- MongoDB is a flexible, scalable alternative to relational databases, well-suited for fast-growing organisations that are rapidly embracing digital transformation
- It stores data as “fields and value pairs” instead of “rows and columns”
- It can also store documents, a group of which is called collections
Over the last few years, Toyota Material Handling Europe has embraced Industry 4.0 and streamlined operational processes across the value chain with the use of cutting-edge digital technologies.
As part of this digital transformation effort, the company wanted to access data in real-time from its 100,000 warehouse trucks. By using IoT and telematics, business leaders and decision-makers have a finger on the pulse of all data across the manufacturing, supply chain, and logistics workflows.
As the company automated key processes and migrated large-scale data to the cloud, the monolithic SQL databases they were using proved to be very limiting. The company opted for a microservices approach and required a NoSQL database.
MongoDB Atlas, a fully managed, global cloud database service, was found to be the best fit because of the following reasons:
- MongoDB’s JSON document data model provides flexibility to let customers manage and incorporate data in any structure
- It is easily scalable and can handle the large volumes of data generated from IoT devices
- Its rich indexing and querying capabilities, including aggregations, geospatial and text search made it easy to run analytics and capture insights in real-time
MongoDB: Why it is an ideal choice for document-oriented data
The equivalent of relational database tables in MongoDB is collections, referring to sets of documents. One of the disadvantages of MongoDB though is that while it can be any data type, it cannot be spread across different databases.
Read about other advanced tools recommended by Merit’s data engineering experts for powering and optimising your BI Stack.
The History of MongoDB
Dwight Merriman and Eliot Horowitz, while building web applications at DoubleClick, an online advertising company that is now owned by Google Inc, encountered development and scalability issues with traditional relational database approaches. They created MongoDB and named it after a derivative of the word ‘humongous’ to reflect the large volumes of data it can support.
The DBMS was released as open-source software in 2009 by 10Gen Inc., which the two helped form in 2007 and eventually renamed to MongoDB Inc. in 2013, to commercialize the database and related software.
MongoDB is available in two forms: the open-source Community Edition and the Enterprise Server version with additional security features, an in-memory storage engine, administration & authentication features, and monitoring capabilities through Ops Manager.
MongoDB Compass, a graphical user interface (GUI), facilitates working with the document structure, conduct queries and index data, among other features. It comes with a Connector for BI that lets users connect the NoSQL database and their business intelligence tools, thereby enabling visualisation of data and creation of reports using SQL queries.
The cloud version of the database, called MongoDB Atlas, was released in 2016 and runs on AWS, Microsoft Azure, or Google Cloud Platform, depending on what the customer wants. For application development on MongoDB Atlas, Stitch platform was made available and this really made life easy for developers.
Since 2018, it has started supporting multi-document ACID (atomicity, consistency, isolation and durability) transactions, assuring accuracy and reliability.
How MongoDB Works
The BSON document storage and data interchange format provide a binary representation of JSON-like documents. Data in a MongoDB collection is distributed across multiple systems by automatic sharding, allowing for horizontal scalability when data volumes and throughput requirements increase.
Data consistency is ensured by the automatic replication of operations from the single master architecture to secondary databases for automatic failover.
Some of the key features of MongoDB include:
Schema-less Database: Being schema-less, one collection can hold many types of documents with varying numbers of fields, content and size, making it flexible.
Document Oriented: Unlike the tables in RDBMS, MongoDB stores documents in fields (key-value pair) instead of rows and columns, with each field having a unique object ID.
Indexing: Searching is made easy in MongoDB database due to every field in the documents being indexed with primary and secondary indices.
Scalable: Sharding makes MongoDB horizontally scalable by distributing data on multiple servers. Vast amounts of data are partitioned into data chunks using the shard key and evenly distributed across shards located in many physical servers. New machines can also be added to a running database.
Replication: Replication increases availability and redundancy. The multiple copies of the data it creates are sent to a different server so that in case of one server failing, data can be retrieved from the other.
Aggregation: Similar to the SQL GROUPBY clause, MongoDB performs operations on grouped data to get a single result or computed result. Three types of aggregations are possible: aggregation pipeline, map-reduce function and single-purpose aggregation methods.
All these features improve the performance of MongoDB with data persistence.
Differences Between MongoDB and RDBMS
Key differences between MongoDB and RDBMS at a glance:
|A non-relational and document-oriented database||Relational database|
|Allows hierarchical data storage||Not suitable for hierarchical data storage|
|Dynamic schema||Predefined schema|
|Focused on CAP theorem (Consistency, Availability, and Partition tolerance)||Complies with ACID|
Various Use Cases for MongoDB
There are many use cases for MongoDB. Some of the popular ones include:
- Big Data Management
- Content management systems
- Product Data Management
- Operational Intelligence
- Mobility and Scaling
- Real-Time Data Integration
About Merit Group
At Merit Group, we work with some of the world’s leading B2B intelligence companies like Wilmington, Dow Jones, Glenigan, and Haymarket. Our data and engineering teams work closely with our clients to build data products and business intelligence tools. Our work directly impacts business growth by helping our clients to identify high-growth opportunities.
Our specific services include high-volume data collection, data transformation using AI and ML, web watching, and customized application development.
Our team also brings to the table deep expertise in building real-time data streaming and data processing applications. Our expertise in data engineering is especially useful in this context. Our data engineering team brings to fore specific expertise in a wide range of data tools including Kafka, Python, PostgreSQL, MongoDB, Apache Spark, Snowflake, Redshift, Athena, Looker, and BigQuery.
If you’d like to learn more about our service offerings or speak to a Kafka expert, please contact us here: https://www.meritdata-tech.com/contact-us/
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