Choosing the right database is a critical choice when building any software application. All databases have different strengths and weaknesses when it comes to performance, so deciding which database has the most benefits and the most minor downsides for your specific use case and data model is an important decision. Below you will find an overview of the key concepts, architecture, features, use cases, and pricing models of Azure Data Explorer and MongoDB so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how Azure Data Explorer and MongoDB perform for workloads involving time series data, not for all possible use cases. Time series data typically presents a unique challenge in terms of database performance. This is due to the high volume of data being written and the query patterns to access that data. This article doesn’t intend to make the case for which database is better; it simply provides an overview of each database so you can make an informed decision.
Azure Data Explorer vs MongoDB Breakdown
ADX can be deployed in the Azure cloud as a managed service and is easily integrated with other Azure services and tools for seamless data processing and analytics.
MongoDB uses a flexible, JSON-like document model for storing data, which allows for dynamic schema changes without downtime. It supports ad hoc queries, indexing, and real-time aggregation. MongoDB can be deployed as a standalone server, in a replica set configuration for high availability, or as a sharded cluster for horizontal scaling. It is also available as a managed cloud service called MongoDB Atlas, which provides additional features like automated backups, monitoring, and global distribution.
SSPL for community edition, commercial licenses for other versions
Log and telemetry data analysis, real-time analytics, security and compliance analysis, IoT data processing
Content management systems, mobile applications, real-time analytics, IoT data management, e-commerce platforms
Highly scalable with support for horizontal scaling, sharding, and partitioning
Horizontally scalable with support for data sharding, replication, and automatic load balancing
Azure Data Explorer Overview
Azure Data Explorer is a cloud-based, fully managed, big data analytics platform offered as part of the Microsoft Azure platform. It was announced by Microsoft in 2018 and is available as a PaaS offering. Azure Data Explorer provides high-performance capabilities for ingesting and querying telemetry, logs, and time series data.
MongoDB is a popular, open-source NoSQL database launched in 2009. Designed to handle large volumes of unstructured and semi-structured data, MongoDB offers a flexible, schema-less data model, horizontal scalability, and high performance. Its ease of use, JSON-based document storage, and support for a wide range of programming languages have contributed to its widespread adoption across various industries and applications.
Azure Data Explorer for Time Series Data
Azure Data Explorer is well-suited for handling time series data. Its high-performance capabilities and ability to ingest large volumes of data make it suitable for analyzing and querying time series data in near real-time. With its advanced query operators, such as calculated columns, searching and filtering on rows, group by-aggregates, and joins, Azure Data Explorer enables efficient analysis of time series data. Its scalable architecture and distributed nature ensure that it can handle the velocity and volume requirements of time series data effectively.
MongoDB for Time Series Data
Although MongoDB is a general-purpose NoSQL database, it can be used for storing and processing time series data. The flexible data model of MongoDB allows for easy adaptation to the evolving structure of time series data, such as the addition of new metrics or the modification of existing ones. MongoDB provides built-in support for time-to-live (TTL) indexes, which automatically expire old data after a specified time period, making it suitable for managing large volumes of time series data with a limited storage capacity. MongoDB has also recently added a custom columnar storage engine and time series collection for time series use cases, meant to improve performance over the default MongoDB storage engine in terms of data compression and query performance.
Azure Data Explorer Key Concepts
- Relational Data Model: Azure Data Explorer is a distributed database based on relational database management systems. It supports entities such as databases, tables, functions, and columns. Unlike traditional RDBMS, Azure Data Explorer does not enforce constraints like key uniqueness, primary keys, or foreign keys. Instead, the necessary relationships are established at query time.
- Kusto Query Language (KQL): Azure Data Explorer uses KQL, a powerful and expressive query language, to enable users to explore and analyze their data with ease.
- Extents: In Azure Data Explorer, data is organized into units called extents, which are immutable, compressed sets of records that can be efficiently stored and queried.
MongoDB Key Concepts
Some key terminology and concepts specific to MongoDB include:
- Database: A MongoDB database is a container for collections, which are groups of related documents.
- Collection: A collection in MongoDB is analogous to a table in relational databases, holding a set of documents.
- Document: A document in MongoDB is a single record, stored in a JSON-like format called BSON (Binary JSON). Documents within a collection can have different structures.
- Field: A field is a key-value pair within a document, similar to an attribute or column in a relational database.
- Index: An index in MongoDB is a data structure that improves the query performance on specific fields within a collection.
Azure Data Explorer Architecture
Azure Data Explorer is built on a cloud-native, distributed architecture that supports both NoSQL and SQL-like querying capabilities. It is a columnar storage-based database that leverages compressed, immutable data extents for efficient storage and retrieval. The core components of Azure Data Explorer’s architecture include the Control Plane, Data Management, and Query Processing. The Control Plane is responsible for managing resources and metadata, while the Data Management component handles data ingestion and organization. Query Processing is responsible for executing queries and returning results to users.
MongoDB’s architecture is centered around its flexible, document-based data model. As a NoSQL database, MongoDB supports a schema-less structure, which allows for the storage and querying of diverse data types, such as nested arrays and documents. MongoDB can be deployed as a standalone server, a replica set, or a sharded cluster. Replica sets provide high availability through automatic failover and data redundancy, while sharded clusters enable horizontal scaling and load balancing by distributing data across multiple servers based on a shard key.
Free Time-Series Database Guide
Get a comprehensive review of alternatives and critical requirements for selecting yours.
Azure Data Explorer Features
High-performance data ingestion
Azure Data Explorer can ingest data at a rate of 200 MB per second per node, offering fast and efficient data ingestion capabilities.
Azure Data Explorer integrates seamlessly with popular data visualization tools like Power BI, Grafana, and Jupyter Notebooks, allowing users to easily visualize and analyze their data.
The Kusto Query Language (KQL) supports advanced analytics features such as time series analysis, pattern recognition, and anomaly detection, enabling users to gain deeper insights from their data.
Unlike traditional relational databases, Azure Data Explorer does not enforce constraints like key uniqueness, primary keys, or foreign keys. This flexibility allows for dynamic schema changes and the ability to handle semi-structured and unstructured data.
Flexible Data Model
MongoDB’s schema-less data model allows for the storage and querying of diverse data types, making it well-suited for handling complex and evolving data structures.
MongoDB’s replica set feature ensures high availability through automatic failover and data redundancy.
MongoDB’s sharded cluster architecture enables horizontal scaling and load balancing, allowing it to handle large-scale data processing and querying.
Azure Data Explorer Use Cases
Azure Data Explorer is commonly used for log analytics, where it can ingest, store, and analyze large volumes of log data generated by applications, servers, and infrastructure. Organizations can use Azure Data Explorer to monitor application performance, troubleshoot issues, detect anomalies, and gain insights into user behavior. The ability to analyze log data in near real-time enables proactive issue resolution and improved operational efficiency.
Azure Data Explorer is well-suited for telemetry analytics, where it can process and analyze data generated by IoT devices, sensors, and applications. Organizations can use Azure Data Explorer to monitor device health, optimize resource utilization, and detect anomalies in telemetry data. The platform’s scalability and high-performance capabilities make it ideal for handling the large volumes of data generated by IoT devices.
Time series analysis
Azure Data Explorer is used for time series analysis, where it can ingest and analyze time-stamped data points collected over time. This use case is applicable in various industries, including finance, healthcare, manufacturing, and energy. Organizations can use Azure Data Explorer to analyze trends, detect patterns, and forecast future events based on historical time series data. The platform’s advanced query operators and real-time analysis capabilities enable organizations to derive valuable insights from time series data.
MongoDB Use Cases
Content Management Systems
MongoDB’s flexible data model makes it an ideal choice for content management systems, which often require the ability to store and manage diverse content types, such as articles, images, and videos. The schema-less nature of MongoDB allows for easy adaptation to changing content structures and requirements.
IoT Data Storage and Analytics
MongoDB’s support for high data volumes and horizontal scalability makes it suitable for storing and processing data generated by IoT devices, such as sensor readings and device logs. Its ability to index and query data efficiently allows for real-time analytics and monitoring of IoT devices.
MongoDB’s flexibility and performance features make it an excellent choice for e-commerce platforms, where diverse product information, customer data, and transaction records need to be stored and queried efficiently. The flexible data model enables easy adaptation to changes in product attributes and customer preferences, while the high availability and scalability features ensure a smooth and responsive user experience.
Azure Data Explorer Pricing Model
Azure Data Explorer’s pricing model is based on a pay-as-you-go approach, where customers are billed based on their usage of the service. The pricing is determined by factors such as the amount of data ingested, the amount of data stored, and the number of queries executed. Additionally, customers can choose between different pricing tiers that offer varying levels of performance and features. Azure Data Explorer also provides options for reserved capacity, which allows customers to reserve resources for a fixed period of time at a discounted rate.
MongoDB Pricing Model
MongoDB offers various pricing options, including a free, open-source Community Edition and a commercial Enterprise Edition, which includes advanced features, management tools, and support. MongoDB Inc. also offers a fully managed cloud-based database-as-a-service, MongoDB Atlas, with a pay-as-you-go pricing model based on storage, data transfer, and compute resources. MongoDB Atlas offers a free tier with limited resources for users who want to try the service without incurring costs.
Get started with InfluxDB for free
InfluxDB Cloud is the fastest way to start storing and analyzing your time series data.