by Spencer Nguyen • May 9, 2022
Snowflake and Oracle Autonomous Data Warehouse are two cloud data warehouses that provide you with a single source of truth (SSOT) for all the data that exists in your organization. You can use either of these warehouses to run data through business intelligence (BI) tools and automate insights for decision-making. But which one should you add to your tech stack? In this guide, learn the differences between Snowflake vs. Oracle and how you can transfer data to the warehouse of your choice.
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Snowflake is a data warehouse built for the cloud. It centralizes data from multiple sources, enabling you to run in-depth business insights that power your teams.
Oracle is available as a cloud data warehouse and an on-premise warehouse (available through Oracle Exadata Cloud Service). For this comparison, DreamFactory will review Oracle’s cloud service.
Like Snowflake, Oracle provides a centralized location for analytical data activities, making it easier for businesses like yours to identify trends and patterns in large sets of big data.
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).
Snowflake automatically applies all upgrades, fixes, and security features, reducing your workload. 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.
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. 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.
Snowflake and Oracle take data security seriously, with features such as data encryption, IP blocklists, multi-factor authentication, access controls, and adherence to data security standards such as PCI DSS.
Users should be aware of data governance principles when transferring data to Snowflake or Oracle. Legislation such as GDPR and HIPAA mean businesses can incur expensive penalties for incorrectly moving sensitive information between data sources and a warehouse. Both platforms handle data governance adequately, with the ability to manage data quality rules and data stewardship workflows.
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.
ETL, ELT, and other data integration methods require a specific skill set because these processes are so complicated. Using DreamFactory can provide a solution to this problem. It connects data sources to Snowflake or Oracle through a live, documented, and standardized REST API, offering an alternative to data warehousing.
When comparing Snowflake vs. Oracle, realize that both providers offer superior data warehouses that help you operationalize and analyze real-time data in your organization. Snowflake might be easier to use and work out cheaper because of its ability to pause clusters when not running queries. However, Oracle comes with support for cursors and in-built machine learning capabilities, helping you program and generate advanced insights from workloads.
You can also compare Snowflake vs Oracle with other data warehouses such as Amazon (AWS) Redshift, Microsoft Azure, and Google BigQuery. Whatever option you choose, think about how your business will transfer data to a warehouse.
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