What is AnalyticsOps, and how could it improve your business decisions?

Analytics operations, or AnalyticsOps for short, is a concept that has evolved beside the advent of cloud computing, democratized analytics, and advanced analytics techniques such as AI/ML.  Don't confuse AnalyticsOps with Operational Analytics, which is an entirely different concept, nor the older concept of the BI center of excellence (BICOE).  AnalyticsOps works alongside, and doesn't replace, DevOps and MLOps to improve business outcomes. Implementing AnalyticsOps as a formal function is a key step in the journey to achieve analytics-mastery.

What is AnalyticsOps (analytics operations)?

AnalyticsOps is a set of operational and administrative processes, tools, and tasks for an analytics platform deployment. It involves the integration of analytics and operations to drive user adoption, improved analytics and KPIs, better decision-making, and optimization of business processes toward achieving strategic goals. In simpler terms, AnalyticsOps is the practice of using data analytics and operational tools to increase the value of an analytics platform and achieve desired outcomes. AnalyticsOps enables organizations to collect, process, analyze, and act on data in real-time. AnalyticsOps aims to create an analytics-driven culture within the organization, where data is integrated into decision-making at all levels - from daily operations to strategic planning - to optimize efficiency, minimize costs, and maximize revenue.

For example, Oracle has a dedicated Global Revenue Operations (Rev Ops) team responsible for developing and maintaining the Oracle Global Analytics, Reporting, and Sales Intelligence Platform (SI), which supports over 11,000 internal stakeholders from various departments, including sales, marketing, and finance. The team proactively monitors a wide range of AnalyticsOps KPIs using Oracle Analytics Cloud (OAC), ensuring that all queries essential to the monthly review process have a maximum response time of 5 seconds. This level of service has led to increased efficiency and accuracy, because Oracle's sales management has agreed to review live data during meetings instead of relying on error-prone spreadsheets or slide show presentations.

Additionally, the Rev Ops team analyzes poorly performing queries and reaches out to the specific users who initiated the request, offering to help them build more efficient queries. By examining the telemetry data of the OAC instance, the team can identify opportunities for efficiency, such as educating users on scheduling data flows to not run jobs hourly when the source data system updates every 24 hours, which can help reduce the operational load on the system. These efforts have had a significant positive impact that has led to improved stability and performance of the analytics platform, especially given the large and active user community of over 11,000 stakeholders.

How is AnalyticsOps related to DevOps and MLOps?

DataOps focuses on streamlining and automating data pipelines to improve the speed and quality of analytics. MLOps is an extension of DataOps that specifically addresses the unique challenges of deploying and managing ML models at scale. Conversely, AnalyticsOps integrates analytics and operations specifically to improve the analtyics workflows to drive better decision-making and optimize business processes.  All three - DataOps, MLOps, and AnalyticsOps - are crucial for business success in today's data-driven economy.

How does AnalyticsOps differ from the BI center of excellence?

The BI center of excellence (BIoE) is an older approached that creates a centralized team, usually residing with IT, responsible for managing the organization's business intelligence (BI) efforts, including data governance, data quality, reporting, and analytics. That team’s responsibilities included providing standardized reports and dashboards to business users, who then used this information to make decisions.  Essentially, a BIoE established, deliverd, and maintained policies and processes that delivered a mode 1  (as defined by Gartner) style of analytics. The focus was on delivering content through a given BI tool but didn’t incorporate other operational aspects and technologies related to a comprehensive analytics deployment.  

In 2016, Gartner declared that “the BICC is dead…” recognizing the evolving landscape of analytics tools, including the emergence of predictive technologies and the groundwork for machine learning (ML). Gartner's observation indicated that the successor to the BICC should prioritize empowering businesspeople with self-service capabilities through training, education, and coaching – which aligns with what we now know as AnalyticsOps.

AnalyticsOps takes a broader approach toward what Gartner refers to as mode 2 analytics. It integrates analytics and operations, including promotion of advanced analytics techniques such as AI/ML, to create an analytics-driven culture within the organization. Instead of relying on a centralized team to provide reports and dashboards, AnalyticsOps empowers business users to analyze and interpret data in real-time, allowing them to make faster, more informed decisions. In addition, it ensures all processes, policies, and tools are in place to ensure the deployment and availability of the analytics platform that supports modern analytics

How can AnalyticsOps help reduce “Data Debt?”

“Data debt” refers to the accumulation of data-related issues and problems that haven't been addressed or resolved over time, resulting in a backlog of work that IT hasn’t been able to complete. In the context of corporate data analytics, data debt can arise from incomplete or inaccurate data or data models, outdated data infrastructure, or inefficient data processes that haven’t been adequately maintained. This can impede an organization's ability to make informed analytics-driven decisions. To address data debt, organizations need to invest in data management and governance processes that ensure data quality, accuracy, and consistency for an analytics platform, which can help them make better decisions and stay ahead of the competition. By adopting an AnalyticsOps approach, organizations can better manage their data analytics processes and reduce the risk of accumulating data debt. This helps increase the accuracy and trustworthiness of data and analytics that support the decision-making process. 

To illustrate, consider a digital marketing company that has been collecting customer data from multiple sources including social media, online orders, and service calls, over the course of several years. During this time, the company underwent changes such as switching database vendors, acquiring companies, and revamping their customer loyalty program, resulting in a significant amount of customer data stored across multiple locations in different formats.  As a result, the business users struggle to report accurately on customers, often making mistakes due to inconsistent or incomplete data.  In this scenario, the company is grappling with overdue issues, or data debt. By implementing AnalyticsOps, the company can address the root cause of its customer data problems by analyzing its current data definitions and creating customer data standards with improved automated data collection, cleansing, and validation processes. AnalyticsOps has simplified the customer data definitions, making it easier for business users to correctly access and report on their customers.

What are the benefits of AnalyticsOps?

Unlike DataOps and DevOps, AnalyticsOps takes into account the last mile of the data journey at the Analytics layer. Say goodbye to low adoption rates, lack of trust, high costs, and difficulties with regulatory compliance.

• Improved stability and performance: Stakeholders have greater confidence in using the analytics platform to make business decisions. Ensuring the best performance reduces time wasted waiting and instead focuses businesspeople on strategic tasks.
• Better analytics integrity: AnalyticsOps actively monitors queries and activity to ensure the integrity of the analytics platform by quickly identifying and resolving slow or malformed queries through updates to the data model or data source, or by providing end-user education.
• Better decision-making: By having access to accurate, timely, and relevant data, businesspeople make informed decisions by being data-driven based on data insights rather than assumptions or gut feelings. This improves accuracy of decision-making and reduces the risk of errors.
• Boosted user adoption: Success of the analytics platform can be directly measured in terms of user adoption. Higher adoption means better ROI on the analytics platform. AnalyticsOps benefits ensure the best user experience and business outcomes, which leads to greater usage (and adoption)
• Enhanced agility: AnalyticsOps can enable businesses to spot potential risks and to adapt quickly to market shifts and changing customer needs. Real-time data insights allow businesses to make quick decisions and adjust their strategies accordingly.
• More competitive advantage: By using data insights, businesses can gain a competitive advantage because through using techniques such as predictive analytics, they can take advantage of new and potentially unseen opportunities before their competitors.
• Increased efficiency: AnalyticsOps can help to automate analytics processes and workflows, reducing the time and effort required to collect, process, and analyze data. AnalyticsOps also helps with cleaning up the catalog (identifying and deleting unused workbooks, data flows, and datasets).
• Improved collaboration: AnalyticsOps can foster structured collaboration between different people within an organization, including IT, data science, and business groups. This leads to better alignment of objectives, as well as more effective communication and faster decision-making.

What are the costs of AnalyticsOps?

The costs of establishing AnalyticsOps aren't just limited to financial investments in analytics tools and technologies but are primarily indirect costs. These costs are associated with the time, effort, and resources required to provide AnalyticsOps and include:

• Training and upskilling: Establishing AnalyticsOps requires the upskilling of existing employees to enable them to understand not only analytics tools and technologies, but also the data sources and definitions across the organization’s data estate.
• Time and resources: Creating an analytics-driven culture requires investment of time and resources. This includes time spent on data collection, preparation, analysis, and interpretation, as well as the resources required to maintain data pipelines and systems.
• Data governance: AnalyticsOps requires a strong data governance framework to ensure that data is accurate, trusted, and secure.  This requires additional effort and resources to develop and maintain.
• Change management: Implementing AnalyticsOps may require changes to existing processes and workflows. This can create resistance from employees who are accustomed to working in a certain way with certain tools (such as Microsoft Excel) and may require additional time and effort to manage.
• Collaboration and communication: AnalyticsOps requires collaboration between different teams within the organization, including IT, data science, and business groups. This requires effective communication and coordination, which may be challenging to establish and coordinate.

Summary

AnalyticsOps is a critical and proactive methodology that integrates analytics and operations to improve business outcomes, to drive user adoption, and to achieve strategic goals. It's a decentralized approach that empowers business users to analyze and interpret data in real-time, allowing them to make faster, more informed decisions. AnalyticsOps also emphasizes the importance of agility and continuous improvement. It uses automation and machine learning to streamline data processes and optimize business performance. This enables organizations to quickly adapt to changing market conditions and stay ahead of the competition. It's a key step in the journey toward analytics-mastery, and organizations that embrace it will thrive in the data-driven economy.

Source: oracle.com

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Understanding Database Management Systems (DBMS)

In the realm of digital infrastructure, the term Database Management System (DBMS) holds immense significance. A DBMS serves as a cornerstone for organizing and managing data efficiently within an organization. Let's delve into the intricacies of what constitutes a DBMS and its pivotal role in modern-day information management.

What is a Database Management System?

A Database Management System (DBMS) is a software suite designed to facilitate the storage, retrieval, and manipulation of data in a structured format. It acts as an intermediary between users and databases, providing an interface for users to interact with the data while ensuring its integrity, security, and accessibility.

Components of a DBMS

A DBMS comprises several key components, each playing a crucial role in its functionality:

1. Data Definition Language (DDL)

The DDL component of a DBMS enables users to define the structure of the database, including tables, fields, and relationships. It allows for the creation, modification, and deletion of database objects, ensuring data integrity and consistency.

2. Data Manipulation Language (DML)

The DML component facilitates the manipulation of data within the database, allowing users to insert, update, delete, and retrieve records. It provides the necessary commands and operations to interact with the data stored in the database.

3. Data Query Language (DQL)

DQL enables users to retrieve specific information from the database using queries. It allows for the extraction of data based on predefined criteria, enabling users to retrieve relevant information efficiently.

4. Data Control Language (DCL)

The DCL component governs the access and permissions associated with the database. It regulates user access rights, security privileges, and data integrity constraints, ensuring that only authorized users can manipulate the data as per defined policies.

Types of Database Management Systems

DBMSs can be categorized into various types based on their underlying architecture and functionality. Some common types include:

1. Relational DBMS (RDBMS)

RDBMS organizes data into tables with rows and columns, establishing relationships between them. It employs Structured Query Language (SQL) for data manipulation and retrieval, offering a robust and standardized approach to data management.

2. NoSQL DBMS

NoSQL databases diverge from the traditional relational model, offering flexible schemas and scalable architectures. They are well-suited for handling unstructured or semi-structured data types and can accommodate high volumes of data with distributed computing capabilities.

3. Object-Oriented DBMS (OODBMS)

OODBMS stores data as objects, encapsulating both data and methods within a single entity. It provides support for complex data structures and inheritance relationships, making it suitable for object-oriented programming paradigms.

4. Graph DBMS

Graph databases represent data as nodes, edges, and properties, facilitating the storage and traversal of interconnected data structures. They excel in modeling complex relationships and are widely used in applications involving social networks, recommendation systems, and network analysis.

Importance of DBMS in Business Operations

The adoption of a robust DBMS offers several benefits for organizations across various industries:

• Data Centralization: A DBMS centralizes data storage, enabling organizations to maintain a single source of truth for their information assets.
• Data Consistency: DBMS ensures data consistency by enforcing integrity constraints and validation rules, thereby minimizing the risk of errors and inconsistencies.
• Data Security: DBMS implements access controls and encryption mechanisms to safeguard sensitive data from unauthorized access and cyber threats.
• Scalability: DBMS offers scalability features to accommodate growing data volumes and user demands, ensuring optimal performance and resource utilization.
• Decision Support: DBMS provides tools and utilities for data analysis, reporting, and visualization, empowering decision-makers with actionable insights derived from data-driven analysis.

Conclusion

In essence, a Database Management System (DBMS) serves as a foundational pillar for efficient data management, storage, and retrieval within organizations. By leveraging the capabilities of a robust DBMS, businesses can streamline their operations, enhance data integrity, and unlock valuable insights to drive informed decision-making.

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Unveiling the Power of Oracle Database 23c: Revolutionizing Data Management

In the ever-evolving landscape of data management, Oracle has yet again set a new standard with the introduction of Oracle Database 23c. This groundbreaking release brings forth a plethora of features and enhancements designed to streamline operations, enhance security, and elevate performance to unprecedented levels. In this comprehensive guide, we delve into the intricate details of Oracle Database 23c, exploring its key features, benefits, and the transformative impact it can have on your organization's data infrastructure.

Enhanced Security Measures

Security is paramount in today's digital age, and Oracle Database 23c delivers on this front with an array of enhanced security measures. With features such as Data Safe and Transparent Data Encryption, organizations can fortify their data against unauthorized access and cyber threats. Additionally, Oracle Label Security provides granular control over data access, allowing organizations to enforce fine-grained security policies based on user roles and data classifications.

Advanced Machine Learning Capabilities

Harnessing the power of machine learning, Oracle Database 23c empowers organizations to unlock valuable insights from their data like never before. Through features such as AutoML, organizations can automate the process of building and deploying machine learning models, enabling data-driven decision-making at scale. Furthermore, Adaptive Query Optimization leverages machine learning algorithms to optimize query performance, ensuring efficient utilization of resources and maximizing throughput.

Seamless Scalability and Performance

Scalability and performance are cornerstones of any robust data management solution, and Oracle Database 23c excels in both areas. With support for multitenant architecture and in-memory processing, organizations can effortlessly scale their databases to accommodate growing workloads while maintaining optimal performance levels. Moreover, Automatic Indexing leverages machine learning algorithms to automatically create and manage indexes, further enhancing query performance and reducing administrative overhead.

Cloud-Native Innovations

As organizations increasingly embrace cloud-native technologies, Oracle remains at the forefront with Oracle Database 23c's cloud-native innovations. With support for Oracle Autonomous Database and Oracle Cloud Infrastructure, organizations can seamlessly migrate their on-premises databases to the cloud, unlocking greater agility, scalability, and cost-efficiency. Additionally, Oracle Exadata Cloud@Customer brings the power of Exadata to the cloud, allowing organizations to leverage the industry's leading database platform without the need for upfront infrastructure investments.

Enhanced Data Integration and Management

Efficient data integration and management are essential for driving actionable insights and maximizing the value of data assets. Oracle Database 23c offers a comprehensive suite of tools and features for data integration, data warehousing, and real-time analytics, empowering organizations to extract, transform, and load data from disparate sources with ease. With Oracle Data Integrator and Oracle GoldenGate, organizations can streamline data movement and replication, ensuring data consistency and integrity across the entire ecosystem.

Future-Proof Your Data Infrastructure with Oracle Database 23c

In conclusion, Oracle Database 23c represents a paradigm shift in the realm of data management, combining unparalleled security, advanced machine learning capabilities, seamless scalability, and cloud-native innovations to deliver a comprehensive solution for modern organizations. Whether you're looking to enhance security, boost performance, or embrace cloud-native technologies, Oracle Database 23c has you covered. Embrace the future of data management with Oracle Database 23c and stay ahead of the curve in today's data-driven world.

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How to help AI models generate better natural language queries

Using natural language to query your data is an easy way to answer business questions. One question I’m often asked is, “how can this work on my data? Have you seen my table and column names? The names are meaningless.”  Fear not! It is possible when you’re using Autonomous Database.

There is no magic. If your table and column names aren’t descriptive, you can help the large language model (LLM) interpret the meaning of tables and columns by using a built-in database feature called “comments”. Comments are descriptions or notes about a table or column’s purpose or usage. And, the better the comment, the more likely the LLM will know how to use that table or column to generate a the right query.

Adding Comments to your tables and columns

Let’s take an example. My database has 3 tables. The table names and columns are meaningless:

TABLE1 CREATE TABLE table1 ( c1 NUMBER, c2 VARCHAR2(200), c3 NUMBER

TABLE2 CREATE TABLE table2 ( c1 TIMESTAMP, c2 NUMBER, c3 NUMBER, c4 NUMBER, c5 VARCHAR2(100), c6 NUMBER, c7 NUMBER )

TABLE 3 CREATE TABLE table3 ( c1 NUMBER, c2 VARCHAR2(30) )

There is zero chance that a natural language query will know that these tables represent movies, genres and streams. We can fix that ambiguity by adding database comments:

TABLE1

COMMENT ON TABLE table1 IS 'Contains movies, movie titles and the year it was released';

COMMENT ON COLUMN table1.c1 IS 'movie ids. Use this column to join to other tables';

COMMENT ON COLUMN table1.c2 IS 'movie titles';

COMMENT ON COLUMN table1.c3 IS 'year the movie was released';

TABLE2

COMMENT ON TABLE table2 IS 'transactions for movie views - also known as streams';

COMMENT ON COLUMN table2.c1 IS 'day the movie was streamed';

COMMENT ON COLUMN table2.c2 IS 'genre ids. Use this column to join to other tables';

COMMENT ON COLUMN table2.c3 IS 'movie ids. Use this column to join to other tables';

COMMENT ON COLUMN table2.c4 IS 'customer ids. Use this column to join to other tables';

COMMENT ON COLUMN table2.c5 IS 'device used to stream, watch or view the movie';

COMMENT ON COLUMN table2.c6 IS 'sales from the movie';

COMMENT ON COLUMN table2.c7 IS 'number of views, watched, streamed';

TABLE3

COMMENT ON TABLE table3 IS 'Contains the genres';

COMMENT ON COLUMN table3.c1 IS 'genre id. use this column to join to other tables';

COMMENT ON COLUMN table3.c2 IS 'name of the genre';

 

That’s it! The meaningless table and column names can now be understood by the LLM using Select AI.

Set up your Select AI profile to use comments

A Select AI profile encapsulates the information needed to interact with an LLM. It includes the AI provider, the model to use, the source tables used for natural language queries – and whether comments should be passed to the model for SQL generation.

begin

  dbms_cloud_ai.create_profile(

    profile_name => 'myprofile',

    attributes =>       

        '{"provider": "azure",

          "azure_resource_name": "my_resource",                    

          "azure_deployment_name": "my_deployment",

          "credential_name": "my_credential",

          "comments":"true",  -- enable the use of comments

          "object_list": [

            {"owner": "moviestream", "name": "table1"},

            {"owner": "moviestream", "name": "table2"},

            {"owner": " moviestream", "name": "table3"}             

          ]          

          }'

    );

    dbms_cloud_ai.set_profile(

        profile_name => 'myprofile'

    );

end;

/

Run your queries

You can now start asking questions using natural language against your complex schema. Even though the table and column names are meaningless, the LLM is able to identify the appropriate tables and columns through the comments and generate a query:

Source: oracle.com

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Announcing the Oracle APEX Sample Document Generator App!

Today, we are releasing the Sample Document Generator App, which showcases the integration of the Document Generator in APEX. The app is now available in the APEX 23.2 Gallery.

This app includes examples to generate PDF documents using a custom plug-in, which invokes the Document Generator Function. Feel free to explore the plug-in code, look at MS Word templates stored in Static Application Files, and the JSON.

Cost-effectiveness

In order to create the Pre-Built Document Generator function, you need to have a paid account but the pricing is extremely cost-effective. With Oracle Cloud Functions, you only pay for the resources consumed during execution, eliminating the need for upfront infrastructure investment.

Installation on Oracle Cloud is required

The sample app uses a plug-in, which leverages the Oracle Cloud Infrastructure SDK for PL/SQL, which makes it very easy to manage / invoke OCI resources. This SDK is only available on the Autonomous Database, which means the sample app requires an Autonomous Database. This does not mean you can’t use the Document Generator in another APEX instance.

There are several prerequisites we must take care of before we can use the sample app to generate PDF documents. These steps are outlined below.

Configure the Document Generator Function

On OCI, open the navigation menu and select Pre-Built Functions in Developer Services.

Select Document Generator. 

Click the Create Function button. If a suitable application doesn't already exist in the current compartment, click Create new application.

Tip: when you are using Document Generator in production, you can enable provisioned concurrency to reduce initial provisioning time and ensure hot starts.

Click the Create button to finish the wizard and you can see the function is now deployed in the application.

Create an Object Storage Bucket

Open the navigation menu and select Buckets from Storage.

Create a new bucket if needed and provide a name.

The sample app will use this bucket later to store the MS Word templates and PDF documents.

Configure the Database

In the next steps we make sure the database has the rights to invoke the Document Generator function and manage objects in Object Storage Buckets.

We start by creating a Dynamic Group for the database.

Open the navigation menu and select Domains from Identity & Security. Select the identity domain you want to work in and click Dynamic Groups. Create a new Dynamic Group if needed by specifying a name, description, and a rule using the OCID of your Autonomous Database as resource ID. Remember the name because we will need it afterwards.

The matching rule is defined as:

resource.id = '<db_ocid>'

Next, we create a new Policy for this Dynamic Group. Open the navigation menu and select Policies from Identity & Security.

Create the following policy.

Allow dynamic-group <group_name> to manage objects in compartment <compartment_name>

Allow dynamic-group <group_name> to manage buckets in compartment <compartment_name>

Allow dynamic-group <group_name> to use functions-family in compartment <compartment_name>

As final step in OCI we have to execute the following statements as the ADMIN user in the Autonomous Database.

Go to the Automous Database and Click SQL under Database Actions.

Execute the following code as the ADMIN user.

begin

    DBMS_CLOUD_ADMIN.ENABLE_RESOURCE_PRINCIPAL();

    DBMS_CLOUD_ADMIN.ENABLE_RESOURCE_PRINCIPAL(username => '<WORKSPACE_SCHEMA>');

end;

/

grant DWROLE to <WORKSPACE_SCHEMA>;

The resource principal is used to authenticate and access Oracle Cloud Infrastructure resources. The DWROLE enables you to use the OCI PL/SQL SDK.

Quickly Installing the Document Generator Sample App

Perform the following steps to install the app:

1. Go to the App Gallery.

2. Search for the Sample Document Generator app.

3. Click install.

4. Go to application and navigate to Shared Components > Component Settings

5. Specify values for the empty attributes:

• Region Name: The OCI region of the Object Storage Bucket and Document Generator function.
• Document Generator Function OCID: The OCID of the function in the OCI application.
• Bucket Namespace: The OCI namespace of the Object Storage Bucket.
• Bucket Name: The name of the Object Storage Bucket.

Now we have completed the prerequisites and are ready to use the app!

Source: oracle.com

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Introducing Zero to low-cost Autonomous Database for Developers

Oracle has recently been recognized as a leading cloud service provider (CSP), providing a full suite of cloud computing solutions including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and industry-specific application solutions via Software as a Service (SaaS).

To accomplish this, we created a next-generation cloud experience that focuses on enterprise performance, availability, security and cloud economics where you pay for what you use. The economic benefits of Oracle Cloud Infrastructure (OCI) are substantial, enabling workloads such as AI and Oracle Database to achieve outstanding price-performance.

We are now further improving OCI economics for our Oracle Database cloud service portfolio with the introduction of Oracle Autonomous Database for Developers, which provides Autonomous databases for developers on Dedicated Exadata Infrastructure and Exadata Cloud@Customer at no additional cost. 

Oracle Autonomous Database is an ideal database for developers. It provides multi-model database capabilities for many types of data (including relational, JSON, spatial, graph, multimedia, XML, files, and more), many workloads (transactional, data warehouse, and analytics), and typical developer interfaces (full SQL, REST data access, and language drivers). It comes with free development tools such as Database Actions, Oracle Application Express (APEX) for low-code app creation, and Oracle REST Data Services. It also includes in-database machine learning algorithms and Select AI which enables users to query data using generative AI powered natural language processing.

Autonomous Database for Developers enables developers to experiment with Autonomous Database and build applications with no additional cost. The free developer databases are intended solely for development and functional testing. There is no limit on the number of developer databases users can create on their Exadata and these databases have no expiration dates. Autonomous Database for Developers supports Transaction Processing and Data Warehousing workload types. Each developer database instance has 4 ECPUs, 20 GB of data storage, and supports up to 30 concurrent database sessions.

Except for Autonomous Data Guard, Database In-Memory, Autoscaling, and long-term backups, all other features of Autonomous Database, such as backup and restore, cloning, patching, APEX, Database Actions, ORDS, Performance Hub, Select AI, APIs, metrics, and notifications are included in Autonomous Database for Developers. While developer databases may lack a few production database features, they are otherwise 100% compatible with the Autonomous databases used in production environments, letting developers create and test their applications against identical database environments.

Developer databases are automatically patched following the same schedule as regular Autonomous databases. Developers can file service requests (SR) to Oracle Support to get assistance with their developer databases; however, there is no severity 1 SR support or critical one-off patches. Autonomous Database for Developers adheres to a 99.5% service level objective (SLO).

While Autonomous Database for Developers is for development and functional testing only, users can access the full suite of Autonomous Database features or scale up the database for non-development deployments such as load/stress testing and production by cloning a developer database to a full-featured Autonomous Database instance and running there.

Oracle Autonomous Database offers developers a powerful and user-friendly platform for building and deploying mission-critical applications with high performance, scalability, and built-in security while minimizing administrative overhead and costs. With the introduction of Autonomous Database for Developers and it’s free Autonomous databases for developers, there is now an even more compelling reason to start all new application development with Autonomous Database.

Source: oracle.com

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