Oracle Data Warehouse and Amazon Redshift are two popular data warehousing solutions, but which one offers the ideal features and capabilities for your business? Read this comparison to find out.
Data warehousing solutions enable users to process their enterprise data and gain more insights from their data analysis. But with so many different types and vendors of data solutions on the market, finding the best data warehousing product for your team can be difficult. By learning about the data usage capabilities of your data solution options, you can determine which aspects of each tool better serve your data needs. This article discusses the features and functionality of two popular data warehousing solutions: Oracle Data Warehouse and Amazon Redshift.
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What is Oracle Data Warehouse?
Oracle DataWarehouse is a cloud-native analytics and data warehousing solution. Its features and capabilities simplify data warehouse management for users by automating and supporting business intelligence activities.
What is Redshift?
Amazon Redshift is a data warehouse tool that uses SQL to analyze data. The software assists users in managing their database migrations and large datasets.
Feature comparison of Oracle Data Warehouse and Redshift
Oracle Data Warehouse can connect and load data from Oracle Object Store, AWS S3 or on-premises data sources. It can consolidate data from multiple data sources, such as applications and spreadsheets, into a query-optimized data store using self-service data tools, enabling users to generate actionable insights. The Oracle SQL Developer Tool makes it quick and easy to transfer data to the Oracle Cloud, and the Migration Workbench tools support many database vendors. Additionally, users can leverage and mine data from third-party solutions as the Oracle Cloud model supports it. All data can be loaded and managed within the platform, and customers can efficiently use drag-and-drop capabilities for data connectors, data models, third-party integrations, and more.
Amazon Redshift allows users to access their data programmatically within the platform using the Data API. The software can leverage structured and semi-structured data from cloud-native, traditional, containerized, serverless web service-based, and event-driven applications across any operational database, data warehouse, and data lake. Its integrations allow users to sync and transform data from third-party sources with data integration partners. In addition, the tool can stream and ingest data from multiple Kinesis data streams simultaneously. Redshift’s API enables data analysis in various formats and programming languages. This includes data from TSV, HSON, Apache logs and CSV data source formats, as well as data in supported platforms such as Ruby, Go, PHP, C++, Java and more.
Oracle’s data warehouse solution has built-in support for data workloads, including in-database machine learning, spatial, graphical, and analytical SQL. The tool allows users to gain insights by asking questions about their data. The software’s powerful analytics and support for other popular BI tools enable users to gain immediately actionable insights from their data. Users can transform, manage, control, visualize, analyze, and create machine learning models to gain insights from their datasets through the Oracle solution.
It has built-in support for optimizing data from multiple sources and running multiple workloads, including analytical SQL, in-database machine learning, Oracle Spatial, and Graph. Graph analytics allows users to manage relationships in data for deeper analysis and insights. Users can also benefit from leveraging easy integrations with Oracle Analytics Cloud or other popular BI tools. It is possible to create and deploy your own machine learning models for a broader range of analysis functions, precisely tailored to the needs of the users’ organization. By applying data science capabilities and analysis, users can understand context for actionable events and create an informed response.
Redshift software can analyze exabytes of data and run complex analytical queries on data using AWS-engineered hardware, machine learning, and SQL. Users only need to load data into the data warehouse and query to gain valuable information through data analysis. The software can then run analytical workloads. In addition, it can process data and provide users with 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 more functionality. Redshift even allows users to leverage machine learning with Redshift ML to develop and maintain Amazon SageMaker models using SQL for predictive analytics, forecasting, risk assessment, and more. Redshift supports standard scalar data types and native support for various processes for advanced analytics. And for a simplified analysis experience, the Query Editor v2 feature allows users to quickly visualize query results with just one click.
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Oracle Data Warehouse uses automation in a variety of ways to help users manage their data and derive insights from it. Analysis tools can automate data management for easier analysis, modeling, and visualization. For example, Graph Studio enables automated graph modeling, installation, updating, deployment, auto-save, scheduled analysis capabilities, and more. With the autonomous database cloud service that is self-optimizing and preconfigured with automated self-patching and upgrades for optimal performance, users don’t have to worry about mismanaging and maintaining their data warehouse solution. In addition, through its machine learning capability, the software automatically optimizes caching and indexing to reduce CPU consumption and help users save costs and reduce risks.
Amazon Redshift has many automation features and capabilities to take care of delivery and manage 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 as the product automatically scales up capacity when it’s busy and scales down when it’s not, resulting in reduced expenses. Redshift’s automatic table tuning configures sorting and distribution settings to improve cluster performance and optimize query speed without requiring administrator intervention. Other ways Redshift manages workloads with sophisticated algorithms to improve the layout of data include features like automatic table sorting, automatic analysis, and automatic vacuum deletion.
So which is the better solution – Oracle Data Warehouse or Redshift?
The better data warehousing and analytics tool is not always obvious, and the answer may differ from one organization to another based on their specific needs. For example, a company that already uses many Oracle applications and tools may choose Oracle Data Warehouse as the best option for easy integrations with other Oracle products. However, other users may require advanced query performance capabilities, which are provided by Redshift through its distribution keys and sort keys. By analyzing the needs and aspects of their enterprise datasets, data sources, and BI solutions, users can compare data solution options to find the best tool for their organization.