When you create a dataset in Oracle Analytics, the system performs column-level profiling to generate semantic recommendations for data enrichment. Additionally, when creating workbooks, you can enhance visualisations by including knowledge enrichments from the Data Panel. These recommendations are based on automatic detection of specific semantic types during the profiling process. 
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Semantic types include geographic locations identified by city names, recognisable patterns (such as credit card numbers, email addresses, and social security numbers), dates, and recurring patterns.

In the share price dataset below which has a date column selected, the panel on the right hand side provides semantic recommendations related to the date column which can be added to the dataset to provide additional classifications in addition to the original date from the data set like the month, quarter and year. 

This is very helpful in adding new ways to look at data but this is limited to the system built-in semantic recommendations  that come with the Oracle Analytics platform. Most companies have domain specific knowledge which would be useful in this context to provide enrichment possibilities to users. 

In the sample file below which contains geographic information about airport codes, this can be used to give additional geographic context to datasets that only contain the 3-digit airport code. 

To demonstrate this feature, we will be adding some custom knowledge that can be used to enrich 3-digit airport codes with additional information about the airport name, country and continent. 

As an Administrator, click the Reference Knowledge button in the Oracle Analytics administration console. 

Select the file (either an XSLX or CSV format) with the custom knowledge to upload.  I have noticed that the first column in the data to be uploaded to custom knowledge should be the unique key used to identify each record uniquely.  In the case above, the 3-digit airport code is unique and should be the first column in the data. 

​Review the content in the window below and click OK if all looks good. 

Now that the custom knowledge has been uploaded to Oracle Analytics, it is now available to be used in workbooks that have the airport code in their datasets. This workbook has a list of London airports and their approximate passenger numbers form 2014 - 2023.  We can make use of the custom knowledge to enrich this workbook.

Navigate to the Data tab and you should now see recommendations for the Airport column with options as seen in the screenshot below. 

The enrichments can be seen here and will now be available to use within the workbook. 

Alternatively, the enrichments can also be seen directly in the workbook data panel enabling non-administrator users to have access to these enrichments. 

What would improve this feature would be having an option to derive the custom knowledge from a database source for the purposes of data governance. A refresh schedule would be a welcome addition to the feature as well ensuring that the custom knowledge is sourced directly from a database but also routinely refreshed to ensure that the custom knowledge is kept up to date. ​

This functionality serves as a valuable tool that can empower users to achieve a deeper understanding of their data and extracting insights that have greater relevance to the end user. 

In data visualisation, it's often desirable to present both high-level summaries and detailed breakdowns within the same view. This enables users to have a view of the bigger picture while still having access to the underlying granular data points. This seamless integration can be challenging, especially when working with disparate data sources or attempting to convey complex relationships.
Fortunately, Oracle Analytics Cloud offers a powerful charting feature known as the overlay chart, which elegantly solves this problem. By combining multiple chart types into a single visualisation, the overlay chart enables you to layer summary-level metrics on top of more detailed visualisations, providing a comprehensive and cohesive view of your data.

In the data panel, select the visualisations tab and then select the Overlay Chart. 

The overlay chart is a powerful data visualisation tool in Oracle Analytics Cloud that enables seamless integration of high-level metrics and granular details within a single view. By layering multiple chart types, you can construct rich, multifaceted data stories. Whether unveiling executive summaries backed by granular evidence or pinpointing critical operational insights amidst a sea of data, the overlay chart empowers you to communicate with clarity and impact.

AI is rapidly becoming a critical technology that organisations across industries cannot afford to ignore. Those that proactively embrace AI, build the required capabilities, and integrate it into their strategies and operations will be better positioned to navigate the disruptions and capitalise on the opportunities presented by this transformative technology.

Democratising Machine Learning with Oracle Analytics Cloud ‘s AI Integration
At the heart of Oracle Analytics Cloud's capabilities lies the integration of AI and machine learning technologies. AI, which enables computers and digital devices to simulate human-like intelligence by learning, reasoning, and making decisions, has revolutionised various industries, from healthcare to finance. Machine learning, a subset of Artificial Intelligence is a technique that allows systems to automatically learn and improve from experience without being explicitly programmed.

Oracle Analytics Cloud leverages machine learning capabilities to empower users with a user-friendly interface for building, training, and deploying predictive models. This democratisation of machine learning empowers even non-technical users to harness the power of data-driven insights for decision-making.

Integrated Machine Learning Algorithms
​Oracle Analytics offers a set of built-in machine learning (ML) algorithms that you can leverage for various data analysis tasks. These algorithms are designed to be user-friendly and accessible through a drag-and-drop interface, eliminating the need for coding knowledge.  The following algorithms are available:

• Numeric Prediction: Forecast future values for a continuous numerical variable based on historical data.
• Multi-classifier: Classify data points into multiple predefined categories.
• Binary Classifier: Classify data points into two categories.
• Clustering: Group similar data points together based on their characteristics.

A recent update to this capability was the addition of an Auto ML step which analyses your data, calculates the best algorithm to use. This is available for Oracle Autonomous Data Warehouse data sources. The data preparation features can be used to ensure that the data is suitable for the chosen Machine Learning model. 
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You can find out more about the various OCI AI integrations currently available in Oracle Analytics Cloud.

In addition to these user-friendly features, OAC offers access to a wide range of built-in machine learning algorithms, catering to diverse analytical needs and user personas, from casual end-users to data scientists.

Benefits of AI-Powered Analytics with OAC
Integrating AI and machine learning capabilities into the analytics workflow offers numerous benefits for organisations:

1. Actionable insights for better decision-making: By leveraging machine learning algorithms and automated insights, businesses can uncover hidden patterns, trends, and relationships within their data, enabling more informed and data-driven decision-making processes.
2. Automation of repetitive tasks and process optimisation: AI and machine learning can automate tedious and time-consuming tasks, such as data preparation and cleansing, freeing up valuable human resources for higher-level strategic analysis.
3. Improved customer experiences and operational efficiency: By harnessing the power of predictive analytics and machine learning models, organisations can anticipate customer needs, optimise processes, and enhance operational efficiency, resulting in improved customer experiences and cost savings.
4. Driving innovation through data-driven insights: The ability to extract meaningful insights from complex data sources empowers organisations to identify new opportunities, develop innovative products and services, and stay ahead of the competition in an ever-changing business landscape.

The Evolving Role of AI in Analytics
As AI and Machine Learning technologies continue to advance, their role in the analytics domain is evolving. Traditionally, the focus has been on automating repetitive tasks and streamlining data preparation processes. However, the true power of AI lies in its ability to unlock predictive capabilities, enabling organisations to go beyond descriptive and diagnostic analytics.

With AI-powered analytics platforms like OAC, businesses can leverage predictive recommendations and future trend forecasts, gaining a competitive edge by anticipating market shifts, customer preferences, and potential risks or opportunities.

Upcoming Oracle Analytics Generative AI Features
Oracle is at the forefront of integrating cutting-edge AI capabilities into its analytics platform. According to a forward looking statement from James Richardson's blog, here are two highly anticipated features that we might see incorporated into Oracle Analytics:

1. Oracle Analytics AI Assistant: This natural language interface will enable users to query data and generate insights using conversational language, significantly reducing the barrier to entry for non-technical users and democratising data access.
2. Oracle Analytics Story Exchange: This open format will incorporate both data and visualisations, allowing for seamless integration with AI tools. By combining data and visual elements, users can leverage the power of generative AI to create rich, insightful narratives and presentations.

The Future of AI in Oracle Analytics
Oracle's roadmap for AI integration in its analytics platform is ambitious and forward-thinking. While the first AI integrations will be "behind the scenes" to improve productivity, the ultimate goal is to augment human intelligence with AI-powered insights.

Enhancements to features like Auto Insights and the AI Assistant are on the horizon, leveraging the capabilities of generative AI to provide more comprehensive and contextual insights. As AI continues to evolve, its role in analytics will shift from mere automation to true augmentation, empowering users with AI-driven recommendations, forecasts, and actionable insights.

As the volume and complexity of data continue to grow exponentially, embracing AI-enabled analytics platforms like Oracle Analytics Cloud will be crucial for organisations seeking to stay competitive and data-driven. By harnessing the power of AI and machine learning, businesses can uncover actionable insights, automate tasks, and make informed decisions that drive innovation, enhance customer experiences, and ultimately fuel success in the ever-changing digital landscape.

REST (REpresentational State Transfer) APIs (Application Programming Interface) are a way for software systems to communicate and share data over the internet using HTTP requests. REST APIs enable different systems to exchange data in a standardised way.  Simply put, an API is a set of rules which enable different software applications to communicate with each other defining how the software applications should interact without having to know about the internal complexities of each application. 

This analogy further explains an API; think of an API as a waiter in a restaurant. When you (the client) want to order food which is analogous to requesting information or services, you do not need to fully understand what the Chef needs to do to get your order prepared.  You, the client made your request to the waiter who acts as an intermediary (in the way that an API is the go-between) ensuring that communication between the client and the kitchen is executed and then returns the food ordered to the client.

Some key aspects of REST APIs:

1. They use HTTP methods like GET, POST, PUT, DELETE to perform operations.
2. Data is structured in either JSON or XML format.
3. APIs are organised around resources which are identified by URIs.
4. APIs are stateless, meaning no client context is stored on the server between requests.

The January 2024 update includes an extension to the available REST API endpoints that now provide catalog management capabilities. In Oracle Analytics Cloud, REST APIs allow external applications to integrate with and extend the capabilities of OAC. Some examples of how REST APIs are used:
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1. Catalog object deployment and management of object permissions
2. OAC system settings configuration
3. Querying data from OAC datasets and subject areas. The API can return data in JSON format.
4. Automating processes by chaining together sequences of API calls.

Mike Durran from the Oracle Analytics Product Management team has put together a video available on YouTube that walks through these endpoints.

REST APIs provide a standardised way for Oracle Analytics Cloud to exchange data and interoperate with other systems. The APIs allow OAC capabilities to be leveraged from external apps and workflows. REST APIs can help enable clean handoffs between developers and production support teams for Oracle Analytics Cloud deployments, without the production support teams requiring extensive OAC expertise from the operational perspective of Oracle Analytics.

Some of the new REST API endpoints released in the January 2024 update that now allow for catalog management. Here is a list of the new Oracle Analytics Cloud REST API endpoints:
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1. Copy a Catalog item
2. Create a folder
3. Delete a folder
4. Get Catalog item ACL
5. Get Catalog item details
6. Get Catalog item by type
7. Move a Catalog item
8. Update a Catalog item ACL

​The curl command below is an example of how one of these endpoints looks like.

The {{OAC_URL}} portion of the URL (above) to use in the REST API endpoints can be retrieved from your Analytics Instance detail page as below:

​Developers can build automated deployment pipelines and operations runbooks that call OAC REST APIs to perform releases. For example, the pipeline can use APIs to validate the system health, check for availability of required roles/capacity, import metadata like dashboards or reports, and switch traffic to the new version. 

The operations team does not need deep application knowledge, just the ability to invoke APIs and monitor for errors. The actual deployment logic is encapsulated in code and triggered through API calls. This allows a clear separation of development teams from production environments - developers own building the logic and automation leveraging their OAC expertise, while operations teams focus on running and monitoring the operational aspects.

The APIs serve as the interface layer between the two. This prevents fragile handoffs where operations need to learn and understand OAC internals, rely on manual processes or even rely upon the development teams to carry out these activities in production environments. Access to production can be minimised once APIs are implemented.

Overall, OAC REST APIs enable easier collaboration between developers and production teams. Releases can be made reliable and repeatable through automation using APIs, reducing risks and errors caused by manual processes. The APIs abstract away the complexity and provide self-service capabilities to operations teams.

The second part of this blog will dive into more technical detail on how these REST APIs are set up  using an API client tool to interact with the API endpoints. We'll also look at how these REST APIs can be used  by operations teams.

Parameter binding in Oracle Analytics Cloud refers to the ability to bind prompt values to filter components (for example) in data visualisation workbooks. The November 2023 update includes an extension to the parameter functionality which allows you to be able to bind a parameter to a range filter in a workbook.
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Data Visualisation parameters in Oracle Analytics Cloud allow you to vary aspects of a visualisation at runtime using prompt selections. Some key points:

1. Parameters can be set on visualisations like charts, graphs, maps etc. 
2. You can vary measures, dimensions, filters, etc by binding prompt values. For example, a parameter could allow the X axis measure or category dimension of a bar chart to change based on a prompt.
3. Instead of having static hard-coded values, parameters let selections made at runtime drive the visualisations.
4. Data Visualisation Parameters make it possible to create more flexible, interactive visualisations within a single workbook.

Data Visualisation parameters enhance flexibility, interactivity, and reuse by linking runtime user selections to the visualisations in an Oracle Analytics Cloud workbook.

In the workbook above, a parameter has been bound to the Cost Date attribute. The filter on the visualisation at the top of this workbook was set up as a range filter on the Cost Date.

In the visualisation that you would like to apply a range filter, follow the steps below:

1. Right click on the filter and select Modify Filter
2. In this case, select Date Range
3. Choose Range
4. Clink on the Bind to Parameter icon for the low range option and select Create Parameter
5. Repeat the same for the high range option​

These steps create 2 parameters and binds the filter range to these parameters which you can see in the parameters tab in the Data panel. You can modify the automatically generated parameters if required by right clicking on the parameter. 

Useful Use Case

​You may have a situation where you want to show the full history on a canvas and also to show a subset of this in another visualisation. You can achieve this by creating the 2 visualisations. In the visualisation that should show the subset of data, you will need to create a range filter and bind to a parameter as we have done above.

​The next step would be to drag the parameters from the data panel to the filters panel

This will enable you to have the visualisation with a subset of data as well as another visualisation with the full history on the same canvas.

Oracle Analytics Cloud (OAC) and Microsoft Power BI are two of the most popular platforms organisations use for data visualisation, reporting, and business analytics.
It is common for companies today to have a "hybrid analytics ecosystem" meaning they utilise both OAC and Power BI, rather than standardising on just one. There are several reasons a hybrid approach may emerge:
Oracle Analytics and Microsoft Power BI are 2 popular choices for businesses around the world for their Analytics solutions. In many cases, there is what I'll refer to as a "hybrid analytics ecosystem".

Hybrid Analytics Ecosystem

Many organisations today have a "hybrid analytics ecosystem" - meaning they use both Power BI and Oracle Analytics Cloud (OAC) or any other combination of analytics platforms for different analytics and reporting needs. There are several reasons this hybrid approach may emerge:
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• Through mergers and acquisitions, a company may end up with both OAC and Power BI if different units or mergers used different analytics platforms historically.
• Organisations may choose different tools for different use cases. For example, OAC for governed enterprise reporting and Power BI for self-service data exploration.
• Companies may adopt both but be in transition from one to the other. Using both allows a gradual migration approach.
• Global companies may use one platform in some regions and the other platform in other regions.
• In some cases, companies get into this situation because there is no centralised reporting/analytics strategy.

Whatever the reason, it's common for organisations to have a footprint in both Power BI and OAC (or any other combination of analytics platforms). This used to be seen as a challenge - "which one do we standardise on?" But modern integration and alignment features allow both platforms to coexist and complement each other rather than compete. This provides flexibility to leverage the strengths of both tools.
The goal of this post is to show how Power BI and OAC can work together effectively in a hybrid analytics model to deliver insights across the business.

Benefits of each platform

An important benefit of Power BI is its tight integration with Microsoft 365 and components like Excel. Users are already familiar with Excel for ad hoc analysis, and Power BI makes it easy to share those same models and data to the entire organisation via dashboards and apps. The Microsoft stack alignment enables a seamless flow between data, analysis, and organisational sharing. For organisations invested heavily in Microsoft technology, Power BI is a natural fit to extend that analytics foundation.

A key strength of Oracle Analytics Cloud is its semantic model that abstracts the complexity of the underlying data and technology stack from end users. Business analysts can access and analyse data without needing to understand the physical tables and relationships. OAC also enables strong data governance with centralised security rules, data logic definitions, and stewardship processes applied consistently across the semantic models. This top-down approach ensures quality and trust in the data while still empowering decentralised analytics.

How They Can Work Together

There is Power BI supported connectivity to Oracle Analytics that gives Power BI users access to Oracle Analytics content.

Oracle Analytics provides an easily configured connector that enables Power BI Desktop access to the Oracle Analytics semantic model. More details on how to set it up can be found here. 

​It should be noted that there are a number of limitations:

1. The connectivity to Oracle Analytics only works from Power BI desktop.and nor the Power BI service. 
2. Power BI can only access cached data in Oracle Analytics that has been previously loaded into OAC. It does not allow direct queries or live connections to data sources. This means data may be stale if the cache has not been refreshed.

Conclusion

Power BI and Oracle Analytics Cloud both offer compelling capabilities for data visualisation, reporting and analytics. Rather than viewing them as competing tools, organisations should consider how they can work together in a hybrid analytics environment.
The key is to play to the strengths of each platform:

• Use OAC as the governed source of truth for enterprise data models, metrics and KPIs. Take advantage of its advanced modelling, calculation and data preparation features.
• Leverage Power BI's ease of use and flexibility for self-service data exploration and quickly sharing insights through interactive reports and dashboards as well at its tight integration with Microsoft 365 products like Excel and PowerPoint.

With strong data governance, user training, and integration between the platforms, Power BI and OAC can complement each other to deliver insights to users across the organisation. There doesn't need to be an "either/or" choice.
By taking a hybrid approach, both business users and data experts can win with the analytics capabilities they need. Rather than analytics chaos, a "best of both worlds" environment can emerge. Organisations can optimise their analytics investments and empower people with the right insights at the right time.
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Unless you’ve been living under a rock, you won’t be surprised to know that the main theme of CloudWorld this year at Las Vegas was generative AI.

Almost every Oracle product from databases, to Fusion applications, to OCI, APEX and Analytics to name but a few announced generative AI functionality at the recently concluded Oracle CloudWorld 2023 in Las Vegas that I attended.

My journey from the U.K. to Las Vegas was "eventful" and could be a separate blog in its own right!

​Another point made that I was admittedly unaware of is that Oracle Analytics Server and Oracle Analytics Cloud only share a very small amount of source code. This is the main reason why there is a significant difference in the Oracle Analytics Cloud and Oracle Analytics Server release cycles; every 2 months for Oracle Analytics Cloud and annually for Oracle Analytics Server.

​In the integrated data preparation area, PII (Personally Identifiable Information) is coming soon. My take on this is that this will be another recommendation on the data preparation step. The web based semantic modeller support for EPM & Essbase data sources is also something eagerly awaited and, on the roadmap, marked as “later”.

The information from this strategy update is publicly available and can be found here.

Catalog Manager
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It was recently announced that the Catalog Manager GUI will be desupported from the January 2024 Oracle Analytics Cloud update. The command line interface will still be available to use from that point. The January 2024 update will not ship with the Catalog Manager client tool however, you’ll still be able to use existing installations of the Catalog Manager client if needed. The strategic plan is for the Catalog Manager client tool to be completely replaced by a web-based Catalog Manager. There are also OAC catalog APIs in the works as well.
 
T.K. Anand had a session which I couldn’t attend that provided some more context and information about Larry Ellison’s announcement about Oracle Cloud Data Intelligence Platform. I think that this is going to bring Oracle Analytics and the Fusion Analytics platform under one umbrella in conjunction with generative AI. If this is the case, then it will simplify the Oracle Analytics offering doing away with all the confusion around which of the products are best suited to specific customers. I’ll be keeping an eye on developments on this.
 
My PresentationI presented at CloudWorld for the first time, and it was an interesting experience. Tim German, the Version 1 US EPM Practice Head was a co-presenter. The topic we covered was “from data to insights with Oracle Analytics”. We had a really great turnout with standing room only and about 100 people in attendance, we had to cram a lot of content into a 20-minute theatre session to demonstrate the capability of Oracle Analytics to enable users to gain insights from their data assets. The key takeaways of the session were that Oracle Analytics:
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•       allows users to connect to a variety of data sources.
•       users can easily visualise and analyse data.
•       built-in machine learning and artificial intelligence capabilities
•       tools for data preparation.
•       collaboration and sharing features.

CloudWorld has been a positive experience, and I had the opportunity to meet a lot of people in person for the first time that I’ve only seen on Zoom and Teams calls! As an Oracle ACE Pro, I enjoyed the Oracle ACE events which were good fun where there were over 100 Oracle ACEs in attendance at the ACE dinner! I also got to meet some of the Version1 Oracle ACEs in person too.

​I just hope that my luggage won’t be overweight on my way home with all the swag that I picked up!

In the dynamic landscape of data analytics, businesses are in search of a comprehensive solution that seamlessly integrates data governance, self-service analytics, augmented insights, and robust reporting. Oracle Analytics emerges as a unified platform that stands out amidst the competition. Let's draw a comparison with other leading tools – Tableau, Power BI, and Qlik – and delve into Oracle's key differentiator: the Semantic Model, and how it empowers cross-platform compatibility.

Comparing Solutions:

In a landscape where specialised tools often excel in specific areas; some examples stand out:
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• Tableau (Self-Service Analytics): Tableau offers user-friendly self-service analytics, allowing users to create captivating visualisations. However, it lacks the depth of governed analytics and comprehensive AI-powered insights.
• Power BI (AI-driven Insights): Power BI shines with its AI-driven insights and automation of data analysis. However, its capabilities can sometimes be limited when integrating insights with external reporting tools.
• Qlik (Data Governance): Qlik emphasises data governance and provides strong analytics capabilities. But it lacks the seamless integration required for a comprehensive analytics ecosystem.

In contrast, Oracle Analytics emerges as the bridge that seamlessly connects and combines these functionalities, underpinned by the powerful and unique Semantic Model. Unlike other vendor solutions that focus on one specific area of Analytics, Oracle Analytics unites governed analytics, self-service analytics, and augmented analytics within a single, cohesive platform.
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• Business Analysts - Oracle Analytics provides self-service data visualisation and discovery tools for business analysts to easily access, prepare, analyse, and visualise data to gain insights. Features like data preparation, auto insights, smart visualisations and natural language processing enable faster and easier analysis.
• Data Scientists - For more advanced analytics, data scientists can use Oracle Analytics to build and compare machine learning models, perform clustering and predictions, and leverage R and Python scripts for advanced analysis. Oracle Analytics includes auto ML to make model building faster.

• Developers - Developers can embed analytics into applications using Oracle Analytics Cloud SDKs and REST APIs. They can build custom visualisations, dashboards, and data flows to extend Oracle Analytics capabilities. The Semantic Modeller incorporates data governance capabilities like business-friendly data sets, stewardship, master data management etc. Developers can build on these capabilities for enterprise data governance needs.
• Casual Business Users - With interactive dashboards, natural language query and conversational analytics, casual business users can intuitively explore data and find insights through guided analysis and AI assistant. Smart visualisation recommendations make it easy for novice users.
• Executives/Management - Interactive dashboards provide executives with ability to visualise and track KPIs and metrics to monitor performance. Natural language makes it easy to ask questions of data. Data storytelling features create narrated stories to simplify sharing insights.

The goal of Oracle Analytics is to empower all personas across organisations to make data-driven decisions through easy, intelligent, and connected analytics experiences. The versatility caters to both highly technical and business casual users.

A Path Forward: Embrace Unity with Oracle Analytics:
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In the July 2023 update, Oracle Analytics Cloud has introduced a new feature called AutoML. Oracle AutoML, or Oracle Automated Machine Learning, is a feature within the Oracle Cloud Infrastructure (OCI) platform that has been accessible within OCI in the Autonomous Database. Its main objective is to automate the process of developing and deploying machine learning models. AutoML is designed to simplify and accelerate the model building process, particularly for users who may not have extensive expertise in data science or machine learning.
 
What exactly is AutoML?
​
Oracle AutoML provides a user-friendly interface that allows users to upload their data, specify the target variable they want to predict or classify, and set various model configuration options. The platform then automatically explores different algorithms, feature transformations, and hyperparameter settings to find the best-performing model for the given task.

​The AutoML process involves several steps, such as data preparation, feature engineering, model selection, hyperparameter tuning, and model evaluation. Oracle AutoML takes care of these steps behind the scenes, leveraging advanced techniques and computational resources to optimise the model building process.
Once the best model is identified, Oracle AutoML enables users to deploy the model for real-world use cases. The deployed model can be integrated into applications or used for making predictions and decisions based on new data.
Overall, Oracle AutoML aims to democratize the use of machine learning by automating complex tasks and reducing the need for specialized knowledge. It enables users to leverage the power of machine learning without the extensive time and effort typically required for model development, allowing them to focus more on deriving insights and value from their data.
AutoML in Oracle Analytics CloudAutoML has now been embedded in Oracle Analytics in the July 2023 update enabling users with no prior Machine Learning experience to use the right models with the correct hyperparameter settings.

Right clicking on the model and selecting the Inspect menu option gives you more information about the ML model. ​

The Oracle Analytics REST API Connector is now available in the September 2022 Oracle Analytics update in preview mode.

The connector now allows access to data directly from REST API endpoints.

​To access the REST API Connector, you will need to enable the feature in the Console System Settings preview section by switching on the toggle.

You will need to log off and back on again to apply the change to your instance. You should now see the REST API connector in the create > connections menu on the Oracle Analytics homepage.

In the next screen, you enter details of your REST API that you want to access. The basic information required for connecting are:
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1. Base URL of the REST API
2. REST API endpoints
3. Authentication type
4. Authentication credentials

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You can either enter the information in here directly or you can use a JSON template to enter all of the REST API information. A sample JSON template file can be found at the bottom of this post.

If you decide to enter information of the REST API for your connector using a JSON file, you will need the contents to match the syntax below. If your API has several endpoints that you will be using then enter each endpoint URL in the endpoints section.

​Ensure that the correct authentication type is entered. Once all completed, the connection can be used as a standard connection to create a dataset. .

During the testing of this new functionality, the REST API used BasicAuth authentication and the API key was supposed to be entered in the username field. The issue here is that the password is a mandatory field and can't be left blank. The way round this was to re-enter the API key in the password field as well.

Once the connection has been created, you can create a dataset using the REST API connection in the normal fashion.

In the connection panel of the New Dataset window expand the REST API connection to the AUTOREST schema to access the result sets of the endpoints. The RESULTS tables contain the data from the endpoints and the others contain metadata of the endpoints.

The new REST API connector is a great addition to the Oracle Analytics capability. It now gives users another means of bringing additional data in to uncover even more insights from your enterprise data assets.