5 Data Lake Examples That Prove They’re Not Just a Buzzword

A data lake is essentially a vast digital dumping ground where companies toss all their raw data, structured or not.

A modern data stack can be built on top of this data storage and processing layer, or a data lakehouse or data warehouse, to store data and process it before it is later transformed and sent off for analysis.

Let’s dive into some data lake examples that showcase how industry giants like Uber, Nestlé, Accenture, Netflix, and Capital One are using these digital reservoirs to make waves in their respective sectors.

Uber’s Data Lake

Uber is the first of our data lake examples. When you think of Uber, you likely think of ride-sharing or food delivery. But behind the scenes, Uber is also a leader in using data for business decisions, thanks to its optimized data lake.

• Incremental Data Processing with Apache Hudi: Uber’s data lake uses Apache Hudi to enable incremental ETL processes, processing only new or updated data instead of recomputing everything. This shift from batch to incremental processing keeps data fresh and reduces costs.
• Enhanced Data Quality and Consistency: Uber’s incremental approach ensures data consistency across its global data centers. Pre-commit validators perform real-time data quality checks before data enters production, crucial for a data-driven company like Uber.
• Significant Performance and Cost Efficiency Gains: For Uber, this approach reduced ETL run times by up to 82% and cut costs by over 78%, significantly boosting both performance and efficiency.

Nestlé’s Data Lake

Next up in our data lake examples is Nestlé. Nestlé isn’t just the world’s largest food and beverage company; it’s also a leader in data management. With Deloitte, Nestlé built a centralized data lake that transformed its data strategy.

• Centralized Data Lake Development: Nestlé USA moved from multiple siloed, on-premises systems to a Microsoft Azure-based data lake, eliminating redundancy, improving consistency, and boosting collaboration across units.
• Enhanced Analytics and Machine Learning: With the data lake, Nestlé built the Sales Recommendation Engine (SRE), now used by 1,500 sales reps weekly, driving significant sales growth and efficiency.
• Substantial Business Value and Adoption: The data lake decommissioned 17 systems and onboarded over 2,000 users. In four years, it generated $200 million in value, showing the impact of a strong data lake strategy.

Accenture’s Data Lake

Up next in data lake examples? Accenture. Accenture, a global professional services firm, needed a scalable, secure data solution to handle its massive datasets. The solution? A cloud-native data lake on Google Cloud.

• Cloud-Native Data Lake for Scalability and Security: By centralizing over 400 terabytes of business data on Google Cloud, Accenture enhanced data visibility and security—vital for handling sensitive client information.
• Automated Data Operations: Using tools like Google BigQuery and Cloud Composer for data ingestion, processing, and workflow management, Accenture reduces manual work and improves consistency.
• Foundation for Advanced Analytics and Cost Efficiency: Accenture manages costs effectively with a pay-as-you-go model while supporting advanced analytics and AI, including contract analysis and anomaly detection.

Netflix’s Data Lake

Netflix is another great callout when considering data lake examples. When you stream on Netflix, data lakes are the last thing on your mind—but they’re key to Netflix’s success. The company has built a leading machine learning ecosystem in entertainment.

• Scalable and Efficient Data Infrastructure: Netflix’s data lake on AWS with Amazon S3 handles petabytes of data daily, supporting everything from personalized recommendations to streaming quality optimization.
• Advanced Analytics and Machine Learning: Netflix uses AWS tools like Apache Spark on Amazon EMR and Amazon Redshift to predict user engagement and optimize content delivery, enhancing customer satisfaction.
• Democratized Data Access and Innovation: With self-service access to trusted datasets, Netflix promotes data-driven decisions, accelerating innovation and the rapid development of new features.

Capital One’s Data Lake

The last of our data lake examples is Capital One. In banking, data security and compliance are crucial. Capital One’s move to a centralized data lake on AWS demonstrates how to do it right.

• Modernized Data Ecosystem with Enhanced Security: Moving to a data lake on Amazon S3 allowed Capital One to consolidate siloed data into a single, secure platform, ensuring strong compliance and governance.
• Streamlined Data Operations and Real-Time Analytics: Using AWS services like AWS Glue and Amazon Kinesis, Capital One streamlined ETL processes and enabled real-time analytics to detect fraud, assess credit risk, and enhance customer experiences.
• Empowering Teams with Self-Service Analytics: With easy data access, Capital One’s teams can analyze data more effectively, supporting agile development and rapid deployment of machine learning models.

Get transparency into these data lake examples with data observability

As these industry giants show, a well-executed data lake strategy can revolutionize your business—but only when the data is trustworthy. With Monte Carlo’s data observability platform, you gain the end-to-end visibility needed to ensure your data lake remains accurate, reliable, and ready to drive the kind of transformative insights that Uber, Nestlé, and Netflix rely on every day. Ready to elevate your data strategy?

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