What are Data Lakes?

July 22, 2025

Data lakes let you store all kinds of raw data in one place, structured or not, so you can explore, analyze, and extract insights when you need them.

Data lakes are reasonably priced centralized repositories that can deal with huge amounts of raw data available in any format, like structured, unstructured, or semi-structured, at any scale. Most data lakes utilize cloud-based object storage, including Google Cloud Storage, AWS S3, or IBM Cloud Object Storage.

The good thing about a data lake is it doesn’t ask to structure the data first and run various kinds of analytics. As data lakes use cloud computing to their advantage and storage of data becomes more affordable and scalable. Organizations consider data lakes as their core elements because of this feature. It is predicted that the global market of data lakes will witness a worthwhile expansion of 21.3% CAGR in the following years.

Key Characteristics of Data Lakes

When organizations understand the key characteristics of data lakes, they can use them more effectively. Data lakes possess some key characteristics, like:

1. Schema-on-read

Data lakes are different from traditional databases, as the latter use an approach of schema-on-write. On the contrary, data lakes use schema-on-read. It means data gets applied to a schema when it is read only, instead of when it is stored. Hence, a user can store data in the unstructured form and also define the structure.

2. Elastic Storage

As data lakes offer a large storage capacity, they can accommodate data of varying sizes. Furthermore, their performance does not degrade during this process. Additionally, data lakes allow elastic scalability. Thus, it becomes possible to scale data up and down without making any alterations to its configuration or architecture.

3. Integrated Analytics

Most often, data lakes comprise analytics tools. Again, they also combine with the current analytics tools of an organization. Thus, enables deeper analysis on the preserved data directly. Users prefer to use this integration to carry out tasks like big data analytics and real-time analytics.

4. Multi-tenancy

Data lakes allow several departments and users within organizations to access only one infrastructure. This way, they support multi-tenancy. It helps in resource sharing and collaboration while maintaining the security of the data using access controls.

Factors That Make Data Lake and Data Warehouse Different

Both data lakes and data warehouses work as storage systems, but they differ in the way they deal with and process information. Some key differences between them are:

1. Storage of data

Data lakes contain all data in an unstructured and raw form for either future or immediate use. But data warehouses store only the cleaned and transformed data, which has been arranged in a predefined schema.

2. Users

Engineers and data scientists use data lakes when they opt to study raw data for gaining exclusive and new business insights, whereas business end users and managers use a data warehouse when they look forward to gaining insights from a business KPI perceptive.

3. Processing

A data lake uses ELT (Extract, Load, Transform), where data is extracted right from its source for being stored and structured when required. In contrast, data warehouses use ETL. Here, data is mined using its source. Afterward, it is scrubbed and structured. This way, it becomes prepared for business-end analysis.

4. Schema

Data lakes use schema-on-read, and it is applied if the data is read instead of when the data is loaded. It suggests you can keep on including new data without predefining a schema. Conversely, data warehouses use schema-on-write. It ensures that the data is structured steadily right from scratch.

Benefits of Data Lakes

Data lakes offer scalability, cost-efficiency, and, most importantly, convenient storage. Thus, they can assist businesses in realizing the importance of data in several ways:

1. Centralized Data

Data lakes aggregate data, and it helps them in avoiding silos. Additionally, they make every vital piece of business information available using only a centralized location.

2. Quicken Analytics

A data lake seems pivotal for data analytics. When businesses use these pools of information, they can utilize modern analytic procedures for guiding decision-making processes and accessing real-time market insights. In 2022, the value of the market of data lakes was $5.80 billion, and it is anticipated that by 2030, it will expand to an impressive $34.07 billion.

3. Cost-effective and Scalable Storage

Every business needs data to thrive; hence, they opt to use a data lake, as it helps organizations structure their data at the time of the intake process.

4. Enhance Collaboration

When data is consolidated from different sources into a centralized repository, then organizations can access it widely. Data lakes remove hindrances, and this way, they foster collaboration between teams in different departments.

5. Reliable AI Enablement

With data lakes, you can develop AI initiatives on a diverse and huge data foundation, and it seems perfect to train machine learning models and AI models to customize the experiences of customers. This foundation also helps in making informed decisions and thus leading to AI-driven personalization to a much greater extent.

Challenges of Data Lakes

The challenges that data lakes pose include security, performance issues, data governance, etc. Let us have a look at the challenges one by one: 

1. Data Quality – When a data lake contains raw data, it is found to be incomplete and contain errors. Hence, implementing regular data quality checks is important for dependable analysis.

2. Data Governance – Forming clear policies and ownership is important to manage a huge chunk of data in data lakes, especially when there are diverse sources and data types.

3. Performance – At times, processing big datasets in data lakes seems challenging because of ineffective execution of queries and unstructured data formats. In this condition, optimizing storage structures seems pivotal for performance.

4. Safety – It becomes essential to use strong security measures in data lakes, such as access controls, monitoring, and encryption, so that data does not become susceptible to security breaches.

5. Integration of Data – Sometimes, it seems tough to integrate data from different sources into data lakes. In this condition, ideal data integration procedures seem important.

6. Schema Mismatch – As a data lake ingests data from various sources, sometimes ensuring steady schema matching and encoding looks tough.

Articles Referenced:

• What is a Data Lake?
• Advantages of Data Lakes
• What are some data lake use cases?
• Data lake challenges
• Data lake vs. data warehouse