Dataflows gets better and smarter

Data flows in latest version of Oracle Analytics Cloud is more powerful and useful than ever. Lets take a look.

Data flows in Oracle Analytics lets users take one ore more data sources, join them and transform the data to produce a curated set of data that users can use to visualize and analyze. Data flows have various inbuilt functions/nodes like Adding new columns/calculations, Removing columns, Grouping, Binning, Training different Machine Learning Models, Forecasting, Sentiment Analysis etc to transform and enrich the data.

In the latest version of Oracle Analytics more new features like Dataset Prompts, Branching, Incremental data processing, Output columns metadata management etc are added to dataflows. We will go through each of those new features in detail in this blog.

Dataset Prompts: 

Dataset Prompts feature lets users choose input or output datasets to a dataflow on the fly at the time of executing/running the dataflow. Prompt option is useful in cases where a user would like to reuse a complex dataflow with another dataset or to return output dataset with a different name without having to edit the flow by opening it. Prompt option can parametrize the input and output datasets of a dataflow. By default this option is disabled and users have to enable it by clicking on Prompt check box. Here are a few snapshots that show how to enable Prompts for dataflows:


                               
Name field takes the default dataset name as input. This should be the name of actual dataset present in the instance.
Prompt field takes the prompt text to be shown when running the dataflow.
Prompt option is available for both input and output datasets of the dataflow. If Prompt option is not selected dataflow will run with the dataset selected during dataflow creation/edit phase.

This is how the prompt window looks like when a dataflow with prompts enabled is run:


To summarize Prompt option adds a great deal of flexibility to running the dataflows by specifying the input and output datasets on the fly without having to edit the dataflow.

Here is a video which demonstrates Dataset prompt feature in dataflows:

Branching:
Branching option allows users to branch the output of a node(except Train ML Model node) in the dataflow into 2 or more branches. Users can apply different transformations on different branches and save the outputs of these different branches to different datasets. End node of each branch will always be Save Data. To add a branch node click on + and select branch node. This is how the branch node and its options look like:

                   
Number of branches can be incremented or decremented by entering the value or using UI options. Each branch can process disjoint subsets of data and return distinct outputs. For example in the below snapshot 3 branches are added to Sample Order Lines dataset.

1st branch computes Sales by Customer Segment and saves it in a dataset.
2nd branch computes Sales by Product Category and saves it in a different dataset and
3rd branch adds month column and saves the entire result in a different dataset.

On running/executing this dataflow three different datasets will be created. Here is a snapshot of the output of these 3 branches:

                                                 

To summarize Branch option is useful when user wants to do different transformations on different subsets of data and save it separately.

Here is a quick video tutorial that shows how branch option can be used:

Output Controls:
Output controls feature lets users decide how an output column of a dataflow should be treated and saved as (Attribute or Metric) in the output dataset. For metric columns default aggregation can also be chosen. On adding Save Data node to the dataflow users are provided with option to decide how each of the output columns should be treated as. Users can select Attribute or Measure from the drop down list. For metric columns users can select the default aggregation rule. Here is a quick snapshot that shows how users can change the data types and default aggregation rule for columns:



To summarize this feature provides more control to users over datatype of output column.
Here is a quick video tutorial that demonstrates this feature:

Incremental Data Processing:
Incremental data processing feature allows users to run the dataflow for incremental data/rows that become available between batch runs. This feature helps in efficient usage of resources to run dataflows only on incremental data rather than re-running on data which is processed already. This option is available only for datasets created from Database connections and this can be enabled only for a single input dataset within a dataflow.

Enabling incremental processing for a dataset is a two step process:
1) First step is to set New Data Indicator column while creating the dataset from a database. To enable incremental processing set New Data Indicator field to one of the columns from the dataset in configuration page. New data added to the database will be identified based on this indicator column. Here is a quick snapshot which shows how to configure the new data indicator column:



In this case New Data Indicator column is set to TIME_BILL_DT (date) column.

2) After adding the newly created dataset as an input to the dataflow, select "Add New Data Only" field to enable incremental processing for this dataset. Here is a quick snapshot that shows how this should done:

                                             

Now this dataset is enabled for incremental processing and any updates to this dataset/table will be processed incrementally in the dataflow when the dataflow is run after making changes to the dataset.

Output of the dataflow for the incremental data can either be appended to the existing output or can replace the existing output. Here is a quick snapshot which shows where to choose this option while saving the output dataset:

                                     
Now all the required parameters are set for incremental processing. When we save and run this dataflow for the first it will be run for the entire dataset and for subsequent runs, dataflow would be run only for the changed(added or removed) data in the configured dataset.

Here is a quick video tutorial that demostrates incremental data processing capabilities of dataflows:

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Replace Dataset Feature

In this blog we will talk about a new feature in the latest version of Oracle Analytics Cloud called Replace Dataset. This feature allows users to intelligently apply (or map) data visualization analyses created using one dataset on to another dataset with few clicks and intuitive column mapping.

Oracle DV allows users to create in-depth and insightful analyses, add sophisticated calculations,  create nice looking infographics and more. Sometimes after spending quite some time and energy on a project, they realize they want to do "similar" analysis on different  dataset or substitute certain columns within those analyses. Replace Dataset feature lets users to capitalize on the efforts they have already put in creating a project by offering a intelligent tool to swap the dataset/columns with its corresponding new dataset/columns. This feature come handy also in situations where you do analysis on one batch of data and later you want to perform the same analyses on a different batch of the same data set (perhaps for a different date range or region or product).

For demonstration purpose in this blog we will use a sample project which analyses Quarter Value growth, Quarter indexed growth, and Quarter Ago growth for a sample sales data. Here are quick snapshots to give you a quick overview of this project:

This analysis looks good and as a end business user I would like to do exact same analysis but for a different data that I have: Expenses data. To achieve this goal I will use Replace Data Set feature and in this process I will walk you through this feature in detail.

Replace dataset feature is quite easy and intuitive to use. To replace dataset in an existing project with another dataset, user has to simply right click on the dataset and select "Replace Data Set" option. Please note Replace Data Set option is available only for non-joined datasets. Here is a quick snapshot that shows how to invoke this option:

This will open a prompt window which lets users select or map the columns of existing dataset to the columns from new dataset. Oracle Analytics identifies columns which are used in the project (visualizations and custom calculations) and prompts user to map only the used columns to relevant columns in the new dataset. Here is a quick snapshot that shows how the prompt window looks like:

Once user clicks Replace button, all the existing visualizations will be updated with metric and dimension columns from the new dataset without any manual intervention. In this case we are replacing the Sample Order Lines dataset with Expenses dataset. This is how the canvases look like with Expenses dataset (with a filter that removes data for 2016, as we do not have data for entire 2016):

Another important functionality of this feature is, if the user would like to replace a column in this dataset with another column in the same dataset, he/she can use Replace Data Set option -> select the same dataset and then map the column to be replaced with a new column in the dataset. In this example if user would like to replace Business unit with Expense type, he/she has to map Business Unit column to Expense Type column.

To summarize, Replace Data Set feature lets users choose a project as a template and use the same analysis for other datasets with few clicks and without having to manually re-create the entire project which is tedious and time taking.

Here is quick video tutorial that demonstrates Replace Data Set feature:

 

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