Data Manipulation in Alteryx
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Learn to combine data with unions, appends and joins in Alteryx, parse text and date fields, and speed up slow workflows.
Data Manipulation in Alteryx deals with records that sit in several sources and raw fields that need parsing before analysis. You stack or append datasets, combine data from multiple tables with joins, split text into separate columns, and convert date and time values into a usable form. The final part turns to workflows that slow down, showing how to locate the cause and get them running smoothly again on large datasets.
What You Will Achieve
- Choose between a union and an append when compiling records into one dataset
- Pick the right join type to merge tables and use a join to filter records
- Split raw text into columns and convert date and time values for analysis
- Find bottlenecks in a workflow and speed it up with profiling, caching and leaner data
Program Overview
Combining Data with Unions, Appends and Joins
Covers the two ways of stacking or adding data in Alteryx, then the join types used to combine data from multiple tables.
- Unions versus appends and when each fits
- The Union and Append tools, including summarizing before a union
- Inner, left, right and full outer joins
- Using a join as a filter
String and Date/Time Parsing
Shows how to pull structured values out of raw text and turn temporal data into formats that analysis can use.
- Choosing a parsing technique
- Text to Columns parsing
- Inputting order status
- Date and time conversion
Performance Optimization in Alteryx Workflows
Deals with the bottlenecks that slow a workflow, such as data volume, tool limits, design and resource use, and ways to address them.
- Identifying the type of optimization needed
- Reducing data volume and improving workflow design
- Performance profiling
- Caching inputs and disabling unnecessary outputs
Certificate Awarded (Paid)
Job Outlook
Combining, parsing and tuning data workflows in Alteryx is routine work in analytics and business intelligence teams that prepare data from several systems for reporting and analysis.
Good for: Analysts building Alteryx workflows who need to merge data held in several places, clean up text and date fields, and speed up slow runs.
Keep in mind: Every exercise is built from Alteryx tools such as Union, Append and Join, so the techniques are taught inside Alteryx workflows rather than in code.
Bottom line: Four core Alteryx data techniques in one course, closing with practice on finding and fixing the bottlenecks that slow a workflow down.
Frequently Asked Questions
Which join types does the course work with?
The joins chapter practices inner, left, right and full outer joins with the Join tool. It includes a lesson built around the right join and another that uses a join as a way to filter records.
What kinds of parsing are taught?
The parsing chapter handles both string and date/time data. Lessons cover choosing a suitable parsing technique, splitting text with Text to Columns, an exercise on inputting order status, and converting date and time formats.
Which workflow bottlenecks does the final chapter address?
It looks at slowdowns caused by data volume, tool-specific limitations, workflow design and resource management. The remedies practiced include performance profiling, reducing data volume, disabling unnecessary outputs and caching inputs.
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