While modern-day analytics focuses on cutting-edge advances in machine learning algorithms, the day-to-day drudgery of data analysis is still a manual process of finding, compiling, and wrangling disparate data types.įor the financial analyst, data often arrives as an Excel spreadsheet, but just as often, it is a data dump into a CSV or a query into a SQL database. In this age of data lakes and petabyte-scale databases, it is shocking how frequently I still receive data in the form of CSV, text, and Excel files.
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Customizing with code: The M language is the functional code used within Get & Transform, and it is possible to write custom queries for more bespoke requests.Different data sources: With a wide range of input files accepted, it is possible to work with disparate sources while maintaining consistent and normalized output quality.Handling text strings: As a significant improvement over the Text to Columns functionality in Excel, Get & Transform can quickly parse through and separate combined text and number strings into separate columns.These instructions can be saved and then repeated for future imports. Loading CSVs: Importing a CSV via Get & Transform allows for it to be cleaned and made "narrower" or "wider" to assist with data pivoting.In Power BI it exists in the External Data section of the Home tab. Access in Excel is via the Get & Transform Data section within the Data tab.
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