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 MySQL 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 MySQL 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 MySQL 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.
MySQL uses a client-server model with a multi-layered server design. It supports the SQL query language and offers various storage engines, such as InnoDB and MyISAM, for different use cases. MySQL can be deployed on-premises, in the cloud, or as a managed service.
GNU General Public License v2 (for the open-source Community Edition)
Log and telemetry data analysis, real-time analytics, security and compliance analysis, IoT data processing
Web applications, e-commerce, data warehousing, content management systems, business applications
Highly scalable with support for horizontal scaling, sharding, and partitioning
Supports vertical scaling by adding more resources to a single node; horizontal scaling can be achieved through replication, sharding, and third-party tools
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.
MySQL is an open source relational database management system that was first released in 1995. It is one of the most popular databases worldwide due to its ease of use, reliability, and performance. MySQL is widely used for web applications, online transaction processing, and data warehousing. Oracle Corporation acquired MySQL in 2010, but it remains open source software with an active community of contributors.
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.
MySQL for Time Series Data
MySQL can be used for storing and analyzing time series data, but it will not be as efficient as a dedicated time series databases. MySQL’s flexibility and support for various indexing techniques can make it a suitable choice for small to medium sized time series datasets. For large-scale time series data workloads, with high write throughput or use cases where low latency queries are required, MySQL will tend to struggle unless highly customized.
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.
MySQL Key Concepts
- Table: A collection of related data organized in rows and columns, which is the primary structure for storing data in MySQL.
- Primary Key: A unique identifier for each row in a table, used to enforce data integrity and enable efficient querying.
- Foreign Key: A column or set of columns in a table that refers to the primary key in another table, used to establish relationships between tables.
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.
MySQL is a relational database management system that uses SQL for defining and manipulating data. It follows the client-server model, where a MySQL server accepts connections from multiple clients and processes their queries. MySQL’s architecture includes a storage engine framework that allows users to choose from different storage engines, such as InnoDB, MyISAM, or Memory, to optimize the database for specific use cases.
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.
MySQL supports transactions and adheres to the ACID (Atomicity, Consistency, Isolation, Durability) properties, ensuring data integrity and consistency.
MySQL can scale both vertically and horizontally, depending on the storage engine and configuration.
Replication and high availability
MySQL supports various replication techniques, including master-slave and master-master replication, to provide high availability and fault tolerance.
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.
MySQL Use Cases
MySQL is a popular choice for powering web applications, content management systems, and e-commerce platforms due to its flexibility, ease of use, and performance.
Online transaction processing (OLTP)
MySQL is suitable for OLTP systems that require high concurrency, fast response times, and support for transactions.
While not specifically designed for data warehousing, MySQL can be used for small to medium-sized data warehouses, leveraging its support for indexing, partitioning, and other optimization techniques.
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.
MySQL Pricing Model
MySQL is available in multiple editions with different feature sets and pricing models. The MySQL Community Edition is open source and free to use, while the MySQL Enterprise Edition includes additional features, such as advanced security, monitoring, and management tools, and requires a subscription. Pricing for the Enterprise Edition depends on the number of server instances and the level of support required.
Get started with InfluxDB for free
InfluxDB Cloud is the fastest way to start storing and analyzing your time series data.