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Begin by logging into your Zendesk account. Navigate to the Admin Center, and locate the Reports or Analytics section, depending on your Zendesk version. Use the available options to export chat data, typically available in CSV or JSON format. Ensure you select the relevant fields and date ranges needed for your analysis.
Once your data is exported, review the file to understand its structure. Open the CSV or JSON file in a suitable editor (Excel for CSV, a text editor for JSON) and examine the columns or keys. Plan which data fields are necessary for your Starburst Galaxy queries.
Clean the data by removing any unnecessary columns and correcting any formatting issues. This may involve standardizing date formats, removing null values, or renaming column headers for consistency. Use spreadsheet tools or scripts to automate this process if the data set is large.
Transform the cleaned data into a format compatible with Starburst Galaxy. This may involve converting the CSV to a more suitable format, such as Parquet or ORC, which are optimized for analytical queries. Use command-line tools or scripts like Python with Pandas or Apache Arrow for this conversion.
Log into your Starburst Galaxy account. Set up the necessary environment by creating a new catalog and schema if they do not already exist. Ensure you have the correct permissions and access to create tables and upload data.
Use the Starburst Galaxy web interface or command-line tools to upload your transformed data. Create a new table within your designated schema and catalog, ensuring the table structure matches your data file. Use the `CREATE TABLE` statement to define the table's schema and the `COPY` command to upload the data.
After the upload, run a series of queries to verify the integrity of the data. Check for row counts, data types, and sample entries to ensure the data accurately reflects the original export from Zendesk. Make any necessary adjustments by re-uploading corrected data or altering table definitions as needed.
This process provides a direct method to move data between Zendesk Chat and Starburst Galaxy without relying on third-party tools, focusing on manual data handling and transformation.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
A software developed to optimize communication for small businesses and enterprises worldwide, Zendesk Chat is a live chat application that enables businesses to establish a more personal touch in their customer support. Designed to work on iPhone and iPad as well as computers, Zen Chat provides the ability to monitor, manage, and engage with website visitors from any location; sends notifications when visitors are on a website; features shortcuts to reduce typing time and improve agents’ response time; and more.
Zendesk Chat's API provides access to a wide range of data related to customer interactions and support activities. The following are the categories of data that can be accessed through the API:
1. Chat data: This includes information about chat sessions, such as chat duration, chat transcripts, and chat ratings.
2. Agent data: This includes information about agents, such as their availability status, chat history, and performance metrics.
3. Visitor data: This includes information about visitors, such as their location, browser type, and chat history.
4. Ticket data: This includes information about support tickets, such as ticket status, priority, and tags.
5. Analytics data: This includes information about chat and support activity, such as chat volume, response times, and customer satisfaction scores.
6. Custom data: This includes any custom data that has been added to the Zendesk Chat platform, such as custom fields or tags.
Overall, the Zendesk Chat API provides a comprehensive set of data that can be used to analyze and improve customer support operations.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What should you do next?
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