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Begin by exporting your data from Delighted. Log in to your Delighted account and navigate to the "Export" section. Choose the format you prefer, such as CSV or JSON, and export the required data set to your local system. Ensure that you have all the necessary data fields included in your export.
Once the data is exported, open the file to verify its integrity. Check for completeness, accuracy, and consistency. Look for any anomalies or missing values that need to be addressed before loading into the Databricks Lakehouse.
Depending on the export format, you may need to preprocess the data. If you exported a CSV file, ensure it's properly formatted with consistent delimiters and correct encoding, such as UTF-8. If using JSON, confirm that the structure is valid and consistent with expected schemas.
Log in to your Databricks account and create a new cluster if needed. Ensure the cluster is configured with the appropriate resources and libraries necessary for data processing. This step is crucial for preparing the environment where you'll load and manipulate the data.
Use the Databricks UI or a command-line interface to upload the exported Delighted data to the Databricks File System. Navigate to the "Data" tab in Databricks, select "Add Data," and upload the file from your local system to a desired location in DBFS.
Using a Databricks notebook, write a script to load the data into a Databricks table. If the data is in CSV format, use Spark's `spark.read.csv` method to read the file from DBFS and create a DataFrame. For JSON, use `spark.read.json`. Then, use the `write` method to save the DataFrame as a table in the Databricks Lakehouse.
```python
# Example for CSV
df = spark.read.csv('/dbfs/path/to/delighted_data.csv', header=True, inferSchema=True)
df.write.format('parquet').saveAsTable('delighted_data_table')
```
After loading the data, perform validation checks to ensure it was loaded correctly. Run queries against the new table to verify row counts, data types, and any transformations applied. It's essential to confirm that the data in the Databricks Lakehouse matches the original data from Delighted.
By following these steps, you can efficiently move data from Delighted to Databricks Lakehouse without relying on third-party connectors.
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.
Delighted assists businesses connect with their customers learning, improving, and delighting.It is well known for delivering some of the most innovative functionality for customer experience management. Delighted is completely the self-serve experience management platform of choice for the worldwide top brands. It helps to collect and analyze survey feedback through Delighted. Get set up in minutes, no technical knowledge needed. Delight helps to build long-lasting relationships and deliver great service experience.
Delighted's API provides access to various types of data related to customer feedback and satisfaction. The categories of data that can be accessed through Delighted's API are:
1. Survey Responses: This includes all the responses received from customers through Delighted's surveys. It includes both quantitative and qualitative data.
2. Metrics: This includes various metrics related to customer satisfaction, such as Net Promoter Score (NPS), Customer Satisfaction Score (CSAT), and Customer Effort Score (CES).
3. Trends: This includes trends related to customer feedback and satisfaction over time. It helps businesses to identify patterns and make data-driven decisions.
4. Segmentation: This includes data related to customer segments, such as demographics, location, and behavior. It helps businesses to understand their customers better and tailor their offerings accordingly.
5. Integrations: Delighted's API also provides access to data from various integrations, such as Salesforce, HubSpot, and Slack. It helps businesses to streamline their workflows and improve their customer experience. Overall, Delighted's API provides a comprehensive set of data that businesses can use to measure and improve their customer satisfaction.
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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