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Free your time from maintaining connectors, with automation
- Automated schema change handling, data normalization and more
- Automated data transformation orchestration with our dbt integration
- Automated workflow with our Airflow, Dagster and Prefect integration
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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.
For huge analytical tables, Apache Iceberg is a high-performance format. Using Apache Iceberg, engines such as Spark, Trino, Flink, Presto, Hive and Impala can safely work with the same tables, at the same time, providing the reliability and simplicity of SQL tables to big data. With Apache Iceberg, you can merge new data, update existing rows, and delete specific rows. Data files can be eagerly rewritten or deleted deltas can be used to make updates faster.
1. Ticket ID: The unique identifier for each ticket in Intercom.
2. Ticket Status: The current status of the ticket, such as open, pending, or closed.
3. Ticket Priority: The level of urgency assigned to the ticket, such as high, medium, or low.
4. Ticket Assignee: The team member or group responsible for handling the ticket.
5. Ticket Tags: The labels or categories assigned to the ticket for organization and tracking purposes.
6. Ticket Subject: The brief summary of the issue or request submitted by the customer.
7. Ticket Description: The detailed explanation of the issue or request submitted by the customer.
8. Customer Information: The name, email address, and other relevant details of the customer who submitted the ticket.
9. Ticket Creation Date: The date and time when the ticket was created.
10. Ticket Update Date: The date and time when the ticket was last updated or modified.
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.
