Export conversations to BQ

Qwiklabs-Courses

12 Apr 202402:38

Summary

TLDRThe script explains how to export conversation data from CCAI Insights to BigQuery for detailed analysis and visualization. It outlines the steps to create an empty table in BigQuery, apply filters, configure the export job, and monitor its progress. The export supports various write dispositions and can be filtered by specific criteria.

Takeaways

📊 Conversations can be exported to BigQuery for advanced analysis and visualization of Insights data.
🔍 Filtering conversations is an optional step before exporting to BigQuery, allowing for targeted data analysis.
📝 An empty table must be created in BigQuery prior to exporting data from Insights.
💾 The schema for the BigQuery table is dynamically set during the export job and does not need to be pre-defined.
🔗 After table creation, the export process is initiated from the Insights console by reviewing filters and conversation counts.
📋 Users should input the correct BigQuery dataset and table information to ensure accurate data transfer.
🚀 The export process is a long-running job, and its progress can be monitored through the Insights console's notification icon.
⚠️ The Insights interface's Export button has limitations on the number of records that can be exported and does not support data appending.
🔄 BigQuery supports two write disposition options for the export: WRITE_TRUNCATE and WRITE_APPEND.
🔒 The export feature also supports writing data to tables protected by Customer-Managed Encryption Keys (CMEK).
🌐 Export to BigQuery is compatible with all filter combinations that can be applied to conversation queries, allowing for complex data sampling.

Q & A

What is the purpose of exporting conversations into BigQuery?
-The purpose is to enable custom in-depth analysis and visualization on Insights data, allowing for more detailed insights into the conversation data.
Is it necessary to filter conversations before exporting to BigQuery?
-No, filtering conversations is an optional step. You can export all the conversations if required.
What is the first step before triggering an export job to BigQuery?
-The first step is to create an empty table in BigQuery where the Insights data will be loaded.
How do you create a new table in BigQuery?
-Go to the BigQuery console, select a dataset or create one if it doesn’t exist, and then click on 'Create a new table'. Input the required fields without setting a schema, as it will be done dynamically during the export job.
What is the role of the 'Export' button in the CCAI Insights console?
-The 'Export' button in the CCAI Insights console is used to initiate the export process to BigQuery, allowing users to review applied filters and conversation count before proceeding.
How can the progress of an export job be checked?
-The progress of an export job can be checked by clicking on the notification icon at the top right corner of the Insights console.
Why should the Export button in the Insights interface not be used?
-The Export button in the Insights interface should not be used due to current limits on the number of records that can be exported and the inability to append data to the target table.
What are the write disposition options supported by the CCAI Insights export to BigQuery via the API?
-The supported write disposition options are 'WRITE_TRUNCATE', which overwrites existing table data with the schema from the query result, and 'WRITE_APPEND', which appends data to an existing table.
Can data be exported to customer-managed encryption key (CMEK) protected tables?
-Yes, the export supports writing data to customer-managed encryption key (CMEK) protected tables.
Is the export to BigQuery compatible with all combinations of filters applied to conversation queries?
-Yes, the export to BigQuery is compatible with all combinations of filters that can be applied to conversation queries.
Can you provide an example of a sample export query?
-An example query could be to export all conversations with 10 or more turns handled by agent_id '007' between January 1st 2021 and January 2nd 2021 Pacific Standard Time.

Outlines

00:00

📊 Exporting Conversations to BigQuery for Analysis

This paragraph explains the process of exporting conversation data into BigQuery for advanced analysis and visualization. It highlights the ability to apply filters to select specific subsets of conversations before exporting. The user is instructed to create an empty table in BigQuery to load the Insights data. The export job is initiated from the CCAI Insights console, and the progress can be monitored through notifications. The paragraph also mentions the limitations of using the Export button in the Insights interface and outlines the supported write disposition options in BigQuery, such as WRITE_TRUNCATE and WRITE_APPEND. Additionally, it covers the compatibility of export with various filters and the ability to write to customer-managed encryption key (CMEK) protected tables.

Mindmap

Keywords

💡BigQuery

BigQuery is a fully-managed, serverless data warehouse that enables super-fast SQL queries using the processing power of Google's infrastructure. In the context of the video, it is used to export and analyze conversation data from CCAI Insights for in-depth analysis and visualization. The script mentions creating an empty table in BigQuery to load Insights data.

💡Custom Analysis

Custom analysis refers to the tailored examination of data to derive insights specific to the needs and interests of the user. The video script emphasizes the ability to perform custom in-depth analysis on exported conversation data, showcasing the flexibility and depth of insights that can be gained.

💡Filters

Filters are tools used to narrow down data sets to include only the desired records. In the script, filters are applied to conversations before exporting them to BigQuery, allowing users to select a subset of data for more focused analysis.

💡Export Job

An export job is a process that transfers data from one system to another. The video script describes configuring an export job to move conversation data from the Insights console to a BigQuery table, highlighting the importance of having a pre-created table for this process.

💡Dataset

A dataset in BigQuery is a collection of tables. The script instructs viewers to select or create a dataset in the BigQuery console, which will contain the new table for the exported conversation data.

💡Schema

In the context of databases, a schema defines the structure of a database, including the tables and the relationships between them. The script notes that the schema for the new table in BigQuery does not need to be set initially, as it will be determined dynamically during the export job.

💡Conversation Hub

The Conversation Hub appears to be a central location within the CCAI Insights console where users can manage and interact with conversation data. The script mentions returning to the Conversation Hub to initiate the export process.

💡WRITE_TRUNCATE

WRITE_TRUNCATE is a write disposition option in BigQuery that overwrites the existing data in a table with new data from the query result. The script specifies this as the default behavior when exporting to a pre-existing table in BigQuery.

💡WRITE_APPEND

WRITE_APPEND is another write disposition option that allows new data to be added to an existing table in BigQuery without overwriting the current data. The script mentions this as an alternative to WRITE_TRUNCATE during the export process.

💡Customer-Managed Encryption Key (CMEK)

CMEK refers to a feature that allows customers to manage their own encryption keys for data stored in cloud services. The script indicates that the export process supports writing data to tables protected by CMEK, offering an additional layer of security and control over data.

💡Long Running Job

A long running job is a process that may take an extended period to complete, often due to the volume of data being processed. The script describes the export to BigQuery as a long running job, the progress of which can be monitored through the Insights console.

Highlights

Conversations can be exported directly into BigQuery for further analysis, enabling custom in-depth analysis and visualization on Insights data.

Exporting to BigQuery requires an empty table to be created first, where Insights data will be loaded.

Filtering the conversation is optional; all conversations can be exported if required.

BigQuery console is used to select or create a dataset and then create a new table for Insights data.

The schema for the BigQuery table does not need to be set initially; it will be done dynamically during the export job.

After creating the BigQuery table, the Export button in the CCAI Insights console is used to initiate the export process.

The export job allows reviewing applied filters and the conversation count before execution.

The correct BigQuery dataset and table must be specified during the export configuration.

A long-running job is triggered by the Export button, with progress viewable through the Insights console's notification icon.

There are limits on the number of records that can be exported using the Export button in the Insights interface.

Appending data to the target table is not possible with the Insights interface's Export button.

CCAI Insights export supports WRITE_TRUNCATE and WRITE_APPEND write disposition options via the API.

WRITE_TRUNCATE overwrites existing table data with the schema from the query result, which is the default option.

WRITE_APPEND allows BigQuery to add data to an existing table.

The export supports both filtering and writing data to customer-managed encryption key (CMEK) protected tables.

Export to BigQuery is compatible with all combinations of filters that can be applied to conversation queries.

An example provided shows how to craft a query to export all conversations with 10 or more turns handled by a specific agent within a date range.