5 ways to join your data in a seamless process with Alteryx

In this article we will focus on the different possibilities to join data with Alteryx.

As an analyst you are faced with the reality that not all data is in one system or location. Getting access to that data is one step in the analytics process, blending and combining that data is a key component to create an analytical dataset that companies can use to take informed decisions.

This is a sample of the tools available in Alteryx Designer.

Append field

Append Field

Append the fields from a source input to every record of a target input. Each record of the target input will be duplicated for every record in the source input.

Some practical examples

  • Add total national sales across all your store sales to calculate a market share.
  • Add the date of the extract to each record.

Tips & tricks

  • Ensure that your larger data set is the target and the smaller the source.
  • Clean (Deselect & Rename fields) your data directly in the tool.

Find and
replace

Find and replace

Search for data in one field from one datastream and replace it with a specified field from a different stream, or append the field to a record.

Some practical examples

  • Replace abbreviations at once in one field
  • Add a new field with matching values, as the city of your store matched with the store ID

Tips & tricks

  • Similar to a VLOOKUP in Excel

Join

Join

Combine two data streams based on common fields (or record position). In the joined output, each row will contain the data from both inputs.

Some practical examples

  • Combine your sales data & your store data based on the store ID which is the common field in both files.
  • Check if all your records are matched with a second file, or if there are errors.

Tips & tricks

  • Thanks to the three outputs (Left, Join & Right outputs) it is easy to check if all records were joined.
  • Clean (Deselect & Rename fields) your data directly in the tool.

Union

union

Combine two or more data streams with similar structures based on field names or positions. In the output, each column will contain the data from each input.

Some practical examples

  • Combine two or more years of sales data into one file, if the input files have similar fields.

Tips & tricks

  • You can configure the output fields manually, so you can easily detect errors.

Movie: what is an optimal point of sale network?