Kafka data output
This article provides an introduction to Apache Kafka along with a guide on how to create a Kafka data output using Upsovler.
What is Apache Kafka?
Apache Kafka is an open-source stream-processing software platform developed by the Apache Software Foundation, written in Scala and Java. The project aims to provide a unified, high-throughput, low-latency platform for handling real-time data feeds.
Create a Kafka data output
1. Go to the Outputs page and click New.
2. Select Kafka as your output type.
3. Name your output and select whether the output should be Tabular or Hierarchical. After adding your Data Sources, click Next.
4. Click the information icon
in the fields tree to view information about a field.
The following will be displayed:
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.
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.
5. Click the information iconnext to a hierarchy element (such as the overall data) to review the following metrics:
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.
6. 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 Output Column Name.
7. Add any required calculated fields and review them in the Calculated Fields tab. See: Adding Calculated Fields
8. Add any required lookups and review them under the Calculated Fields tab.
9. Through the Filters tab, add a filter like WHERE in SQL to the data source.
See: Adding Filters
10. 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
11. In the Aggregation Calculated Fields area under the Calculated Fields tab, add any required calculated fields on aggregations. See: Functions, Aggregation Functions
12. Click Run and fill out the following fields:
Kafka Hosts: How to connect to Kafka
Topic Name
Additional Kafka Properties (Optional)
Intermediate Storage: Where Upsolver will store the intermediate bulk files before loading them into Kafka
See: Running an output
13. Click Next and complete the following:
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.
14. 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 data to your Kafka topic.
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