Oracle Data Warehouse vs Redshift: Compare data warehouse software

Oracle Data Warehouse and Amazon Redshift are two popular data warehousing solutions, but which one has your organization’s ideal features and capabilities? Read this comparison to find out.

Data warehousing solutions enable users to process their organizational data and gain more insights from their data analysis. But with so many different types and vendors of data solutions on the market, it can be challenging to identify the best data warehousing product for your team. By learning about the data use capabilities of your data solution options, you can determine which aspects of each tool would better satisfy your data needs. This article will discuss the features and functionalities of two popular data warehousing solutions: Oracle Data Warehouse and Amazon Redshift.

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What is Oracle Data Warehouse?

Oracle Data Warehouse is a cloud-native analytics and data warehousing solution. Its features and capabilities simplify data warehouse management for users through automation and support of business intelligence activities.

What is Redshift?

Amazon Redshift is a data warehouse tool that uses SQL to analyze data. Its software aids users in managing their database migrations and large-scale data sets.

Oracle Data Warehouse vs. Redshift feature comparison

Data syncing

Oracle Data Warehouse can connect and load data from the Oracle Object Store, AWS S3, or on-premises data sources. It can consolidate data from multiple data sources like applications and spreadsheets into a query-optimized data store using self-serve data tools so users can gain actionable insights. With the Oracle SQL Developer tool, data can be swiftly and easily transferred into the Oracle Cloud, and the migration workbench tools support many database providers. In addition, users can utilize and gain data from third-party solutions, as the Oracle Cloud model supports this. Any data can be loaded and managed within the platform, and customers can efficiently utilize drag-and-drop capabilities for data connectors, data models, third-party integrations and more.

Amazon Redshift enables users to programmatically access their data within the platform with the Data API. The software can utilize structured and semi-structured data from cloud-native, traditional, containerized, serverless web services-based and event-driven applications across all operational databases, data warehouses and data lakes. Its integrations allow users to sync and transform data from third-party sources with Data Integration Partners. Additionally, the tool can stream and ingest data from multiple Kinesis data streams at a time. Redshift’s API enables data parsing in various formats and programming languages. These include data from TSV, HSON, Apache logs and CSV data source formats, and data in supported platforms such as Ruby, Go, PHP, C++, Java and more.

Data analysis

Oracle’s data warehouse solution has built-in support for data workloads, including in-database machine learning, spatial, graph and analytical SQL. The tool enables users to gain insights by asking questions of their data. The software’s high-performance analytics and support for other popular BI tools allow users to gain actionable insights from their data immediately. Users can transform, manage, govern, visualize, analyze and build machine learning models to gain insights from their datasets through the oracle solution.

It comes with built-in support to optimize data from multiple sources and perform multiple workloads, including analytical SQL, in-database machine learning, Oracle Spatial and Graph. Graph analytics can enable users to manage relationships in data for deeper analysis and insight discovery. Users may also benefit from utilizing the simple integrations with Oracle Analytics Cloud or other popular BI tools. Building and deploying one’s own machine-learning models is possible for a broader range of analysis capabilities fine-tuned to the needs of the users’ organization. By applying data science capabilities and analytics, users can understand the context for actionable events and create a well-informed response.

Redshift software can analyze exabytes of data and run complex analytical queries on data using AWS-designed hardware, machine learning, and SQL. Users need only to load and query data in the data warehouse to gain valuable information through data analysis. The software can then run analytic workloads. In addition, it can process data and provide users valuable insights through ad-hoc analysis methods, such as anomaly detection, what-if analysis and machine learning-based forecasting.

The system’s reporting can also provide actionable insights. With Redshift, users can run queries within the Redshift platform or connect SQL client tools, libraries or data science tools for greater functionality. Redshift even enables users to utilize machine learning with Redshift ML to develop and manage Amazon SageMaker models with SQL for predictive analyses, forecasting, risk scoring and more. Redshift supports standard scalar data types and native support for various processes for advanced analytics. And for a more simplified analysis experience, the Query Editor v2 feature lets users visualize query results quickly with just one click.

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Automation capabilities

Oracle Data Warehouse utilizes automation in many ways to help users manage and gain insights from their data. Analysis tools can automate data management for easier analysis, modeling and visualization. For example, Graph Studio enables automated graph modeling, installation, upgrading, provisioning, autosave, scheduled analysis capabilities and more. Users don’t need to worry about erroneously managing and maintaining their data warehouse solution with the autonomous database cloud service, which is self-tuning and preconfigured with automated self-patching and upgrades for optimal performance. In addition, through its machine learning feature, the software automatically optimizes caching and indexing to reduce CPU consumption and helps users save costs and reduce risk.

Amazon Redshift has many automation features and functions to take care of provisions and manage the infrastructure for analytics workloads. For example, the tool can automatically scale data warehouse capacity, so performance is always fast and efficient. Users can also benefit from cost optimization since the product automatically scales up capacity when busy and scales down when not, resulting in less spending. Redshift’s Automatic Table Optimization configures the sort and distribution settings to improve cluster performance and optimize query speeds without requiring administrator action. Other ways that Redshift manages workloads with sophisticated algorithms to enhance the layout of data involve features including Automatic Table Sort, Automatic Analyze and Automatic Vacuum Delete.

So which is the better solution — Oracle Data Warehouse or Redshift?

The better data warehousing and analysis tool isn’t always obvious, and the answer may change from one organization to another based on their specific needs. For example, an organization that already utilizes many Oracle applications and tools may choose Oracle Data Warehouse as the best option for easy integrations with other Oracle products. But other users may require advanced query performance capabilities provided by Redshift through its distribution keys and sort keys. By analyzing the needs and aspects of their organizational datasets, data sources and BI solutions, users can compare data solution options to find the best tool for their organization.