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Begin by exploring Looker's native data exporting capabilities. Looker allows users to download data in various formats, such as CSV, JSON, or Excel. Navigate to the Looker interface and identify the specific reports or dashboards you are interested in exporting. Choose a suitable format, typically CSV for ease of use, ensuring it matches the data structure required by Starburst Galaxy.
Once you've identified the data sets, use Looker's export function to download the data. This can be done by viewing the report, selecting the download option, and choosing your preferred format (such as CSV). Ensure you save the exported files to a secure location on your local machine or a cloud storage service that you can access later.
After exporting, inspect the data files to ensure they are correctly formatted and contain no errors. Open the CSV files using a spreadsheet application or text editor to verify the data's integrity. Clean up any inconsistencies or formatting issues that might affect the upload process into Starburst Galaxy, such as removing null values or correcting data types.
Log into your Starburst Galaxy account and navigate to the data management or catalog section. Ensure you have the necessary permissions to create new tables or modify existing ones. Familiarize yourself with the user interface and locate the data upload feature, which will allow you to import your cleaned data files.
Prior to uploading your data, define a schema in Starburst Galaxy that matches the structure of your data. This involves setting up tables and specifying column data types that align with those in your exported CSV files. Use Starburst Galaxy's SQL interface to create and configure these tables, ensuring they are ready to receive data.
With your schema in place, begin the data upload process. Use Starburst Galaxy's data import feature to upload the CSV files. Follow any provided guidelines or wizards offered by Starburst Galaxy to map the CSV columns to the corresponding table columns correctly. Ensure that all data is correctly inserted into the tables, and resolve any import errors that may arise.
Once the data is uploaded, perform a thorough verification to ensure the data is complete and correctly structured. Run sample queries to check for data accuracy and consistency. Validate that the data can be used effectively within Starburst Galaxy for your intended analytics or reporting tasks, and address any issues discovered during this validation phase.
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:
