How to load Azure Data Lake Storage data into Elasticsearch via Logstash

Elasticsearch is a popular distributed full-text search engine. By centrally storing data, you can perform ultra-fast searches, fine-tuning relevance, and powerful analytics with ease. Elasticsearch has a pipeline tool for loading data called "Logstash". You can use CData JDBC Drivers to easily import data from any data source into Elasticsearch for search and analysis.

This article explains how to use the CData JDBC Driver for Azure Data Lake Storage to load data from Azure Data Lake Storage into Elasticsearch via Logstash.

Using CData JDBC Driver for Azure Data Lake Storage with Elasticsearch Logstash

• Install the CData JDBC Driver for Azure Data Lake Storage on the machine where Logstash is running.
• The JDBC Driver will be installed at the following path (the year part, e.g. 20XX, will vary depending on the product version you are using). You will use this path later. Place this .jar file (and the .lic file if it's a licensed version) in Logstash.
  C:\Program Files\CData\CData JDBC Driver for ADLS 20XX\lib\cdata.jdbc.adls.jar
  
• Next, install the JDBC Input Plugin, which connects Logstash to the CData JDBC driver. The JDBC Plugin comes by default with the latest version of Logstash, but depending on the version, you may need to add it.
  https://www.elastic.co/guide/en/logstash/5.4/plugins-inputs-jdbc.html
• Move the CData JDBC Driver’s .jar file and .lic file to Logstash's "/logstash-core/lib/jars/".

Sending Azure Data Lake Storage data to Elasticsearch with Logstash

Now, let's create a configuration file for Logstash to transfer Azure Data Lake Storage data to Elasticsearch.

• Write the process to retrieve Azure Data Lake Storage data in the logstash.conf file, which defines data processing in Logstash. The input will be JDBC, and the output will be Elasticsearch. The data loading job is set to run at 30-second intervals.
• Set the CData JDBC Driver's .jar file as the JDBC driver library, configure the class name, and set the connection properties to Azure Data Lake Storage in the form of a JDBC URL. The JDBC URL allows detailed configuration, so please refer to the product documentation for more specifics.

Authenticating to a Gen 1 DataLakeStore Account

Gen 1 uses OAuth 2.0 in Azure AD for authentication.

For this, an Active Directory web application is required. You can create one as follows:

1. Sign in to your Azure Account through the .
2. Select "Azure Active Directory".
3. Select "App registrations".
4. Select "New application registration".
5. Provide a name and URL for the application. Select Web app for the type of application you want to create.
6. Select "Required permissions" and change the required permissions for this app. At a minimum, "Azure Data Lake" and "Windows Azure Service Management API" are required.
7. Select "Key" and generate a new key. Add a description, a duration, and take note of the generated key. You won't be able to see it again.

To authenticate against a Gen 1 DataLakeStore account, the following properties are required:

• Schema: Set this to ADLSGen1.
• Account: Set this to the name of the account.
• OAuthClientId: Set this to the application Id of the app you created.
• OAuthClientSecret: Set this to the key generated for the app you created.
• TenantId: Set this to the tenant Id. See the property for more information on how to acquire this.
• Directory: Set this to the path which will be used to store the replicated file. If not specified, the root directory will be used.

Authenticating to a Gen 2 DataLakeStore Account

To authenticate against a Gen 2 DataLakeStore account, the following properties are required:

• Schema: Set this to ADLSGen2.
• Account: Set this to the name of the account.
• FileSystem: Set this to the file system which will be used for this account.
• AccessKey: Set this to the access key which will be used to authenticate the calls to the API. See the property for more information on how to acquire this.
• Directory: Set this to the path which will be used to store the replicated file. If not specified, the root directory will be used.

Executing data movement with Logstash

Now let's run Logstash using the created "logstash.conf" file.

A log indicating success will appear. This means the Azure Data Lake Storage data has been loaded into Elasticsearch.

For example, let's view the data transferred to Elasticsearch in Kibana.

    GET adls_table/_search
    {
        "query": {
            "match_all": {}
        }
    }

We have confirmed that the data is stored in Elasticsearch.

By using the CData JDBC Driver for Azure Data Lake Storage with Logstash, it functions as a Azure Data Lake Storage connector, making it easy to load data into Elasticsearch. Please try the 30-day free trial.