Genesys Cloud in AVRO Format

In this article, we will explore how to export Genesys Cloud raw data in AVRO format using the Repo361 API and analyze it in Databricks for deeper insights.

Why Use AVRO Format?

AVRO is a data serialization format that is highly efficient for big data applications. Some key benefits include:

• Compactness: AVRO is a binary format, which makes it more space-efficient compared to text-based formats like CSV.
• Schema Evolution: AVRO stores schema information along with the data, making it easier to evolve the schema over time without breaking compatibility.
• High Performance: AVRO works well with distributed data processing frameworks like Apache Spark, which Databricks is built on.

Step-by-Step Guide: Exporting and Using Genesys Cloud Raw Data in AVRO Format with Databricks

Step 1: Configure Repo361 API Credentials

Before you can request data, make sure you have the proper credentials for accessing the Repo361 API. These credentials include:

• Client ID and Client Secret for authentication.
• OAuth2 Token to access raw data files.

You can generate these credentials in the Repo361 platform by following these steps:

1. Log in to Repo361 with your credentials.
2. Navigate to the settings page and generate a client secret.
3. Store the client secret in a secure location, as it will only be shown once.

Step 2: Request Raw Data in AVRO Format

Once your credentials are set up, you can use the Repo361 API to request raw data from Genesys Cloud in AVRO format. Here’s an example of how to send the API request:

POST Request to Export Raw Data in AVRO Format:

POST https://api.repo361.com/rawdata/export
Headers: {
    "Authorization": "Bearer {access_token}",
    "Content-Type": "application/json"
}
Body:
{
    "date": "20240905",  // Specify the date for data export in YYYYMMDD format
    "format": "avro"     // Request data in AVRO format
}

The response will include a download URL for the raw data in AVRO format. The URL is valid for 1 hour, so ensure you download the file within that time.

Step 3: Save AVRO Files to Databricks

To process the raw data in Databricks, we need to save the AVRO files to the Databricks File System (DBFS). This can be done using Databricks’ file management capabilities or a direct upload via the Databricks UI.

If you’ve downloaded the AVRO file, you can upload it to a directory in DBFS. For example:

1. In Databricks, open your workspace and navigate to the Data tab.
2. Upload the file to a directory, e.g., /dbfs/tmp/genesys_data/.

Step 4: Load AVRO Files into Databricks

To enhance the workflow by utilizing fastavro for efficient reading and processing of AVRO files in Databricks, you can skip Spark’s built-in AVRO support and directly load the AVRO data with fastavro, then map the data to a Spark DataFrame. This approach improves performance when working with AVRO files by reducing overhead.

Here’s a short description of the idea:

1. Use fastavro to Read the AVRO File:
   Instead of loading the AVRO file directly into Spark, you first use the fastavro library to read the AVRO data. This method is faster, especially when handling large datasets.

2. Create a Spark Schema:
   After reading the AVRO data with fastavro, you create a corresponding Spark schema based on the AVRO file's structure. This schema is then used to convert the fastavro data into a Spark DataFrame for further processing and analysis.

By using fastavro, you get more control over the data loading process while also improving performance.

Step 5: Analyze the Data in Databricks

Now that the raw Genesys Cloud data is loaded into Databricks, you can perform various analyses, such as:

• Interaction Analysis: Analyze customer interactions, response times, and resolution patterns.
• Agent Performance: Evaluate agent productivity and identify areas for improvement.
• Customer Behavior Trends: Uncover trends in customer behavior across different channels.

You can also create visualizations, build dashboards, or export the results to other systems, such as Power BI or Tableau, for reporting purposes.

Conclusion

Using Repo361’s API to export Genesys Cloud raw data in AVRO format and analyzing it with Databricks enables powerful insights into customer interactions and operational performance. AVRO’s compact format and Databricks’ scalable analytics capabilities provide an efficient solution for processing large datasets.

Whether you're tracking agent performance, customer behavior, or operational metrics, integrating Genesys Cloud raw data into Databricks unlocks the potential for data-driven decisions and strategic improvements.

By combining the flexibility of AVRO format with the processing power of Databricks, you can gain deeper insights into your customer interactions, enhance reporting, and improve business outcomes.