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Step List

Pull from File​

Extracting data from a file uploaded by user (CSV, XLSX, JSON etc). It typically reads the contents of the file(s) and prepares them for further processing or analysis within a data flow.

Pull from Google Sheets​

Retrieving data from Google Sheets. It allows access to structured data stored in Google Sheets for use in data pipelines, analysis, or integration with other systems.

Pull from MySQL​

Extracting data from a MySQL database using direct SQL SELECT query. It enables accessing structured data stored in MySQL tables for use in data pipelines, analysis, or integration with other systems.

Pull from Shopify​

Extracting live data from Shopify. It enables access to various types of data such as orders, customers, products, varians, locations, and inventory stored in Shopify for integration, analysis, or reporting purposes.

HTTP Request​

Making an HTTP request to retrieve data from a web server (API endpoint) or put data (API, GraphQL etc). It allows fetching data in various formats (JSON, XML, etc.) from external sources or send (PUT, POST, DELETE) data.

Send Email​

Sending an email with data or information from data pipeline. It allows for automating the distribution of reports, notifications, or data extracts to specified recipients via email, enhancing communication and workflow automation.

Send to File​

Saving data from a data pipeline into a virtual file. The file can be used for download or sending by email. Then name of file also support expression (dynamic naming).

Send to Google Sheets​

Sending tabular data into Google Sheets. It allows for exporting or storing structured data from a data pipeline into Google Sheets for reporting, collaboration, or further analysis purposes.

To HTML​

Converting structured data from a data pipeline into HTML format. It allows for generating HTML documents or reports that can be displayed in web browsers or integrated into web-based applications for data visualization or sharing purposes. The step uses Liquid language for create HTML markup and handle special directives.

Loop over rows​

Iterating over each row in a dataset sequentially. It allows for performing repetitive operations, such as calculations, API request, send email or file on each row individually. This capability is useful for applying complex logic or custom business rules across multiple data entries within a data pipeline or workflow.

Wait​

Pause or delay in the execution of a workflow. It allows for controlling the timing between steps, ensuring that subsequent operations commence after a specified duration.

Output​

Output the data to a virtual table that visible on the workflow home page. Useful for debuging or dispalying data from particular steps.

Code​

Using JavaScript code to manipulate or transform data within a dataset. It allows for custom data transformations, calculations, or validations that are not directly supported by standard data manipulation steps. This flexibility is useful for complex data processing tasks, creating json objects or implementing specific business logic requirements.

Remove duplicates​

Process of identifying and removing duplicate rows from a dataset to ensure data integrity and accuracy. It is essential for cleaning data, optimizing storage, and improving overall data quality.

JSON to Table​

Converts JSON (JavaScript Object Notation) data into a tabular format, transforming nested or hierarchical JSON structures into rows and columns. It facilitates easier analysis and integration of JSON data into relational databases or other tabular data formats.

Arrange (Sort)​

Sorting the rows of a dataset based on one or more columns. It allows for organizing data in ascending or descending order according to specified criteria, facilitating easier analysis, visualization, or reporting of ordered data sets.

Filter table​

Selecting rows from a dataset based on specified columns conditions or expressions. It allows for extracting subsets of data that meet specific requirements, such as filtering by values, dates, or other conditions. This operation is useful for focusing analysis on relevant data or preparing data for subsequent processing within a data pipeline.

Combine tables​

Merging or combining data from two tables based on common columns or keys, it's used to integrate related information from different sources into a single dataset, facilitating comprehensive analysis or reporting.

Limit rows​

Restricting the number of rows in a dataset to a specified maximum. It is commonly used to sample data for testing, limit resource consumption, or focus analysis on a subset of the dataset.

Create Column​

Adding a new column to a dataset or modifying an existing one based on specified transformations or calculations (expressions). It allows for the creation of derived data that enhances analysis or meets specific reporting requirements.

Edit columns​

Selecting, renaming and arranging columns for the dataset. It allows for refining the dataset by focusing only on relevant columns needed for analysis, reporting, or downstream processing within a data pipeline.

Summarize (Group By)​

Aggregating data in a dataset by grouping rows based on one or more columns. It allows for calculating summary statistics (such as counts, sums, averages, mins or maxs) or applying custom aggregations within each group. This operation is useful for generating insights from data and preparing it for further analysis or reporting.