Basic Elements of Upsolver
Learn the fundamentals of using Upsolver to ingest your data.
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Learn the fundamentals of using Upsolver to ingest your data.
Upsolver has been designed to help you build pipelines quickly and easily. Read the following FAQs to discover the basic elements of Upsolver.
Upsolver includes pipeline templates to help you get started with popular use cases, however, you can also use SQL snippets to build your own pipelines if you prefer. Templates are available under the Worksheets section, or you create a blank worksheet to write your own code.
Pre-built templates help you to jump-start your Upsolver journey. Use templates with sample data to create a worksheet and start ingesting data. Templates include code to create data sources and target connections, tables, and jobs.
Worksheets provide an interface for creating and submitting SQL queries, as well as performing most other DML and all DDL operations, and viewing the results as your statements complete.
In Upsolver, you will find worksheets that are:
Private - only accessible to you.
Public - visible and accessible to all users within your organization.
The worksheet contains data source and target connections, tables, and jobs, written in familiar SQL syntax.
The Jobs section in Upsolver provides insights into each job and its progress. The Jobs page is helpful for monitoring and troubleshooting your jobs.
Jobs enable you to copy and transform your data. They can read from external sources and write the data into a table, or read from a table and write to another table or an external target.
The clusters section in the Upsolver UI provides a list of the clusters in your Upsolver environment.
The compute cluster is the group of servers that provide the computing power for transforming, aggregating, and enriching the data in Upsolver. They don’t interact with outside processes.
Every time a user triggers an action that requires a response, the request interacts with the API Server.
We have a library of resources, including articles, videos, blogs, and whitepapers on our website: https://www.upsolver.com/resources.
Upsolver handles data stream ordering based on the following rules:
The ordering within each partition or shard is preserved, as long as the number of output shards is equal to or lower than the number of input shards
Data is read based on the Select statement
If a Union operator is used in the statement, the data sources will be processed based on the order in which they appear within the Union (standard ANSI SQL behavior).