![]() ![]() We can then select one of the options at the bottom of the screen to view the data that we have just imported. Once we have uploaded our data, we will see a screen that will looks like the following, and we can click on the Import button:įinally, we name our index master_data_from_csv as follows:Īfter executing the import, we will see a screen that looks like this: These steps are demonstrated in the following screenshot:Īfter performing the above steps, follow the steps on the next screen to upload our CSV data: Click on the Upload file button (in the Import data section).To get to the Data Visualizer do the following: The blog on Importing CSV and Log Data into Elasticsearch with File Data Visualizer shows how the Data Visualizer in Kibana can be used to import CSV data into Elasticsearch. Note that the CSV data should not include any additional spaces, as the current version of the Data Visualizer requires that the data is precisely formatted. "device6","London","Consulting","Computer" "device5","Toronto","Consulting","Computer" "device4","Barcelona","Engineering","Phone" "device1","London","Engineering","Computer" "Device ID","Device Location","Device Owner","Device Type" This data represents devices in an organisation's inventory. For the example given in this blog, we store our Master Data in a file called test.csv. The following example Master Data in CSV format can be imported with Kibana, and later used for enriching documents as they are ingested into Elasticsearch. ![]() Historically, enrichment functionality was only available in Logstash, however since the introduction of the enrich processor in Elasticsearch 7.5.0, it is possible to do enrichments directly in Elasticsearch, without needing to configure a separate service/system.Īs Master Data that is commonly used for enrichment is often created in CSV files, in this blog we give step-by-step instructions about how the Enrich Processor running on an ingest node can be used for enrichment using data from a CSV file. This Master Data could include information such as device location, which team owns a given device, device type, and so on. It is not difficult to imagine a scenario where there are devices logging data into Elasticsearch, and where the data that is sent from these devices needs to be enriched with Master Data. While the GeoIP processor is a good example for understanding the power of enrichment, there are many other cases where documents may need to be enriched with custom data. Enriching documents with a geographical location at ingest-time is useful because it permits fast query-time operations such as queries by location or the efficient presentation of information on a map. Enrichment is the process of merging data from an authoritative source into documents as they are ingested into Elasticsearch.įor example, enrichment can be done with the GeoIP Processor which processes documents that contain IP addresses and adds information about the geographical location associated with each IP address. When ingesting data into Elasticsearch, it is often beneficial to enrich documents with additional information that can later be used for searching or viewing the data. ![]()
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