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Why Oracle Autonomous Data Warehouse over Snowflake

Oracle Autonomous Data Warehouse is a cloud data warehouse service that eliminates virtually all the complexities of operating a data warehouse, securing data, and developing data-driven applications. It automates provisioning, configuring, securing, tuning, scaling, patching, backing up, and repairing of the data warehouse. Unlike other fully managed cloud data platform solutions that only patch and update their service, it features elastic, automated scaling, performance tuning, security, and a broad set of built-in converged database capabilities that enable simpler queries across multiple data types, machine-learning analysis, simple data loading, and data visualizations. It’s available in both the Oracle public cloud and customers’ data centers with Oracle Cloud@Customer.

Customers choose Oracle over Snowflake for several reasons:


1. Only Autonomous Data Warehouse fully automates database administration

Autonomous Data Warehouse’s self-managing capabilities make it easy to set up, maintain, and secure data warehouses with minimal levels of DBA expertise and effort. Due to poor workload management, Snowflake requires database administrators to experiment with different warehouse sizes and cluster configurations to find the ones that best suit their workloads and then manually scale the warehouses up or down to the next fixed size to support changes in workload complexity.

Capability and evidence
Oracle Autonomous Data Warehouse
Snowflake
Can organizations run data warehouses without extensive DBA skills?
Autonomous Data Warehouse automatically tunes and scales the database, running customer workloads with changing query complexity and concurrency at peak performance while eliminating the need for DBA involvement.

Snowflake requires DBAs to manually experiment with different fixed warehouse sizes to find the best configuration for each workload. They have to manually maintain multiple warehouses running for different workloads, as you cannot mix different workloads in the same warehouse. When query complexity changes, DBAs must manually increase or decrease the size of the cluster running the Snowflake data warehouse.
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no
Does the service allow you to only pay for the compute you need?
Autonomous Data Warehouse’s autoscaling capabilities automatically increase and decrease compute resources with no downtime or manual involvement. Compute resources are scaled up on a granular, processor-by-processor basis to support higher workload demands, and then automatically scaled back down when the spike in query load subsides, reducing total customer costs. No administrator involvement is needed.

Scaling Snowflake warehouses requires administrators to manually change the warehouse to the next warehouse size. For instance, scaling up a 32-node warehouse requires jumping to the next warehouse size of 64 nodes, even though only a small increment of compute resources may be needed. Relying on fixed compute sizes that double warehouse sizes and costs every time a little more query performance is needed leads to tremendous waste as organizations are required to overprovision based on expected peak demand.
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Will your database scale up to support high query concurrency without provisioning numerous, expensive replicas?
Autonomous Data Warehouse easily handles concurrent requests by scaling resources based on total workload demands—including query complexity and concurrency. It manages query concurrency using a set of consumer groups, which allow customers to control how resources are used to support their workloads. There is never a need to create additional copies of the warehouse.

Scaling Snowflake warehouse size is not designed to handle high levels of query concurrency. Snowflake lacks a resource manager to assign resources to queries based on their importance and overall system workload. Snowflake customers must meet requirements for additional concurrency by creating additional full copies of the Snowflake warehouse in a multicluster configuration. If a cluster has 8 nodes, creating another cluster will consume a total of 16 credits, doubling the cost. If a multicluster warehouse is resized, for example, from 32 to 64 nodes, the new size applies to all the replicas for the warehouse. As a result, the cost can escalate quickly.
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Arlington Orthopedic logo

Arlington Orthopedic Associates staff employs Oracle Autonomous Data Warehouse and Oracle Analytics to instantly visualize detailed financial scenarios, helping them negotiate the best rates with insurance carriers and create savings that are used to improve patient care. Patient count has doubled and revenue has increased 18% without adding IT resources, maintenance, or support costs.


2. Autonomous Data Warehouse is simpler to deploy and use

Autonomous Data Warehouse’s extensive built-in analytics, machine-learning (ML), security, and developer capabilities eliminate the need for additional services, reducing solution complexity and costs. Snowflake does not offer integrated machine-learning capabilities, extensive security capabilities, or developer tools, requiring customers to subscribe to, integrate, and manage several additional services to provide a complete modern data warehouse solution.

Capability and evidence
Oracle Autonomous Data Warehouse
Snowflake
Are optimized ML capabilities built into the data warehouse?
Autonomous Data Warehouse allows users to build and run ML algorithms inside the database, eliminating the need for a standalone ML service and moving data to it. Familiar Oracle Database tools and Automatic Machine Learning (AutoML) allow users to achieve inference-based insights faster because copy-reformat-transfer times are eliminated.

Snowflake lacks pre-built, in-database machine-learning algorithms. Customers must extract data from the Snowflake warehouse, reformat and transfer their data to a separate, stand-alone ML service, and run their algorithms on that service in order to develop algorithms and obtain predictive insights. Using multiple tools and steps delays insights while increasing complexity, costs, and potential security risks.
yes
no
Are high level security controls built into the data warehouse?
Built-in Autonomous Data Warehouse capabilities transparently secure customers’ data warehouses, eliminating costly and time-consuming application-level changes. Oracle’s capabilities help you understand the sensitivity of data, mask it, audit user activity, and address security compliance requirements. Powerful security controls help customers protect data by blocking privileged account access to application data and control of sensitive operations inside the data warehouse.

Snowflake lacks equivalent built-in functionality, requiring customers to integrate additional services and tools, increasing operational complexity and administrative costs for securing data.
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Are low-code development tools included with the data warehouse?
Autonomous Data Warehouse includes Application Express (APEX), a highly productive, no-code/low-code environment. As described in a Pique Solutions study (PDF), APEX enables developers and power users to build data-driven JSON-centric applications up to 38X faster and with 95% less code than traditional solutions.

Snowflake does not have an equivalent of Oracle APEX. Implementing data-driven applications with Snowflake requires developers to use third-party tools and pay for additional services that may not be as productive as Oracle APEX.
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OUTFRONT logo

OUTFRONT uses Oracle Autonomous Data Warehouse and Oracle Analytics to combine information on more than 500,000 digital and static billboards in North America with third-party media spend to create a comprehensive view of customers’ total advertising spend. OUTFRONT’s sales professionals and executives use interactive dashboards and visualizations to recommend how advertisers can strategically utilize OUTFONT’s products in their media mix. In just minutes, the company loads and merges terabytes of data and securely publishes interactive dashboards, a process that previously took two to three weeks to complete.


3. Oracle costs less with built-in capabilities and autoscaling that closely matches costs to overall workload requirements

Autonomous Data Warehouse’s self-driving capabilities automatically tune the database and adjust consumption to current workload demand while its numerous built-in capabilities eliminate the higher costs incurred when deploying a modern data warehouse using multiple services. Snowflake’s costs are higher because it requires more DBA efforts and has poor workload management to meet changes in query complexity. Customers are also required to pay for additional standalone services.

Capability and evidence
Oracle Autonomous Data Warehouse
Snowflake
Are costs closely aligned to the exact amount of resources needed for query performance and throughput?
Autonomous Data Warehouse automatically increases and decreases compute resources as overall workload requirements change. Scaling to the exact number of resources required to support query complexity and concurrency allows customers to minimize costs while running at peak performance.

Snowflake warehouses do not automatically scale up and down to the exact number of nodes needed as query complexity changes. Snowflake warehouses come in fixed sizes that must be manually scaled to the next instance size to match query complexity. For example, a mid-range data warehouse would need to go from 16 nodes to 32 nodes, even if meeting current query complexity demands would require only one more node. To meet concurrency scaling requirements, Snowflake automatically creates additional copies of the entire warehouse, so if a cluster has 32 nodes, creating another cluster will double total costs.
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Does the warehouse use intelligent storage servers to increase performance and reduce costs?
Autonomous Data Warehouse offloads low-level SQL commands, analytics functions, and machine-learning algorithms to intelligent storage servers and closer to where the data resides to increase performance. Storage-based processing power is continuously available and is included in Autonomous Data Warehouse’s storage pricing.

Snowflake does not offer equivalent functionality. Data must be loaded from storage into the warehouses and all processing is completed on the data warehouse cluster nodes, increasing the amount of paid resources customers must consume to support their workloads.
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no
Can customers build self-service data marts without additional third-party services?
Autonomous Data Warehouse includes comprehensive built-in capabilities that address a broad set of analytical and machine-learning use cases found in modern data warehouses. It does not require implementation of additional third-party services to meet these needs.

Snowflake offers limited built-in analytical capabilities, requiring the use of third-party services to create a complete solution. Organizations wanting to use machine learning, data science, graph analytics, data ingest, or low-code development tools need to subscribe to one or more additional cost services. The cost to integrate and run additional services can cost customers additional tens of thousands of dollars per year (PDF).
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Can customers run their existing data warehouse ecosystems in the cloud without re-architecting them to an entirely new platform?
Autonomous Data Warehouse is 100% compatible with the core Oracle Database capabilities currently running in customers’ data centers. Moving on-premises data warehouses based on Oracle Database to Oracle Cloud Infrastructure is seamless.

Snowflake is not compatible with customers’ existing Oracle Database implementations. It requires time-consuming re-platforming. For example, schemas need to be redesigned to accommodate Snowflake’s write-once storage model.
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Can customers easily manage Oracle data warehouse migration with proven, familiar tools?
Autonomous Data Warehouse provides zero downtime and seamless migration from Oracle Database. Migration of non-Oracle data sources is also seamless with SQL Developer Migration Workbench and Oracle GoldenGate.

Migration from Oracle Database to Snowflake is costly and complicated. It requires implementing third-party migration tools and recoding all schemas and PL/SQL using JavaScript.
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d.light logo

d.light, a provider of affordable solar power to 100M people in 70 countries, uses Autonomous Data Warehouse to consolidate data from different sources, reducing data loading and reporting from 12 hours to minutes and reducing DBA workloads by 75%.


4. Autonomous Data Warehouse provides stronger built-in security to protect against internal and external risks

Autonomous Data Warehouse provides high end-to-end security throughout the data lifecycle with always-on encryption, data discovery and masking, separation of duties administration, and extensive threat detection and remediation that comes standard with Oracle Cloud Infrastructure and Cloud@Customer solutions. Snowflake, even with the more expensive Enterprise or Business-Critical editions, lacks the comprehensive security controls and monitoring of Autonomous Data Warehouse.

Capability and evidence
Oracle Autonomous Data Warehouse
Snowflake
Does the data warehouse include data discovery, user scoring, and data masking tools to secure customer data?
Autonomous Data Warehouse includes built-in security with Oracle Data Safe, helping organizations understand the sensitivity of their data, mask sensitive data, and evaluate risks to data. Customers use Data Safe to implement and monitor security controls, monitor user activity, audit user and administrative activities, and address data security compliance requirements.

Snowflake provides core security features but lacks built-in functionality that is equivalent to Oracle Data Safe. Customers must implement similar Data Safe features using additional services and tools, which increase operational and administrative costs for securing data.
yes
no
Does the data warehouse provide security controls to prevent privileged users from accessing sensitive data?
Oracle Database Vault provides powerful security controls to help customers protect application data from unauthorized access while helping address privacy and regulatory requirements. IT teams use controls to block privileged account access to application data and control of sensitive operations inside the data warehouse. Oracle Database Vault transparently secures data warehouses, eliminating costly and time-consuming changes at the application level.

Snowflake does not have the equivalent of Oracle Database Vault’s separation of duties management, instead relying on a simpler access control method to prevent unauthorized access. It cannot provide preventive controls to block privileged users and DBAs from accessing sensitive data in the data warehouse.
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Does the data warehouse have ISO27017 and ISO27018 certifications?
Autonomous Data Warehouse is certified for ISO27001/ISO27017/ISO27018, international standards for managing information security in a systematic way. These standards help organizations manage their data security by identifying best practices for data management duties, processes, and technology.

Snowflake is not certified for ISO27017 and ISO27018.
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Sensa Analytics logo

Sensa Analytics, a leading healthcare analytics firm, uses an application developed with Oracle Application Express (APEX) and HIPPA-compliant Oracle Autonomous Data Warehouse to gather data from multiple sources to provide real-time visibility into everything from patient satisfaction to revenue processes. Hospitals and surgery centers operate more efficiently with Sensa Analytics running with Autonomous Data Warehouse.


5. Autonomous Data Warehouse makes it easy for organizations to meet data sovereignty requirements

Oracle Autonomous Data Warehouse can be deployed on either shared or dedicated infrastructure in Oracle Cloud Infrastructure regions as well as on Cloud@Customer platforms located in customer data centers. Customers using Cloud@Customer solutions achieve the economic and operational benefits of using a public cloud while also addressing data sovereignty and security requirements. Snowflake is only available in public cloud regions and cannot meet data sovereignty requirements.

Capability and evidence
Oracle Autonomous Data Warehouse
Snowflake
Does the solution provide options for public cloud, hybrid cloud and on-premises deployments?
Autonomous Data Warehouse provides a choice of deployment in OCI regions or in customer data centers via Exadata Cloud@Customer and Dedicated Region Cloud@Customer. Organizations can easily transition from on-premises to cloud and vice versa to meet changing business and regulatory requirements.

Snowflake is available only in the public cloud and does not offer a deployment option in a customers’ data centers.
yes
no
Can the data warehouse address data sovereignty requirements?
For certain applications, industries, and geographies, data must always reside in customers’ data centers located in the country of origin. With Cloud@Customer solutions, Autonomous Data Warehouse can be deployed as a fully managed cloud service in a customer’s own data centers to address data sovereignty and security requirements.

Snowflake only runs in the public cloud. It cannot be deployed in a customer’s own data center to address the requirements for data sovereignty and regulatory compliance.
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RKK Computer Service logo

RKK Computer Service uses Exadata Cloud@Customer to run core business systems for more than 100 municipalities in Japan, achieving high availability, improving IO performance by more than 70%, and reducing costs by 24%. They are using Autonomous Database on Exadata Cloud@Customer to further improve operational efficiency.

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