comparisons
Steampipe vs. CloudQuery
Are you trying to figure out the best tool for syncing and analyzing cloud data? Whether you want to enhance cloud security, optimize your infrastructure costs, or gain deeper insights into your multi-cloud environment, Steampipe and CloudQuery offer robust technical solutions to tackle these challenges. However, there are critical technical differences between the two that you should consider when evaluating which tool best fits your needs. In this post, we’ll break down these differences and explore use cases where one might be a better choice over the other for your cloud management tasks.
Steampipe and CloudQuery are both robust solutions that can help you to sync and analyze cloud data, whether you’re looking to enhance cloud security, optimize your infrastructure costs, or gain deeper insights into your multi-cloud environment, However, there are critical technical differences between the two and its important to analyze these and understand how they interact with your workflow needs before choosing one. This post breaks down these differences and explores use cases where one may be a better choice than the other.
Overview of CloudQuery #
CloudQuery is an open-source data movement tool that simplifies the management and analysis of cloud resources. It reliably collects and processes large volumes of data from several cloud providers and other sources to give an overall view of your cloud environment. Its features include:
- High-performance data ingestion and processing: CloudQuery can move large amounts of data quickly by using Go's concurrency model and Apache Arrow.
- Sync your data to any destination: You can move your data to any data destination.
- Deploy anywhere: CloudQuery can be run as a single-binary executable and deployed and run anywhere. This means you can run it in your CI/CD pipelines, inside your application, locally, or in the cloud.
- Scalability: CloudQuery plugins are stateless and can be scaled horizontally on any platform, such as VMs, Kubernetes, or batch jobs.
- Security and compliance: Reliable security measures protect sensitive data, and compliance features help meet industry standards.
- Open source: Extensible plugin architecture: develop your own plugins in Go, Python, Java, or JavaScript using the CloudQuery SDK.
Overview of Steampipe #
Steampipe is an open-source platform for real-time cloud resources and infrastructure data access. It is a platform for querying and exploring data from various cloud providers, on-premises systems, and SaaS applications. Its primary goals are interactive and exploratory data analysis. Its features include:
- Real-time data access: Steampipe allows users to query and explore live data from cloud resources.
- Large number of plugins: Having more plugins extends Steampipe's capabilities to various data sources.
- Powerful query engine: Steampipe is built on a PostgreSQL-compatible base and offers a SQL-like interface for querying data.
- CLI-first approach: A command-line interface allows for scripting and automation.
- Large community: A large and active community contributes to the platform's development and support.
Technical Comparison #
Here is a comparison of features in Steampipe vs. CloudQuery.
| Feature | CloudQuery | Steampipe |
|---|---|---|
| Data Sources | Cloud providers, Databases, APIs | Cloud providers, Databases, APIs |
| Data Ingestion | Batch and Incremental | Real-time |
| Database Agnostic | Yes, supports any database | No, uses PostgreSQL |
| Database Migrations | Yes | No |
| Historical data | Yes | Yes, with Turbot Pipes |
| CLI | Yes | Yes |
| Cloud Option | Yes | Yes |
| Custom plugin support | Yes | Yes |
| Pricing | By number of rows synced | Amount of compute minutes and storage used |
Use Cases #
This section will cover some different use cases and how CloudQuery and Steampipe can be used.
Use Case 1: Building a Multi-Cloud Asset Inventory #
When building an inventory of all your cloud assets across multiple cloud providers, both CloudQuery and Steampipe can handle this task efficiently. They offer robust connectors and plugins that allow you to gather data from various sources, such as AWS, Azure, GCP, and other infrastructure providers, giving you a comprehensive view of your cloud environment.
- CloudQuery shines in scenarios where you want to persist this inventory data over time. It extracts your cloud asset data and syncs it into your preferred database, allowing you to analyze historical trends, run complex queries, or integrate with BI tools. CloudQuery is a strong choice if you’re looking to build an asset inventory that can be enriched, transformed, or analyzed later using standard data tools.
- Steampipe excels in real-time querying, making it ideal for exploratory analysis and quick access to your cloud assets without permanently storing the data. It allows you to query your cloud resources directly using SQL, which is beneficial for ad-hoc analysis or quick checks across your infrastructure.
Conclusion: Both tools effectively create a multi-cloud asset inventory, but your choice should hinge on what you plan to do with the data afterward. If you need a persistent, analyzable dataset that integrates into your broader data infrastructure, CloudQuery is the better fit. Steampipe may be more suitable if you prefer real-time exploration.
Use Case 2: Querying Synced Data with Your Existing Data Warehouse or Data Lakes #
When you need to run queries against your cloud asset data alongside existing data warehouses or data lakes, CloudQuery and Steampipe offer different strengths.
- CloudQuery integrates with popular data warehouses and data lakes like Snowflake, BigQuery, Amazon Redshift, and more. Since CloudQuery syncs your cloud data into these destinations, it becomes part of your data infrastructure, allowing you to join, transform, and analyze cloud asset data alongside other enterprise data. This makes it an excellent choice if you want to leverage advanced analytics, BI tools, or machine learning models using your existing data pipelines and infrastructure.
- Steampipe, on the other hand, doesn’t inherently integrate with data warehouses or data lakes in the same manner. Its strength lies in querying live cloud resources directly, which means it’s not optimized for integrating with pre-existing large datasets stored in a centralized data warehouse. However, if you’re primarily interested in real-time analysis or exploratory querying without needing historical data integration, Steampipe can still be effective.
Conclusion: For running queries against synced data in your existing data warehouse or data lakes, CloudQuery is the superior option due to its compatibility and ability to persist data in easily queryable formats within those systems. Steampipe, while powerful for real-time access, isn’t designed for this level of integration.
Summary #
Choosing between Steampipe and CloudQuery ultimately depends on your specific needs and how you intend to work with your cloud data. At a high level, CloudQuery excels in scenarios where you need to sync, store, and analyze data over time, making it perfect for long-term data analysis, integration with existing data warehouses, and maintaining historical records. Steampipe, on the other hand, is optimized for real-time querying and exploration, offering a quick and direct way to access your cloud assets without the need for data persistence.
We explored several use cases:
- Building a Multi-Cloud Asset Inventory: Both tools can efficiently build an inventory, but CloudQuery is better suited for scenarios where you need to maintain this data over time, while Steampipe shines for real-time exploration.
- Querying Synced Data with Existing Data Warehouses: CloudQuery stands out here with its seamless integration into existing data lakes and warehouses, allowing you to run complex analyses alongside your broader datasets.
If you’re looking for a solution that integrates well with your data infrastructure and supports deeper analysis over time, CloudQuery is the way to go. Steampipe is a solid choice for those who need quick, real-time insights.
Ready to get started with CloudQuery? You can download and use CloudQuery and follow along with our quick start guide, or explore CloudQuery Cloud for a more scalable solution.
Want help getting started? Join the CloudQuery community to connect with other users and experts, or message our team directly here if you have any questions.
Written by Joe Karlsson
Joe Karlsson (He/They) is an Engineer turned Developer Advocate (and massive nerd). Joe empowers developers to think creatively when building applications, through demos, blogs, videos, or whatever else developers need.
