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“It’s just a quick ask.” Anyone who has worked with data has heard this phrase—a seemingly simple request: “Can we get a slightly modified report?” “What’s the regional breakdown for this other region?” “Just pull the data.” It sounds like something that should take minutes, maybe hours
The way we handle data is fundamentally broken. Across industries, businesses are trapped in a cycle of centralization—pushing everything into a data lake, warehouse, or some shiny new “lakehouse.” Why? Because getting data out of systems is so painful that the instinct is to store it all in one
Data is the lifeblood of modern business, driving everything from customer insights to operational efficiency. Yet, despite all the shiny tools, tech, and talent, most companies are stumbling. They’ve built impressive systems, but when it comes to making data work, they’re failing. Badly. Let’s be clear: this
Working with Strings in Python - Python has a lot of useful features built into its data types, as we’ve seen with our record dictionaries, but strings (text values) can do a lot for us as well.
Removing Fields from a Record - There are two main ways to remove a field from a Record… Or three, if we count simply clearing out the value.
Writing to Records - There are a few different ways that we can modify an existing record, to add a new field and value, change the value on an existing field, or remove a field entirely.
Reading from Records - There are two main ways to access the individual values (or “fields”) of a Record: Indexing and the get() Method.
The Default Transform & Working with Records - Matterbeam allows you to write your own Transformation steps in Python: Functional units of code that will be run on individual records.
Did you know you can send data to Matterbeam over HTTP using our HTTP Collector? Matterbeam provides an always available HTTP endpoint where you can PUT data for immediate use. Simply provide the name of your dataset in the url and a record in the request body. You can get