5 Reasons Why Autonomous Database Is Better than Amazon Redshift

Why Autonomous Database is better than Amazon Redshift

Here are the top five reasons customers choose Autonomous Database over Amazon Redshift.

  • Simplify data management: Autonomous Database has extensive integrated capabilities that eliminate the need for separate, standalone services—making data warehousing and analytics easier to convert into data-driven insights.
  • Build apps and analytics with automated, integrated, and open GenAI: Autonomous Database Select AI provides automated and integrated generative AI with in-database large language models (LLMs); an automated, in-database vector store; scale-out vector processing; and the ability to have contextual conversations in natural language—allowing users take advantage of generative AI without existing AI expertise or data movement. Unlike copilots and advisors, Select AI is immediately available directly for any application that can connect to the database.
  • Lower data management cost at any scale: Autonomous Database costs less due to its built-in features and autoscaling capability, which closely matches consumption costs to overall workload requirements. It delivers significantly lower TCO for all types of data warehousing workloads by eliminating the complexity, latency, cost, and risks of ETL and data duplication across multiple cloud services.
  • Help maximize data protection: Autonomous Database regularly checks for configuration deviations caused by unapproved changes or application patches and alerts administrators. It allows for analysis of user entitlements across all Oracle databases, including access reviews and adherence to password policies. Additionally, it offers privilege analysis for implementing least privilege model to help reduce risk from compromised identities. The Oracle Data Safe service, included with Autonomous Database, helps organizations manage data sensitivity, evaluate risks, mask sensitive data, and monitor security and user activity from a unified console.
  • Benefit from machine learning–powered automation: Autonomous Database provides workload-aware, machine learning–powered automation of various aspects of the application lifecycle, including provisioning, data loading, query execution, and failure handling.

1. Simplify data management

Autonomous Database has extensive integrated capabilities that eliminate the need for separate, standalone services—making data warehousing and analytics easier to convert into data-driven insights.

Capability and evidence
Oracle Autonomous Database
Amazon Redshift*

Self-service reporting capabilities

Can customers build a self-service data mart for reporting without integrating multiple additional services for data ingest, transformation, machine learning, business analytics, graph analytics, geospatial analytics and generative AI application development?
yes
no

Amazon Redshift needs several services to move data from Aurora to Redshift, additional services for graph analytics, machine learning, generative AI applications, data ingestion, and transformation. These extra services can add complexity, security risks, and increase the total cost of ownership (TCO).

Built-in machine learning

Does the data warehouse include built-in machine learning capabilities and Auto-ML features for training and deployment of ML models?
yes
no

Customers must use many different services for machine learning in Redshift. The need to move data out of Redshift and use separate services delays insights while increasing complexity, security risks, and costs.
Does the data warehouse include Auto-ML features to help users with only basic data science knowledge deploy machine learning features to speed up modeling tasks, find new insights, and make predictions quickly.
yes
no

Redshift requires the use of additional AWS services that are not designed for business users.

*As of July 2024



Perspectives from leading analysts

  • WinterCorp

    “The entire process of creating, managing, and operating a database is greatly simplified, costs are in general reduced substantially, agility is increased, and real elasticity is delivered. The author has experimented hands-on with ADB and found these benefits real and substantial.”

    —Richard Winter, WinterCorp

  • Omdia

    “Oracle has waited for the marketplace to mature to the point where an automated converged database that delivers simplicity, governance, security, and manageability will win out over the latest disjointed collection of shiny objects.”

    —Bradley Shimmin, Omdia

  • Cloud Wars

    “Better, hardened security; a fully converged autonomous data warehouse service that greatly simplifies management and maintenance; plus nonstop self-tuning, self-monitoring, and patching...are all big pluses in Oracle’s favor.”

    —Bob Evans, Cloud Wars

Experience the simplicity of Autonomous Database

2. Build apps and analytics with automated, integrated, and open GenAI

Autonomous Database Select AI provides automated and integrated generative AI with in-database large language models (LLMs); an automated, in-database vector store; scale-out vector processing; and the ability to have contextual conversations in natural language—allowing users to take advantage of generative AI without existing AI expertise or data movement. Unlike copilots and advisors, Select AI is immediately available directly for any application that can connect to the database.

Capability and evidence
Oracle Autonomous Database
Amazon Redshift*

In-database machine learning

Can developers and data analysts build, train, deploy, and explain machine learning models within the database? Do data and ML models remain inside the database to speed up results and reduce the latency and risk of data movement between data stores?
yes

(*1 and *2)
no

A separate ML service, such as Amazon SageMaker, is required—even when using Redshift ML. Data is copied to a separate location, and ML models are built outside Redshift.
Additionally, can data scientists use popular open-source Python-based ML algorithms?
yes
no

Redshift’s limited set of ML features do not support using additional Python-based algorithms.
Does the data warehouse include a built-in tool for automated machine learning?
yes
no

Redshift ML requires data science expertise to select the best algorithm and influence the performance, accuracy, and cost of training.
Is the machine learning lifecycle fully automated, including algorithm selection, intelligent data sampling, feature selection, and hyperparameter tuning for all model types?
yes
no

Explainable data models and predictions

Are all models and predictions explainable to help increase trust, fairness, causality, and repeatability and support regulatory compliance?
yes

(*3)
no

Predictions from ML models in Redshift ML aren’t explainable, which may reduce trust, increase risk of bias, and make regulatory compliance more difficult.
Does the data warehouse provide integration with LLM of choice to drive using natural language to build and run SQL queries?
yes

(*4)
no

With Amazon Redshift, users must use a separate service, Amazon SageMaker, to provide LLM models and manually import them into the Redshift database.
Can customers choose from a range of popular LLMs across their preferred cloud to simplify integration and registration, or are they locked into a specific LLM?
yes
no

With Amazon Redshift, users must use a separate service, Amazon SageMaker, to provide LLM models and manually import them into the Redshift database.

Automated generation of vector embeddings

Can the database automate the generation of vector embeddings, including parsing, extracting metadata, creating chunks, and choosing embedding models for data and queries—without requiring AI expertise from the user?

Can all steps be completed in the database, without requiring data movement, to help separate client resources, simplify the process, and reduce costs?
yes
no

Amazon Redshift doesn’t currently support in-database vector processing.

Accelerated vector processing

Is vector processing parallelized across database processors to deliver speedy results?
yes

(*5)
no

Amazon Redshift doesn’t currently support in-database vector processing.

Can you chat with your data warehouse?

Does the database come with a chat-like application to enable the quick use of GenAI within applications?
yes

(*6)
no

Amazon Redshift doesn’t provide an out-of-the-box interface. Users need to use a separate service to build a chatbot, which may increase complexity and costs.

*As of July 2024

1. See the full list of in-database ML capabilities for Autonomous Database.

2. Autonomous Database customers can use popular open source ML algorithms with Oracle Machine Learning for Python.

3. Autonomous Database comes with a fully integrated AutoML user interface as part of the built-in Oracle Machine Learning Notebook.

4. Select AI can be used with today’s most popular LLMs and—given the rapid pace of change in this space—is designed to easily integrate with new LLMs as they become available.
• Refer to DBMS_CLOUD_AI Package (oracle.com) for the most up-to-date model support.

5. Learn more about the prebuilt embedding generation model for Oracle Database 23ai
• Using the ONNX model format, Oracle provides a framework for an augmented pipeline that includes tokenization and all required post-processing steps for generating vector embeddings seamlessly with AI Vector Search.

6. APEX ChatDB accelerates the development of GenAI chat-driven apps that are business-focused.



Perspectives from customers

  • Pianoforte Group

    Pianoforte gains real-time insights with Oracle Cloud.

    Autonomous Database has freed IT specialists to work more closely with business units and spend more time on strategically valuable tasks, such as finding machine learning techniques that create additional insights through clustering and data-driven predictions.

  • Sensa Analytics

    Sensa Analytics speeds healthcare data insights.

    Using OML, Sensa improved payment reimbursement timelines for providers from two to three months to just two weeks. It also processed 100,000 claims per day in minutes to automatically correct medical codes, and reduced accounts receivable outstanding by 39%.

Experience automated and integrated ML and GenAI

3. Lower data management cost at any scale

Autonomous Database costs less due to its built-in features and autoscaling capability, which closely matches consumption costs to overall workload requirements. It delivers significantly lower TCO for all types of data warehousing workloads by eliminating the complexity, latency, cost, and risks of ETL and data duplication across multiple cloud services.

Capability and evidence
Autonomous Database
Amazon Redshift*

Real-time elasticity to exactly match your workload

Can customers increase or decrease the number of nodes to precisely match the requirements of their workload? If so, can they do this without incurring any downtime or read-only time, or rebalancing data across the available nodes?
yes

(*1)
no

With elastic resize, the Redshift cluster is unavailable for four to eight minutes. There are also several limitations to consider. The elastic resize of the Redshift cluster can cause data skew between nodes due to an uneven distribution of data slices, which can severely downgrade query performance.

Self-service data warehouse

Does the database include built-in capabilities that address a broad set of analytical and machine learning use cases in modern data warehouses?
yes
no

Redshift require separate cloud services for graph analytics, spatial analytics, document stores, and machine learning. Integrating multiple services is complex and can increase cost.

Built-in data discovery and masking

Does the data warehouse include automated, intelligent data discovery and masking tools to secure sensitive data?
yes

(*2)
no

Amazon Redshift lacks this level of built-in functionality. Customers must implement Oracle-equivalent features using multiple add-on tools and services, which can increase complexity, potential security risks, and operational costs.

*As of July 2024

1. Autonomous Database automatically scales compute resources up and down to increase the performance of complex data warehouse queries and minimize costs. There is no need to experiment with node types and cluster sizes to determine the best configuration for each workload. With Autonomous Database, scaling takes place instantly on a granular, core-by-core basis without manual intervention by DBAs.

2. See Oracle Data Safe for more detailed information.



Perspectives from leading analysts and customers

  • IDC

    IDC finds that Autonomous Data Warehouse customers obtain a 417%

    “Our research, based on interviews with several customers around the globe, shows that those Oracle ADW customers have achieved approximately 63% reduced total cost of operations while increasing the productivity of data analytics teams by 27%.”

  • Lyft

    Lyft uses Oracle to build a single source of information across finance and operations—and gets data to stakeholders faster.

    “The world’s going a thousand miles an hour, so for me to say, ‘Wait, I need another week’ just doesn’t work anymore… It’s a true transformation at Lyft with the products we’re implementing.”

    —Lisa Blackwood-Kapral, Chief Accounting Officer, Lyft

  • Experian

    Experian improves data workloads with Oracle Cloud.

    Experian moved multiple data workloads onto Oracle’s cloud, including fraud detection, call center analytics, financial analytics, and credit data. The company saw a 40% increase in performance, a 60% reduction in costs, and increased reliability and resilience overall.

4. Help maximize data protection

Autonomous Database periodically assesses the databases and alerts on any configuration deviations from the established standards due to unapproved changes or application patches. Administrators can analyze user entitlements across the entire fleet of Oracle databases, reviewing and reporting on who has access to what and how many users are following password hygiene and rotation policies. Autonomous Database provides privilege analysis to help organizations implement a least privilege model to contain the risk if users’ identities get compromised. The Oracle Data Safe service is included with Autonomous Database to empower organizations to understand data sensitivity, evaluate data risks, mask sensitive data, implement and monitor security controls, assess user security, and monitor user activity—all in a single, unified console.

Capability and evidence
Autonomous Database
Amazon Redshift*

Always-on encryption

Does the data warehouse enable encryption for data in-transit and at-rest by default?
yes
no

Amazon Redshift requires customers to manually turn on encryption—it’s not enabled by default. DBAs must choose a hierarchy of encryption keys to encrypt the database. This manual encryption process may introduce human error and put data at risk.

Comprehensive database security assessments

Does the data warehouse include automatic, periodic assessments of configuration parameter changes to identify deviations from established standards?
yes

(*1)
no

Amazon Redshift does not provide tools to set up established standards, or automatic monitoring and assessment of configuration drifts from the established standards inside the database.

Data masking, data discovery, and user scoring

Does the data warehouse include a data masking, data discovery, and user scoring service to help business users and non-SME user secure customer-related and sensitive data?
yes

(*1)
no

Identification of sensitive data must be done by a data security expert. The data masking and de-identification process must be built and implemented at the application level.

Privileged user safeguards

Does the data warehouse provide security controls to keep privileged users from accessing sensitive data?

Is there an easy way to analyzes the gap between granted privileges and used privileges to create a least-privilege model to contain the risk?
yes

(*1)
no

Amazon Redshift does not have built-in preventive controls to block privileged users and DBAs from accessing sensitive data in the data warehouse.

*As of July 2024

1. Autonomous Database includes built-in security with Oracle Data Safe, which helps organizations determine the sensitivity of their data, mask sensitive data, and evaluate risks to their data. Oracle Data Safe, included at no additional cost, can assist with security configuration, monitoring, and prevention—all via a unified console.



Perspectives from customers

  • Thomson Reuters

    Thomson Reuters rolls out ONESOURCE with zero downtime on Oracle Cloud.

    The high availability of Oracle’s autonomous cloud database enables ONESOURCE staff to update content and functionality for calculating indirect taxes while maintaining critical transaction processing. Meanwhile, Oracle Data Safe and Oracle Cloud Guard enhance data security and insights.

  • Cognizant

    Cognizant modernizes data warehouse and analytics with Oracle Cloud

    The company’s security is tighter than ever with OCI networking security, including OCI Logging, Virtual Private Vault, Oracle Data Safe, and Autonomous Database security. Cognizant has control over the encryption keys for data, while the IT team has ready access to database audit data and centralized event logging.

5. Benefits from machine learning–powered automation

Autonomous Database provides workload-aware, machine learning–powered automation of various aspects of the application lifecycle, including provisioning, data loading, query execution, and failure handling.

Capability and evidence
Autonomous Database
Amazon Redshift*

Automated scaling

Does the platform automatically expand and shrink compute resources to the optimal size based on workload without downtime?
yes

(*1)
no

With Amazon Redshift’s elastic resize, the data warehouse is unavailable for four to eight minutes within the resize period.

Automated query performance tuning

Does the database continuously learn from and improve query performance?
yes

(*2)
no

Query plans aren’t automatically improved using machine learning models.

*As of July 2024

1. Autonomous Database uses expert systems to automatically expand and shrink compute resources as the overall workload changes with zero downtime. The result is optimal performance and cost, since customers only pay for resources via per-second billing.

2. SQL Plan Management is an Oracle database feature that allows you to establish a set of SQL execution plans that will be used even if the database is subject to changes that would otherwise cause execution plan changes to occur.



Perspectives from customers

  • McDonald’s

    McDonald’s Hong Kong leverages machine learning to improve the customer experience.

    “Machine learning can help unleash the potential value of data and drive business growth. It is such an exciting journey of transformation that we can’t afford to miss. We can now be more surgical in coupon planning for our targeted segments.”

    —Keith Chan, CFO and CTO, McDonald’s Hong Kong

  • Halldis

    Halldis finds the ideal stay using Oracle Cloud.

    The company relies on machine learning to help sales and marketing teams provide guests with personalized accommodations, guide advertising decisions, and increase repeat bookings.



Try Autonomous Database for free

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What is an autonomous database?

Oracle Autonomous Database is a cloud native data warehouse service that eliminates the complexities of operating, securing, and developing data-driven analytics.

Its built-in support of rich native data types, diverse algorithms, and native generative AI capabilities enable you to quickly and easily build intelligent applications without multiple siloed and specialized data repositories that weaken overall security and governance, increase costs, and introduce data integration complexities.

Will it work with any cloud?

Autonomous Database is available in Oracle Cloud Infrastructure (OCI), Microsoft Azure, and Google Cloud.

Oracle vs. Amazon Redshift resources

Interested in learning more about Oracle Autonomous Data Warehouse? Let one of our experts help.

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