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:
| Area of Focus | Data Lake | Data Warehouse |
| Data type | Can store any kind of data in any format. Structured, semi-structured, unlabeled, and raw data. | Data has to be pre-processed and structured before it can be stored for later use. |
| Scalability and Agility | Highly agile and very scalable. You can configure a data lake as and when needed. | Data warehouses follow a fixed configuration. They are not as scalable. |
| Target Group | Data lakes are mostly used by machine learning engineers, big data engineers, and data researchers. | Data warehouses are meant for operational clients who need well-prepared reports. They are mostly used by trade intelligence groups. |
| Accessibility | Data in data lakes are open and can be quickly updated whenever. | Data warehouses are restrictive and don't allow public access. Only authorized users can make changes or updates. Additional changes will need supervision and others' approval. |
| Use Cases | Data lakes are used for predictive modeling tasks | Data warehouses are used for operational analytics, reporting, and business intelligence. |
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.
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Get a DemoConclusion
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.
FAQs
In a data lake, raw data is stored in its original form, allowing for various types of data to be kept simultaneously. On the other hand, a data warehouse holds processed and formatted data optimized for SQL queries and business intelligence tools.
Walmart, for instance, utilizes a data lake to manage copious amounts of data from multiple departments. Examples of data lake options include Amazon S3, Azure Data Lake Storage, on-premise Hadoop, and NoSQL databases.
- Versatility: Data lakes can hold large amounts of both well-organized and unstructured data.
- Adaptability: Data lakes are adaptable as they can store diverse types of data.
- Sophisticated Analysis: They support intricate calculations like machine learning and instant processing.
- Economic Savings: By consolidating all data into one place, data lakes make processing large datasets more cost-effective.
Amazon S3 can be considered a data lake because Amazon S3 can store raw data in the native format, including different types of data, and allows users to analyze data.
A data lake is a storage of raw data in its original form, and it can store any type of data, on the other hand, a database is a storage of data in a structured format and is optimized for limited but immediate use.
The former contains raw and unstructured data, the latter is a data lakehouse which is a relatively new concept that incorporates the idea of data lakes but with the structure of data warehouses, solving the problems of data lakes with the help of adding a storage layer.
