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Before moving data, you need to export it from Looker. Go to the dashboard or explore section in Looker where your data is displayed. Use the "Download" option to export the data in a format supported by Firebolt, such as CSV or JSON.
Ensure that your Firebolt environment is ready to receive new data. Log into your Firebolt account and create a database and table structure that matches the schema of your data from Looker. Use the Firebolt web interface or SQL commands to set up the database schema.
Depending on the format you exported from Looker, you may need to transform the data to ensure compatibility with Firebolt. Use a tool like Python or a simple script to clean, reformat, or adjust data types as necessary.
Firebolt requires data to be uploaded to an external storage location before ingestion. Use an Amazon S3 bucket or another supported storage service to upload your transformed data file. Make sure the data is accessible to Firebolt for the next steps.
In Firebolt, create an external table that points to the location of your uploaded data. This step allows Firebolt to read the data directly from your storage service. Use SQL commands in Firebolt to define the external table with the correct schema and data source location.
Once the external table is set up, use the `INSERT INTO` command to load data from the external table into your Firebolt database table. This step involves executing a SQL command to transfer data from the external table to the permanent table in your Firebolt database.
After loading the data, perform checks to ensure data integrity and completeness. Run queries in Firebolt to validate that all data has been imported correctly and matches the original data from Looker. Adjust any discrepancies by revisiting the previous steps as needed.
By following these steps, you can manually transfer data from Looker to Firebolt without relying on third-party tools or 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.
Looker is a Google-Cloud-based enterprise platform that provides information and insights to help move businesses forward. Looker reveals data in clear and understandable formats that enable companies to build data applications and create data experiences tailored specifically to their own organization. Looker’s capabilities for data applications, business intelligence, and embedded analytics make it helpful for anyone requiring data to perform their job—from data analysts and data scientists to business executives and partners.
Looker's API provides access to a wide range of data categories, including:
1. User and account data: This includes information about users and their accounts, such as user IDs, email addresses, and account settings.
2. Query and report data: Looker's API allows users to retrieve data from queries and reports, including metadata about the queries and reports themselves.
3. Dashboard and visualization data: Users can access data about dashboards and visualizations, including the layout and configuration of these elements.
4. Data model and schema data: Looker's API provides access to information about the data model and schema, including tables, fields, and relationships between them.
5. Data access and permissions data: Users can retrieve information about data access and permissions, including which users have access to which data and what level of access they have.
6. Integration and extension data: Looker's API allows users to integrate and extend Looker with other tools and platforms, such as custom applications and third-party services.
Overall, Looker's API provides a comprehensive set of data categories that enable users to access and manipulate data in a variety of ways.
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?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
