Azure Data Explorer vs TDengine
A detailed comparison
Compare Azure Data Explorer and TDengine for time series and OLAP workloads
Learn About Time Series DatabasesChoosing 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 TDengine 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 TDengine 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 TDengine Breakdown
 |
 |
|
| Database Model | Columnar database |
Time series database |
| Architecture | 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. |
TDengine can be deployed on-premises, in the cloud, or as a hybrid solution, allowing flexibility in deployment and management. |
| License | Closed source |
AGPL 3.0 |
| Use Cases | Log and telemetry data analysis, real-time analytics, security and compliance analysis, IoT data processing |
IoT data storage, industrial monitoring, smart energy, smart home, monitoring and observability |
| Scalability | Highly scalable with support for horizontal scaling, sharding, and partitioning |
Horizontally scalable with clustering and built-in load balancing. TDengine also provides decoupled compute and storage as well as object storage support for data tiering in some versions |
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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.
TDengine Overview
TDengine is a high-performance, open source time series database designed to handle massive amounts of time series data efficiently. It was created by TAOS Data in 2017 and is specifically designed for Internet of Things (IoT), Industrial IoT, and IT infrastructure monitoring use cases. TDengine has a unique hybrid architecture that combines the advantages of both relational and NoSQL databases, providing high performance, easy-to-use SQL for querying, and flexible data modeling capabilities.
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.
TDengine for Time Series Data
TDengine is designed from the ground up as a time series database, so it will be a good fit for most use cases that heavily involve storing and analyzing time series data.
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.
TDengine Key Concepts
- Super Table: A template for creating multiple tables with the same schema. It’s similar to the concept of table inheritance in some other databases.
- Sub Table: A table created based on a Super Table, inheriting its schema. Sub Tables can have additional tags for categorization and querying purposes.
- Tag: A metadata attribute used to categorize and filter Sub Tables in a Super Table. Tags are indexed and optimized for efficient querying.
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.
TDengine Architecture
TDengine uses a cloud native architecture that combines the advantages of relational databases (support for SQL querying) and NoSQL databases (scalability and flexibility).
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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.
Data visualization
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.
Advanced analytics
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.
Flexible schema
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.
TDengine Features
Data ingestion
TDengine supports high-speed data ingestion, with the ability to handle millions of data points per second. It supports batch and individual data insertion.
Data querying
TDengine provides ANSI SQL support with additional that allows users to easily query time series data using familiar SQL syntax. It supports various aggregation functions, filtering, and joins.
Data retention and compression
TDengine automatically compresses data to save storage space and provides data retention policies to automatically delete old data.
Azure Data Explorer Use Cases
Log analytics
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.
Telemetry analytics
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.
TDengine Use Cases
IoT data storage and analysis
TDengine is designed to handle massive amounts of time series data generated by IoT devices. Its high-performance ingestion, querying, and storage capabilities make it a suitable choice for IoT data storage and analysis.
Industrial IoT monitoring
TDengine can be used to store and analyze data from industrial IoT sensors and devices, helping organizations monitor equipment performance, detect anomalies, and optimize operations.
Infrastructure Monitoring
TDengine can be used to collect and analyze time series data from IT infrastructure components, such as servers, networks, and applications, facilitating real-time monitoring, alerting, and performance optimization.
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.
TDengine Pricing Model
TDengine is open source and free to use under the AGPLv3 license. TDengine also offers commercial licenses and enterprise support options for organizations that require additional features, support, or compliance with specific licensing requirements.
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