What is a Data Lake? Architecture & Benefits

Every business makes critical decisions. Get the wrong facts, and your organization can come crumbling down. Good data is behind every key decision. And if you are lacking, then it could cause issues later. Data lakes are used for storing data that flows in from diverse and multiple sources, all related to your organization.

In this guide, we will take a look at what data lakes are. We will go deeper into data lakes meanings, applications, architecture, features, and integrations. You'll learn why data lakes are valuable to businesses and also learn the differences between a data lake vs data warehouse.

Data Lake Definition

What is a data lake? Data lakes are authoritative and complete data stores that are used for business intelligence, power data analytics, and machine learning. They serve as a central location for housing raw, unstructured, data, in large volumes, in their raw format. They're not like data warehouses that follow a hierarchy or structure for data storage. There's no schema and you can store data in all stages of its refinement process in them.

Features of Data Lake

1.  Store Raw Data

Data lakes on the other hand store raw data in its original form as it contains all the characteristics of the data. This makes it easier to work with the data because one can manipulate it in various ways and forms.

2.  Support Different Types of Data

Data lakes can be used to store structured data such as database tables, semi-structured such as XML files, and unstructured data such as images, and audio files.

3.  Allow Schema to be Easily Modified

As a result, data lakes offer a schema-on-read architecture, which means the data schema is not defined at the time of the creation of the data lake but at the time when the data is analyzed.

4.  Promote data exploration and discovery

Users can analyze and search for information in more depth and find new information from raw data that is not offered with other methods of data analysis.

5.  Support Advanced Analytics and AI

Data lakes are at the core of machine learning, deep learning, and advanced analytics; hence are critical for organizations that want to adopt AI solutions.

How Data Lakes Works?

A data lake can combine and store data from both on-premises and cloud locations. You can use data lakes for storage and compute.  Your data can be in any format, and it can flow into your data lake without requiring a specific structure schema. Think of your data lake like a river or stream, with information flowing into it whenever you want and being store safe without any leaks.

This data is collected from your business environment, employees, and outside agents that interact with your organization. You can organize, clean it up later, and extract valuable insights with data lake solutions.

Data Lake Vs. Data Warehouse

Here are the core differences between a data lake vs data warehouse:

Key Elements of a Data Lake

1. Storage Layer

The storage layer is used for storing raw data in their native form and it is the last layer in the architecture. This can be, for example, cloud storage such as Amazon S3 or Azure Data Lake Storage.

2. Data Ingestion Layer

This layer is responsible for data acquisition from different sources and loading this data into the data lake optimally and accurately.

3. Data Processing Layer

The data processing layer is essential for processing and preparing the ingested data. This can be batch processing, real-time processing, and machine learning processing.

4. Data Management Layer

This layer is the set of tools and technologies for data governance, quality, security, and metadata. Some of the examples of Data Catalogs are Apache Atlas and AWS Glue.

5. Data Access Layer

The data access layer is also responsible for the provision of interfaces and tools to enable the users to work with the data and these include the SQL query engines, data exploration platforms, and machine learning frameworks.

Data Lake Architecture

The structure of data lake architecture can be divided into several layers that help store, process, and analyze data. These layers include:

1. Raw Data Zone

The raw data zone contains information in its most uncomplicated form or as it has not been changed. This is the first point where all the ingested data is received and processed in this zone.

2. Cleansed Data Zone

In the cleansed data zone the data is processed to make it fit for use and conforming to the required standards. It is used for further differentiation and elaboration of the data received from the preceding zone.

3. Curated Data Zone

The curated data zone is a storage place for data that has been preprocessed and is in a format suitable for analysis. This zone offers data in a format that can be easily utilizable in business intelligence and other similar purposes.

4. Analytics Zone

This is the area of the organization where complex analytical processing, machine learning, and other related activities are conducted. This zone uses the raw, cleansed, and selected data to provide insights.

Benefits of Data Lake

1. Improved Data Agility

They help in the consumption and analysis of big data in real time, and hence, faster decision-making is possible.

2. Enhanced Analytics Capabilities

Data lakes allow for extensive and creative analysis since they store multiple types of data in one place.

3. Increased Scalability

Data lakes can grow horizontally: this means that adding new amounts of data is not a problem for the organization that uses this approach.

4. Reduced Data Silos

Data lakes hold data from different sources in one place so that there is no data fragmentation and data can be easily integrated.

5. Better Data Governance

Data lakes help in data governance since all data stored in a central location can be easily controlled on aspects such as quality, security, and compliance.

Challenges of Data Lake

1. Data Quality

Maintaining data quality can be challenging because data from different sources and in different forms are ingested into the data lake.

2. Data Governance

The task of effective data governance can prove to be complex especially when working with a huge amount of different data.

3. Security

Data security is also a critical feature in a data lake to prevent unauthorized access and data leakage.

4. Performance

The management and optimization of the performance of the data lake can be challenging as the data lake evolves to handle more data.

Examples of Data Lake

Let’s take at look at some of the popular data lake examples in 2025 just to give you an idea of how these data lakes work or are put in use:

Uber's Data Lake

Uber processes over 100 petabytes of data through its Apache Hadoop-based data lake. The platform handles trip data, driver locations, pricing algorithms, and fraud detection systems in real time. You can see when surge pricing kicks in during peak hours - their data lake processes millions of ride requests, traffic patterns, and driver availability data simultaneously. Uber's engineers built Apache Hudi specifically to enable incremental data processing, allowing them to update tables without recomputing entire datasets.

Netflix's Data Lake

Netflix stores viewing behaviors, content metadata, and user interactions in AWS S3 as their primary data lake. It serves as a content personalization engine. The system processes trillions of events daily through Apache Kafka clusters to power recommendation algorithms. When you see personalized movie suggestions, that's their data lake analyzing your viewing history, pause patterns, and completion rates against similar user profiles. Netflix has recently introduced a Media Data Lake specifically for handling video, audio, and image assets to train machine learning models on actual content.

Capital One’s Data Lake

Capital One built their data lake architecture around Snowflake with strong observability and cost monitoring capabilities. Their Slingshot platform provides granular insights into data usage, costs, and performance across all stored datasets. You can track every data transaction and modification through comprehensive audit trails that meet regulatory compliance requirements. Capital One's data lake manages sensitive data while maintaining full visibility into access patterns and data lineage.

Understanding Data Lake Use Cases

Data lakes solve real business problems across multiple industries. Here are specific scenarios where organizations apply them:

• Real-time fraud detection in financial services: Banks store transaction histories, customer behavior patterns, and external threat intelligence feeds together. You can run machine learning models that analyze spending patterns and flag suspicious activities within milliseconds.
• Predictive maintenance for manufacturing equipment: Collect sensor data from machinery, maintenance logs, and environmental conditions in one repository. If you combine historical failure patterns with real-time sensor readings, you can predict equipment breakdowns before they occur.
• Personalized content recommendations for media platforms: Store user viewing histories, content metadata, social media interactions, and demographic data together. You can build recommendation engines that suggest movies, articles, or products based on complex behavioral analysis.
• Supply chain optimization for retail operations: Combine inventory data, weather forecasts, supplier performance metrics, and customer demand patterns. You can predict stock shortages, optimize delivery routes, and adjust purchasing decisions based on multiple data sources.
• Clinical research and drug development: Aggregate patient records, genomic data, clinical trial results, and medical literature in structured formats. If you need to identify treatment patterns or drug interactions, you can query across multiple data types simultaneously.
• Smart city traffic management systems: Store traffic sensor data, public transportation schedules, weather conditions, and event calendars together. You can optimize traffic light timing, predict congestion patterns, and reroute public transport during peak hours.
• Improving customer experiences in telecommunications: Unify billing data, network usage patterns, customer service interactions, and device information. You can identify upselling opportunities, predict customer churn, and resolve network issues faster.
• Risk assessment for insurance underwriting: Combine claims history, property assessments, satellite imagery, and weather data for comprehensive risk evaluation. You can price policies more accurately and identify high-risk properties before issuing coverage.

How does SentinelOne integrate with Data Lake?

SentinelOne's Singularity™ Data Lake can help you get more out of your data. You receive actionable insights from across your organization all in one place. You can turn your data into your strongest security assets.

It helps you:

• Detect threats in real-time with AI-powered threat hunting
• Gain greater visibility by bringing together data from any and every source
• Scale with ease by normalizing all your data into OCSF as your organization grows

Other data lakes often come with limited capabilities and a high price tag. Singularity™ Data Lake is a comprehensive solution with predictable, transparent, and affordable pricing that ensures you get the most value from your investment.

If you'd like threat detection for your cloud data stores, you can also use Singularity™ Cloud Data Security. IT can detect malware and zero-day exploits in milliseconds with AI-powered detection engines. Plus, you can scan objects directly in your cloud data stores and ensure that no sensitive data leaves your environment. It also provides comprehensive coverage and support for regulatory frameworks like GLBA, PCI-DSS, HIPAA, and many others. SentinelOne’s data lake integration is included with its AI-SIEM solution as well which is used for log analytics, real-time data streaming, and ingestion. If you want to capture and analyze your security event data, be sure to check out Singularity™ Data Lake for Log Analytics. It can detect and resolve incidents in real-time. It’s a powerful data visualization tool since it lets you create custom dashboards in just a few clicks by saving queries as dashboards.

Conclusion

By now, you should have a fair idea of what data lakes are and how they work. If you question is: “Do I need a data lake?”, then the answer is yes. Your business will scale up, and you will deal with data coming in from multiple and different sources. Time is money, and so is information. Your next big milestone can unlock itself from the value you find in your data stores. And you don’t want to miss out on that.

If you need help with setting up or configuring your data lake, you can contact the SentinelOne team for assistance. We’re happy to help.

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