When looking at big data storage options for big business, almost all options fall into two categories: data warehouses and data lakes. The latter has been rising in popularity and MongoDB has risen to the occasion of providing a way to implement, manage, and mine these structures with their own framework: MongoDB Atlas Data Lake.

Possible approaches to data

Data warehouses and data lakes entail two very different approaches to handling your decision-making business data. From storing to structuring to processing and, finally, to analysis. So much so that they’re often used in tandem as a total big data stack.

However, storage lakes are gaining ground for the cost-effectiveness and sheer flexibility they have to offer data analysts and businesses. Data lakes are like that one drawer in your kitchen that you throw all of the random things you come across in your house, a flexible, unstructured storage location that you temporarily (even if it’s for years) place things so they are off the counter until you need to organize them later. To put it loosely, data lakes follow a collect first—sort, process, and analyze later approach. This storage of unstructured data, from different sources and in different formats, in a single location helps organizations that have flexible plans with their data or have such large volumes that traditional, structured data storage options may just not work. 

This is particularly useful in contexts where there is an intermingling of business and consumer data, such as e-commerce. It’s also becoming more prevalent as the IoT (Internet of Things) grows and leads to data collection from ever more disparate sources. Manufacturing, multi-nationals as well as giant retailers or e-commerce businesses are natural breeding grounds for this conglomeration of information.

Data lakes also seamlessly gel with cloud-based, serverless storage thanks to the cloud’s intrinsic availability, scalability, and inexpensive storage infrastructure. It’s no surprise that industry leaders in cloud storage, such as Google Cloud and Amazon AWS, offer data lake resources. 

MongoDB Atlas Data Lake is a new form of tool that can help structure data stored in Data Lakes and is what we’ll be looking at here. MongoDB is already used by many businesses globally for their non-relational data platform, and is expanding their tool set to give more power to utilize unstructured data. This provides users a way to immediately act upon data stored within Data Lakes, without having to use a parsing tool to structure the data before it is extracted.

Differences Between Data Lakes and Data Warehouses

While exploring MongoDB Atlas Data Lake, it's crucial to understand how data lakes fundamentally differ from data warehouses, as this shapes your approach to data architecture and implementation:

This distinction is particularly relevant when considering MongoDB Atlas Data Lake implementations. Organizations often find themselves needing both solutions: data lakes for maintaining raw, unstructured data with full flexibility, and data warehouses for structured, quick-access business intelligence. The key is understanding when each tool serves your specific needs best.

For instance, you might use MongoDB Atlas Data Lake to store raw customer interaction data from multiple sources, while maintaining a separate data warehouse for processed, analysis-ready business metrics. This hybrid approach allows you to maintain both the flexibility of raw data storage and the performance benefits of structured data when needed.

MongoDB Atlas Data Lake: A happy medium? 

MongoDB Atlas Data Lake allows you to natively query data stored across both  MongoDB Atlas and Amazon AWS. Data can be queried as long as it’s stored in any of the following formats: JSON, BSON, CSV, TSV, Avro, ORC, and Parquet. Event queries can be made using the mongo shell, MongoDB Compass (the official GUI), or any MongoDB-supported drivers (libraries).

A typical use case scenario for MongoDB Atlas Data Lake would be something like this: A successful online retailer purchases and assimilates another e-commerce store into their business. Let’s say that Store A uses an MongoDB Atlas cluster for its data storage while Store B uses Amazon AWS S3 buckets. Now, how can you start aggregating the data across these two sources while maintaining their “richness” and historical context? 

Examples of analysis you might want to do across these two data sets are:

• A combined list of top-performing/under-performing products from each store with the number of units sold and total profit generated.
• The top customers from both stores as well as which products they bought and how much they’ve spent.
• Pulling together similar/related/identical products from both stores with the relevant metadata.

It would be preferable to keep both data sets intact as separate entities. For one, both data sets may have unique fields. Secondly, while they are now under the same umbrella, both are still very much part of separate entities. If we were to restructure either (or both), it would lose some of its richness and context. What’s needed is a framework that allows us to query both these data sets at the same time and aggregate the information in a useful way.

This is exactly the type of problem that MongoDB Atlas Data Lake and similar technologies are meant to solve.

The superpower of MongoDB Atlas Data Lake

So, how does MongoDB Atlas Data Lake solve this issue? With a conventional data warehouse, you would have to restructure the new data into the same format as the existing data. This is usually in a SQL-like relational database format.

However, by adopting the data lake approach, you can keep each source of data in its own format, schema, and even location. You can then use one of the methods mentioned to run event queries that aggregate and process the information dynamically. 

Assuming we are in the scenario described above, the step-by-step approach will look something like this:

1. You will need data sources. One is a MongoDB Atlas cluster (Store A’s data) and the other is an Amazon AWS S3 bucket (Store B’s data). 
2. Connect the S3 bucket to your Atlas account and define a database and collection that refers to this data.
3. Create a new Data Lake from within your MongoDB Atlas dashboard.
4. Define objects using a query language for both the Atlas collection and S3 stores.
5. Define databases for both of these data sources within the data lake.
6. Create a new database(s) where aggregate data from querying these two data sources can be stored.
7. Code a data pipeline using query syntax with the business logic to execute your requirements. This includes a $out statement to output the results to your new Atlas cluster with its own collection and database.
8. Run event queries that will extract, parse, and combine the data and store it in the unified database.

This video goes through the process of an almost identical scenario as well as providing code snippets to implement it.

The important thing is that this unified data should now have a predictable and structured format. For example, with the first analysis use case above, you might have something like this

• Cluster: unified_data
• Database: data_analysis
• Collection: top_product

This is exactly the type of problem that MongoDB Atlas Data Lake and similar technologies are meant to solve:

Entry #1

• _id: 1470
• units_sold: 671
• profit: $7,986.41

Entry #2

• _id: 0031
• units_sold: 543
• profit: $5,923.22

MongoDB Atlas Alternatives

As organizations scale their data infrastructure needs, choosing the right data lake solution becomes crucial for maintaining performance, ensuring compliance, and retaining control over customer data. While MongoDB Atlas Data Lake offers specific capabilities, it's important to understand how it compares to other enterprise solutions. Here's a detailed comparison of leading data lake platforms, analyzed through the lens of enterprise requirements:

Data Lakes and Server-Side Implementation: The MetaRouter Approach

Architecture Overview

MetaRouter's enterprise data infrastructure platform integrates with MongoDB Atlas Data Lake through server-side integration. The platform collects, processes, and activates customer data while maintaining compliance with data privacy regulations. The zero-trust architecture prevents third-party vendor access to raw customer data.

Technical Challenges of Client-Side Collection

Traditional client-side data collection for MongoDB Atlas Data Lake faces severe technical limitations. Organizations typically run 50+ third-party tags simultaneously, causing significant performance degradation. This leads to site stability issues and inconsistent data capture from ad blockers and browser limitations. The security surface area expands with each third-party script, while conversion tracking shows up to 40% variability.

Server-Side Architecture Benefits

Data Quality Engineering

The server-side architecture enables 300% more event capture compared to third-party pixels through direct server communication. Conversion tracking variability reduces from 40% to 1% through consistent collection methods. The removal of client-side tags delivers a 250ms latency reduction per tag. The system processes billions of monthly events at 99.9%+ uptime, with direct server-side integration through 80+ vendor endpoints.

Identity Resolution Technology

The platform's Sync Injector technology provides advanced user identification capabilities. Custom sync pathways integrate with major platforms like Google and Facebook. First-party cookie implementation extends persistence to 12+ months. The architecture supports event-level filtering and complete data replay functionality for reliability.

Enterprise Implementation Metrics

A recent Fortune 50 retail implementation demonstrated significant performance gains: page load speed improved by 900ms, while event capture increased by 35,000 events in the initial 10 minutes. Match rates showed a 200% improvement, contributing to $246MM in annual revenue growth. Pinterest event capture specifically increased by 300% compared to previous pixel implementation.

Technical Architecture Components

Data Processing Framework

The global transformation layer ensures consistent data processing across sources. Advanced filtering and enrichment occur before lake ingestion, with sophisticated event-level control. The architecture supports custom integration capabilities through BYO Syncs, maintaining real-time data streaming with guaranteed delivery mechanisms.

Security Implementation

The zero-trust architecture implements complete data isolation from third-party access. Regional data sovereignty support enables compliance with local regulations. The system provides granular consent management at the vendor level, supporting GDPR, CCPA, and HIPAA requirements. Custom encryption and data masking protect sensitive information.

Infrastructure Design

The architecture supports cookie-less operations through first-party infrastructure. Deployment flexibility spans major cloud providers and on-premises installations. The platform maintains continuous updates for evolving privacy regulations, with typical implementation timeframes of 2.5-3 weeks. Regular platform updates ensure adaptation to industry changes.

Integration Architecture

Infrastructure Configuration

The system supports deployment across AWS, GCP, Azure, or on-premises environments. Data sovereignty requirements determine regional deployment architecture. Consent management frameworks integrate directly with compliance systems.

Data Strategy Implementation

Advanced filtering rules operate based on specific business requirements. Identity resolution leverages the Sync Injector technology for consistent user tracking. Data replay capabilities ensure complete data delivery through the system.

Performance Engineering

The architecture eliminates client-side tags through server-side alternatives. Critical integrations operate through direct server communication. Real-time monitoring validates system performance and data accuracy.

MetaRouter's server-side architecture creates a robust data infrastructure for MongoDB Atlas Data Lake integration. The platform delivers enterprise-grade data collection, enrichment, and syndication capabilities while maintaining stringent security and performance standards.

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Conclusion: Great for organizations using MongoDB that need to step into hybrid solutions

MongoDB has been one of the most popular NoSQL database platforms for some time now. It’s only natural that its cloud-based framework, Atlas, would be equally popular. With MongoDB Atlas Data lakes, MongoDB is continuing to appeal to modern app developers that deal with more complex and intricate data gathering, storage, and analysis.

However, that doesn’t necessarily mean it’s the best option in each and every scenario. For now, Mongo DB servers a very specific use case of providing flexibility between data sets within MongoDB and AWS S3, providing the native querying of data lakes across MongoDB Atlas and Amazon AWS data sources. 

There’s still something to be said for the uniformity, practicality, and ease of analysis that conventional data warehouses provide. That’s exactly why many businesses with big data use both in their “data storage stack.”

Data lakes can be used to maintain unmanipulated data with its full richness and flexibility. The original data will always be in place should you need it. This data can be queried and aggregated into a more readable and standardized format to store in a data warehouse. Finally, this formatted and structured data can be queried for frequent analysis, insights, and visualizations.

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And if you’re not sure if that’s a perfect fit: keep looking! Amazon S3, Azure Cosmos DB, and Google Cloud provide similar data storage solutions for other contexts and storage ecosystems. 

Photo by will terra on Unsplash