Data Unchained

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Writing Transforms in Python - Part 5

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.

Writing Transforms in Python - Part 4

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 Transforms in Python - Part 3

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.

Writing Transforms in Python - Part 2

Reading from Records - There are two main ways to access the individual values (or “fields”) of a Record: Indexing and the get() Method.

Writing Transforms in Python - Part 1

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.

How to send data to Matterbeam's HTTP collector

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

Connecting Matterbeam to Salesforce

This is a comprehensive guide on how to get set up using Matterbeam's Salesforce Collector to gather your CRM data. With Matterbeam, you can select specific Salesforce objects for collection, ensuring your data is instantly available for transformation and emission to various destinations. To discover how Matterbeam can

Finding and trusting your data with Matterbeam

Does your organization struggle with data sprawl? Do you have valuable information scattered across a myriad of different silos, tools, and personal computers? Are you dealing with duplicates or inconsistently named datasets that complicate the data management process? Or perhaps, your dilemma is having numerous different datasets with the same

Introducing stream joins in Matterbeam

In this short demo we showcase stream joins, our new feature that enables you to do stateful data transformations in Matterbeam. 📍Stream joins are often used in situations where data from one stream needs to be enriched with data from another stream, as well as for various other analytics use

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