Amazon Redshift data output
This article provides an introduction to Amazon Redshift along with a guide on creating an Amazon Redshift data output using Upsolver.
Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud.
An Amazon Redshift data warehouse is a collection of computing resources called nodes, which are organized into a group called a cluster. Each cluster runs an Amazon Redshift engine and contains one or more databases.
1. Go to the Outputs page and click New.
2. Select Amazon Redshift as your output type.
3. Name your output and select your Data Sources.
4. Select New to create a new table or Existing to output to an existing table. Then click Next.
If outputting to an existing table, complete the database options as prompted before clicking Next again. If necessary, create a new Redshift connection.
5. Click the information icon
in the fields tree to view information about a field. The following will be displayed:
Density in Events
Density in Data
How many of the events in this data source include this field, expressed as a percentage (e.g. 20.81%).
The density in the hierarchy (how many of the events in this branch of the data hierarchy include this field), expressed a percentage.
How many unique values appear in this field.
The total number of values ingested for this field.
The first time this field included a value, for example, a year ago.
The last time this field included a value, for example, 2 minutes ago.
Field Content Samples Over Time
The percentage distribution of the field values. These distribution values can be exported by clicking Export.
A time-series graph of the total number of events that include the selected field.
The most recent data values for the selected field and columns. You can change the columns that appear by clicking Choose Columns.
6. Click the information icon
next to a hierarchy element (such as the overall data) to review the following metrics:
# of Fields
# of Keys
# of Arrays
The number of fields in the selected hierarchy.
The number of keys in the selected hierarchy.
The number of arrays in the selected hierarchy.
A stacked bar chart (by data type) of the number of fields versus the density/distinct values or a stacked bar chart of the number of fields by data type.
A list of the fields in the hierarchy element, including Type, Density, Top Values, Key, Distinct Values, Array, First Seen, and Last Seen.
7. Click the plus icon
in the fields tree to add a field from the data source to your output. This will be reflected under the Data Source Field in the Schema tab.
- If required, modify the column name under Schema Column.
- Additionally, click the gear iconto modify other details such as Column Type and Size.
- To remove a field, click the unlink iconto clear the column mapping then the garbage iconto drop the column.
Alternatively, add columns by clicking Add New Column.
- Provide a Column Name as well as select a Column Type.
- If desired, give the column a Default Value then click Save.
- This column will now be added under the Data Source Field in the Schema tab.
Toggle from UI to SQL at any point to view the corresponding SQL code for your selected output.
8. Add any required calculated fields and review them in the Calculated Fields tab. See: Adding calculated fields
9. Add any required lookups and review them under the Calculated Fields tab.
10. Through the Filters tab, add a filter like
WHEREin SQL to the data source. See: Adding filters
11. Click Make Aggregated to turn the output into an aggregated output. Read the warning before clicking OK and then add the required aggregation. This aggregation field will then be added to the Schema tab. See: Aggregation functions
12. In the Aggregation Calculated Fields area under the Calculated Fields tab, add any required calculated fields on aggregations. See: Functions, Aggregation functions
13. To keep only the latest event per upsert key, click More > Manage Upserts then select the following:
- Keys: A unique key identifying a row in the table
- Deletions: The delete key (events with the value true in their deletion key field will be deleted)
Click Preview at any time to view a preview of your current output.
14. Click Run and fill out the following fields:
- Table Name
- Intermediate Storage Location: Where Upsolver will store the intermediate bulk files which it will then load into Redshift using the
15. Click Next and complete the following:
Processing Time Range
Select the compute cluster to run the calculation on. Alternatively, click the drop-down and create a new compute cluster.
The range of data to process. This can start from the data source beginning, now, or a custom date and time. This can never end, end now or end at a custom date and time.
16. Finally, click Deploy to run the output. It will show as Running in the output panel and is now live in production and consumes compute resources.
You have now successfully outputted your table to Redshift.