Oracle provides products and services that
address enterprise information technology (IT) environments. Our products and services
include enterprise applications and infrastructure offerings that are delivered
worldwide through a variety of flexible and interoperable IT deployment models.
Accordingly, we offer choice and flexibility to our customers and facilitate the product, service and deployment combinations that best suit our customers’ needs. Our customers include businesses of many sizes, government agencies, educational institutions and resellers that we market and sell to directly through our worldwide sales force
for example, a global cloud applications
developer that utilizes Oracle Cloud Infrastructure (OCI) to power its
software-as-a-service (SaaS) offerings; a multi-national financial institution
that runs its banking applications using Oracle Exadata Cloud@Customer; and a
global consumer products company that leverages Oracle Fusion Cloud Enterprise
Resource Planning for its accounting processes, risk management, supply chain
and financial planning functions
Oracle SaaS and OCI (collectively Oracle Cloud Services) offerings provide comprehensive and integrated applications and infrastructure services delivered via various cloud delivery models enabling our customers to choose the best option
that meets their specific business needs.
Oracle Cloud Services integrate the IT components, including software, hardware
and services, on a customer’s behalf in a cloud-based IT environment that
Oracle deploys, manages, supports and upgrades for the customer and that a
customer may access utilizing common web browsers via a broad spectrum of devices
Oracle Cloud Services are designed to be
rapidly deployable to enable customers shorter time to innovation; intuitive
for casual and experienced users; easily maintainable to reduce upgrade,
integration and testing work; connectable among
differing deployment models to enable
interoperability and extensibility to easily move workloads among the Oracle
Cloud and other IT and cloud environments; cost-effective by lowering upfront
customer investments and implementing usage based resource consumption costs;
and highly secure, standards-based and reliable. Oracle cloud license and
on-premise license deployment offerings include Oracle Applications, Oracle
Database and Oracle Middleware software offerings, among others, which
customers deploy using IT infrastructure from the Oracle Cloud
or their own cloud-based or on-premise IT
environments. Substantially all customers opt to purchase license support contracts
when they purchase an Oracle license
Oracle hardware products include Oracle Engineered Systems, servers, storage and industry-specific products, among others. Customers generally opt to purchase hardware support contracts when they purchase Oracle hardware products. Oracle also offers professional services to assist our customers and partners to maximize the performance of their investments in Oracle products and services. Providing choice and flexibility to Oracle customers as to when and how they deploy Oracle applications and infrastructure technologies is an important element of our corporate strategy. We believe that offering customers broad, comprehensive, flexible and interoperable deployment models for Oracle applications and infrastructure technologies is important to our growth strategy and better addresses customer needs relative to our competitors, many of whom provide fewer offerings, more restrictive deployment models and less flexibility for a customer’s transition to cloud-based IT environments.
Our investments in, and innovation with respect to, Oracle products and services that we offer through our three businesses (cloud and license, hardware and services businesses, described further below) are another important element of our corporate strategy. In fiscal 2023, 2022 and 2021, we invested $8.6 billion, $7.2 billion and $6.5 billion, respectively, in research and development to enhance our existing portfolio of offerings and to develop new technologies and services.
We have a deep understanding as to how applications and infrastructure technologies interact and function with one another, including through the use of OCI to power our Oracle Fusion SaaS Applications, which we and our customers use to run internal business processes. We focus our development efforts
Snowflake vs. Oracle
Snowflake and Oracle’s cloud data warehouse adopt a pay-as-you-go model, where you only pay for the amount of data you consume. This model can work out to be expensive if you have large amounts of data, but Snowflake might save you more money in the long run. That’s because clusters will stop when you’re not running any queries (and resume when queries run again).
Ease of Use
Snowflake automatically applies all upgrades, fixes, and security features, reducing your workload. SNOW優點 Oracle, however, typically requires a database administrator of some kind, which can add to the cost of data warehousing in your organization. Similar problems exist with scaling these warehouses to meet the needs of your business. Snowflake data warehouse manages partitioning, indexing, and other data management tasks automatically; Oracle usually requires a database administrator to execute any scalability-related changes. Consider these differences when comparing Snowflake vs. Oracle.
Features
What about Snowflake vs Oracle features? Oracle lets you build and run machine learning algorithms inside its warehouse, which can prove incredible for your analytical objectives. Snowflake lacks this capability, requiring users to invest in a stand-alone machine learning platform to run algorithms. 一個缺點 Oracle also offers support for cursors, making it simple to program data.
On the flip side, Snowflake comes with an integrated automatic query performance optimization feature that makes it easy to query data without playing around with too many settings.
What to Consider Before using Snowflake vs. Oracle
While Snowflake and Oracle are effective data warehouses for analytics, both have steep learning curves that many businesses might struggle with. Companies will need coding knowledge (SQL) when operationalizing data in these warehouses and require a data engineer to ensure a smooth transfer of data between sources and their warehouse of choice.
Moving data to Snowflake or Oracle typically involves a process called Extract, Transfer, Load, or ETL. That means users have to extract data from a source like a relational database, transactional database, customer relationship management (CRM) system, enterprise resource planning (ERP) system, or other data platform. After data extraction, users must transform data into the correct format for analytics before loading it to Snowflake or Oracle. Another data integration option is Extract, Load, Transfer, where users extract data and load it to Snowflake or Oracle before transforming that data into a suitable format.
Key Takeaways on Oracle ADW’s Key Differentiators versus Snowflake
Taken together, I view Oracle ADW as clearly differentiated in the five key areas that organizations assign top priority – cost governance, real-time DW workloads, data integrity, ML integration, and deployment flexibility – in comparison to the Snowflake Cloud Data Platform.
First, in terms of cost governance Oracle ADW provides granular compute sizing, whereas Snowflake requires customers to double in size and cost for every step up to meet their expanding compute size requirements. In parallel, Oracle ADW auto-scales instantaneously online and uses governed storage, however I see Snowflake lacking the oversight mechanisms needed to control storage costs.
Second, I see Oracle ADW as providing a definitive edge in the processing of real-time and operational DW workloads. For example, Oracle ADW supports large numbers of concurrent queries, whereas the Snowflake platform defaults to eight concurrent queries per clusters. Oracle ADW furnishes indexes for rapid lookups – a capability that Snowflake completely lacks. In addition, Oracle ADW assures efficient updates, while Snowflake offers only an append-only architecture that impedes real-time updates.
Third, Oracle ADW ensures data integrity by applying enforced unique/primary key, foreign key and check constraints to ensure that the data is correct by preventing simple mistakes like duplicate records. In contrast, the Snowflake Data Cloud Platform does not enforce such meaningful constraints. As a result, Snowflake customers do not have full assurances about the integrity and correctness of their data.
Fourth, Oracle ADW supports and integrates a wide array of built-in, self-service ML algorithms. With Snowflake, customers must license third-party ML tools, install, manage, and learn how to use the tools, delaying time to insight and increasing overall costs. Oracle ADW also includes APEX, a popular no-code/low-code environment that significantly accelerates application development. I do not see Snowflake having equivalent of Oracle APEX.
Fifth, Oracle ADW is available in OCI and on-premises in Oracle Exadata Cloud@Customer and Dedicated Region Cloud@Customer with complete architectural identicality across deployment models. Moving Oracle Database data does not require transformations or re-formatting, Further, Oracle’s Cloud@Customer options enable customers to meet data sovereignty and regulatory requirements. Snowflake only runs in the public cloud and from my perspective does not address the requirements for data sovereignty and regulatory compliance.
Overall, I believe that DB/DW decision makers must prioritize these selection criteria in the evaluation process and and direct comparison of the Oracle ADW and Snowflake propositions. Organizations need to consider the full spectrum of DB and DW requirements or risk selecting a solution that curtails their ability to perform analytics in real-time, lacks fine-grained elastic scaling, does not provide full data integrity, has insufficient tools, and limits deployment flexibility in advancing their cloud DW journey. By separating hype from the underlying realities of DW optimization and administration, organizations can avoid spinning their wheels on subpar outcomes and getting ensnared in data warehousing snowdrifts.
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