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DA2. MongoDB

Statement

MongoDB is used by many companies that deal with big data due to its unique structure and processing speed. Discuss why MongoDB is used within the Big Data sphere and how it can help companies collect and report on captured data.

Answer

Introduction

MongoDB is a popular NoSQL, schemaless, document-based database that forms -along with its tools and cloud services- a full data platform that has many useful use cases in and out of the Big Data sphere. MongoDB 1.0 was released in February 2009 with humble beginnings. Today, it has more than 5K employees, 50K customers, +$1.5 billion in revenue, and reached version 8.0 recently (About MongoDB, 2024).

Why MongoDB is Used in Big Data

MongoDB’s early design philosophy focused on building quick and easy data models, using famous programming languages, being schemaless, supporting horizontal scaling, and only adding essential features, thus there was no need for complex joins, transactions, and foreign keys (MongoDB history, n.d.).

This philosophy made MongoDB suitable for Big Data since Big Data is known for its high variety, thus being schemaless is a big advantage as the shape of the data is not known beforehand. Also, Big Data is known for its high velocity, thus MongoDB’s horizontal on-demand scaling and quick data models are a big advantage especially when eliminating the traditional relational relationships and other checks that slow down data insertion. Finally, Big Data is known for its high volume, this is where MongoDB’s sharding and fault tolerance features -such as replication and load balancing- come in handy.

How MongoDB Helps Companies Collect and Report on Captured Data

MongoDB helps companies during the data collection phase due to its relationship-less and schemaless nature as captured should be stored as is and then it can be categorized and cleaned at the processing phase. MongoDB cloud services -such as Atlas- along with its autoscaling ensure no data loss during the capture phase even for sudden capture spikes. Tools such as MongoDB Realm allow for capturing data from mobile and IoT devices.

MongoDB also helps companies reflect on the data through its complex querying, searching, and aggregation features. MongoDB’s Charts and Compass tools allow easy user-friendly data visualization while its BI Connectors are compatible with popular Business Intelligence and reporting tools. MongoDB’s powerful in-memory caching and indexing help in quick data retrieval and reporting (MongoDB Evolved – Version History, 2024).

Conclusion

MongoDB is not just a database, it is a full-fledged ecosystem that is capable of dealing with the issues of Big Data and is also compatible with popular tools in the industry such as Hadoop, Spark, and Kafka. MongoDB’s unique structure and processing speed make it a popular choice for companies that deal with real-time analytics, IoT, mobile apps, and other Big Data use cases.

References

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