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Begin by logging into your Looker account. Navigate to the dashboard or the specific report you wish to export. Ensure that the data you plan to extract is displayed correctly, as this will be the source of your JSON file.
Once you have the desired data displayed, use Looker's built-in functionality to download the data. Click on the gear icon or the three-dot menu associated with your report or table, and select the option to download. Choose CSV or Excel as the format for downloading. This will allow you to manipulate and convert the data into JSON later.
Open the downloaded CSV or Excel file in a spreadsheet application like Microsoft Excel or Google Sheets. This step is crucial for preparing the data for conversion into JSON format. Ensure that the data is correctly structured, with headers representing the keys and rows representing the records.
Examine the data to ensure it is clean and structured. Remove any unnecessary rows or columns that are not needed in your JSON file. Ensure that your data is in a tabular format with consistent headers, as this will directly translate into the JSON keys.
Use a script or a built-in function in your spreadsheet application to convert the data to JSON. In Google Sheets, you can use a custom script in the Script Editor or utilize a built-in function if available. For Excel, you can use VBA code to loop through the data and construct a JSON object. Alternatively, you can export the spreadsheet to CSV and use a Python script or another programming language to read the CSV and convert it to JSON manually.
After converting the data to JSON format, save it as a file on your local machine. Ensure that the file has a `.json` extension. This can be done directly from the script or function you've used to convert the data, or you can manually create a new file and paste the JSON data into it.
Finally, verify that the JSON file is correctly formatted and contains all the necessary data. Use a JSON validator tool to check for any syntax errors or inconsistencies. Ensure that the JSON structure aligns with your requirements and that all data entries are accounted for.
By following these steps, you can successfully move data from Looker to a JSON file 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.
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:
