us-analytics.png
Ask an Expert

Self-Service Data Transformation: Getting In-Depth with Oracle Data Flow

In part one, I talked about the common concepts and goals that Tableau Prep and Oracle Data Flow share. In part two, I looked at a brief overview of both tools and took an in-depth look at Tableau Prep.

In this third post, let's dive deeper into Oracle Data Flow.

For these examples, I am going to be using Oracle Data Visualization Desktop 12.2.4.3.0 (the latest version as of this writing).

First, I’m going to import the three World Cup Excel files used in the Tableau Prep example. This was accomplished by clicking Create in the top right-hand corner of the ODVD home page, and then selecting Data Flow.

self-service part 3_1

When creating a new Data Flow, the Add Data Set window is displayed and prompts the user to select a source. The window will show any data sources created previously and also allows you to create a new source.

self-service part 3_2

I clicked on Create Data Set and then click File to import the first spreadsheet.

self-service part 3_3

Once a source is selected, the Create Source window is displayed and allows the user to rename the source, add a description, and change some of the basic characteristics of each column before import. DV will automatically try to import number columns as measures, so I needed to change them to attributes so that they are not aggregated.

self-service part 3_4

Clicking OK takes you to the Data Flow editor. From here I need to import the remaining two spreadsheets. To do this, I simply dragged Add Data under Data Flow Steps into the edit window and imported the sources as before.

self-service part 3_5

If I click on the + icon on one of the imported data sets, it brings up all the available options for the current flow.

self-service part 3_6

I’m not going to cover each option at this point, but I do want to highlight a couple of features that are unique to Data Flow.

One of the biggest features is applying machine learning models. While I am not going to go over this feature in great detail, the basic idea is that you can create a Train Model, which uses scripts that define the interface and logic for a task based on known data. You can then use that model to score unknown data or to predict dynamically within a visualization.

Another feature is the ability to apply custom scripts to the data flow. The interface reads the options and parameters defined in the script and then uses this to produce specific types of data sets.

The last feature I want to talk about here is Time Series Forecast. This function takes a time column and a target column and generates forecast values in a new column for the specified number of periods. Although this feature is not unique to Data Flow only (you can accomplish this with forecast functions within DV, OBI Analytics, or Tableau), it is nice that you can include it as part of the output dataset itself rather than doing it after.

For now, I’m going to join my three spreadsheets using the Join option. With the join option selected, the edit window displays a circle selection beside each of the remaining data sets. Clicking on this icon for the desired data set brings up the join interface where you can specify the columns to create the join on and specify matching or all rows for the type (inner, right outer, left outer or full outer).

self-service part 3_7

self-service part 3_8

After joining my data sets, I am going to add a filter for the Year column to only show years that include 1970 and above. I can accomplish this by selecting the filter option and adding each year from a list…

self-service part 3_9

self-service part 3_10

…or create an Expression Filter and write the filter condition manually.

self-service part 3_11

self-service part 3_12

Now, I am going to remove some unwanted columns. This is a very straightforward process. I selected the select column step and then selected the columns I wanted to remove and clicked Remove Selected.

self-service part 3_13

self-service part 3_14

Next, I’m going to add the final step in my Data Flow. By clicking on Save Data I can define the name, description, and type of output I want. If I choose Data Set Storage, the output of the Data Flow is saved as a file in a designated pre-defined location.

self-service part 3_15 

If I click on the dropdown under Save data to, I can select a Database Connection option. This allows me to save the output of my Data Flow as a table in the source database specified by the connection. I am going to use this option and put the data set as a table in my Oracle Database.

self-service part 3_16

self-service part 3_17

self-service part 3_18

Now, I am going to click Execute Data Flow. This is going to run the Data Flow and save the data set in the specified location and create a new data set for use with DV.

self-service part 3_19

self-service part 3_20

self-service part 3_21

self-service part 3_22

I can navigate to SQL Developer and look at my newly created table.

self-service part 3_23

self-service part 3_24

I really like this feature and think it expands the use of Data Flow beyond Oracle DV (creating new models from existing ones, consolidation of complex multi-source projects into an easily accessible source for other analytic tools, requirements that involve sources outside standard enterprise ETL protocol, creating aggregates).

Now, let’s look at combining different sources into a Data Flow. Once again, I’m going to use sales data coming from my Oracle Database and sales target data from my spreadsheet.

I’ll start by creating a new Data Flow and making a connection to my database.

self-service part 3_25

self-service part 3_26

From here, I can either select each table individually and import the columns or construct an SQL statement to consolidate and bring in selected columns from each table in as a single source.

self-service part 3_27

self-service part 3_28

I’m going to choose the first option and bring in all of my tables individually.

self-service part 3_29

As in the previous example, I can join each of my tables together using the Join operation by clicking on the + icon to the right of the source.

self-service part 3_30

Now, I’m going to bring in my Store Targets spreadsheet and join it to my existing flow.

self-service part 3_31

At this point, I’m going to do a little cleanup, remove some unneeded columns, and rename a few of the columns to improve readability.

self-service part 3_32

Now, I need to add in two calculated measures for my variance. I can select Add Columns and type in my calculations in the edit window.

self-service part 3_32.5

self-service part 3_33

With the add columns step, you can add as many calculated columns as needed by clicking the + in the left-hand corner. After each calculation has been defined, you can add them into the Data Flow by clicking Apply.

self-service part 3_34

self-service part 3_35

Now, I’m going to define my aggregation. I’ll select the Aggregate step where I can define the aggregations for each column under the Aggregate pane.

self-service part 3_36

If I want to add an aggregate for a column under the Group By pane, I can click on the desired column and select Aggregate to move it into the Aggregate pane. I am going to leave the default Aggregate selections for now and move to the output step.

Note: Unlike Tableau Prep, it is not possible to define multiple Outputs in Data Flow.

As in the previous example, I’m going to select the Save Data set and choose my database as the output destination and execute my Data Flow.

self-service part 3_37

self-service part 3_38

This creates a new table in my Data Flow schema and creates a new data set to use for visualizations.

self-service part 3_39

self-service part 3_40

Now, let me take a moment to talk about Sequences. Although not technically a part of Oracle Data Flow, it is a part of Oracle DV and requires Data Flow for its input. A Sequence lets you define one or more Data Flow(s) and will execute them in the order for them to exist in the Sequence.

So, let’s say that I have my Data Flow for store sales and targets that I created in the previous example, and I want to make two additional flows to put in a Sequence. One flow will build an Aggregate table that will be saved in my database with measures for Sales and Sales Targets by Month, Continent, and Region. I’ll build a new Data Flow that uses my store sales data set created in the previous example and add the appropriate steps.

self-service part 3_41

self-service part 3_42

The other Data Flow will create a data set based off the Aggregate Table I built in the previous flow for use in Oracle DV. It will show the current year’s sales, sales target and sales vs target by region.

self-service part 3_43

self-service part 3_44

self-service part 3_45

Now, I can build a Sequence that will run each Data Flow that will update each source in order: Store Sales and Targets > Aggregate > Regional Sales and Targets.

self-service part 3_46

self-service part 3_47

self-service part 3_48

This is a fantastic feature that has a wide variety of use cases and makes updating multiple data sets that use the other Data Flow’s a breeze.

Oracle Data Flow — The Good

  • Simple to understand and use
  • Interface is elegant and uncluttered
  • Machine Learning, Scripts, and Forecasting can be used as a part of Data Flow and be inserted directly in the data set
  • Output destination can be set to DV data set or database
  • Data Flows can be executed from the DV interface and don’t require each flow to be opened individually.
  • Sequences can be built to trigger multiple Data Flow’s in any order required
  • Data Flows can be scheduled, currently available on Oracle Analytics Cloud (OAC)
  • Ability to turn off auto preview to aid fast workflow

Oracle Data Flow — The Bad

  • Intermittent errors thrown by joining two data sources – sometimes when using the join operation, an error is thrown if two matching columns are not automatically identified. While not a huge deal, it is a bit annoying having to close the error message box before defining the join manually.
  • Scheduling Data Flows not available on-premises – As of this writing, scheduling a Data Flow is only available on OAC.

Final Thoughts

Both Tableau Prep and Oracle Data Flow introduce some exciting new possibilities in the world of data transformation and integration. While I think both tools have their place, I can’t see Tableau Prep being used in an enterprise-level environment with data that is updated frequently due to its inability to schedule workflows or store passwords for sources that require it. Feel free to share your thoughts and experiences with us in the comments section! Thanks for reading.

You might also be interested in...

connec

Ask an EPM/BI Advisor

If you're here, you've got questions — and we've got answers. Book your consultation to ask us about any range of topics, including:

  • Evaluating EPM or BI technologies
  • Comparing on-prem vs. cloud
  • Planning upgrades and migrations
  • Estimating project costs and timeframes
  • And much more — ask us anything!

Let our experts tackle your toughest questions for you.

Let's Talk