and modify. Furthermore, because data lakes have minimal limits, users may make any updates to the data fast. Data warehouses are more organized by definition. 

One key advantage of data warehouse design is that the processing and organization of data make the data itself easier to comprehend; yet, structural restrictions make data warehouses complex and costly to alter.

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10. The type of targeted user

The majority of users in an organization are “operational” to some extent. They need their daily reports, access to key performance indicators, and the ability to analyze the same information in a spreadsheet. Because it is well-structured, simple to use and comprehend, and specifically designed to address their queries, the data warehouse is often perfect for these users.

The remaining are tasked with conducting additional data analysis. They utilize the data warehouse as a source, but frequently need to return to the original systems to retrieve data that isn’t in the warehouse. They often go beyond the data warehouse’s limitations, even if it is their primary data source.

A small 1% of users will perform an in-depth analysis of data. They combine various data sources to create brand-new inquiries that need to be addressed, and these users may utilize the data warehouse. These users, including data scientists, may employ cutting-edge analytical tools and techniques, including statistical analysis and predictive modeling.

All of these consumers may be accommodated by the data lake strategy. While other users utilize more organized versions of the available data, the data scientists may go to the lake and work with the massive and varied data sets they need.

11. The ecosystem

The organization’s current IT infrastructure should be considered when deciding between data warehouses and lakes. Due to the growing use of Hadoop, an open-source program, data lakes have gained much popularity. This implies that putting data into data lakes may be difficult if your organization does not support open-source technologies.

12. Speed of insight generation 

This distinction is the outcome of several others. Users may access data more quickly with data lakes than with a traditional data warehouse plan since it includes all data types and enables users to access data before it has been transformed, cleansed, and formatted. 

This early access to the data may come at a cost, though. Some or all of the data sources used for analysis may not have the work completed by the data warehouse development team. The first tier of business users might not want to perform that effort, but it puts users in control to investigate and use the data in any appropriate way.

13. The preservation of data

Analyzing data sources, comprehending business processes, and data profiling take up a sizable portion of the time required to create a data warehouse. Consequently, this helps produce a highly organized data model for reporting tasks. Choosing which data to include in the warehouse and which to leave out is a significant element of this process. 

In general, information may be omitted from the warehouse if it isn’t utilized to address particular issues or in a specified report. The data model is typically simplified this way, and space on expensive disk storage, which is needed to power the data warehouse, is also conserved. 

The data lake, however, keeps ALL of the data. This includes not just data utilized now, but data that one could use in the future and even data sets that users may never require. Additionally, information is preserved forever so that we may perform analysis by traveling back in time to any moment. Compared to the warehousing approach, a data lake uses a different type of hardware. Scaling a data lake to terabytes and petabytes is quite affordable because of low-cost storage and standard, off-the-shelf computers.

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Key Similarities Between Data Lake and Data Warehouse

Organizations utilize data lakes and warehouses as central data repositories from which various users and organizational units may access and use data to derive insights and carry out any kind of analysis. When it comes to secure data storage, these are two of the most popular storage options, and they share many characteristics: 

  • They are both locations for storing data.
  • They both provide support for cloud storage.
  • Structured data is present in both data lakes and data warehouses. 
  • Current and historical data are stored in both data lakes and data warehouses.
  • Both strategies help centralize data so that various business units may use it for analysis and insight-gathering.

Data lakes allow IT teams to pick and choose the different metadata, storage, and computing technologies they wish to deploy based on the demands of their systems. They are the do-it-yourself equivalent of a data warehouse.

Today, technologies assist in integrating multiple architectures and data types across lakes and warehouses. You can connect the dots throughout your business no matter where your data resides. These tools help link data from data lakes to data warehouses and vice versa, supporting data scientists and business analysts.

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Takeaways 

Both data warehouses and data lakes are practical tools for modern enterprises. According to TDWI’s Best Practices Report on Building the Unified Data Warehouse and Data Lake (2021), 53% of companies have on-premise data warehouses, and 36% have one on the cloud. However, data lake adoption is still lagging due to its free-flowing nature, larger scale, and architectural complexities. 

Data lakes and data warehouses provide a unique set of pros and cons; your decision to implement either will depend on your enterprise’s current and future data intelligence roadmap.  

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Chiradeep BasuMallick
Chiradeep is a content marketing professional, a startup incubator, and a tech journalism specialist. He has over 11 years of experience in mainline advertising, marketing communications, corporate communications, and content marketing. He has worked with a number of global majors and Indian MNCs, and currently manages his content marketing startup based out of Kolkata, India. He writes extensively on areas such as IT, BFSI, healthcare, manufacturing, hospitality, and financial analysis & stock markets. He studied literature, has a degree in public relations and is an independent contributor for several leading publications.
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