Dataset Caching Improvements

July 2024 Update - Feature Deep Dive

Dataset Caching Improvements

Generated with AI ∙ 16 July 2024 at 11:02 PM

The Oracle Analytics Cloud July 2024 update is on its way out to the general public as this blog is being written. We will focus our attention on one of the new features that significantly improves the benefits of dataset in the Oracle Analytics data visualisation tool.

You can watch this YouTube playlist below to find out details of some of the main features in the July 2024 update. For more information on the update, click here to get a comprehensive list of the features that are included in the July 2024 update.

In this blog, we'll focus on the data caching feature improvements which are part of the July 2024 update. Caching the data in datasets provides a number of benefits which include the potential for improved query performance. Being able to apply changes made to the source data has been possible previously by doing a full reload of the dataset. This can be a long drawn out process if the underlying data source has a large volume of data.

In the July 2024 Oracle Analytics Cloud update, it is now possible to incrementally update datasets. There is now the capability to only insert new rows and updates to apply any changes to existing data. which will be much better than a full data reload. This improves the data load time as well as the execution of queries against the dataset.

These performance improvements that caching provides come with a downside. If you require real time data, caching is not the option for you. If intraday data changes aren't critical to your business area that you are analysing, then data caching will work for you.

Business Benefits

Incrementally loading data into a dataset in Oracle Analytics Cloud (OAC) offers several business benefits:

Reduced System Load and Improved Performance:
 By only loading new or changed records, the system avoids reprocessing the entire dataset. This reduces the overall load on the system, leading to better performance and quicker data refresh times.

Minimised Downtime:
Incremental loading can be scheduled during off-peak hours or more frequently, ensuring that the system is always up-to-date without significant downtime or disruption to users.

Faster Data Refresh:
Since only new or updated data is processed, the time required to refresh datasets is significantly reduced. This allows for more timely and accurate data availability for business analysis and decision-making.

Scalability:
Businesses can scale their data operations more effectively with incremental loading, handling larger datasets without the need for massive resource increases. This supports business growth and the integration of more data sources.

Enhanced Data Accuracy and Relevance:
 With more frequent and efficient data updates, businesses can ensure that their analytics are based on the most current data. This leads to more accurate insights and better-informed decision-making.

Improved User Experience:
End users experience faster query responses and up-to-date data, which enhances their ability to perform timely and effective analysis. This leads to higher user satisfaction and greater adoption of the analytics platform.

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Implementing incremental data loading in Oracle Analytics Cloud supports these benefits, enabling businesses to maintain efficient, scalable, and cost-effective data management practices. Data caching is not available to all data sources. You can get a full list of supported data sources from here.

There are 2 methods that can be used to set the incremental dataset cache refresh; this can be done either in a visualisation or directly within the dataset itself.

Configuration in a Visualisation

​The image below shows you how to set this up in a visualisation.

Change the Data Access from Live to Automatic Caching and then change the Cache Reload Type to Load New and Updated Data. 

Configuration in ​a Dataset

The image below shows how the incremental caching is set up in a dataset.

As described above, you can set up a variety of data sources to refresh incrementally and these can be run manually as above. If there is a requirement for the dataset to be regularly refreshed then a schedule can be set up to enable this. Below, you can see how to set up a schedule that can be used to automate the cache refresh of the dataset. You can also view the details of the schedule and detail of the schedule executions. 

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Incremental data loading of datasets in Oracle Analytics Cloud offers significant business benefits by enhancing system performance and efficiency. By only loading new or updated records, it reduces system load, minimizes downtime, and accelerates data refresh times. This approach optimises resource utilisation, providing cost savings and enabling scalability for larger datasets. Additionally, it ensures data accuracy and relevance, improving the user experience with faster query responses and up-to-date information.

​Overall, incremental loading supports efficient, scalable, and cost-effective data management practices, crucial for informed decision-making and business growth.