A Project is a place in PlaidCloud to manage a set of functions and data objects that serve a business purpose. For example, a Project could be BOM_Build, which is a set of workflows, tables, data imports, and so on that all work together to build the Bill of Materials. A Project is used exclusively in data management and does not include the display of data through a dashboard.
Within a project, you can create and modify tables, views, and dimensions.
A Workflow is a set of steps that load and transform data from raw state into a final form. There can be multiple workflows within a project, and those can be scheduled, run if conditions are met, or run manually. To view the workflows, open a project and go to the Workflows tab.
A Workflow Step is an individual action made within a workflow, such as load from a csv file, insert data into a table, or notify a user via SMS that an error condition occurred. To view the steps in a workflow, go to a project and the Workflow tab, and open a workflow to view all its steps.
There are 2 ways to schedule actions. The first is within the workflow itself by ordering, enabling, and applying conditionals to workflow steps. The second is within the event scheduler, which you can reach through Analyze->Tools menu->Event Scheduler. The Event Scheduler allows for ordering and applying conditionals to one or more workflows.
Data Source Connectors are the means through which data connections are made to external systems to import or export data in or out of PlaidCloud.
Allocations enable values (typically costs) to be split to a more-granular level by applying a driver. Allocations are used for a multitude of purposes, including but not limited to Activity-Based Costing, IT & Shared Service Chargeback, and the calculation of a fully loaded cost to produce and provide a good or service to customers.
The PlaidCloud Data Lakehouse Service (DLS) provides the speed of a Data Warehouse combined with the vast storage capability of a Data Lake. The DLS is based on Databend, a Lakehouse suitable for big data analytics and traditional data warehouse operations while having data lake operations and compatibility with Apache Iceberg, Apache Hive, and Delta Lake. It's extensive analytical optimizations, array of indexing types, and high compression makes it ideal for wide array of uses.