Introduction
In today’s data-driven world, businesses need robust and scalable data warehousing solutions to stay ahead of the competition. Two key players in this domain are Oracle Autonomous Data Warehouse (ADW) and Snowflake Data Cloud. Both platforms offer unique features and capabilities for businesses looking to leverage the power of their data. In this blog post, we will provide a comprehensive comparison of Oracle ADW and Snowflake Data Cloud to help you choose the best option for your organization’s needs.
Architecture and Scalability
Oracle ADW: Oracle’s Autonomous Data Warehouse is built on the Oracle Exadata platform, a high-performance database appliance designed for enterprise workloads. ADW employs a shared architecture, where storage and compute resources are tightly coupled, enabling linear scalability and optimized performance. This allows the platform to automatically scale up or down based on workload requirements, without any downtime or manual intervention.
Snowflake Data Cloud: Snowflake’s unique multi-cluster shared data architecture separates compute and storage resources, enabling independent scaling of each component. This decoupled architecture allows organizations to allocate resources based on their needs and budget, providing greater flexibility and cost control. Additionally, Snowflake’s architecture supports near-infinite concurrency, enabling multiple users and applications to access the same data simultaneously.
Storage
Oracle ADW: is built on the Exadata database machine, a high-performance, fully optimized hardware and software stack developed by Oracle. Here’s how Oracle ADW stores data:
- Columnar Storage: Similar to Snowflake, Oracle ADW also uses a columnar data format for analytical workloads. This approach enables efficient data compression and enhances performance for queries that only need to access a subset of columns in a table.
- Data Compression: Oracle ADW uses advanced compression techniques to reduce storage costs and improve query performance. The Hybrid Columnar Compression (HCC) technology in Oracle allows it to achieve higher compression ratios, especially beneficial for large data warehouse tables.
- Data Blocks and Extents: Oracle databases, including ADW, organize data into units called data blocks, which are then grouped into larger units called extents. These are then allocated to database segments such as tables and indexes.
- Automatic Data Optimization: Oracle ADW supports Automatic Data Optimization (ADO), which allows the system to automatically move and compress data based on usage patterns and pre-defined policies.
Snowflake Data Cloud: uses a unique architecture that separates storage and compute resources, providing scalability and performance. Here’s how Snowflake stores data:
- Micro-partitions: Snowflake automatically divides data into small, immutable chunks called micro-partitions, each typically ranging in size from 50 MB to 500 MB. These micro-partitions store a subset of the data, organized by column, and include metadata to optimize query performance.
- Columnar Storage: Snowflake organizes data in a columnar format, allowing it to compress and read data more efficiently, particularly for analytical workloads.
- Compression: Snowflake automatically compresses data using various compression algorithms, improving storage efficiency and query performance.
- Data Clustering: Snowflake uses clustering keys to automatically organize related data in the same micro-partitions, improving query performance.
- Cloud-based Storage: Snowflake uses cloud-based storage services to store data, such as Amazon S3, Google Cloud Storage, or Azure Blob Storage.
In summary, both Oracle ADW and Snowflake use a combination of columnar storage, data partitioning, and compression to optimize storage and query performance. However, the specific implementation details and additional features differ between the two platforms, reflecting their distinct architectural approaches.
Data Security and Compliance
Oracle ADW: Oracle’s platform offers a wide range of security features, including data encryption, network isolation, and role-based access controls. ADW also incorporates Oracle’s Data Safe, a unified control center for managing data security and compliance, providing automatic detection of security threats and vulnerabilities, as well as recommendations for remediation. Additionally, Oracle adheres to various industry compliance standards such as GDPR, HIPAA, and PCI DSS.
Snowflake Data Cloud: Snowflake provides robust security features, including end-to-end encryption, network segmentation, and multi-factor authentication. It also supports granular role-based access controls, data masking, and data classification, enabling organizations to safeguard sensitive information effectively. Snowflake maintains compliance with a wide range of industry standards, including GDPR, HIPAA, and PCI DSS.
Data Integration and ETL
Oracle ADW: Oracle’s platform supports a variety of data integration tools, such as Oracle Data Integrator, Oracle GoldenGate, and Oracle Data Pump. These tools enable seamless extraction, transformation, and loading (ETL) of data from various sources into the Autonomous Data Warehouse. Additionally, ADW offers native integration with Oracle Analytics Cloud, enabling businesses to derive insights from their data quickly and easily.
Snowflake Data Cloud: Snowflake supports a vast ecosystem of data integration partners, allowing organizations to choose the best ETL tools for their needs. This enables seamless data ingestion from various sources, including structured and semi-structured data formats. Snowflake also offers native integration with leading analytics and BI platforms, providing a seamless experience for data analysis and visualization.
Pricing
Oracle ADW: Oracle offers a pay-as-you-go pricing model for its Autonomous Data Warehouse, with costs based on the number of OCPU or ECPU hours and storage capacity consumed. This allows businesses to scale their costs according to their needs, with the added benefit of Oracle’s performance optimization features that help minimize resource consumption. Oracle early in 2023 reduced its ADW storage price drastically by matching it to Object Storage price. Another price advantage of ADW is the BYOL (Bring Your Own License) model which allows you to transfer your on-premises perpetual licenses to be used with ADW. A very simple pricing example using OCI Cost Estimator for shared deployment:
- https://www.oracle.com/cloud/costestimator.html
- License Included: 8 ECPU for 744 hours + 10TB storage/month = USD 1217.06/month
- BYOL: 8 ECPU for 744 hours + 10TB storage/month = USD 481.98/month
- License Included: 8 ECPU for 744 hours + 10TB storage/month = USD 1217.06/month
Snowflake Data Cloud: Snowflake uses a consumption-based pricing model, where customers pay for the storage and compute resources they consume. This flexible pricing structure allows organizations to allocate resources based on their requirements, providing cost control and scalability. With Snowflake there are shared infrastructure options of choosing between Standard, Enterprise and Business Critical; the latest being the one most compatible with Oracle’s ADW offering. Snowflake’s pricing varies based on the deployment option, cloud provider and region. Considering AWS US-East (Ohio) a simple pricing example would be if the Virtual Warehouse is 100% utilized 744 hours/month:
- https://www.snowflake.com/pricing/
- Business Critical: 4 USD/credit with Large size Warehouse (8 credits/hour) + 10TB storage/month = (4*8*744)+(40*10) = USD 24208/month
- Business Critical: 4 USD/credit with Large size Warehouse (8 credits/hour) + 10TB storage/month = (4*8*744)+(40*10) = USD 24208/month
It is important to note that Snowflake charges for compute only while your queries are running, so it is a bit unfair to consider 100% utilization 24 hours a day. With Oracle ADW we could configure autoscaling for peak utilization, but that is also hard to estimate. Any cost estimation provided is just an estimation and it is hard to be precise since each database engine processes data differently which could lead to more or less compute power.
Conclusion
Both Oracle Autonomous Data Warehouse and Snowflake Data Cloud offer powerful data warehousing solutions for modern businesses. While Oracle ADW’s architecture and tight integration with other Oracle products may appeal to organizations with existing Oracle investments, Snowflake’s decoupled architecture and ecosystem of data offers ease of use and data sharing plus a flexible billing model, extensive resource management and data governance capabilities.