Azure Data Explorer vs StarRocks
A detailed comparison
Compare Azure Data Explorer and StarRocks 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 StarRocks 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 StarRocks 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 StarRocks Breakdown
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| Database Model | Columnar database |
Data warehouse |
| 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. |
StarRocks can be deployed on-premises, in the cloud, or in a hybrid environment, depending on your infrastructure preferences and requirements. |
| License | Closed source |
Apache 2.0 |
| Use Cases | Log and telemetry data analysis, real-time analytics, security and compliance analysis, IoT data processing |
Business intelligence, analytics, real-time data processing, large-scale data storage |
| Scalability | Highly scalable with support for horizontal scaling, sharding, and partitioning |
Horizontally scalable, with support for distributed storage and query processing |
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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.
StarRocks Overview
StarRocks is an open source high-performance analytical data warehouse that enables real-time, multi-dimensional, and highly concurrent data analysis. It features an MPP (Massively Parallel Processing) architecture and is equipped with a fully vectorized execution engine and a columnar storage engine that supports real-time updates.
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.
StarRocks for Time Series Data
StarRocks is primarily focused on data warehousing workloads but can be used for time series data. StarRocks can be used for real time analytics and historical data analysis.
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.
StarRocks Key Concepts
- MPP Architecture: StarRocks utilizes an MPP architecture, which enables parallel processing and distributed execution of queries, allowing for high-performance and scalability.
- Vectorized Execution Engine: StarRocks employs a fully vectorized execution engine that leverages SIMD (Single Instruction, Multiple Data) instructions to process data in batches, resulting in optimized query performance.
- Columnar Storage Engine: The columnar storage engine in StarRocks organizes data by column, which improves query performance by only accessing the necessary columns during query execution.
- Cost-Based Optimizer (CBO): StarRocks includes a fully-customized cost-based optimizer that evaluates different query execution plans and selects the most efficient plan based on estimated costs.
- Materialized View: StarRocks supports intelligent materialized views, which are precomputed summaries of data that accelerate query performance by providing faster access to aggregated data.
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.
StarRocks Architecture
StarRock’s architecture includes a fully vectorized execution engine and a columnar storage engine for efficient data processing and storage. It also incorporates features like a cost-based optimizer and materialized views for optimized query performance. StarRocks supports real-time and batch data ingestion from a variety of sources and enables direct analysis of data stored in data lakes without data migration
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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.
StarRocks Features
Multi-Dimensional Analysis
StarRocks supports multi-dimensional analysis, enabling users to explore data from different dimensions and perspectives.
High Concurrency
StarRocks is designed to handle high levels of concurrency, allowing multiple users to execute queries simultaneously.
Materialized View
StarRocks supports materialized views, which provide precomputed summaries of data for faster query performance.
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.
StarRocks Use Cases
Real-Time Analytics
StarRocks is well-suited for real-time analytics scenarios, where users need to analyze data as it arrives, enabling them to make timely and data-driven decisions.
Ad-Hoc Queries
With its high-performance and highly concurrent data analysis capabilities, StarRocks is ideal for ad-hoc querying, allowing users to explore and analyze data interactively.
Data Lake Analytics
StarRocks supports analyzing data directly from data lakes without the need for data migration. This makes it a valuable tool for organizations leveraging data lakes for storage and analysis.
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.
StarRocks Pricing Model
StarRocks can be deployed on your own hardware using the open source project. There are also a number of commercial vendors offering managed services to run StarRocks in the cloud.
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