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Begin by exporting the data you need from Metabase. Navigate to the dashboard or report that contains your desired data. Use the export feature to download the data in a compatible format such as CSV or Excel. Ensure that the data is structured correctly for easy import later.
Once exported, prepare your data for transfer. This may involve cleaning the data or converting it to a format that is compatible with Starburst Galaxy, such as CSV, JSON, or Parquet. Ensure that the data types are consistent with what Starburst Galaxy supports to prevent import errors.
Access Starburst Galaxy through your web browser. Log in with the appropriate credentials to gain access to the platform's interface where you can manage and import data.
Within Starburst Galaxy, navigate to the administration section and create a new catalog if necessary. This catalog acts as a schema or database where your imported data will reside. Define the necessary configurations and permissions to accommodate your data.
Before importing, you need to create a table in Starburst Galaxy that matches the structure of your data. Use the SQL editor in Starburst Galaxy to define the table and its columns, ensuring they align with the data types and structure of your exported data.
With the table ready, use the Starburst Galaxy interface to upload and import your prepared data file. You can use the "LOAD DATA" SQL command or similar functionality provided in the interface to load your data into the created table. Follow any prompts to map columns and ensure successful data transfer.
Once the data is imported, run queries against the new table in Starburst Galaxy to verify that the data has been transferred accurately. Check for any discrepancies, data type mismatches, or missing data. Make corrections as necessary to ensure the data integrity and accuracy are maintained.
By following these steps, you can move data from Metabase to Starburst Galaxy manually without relying on third-party connectors or integrations.
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.
Metabase is accessible to all. Metabase is a self-service business intelligence software and it is a BI tool with a friendly UX and integrated tooling to let your company explore data on its own. Metabase is the easy, open-source way for everyone in your company to ask questions and learn from data. Metabase is an open-source business intelligence tool that lets you create charts and dashboards using data from a variety of databases and data sources. It generally assists users to create charts and dashboards from their databases.
Metabase's API provides access to a wide range of data types, including:
1. Metrics: These are numerical values that can be used to measure performance or track progress over time. Examples include revenue, website traffic, and customer satisfaction scores.
2. Dimensions: These are attributes that can be used to group or filter data. Examples include date, location, and product category.
3. Filters: These are criteria that can be used to limit the data returned by a query. Examples include date ranges, customer segments, and product types.
4. Joins: These are used to combine data from multiple tables or sources. Examples include joining customer data with sales data to analyze customer behavior.
5. Aggregations: These are used to summarize data by grouping it into categories and calculating metrics for each category. Examples include calculating average revenue per customer or total sales by product category.
6. Custom SQL: This allows users to write their own SQL queries to access and manipulate data in any way they choose.
Overall, Metabase's API provides a powerful tool for accessing and analyzing data from a wide range of sources, making it an ideal choice for businesses and organizations of all sizes.
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:
