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Start by navigating to the Looker dashboard or explore view that contains the data you want to export. Use Looker's built-in export functionality to download the data in a CSV format. Most of Looker's views allow you to export data by clicking on the 'Download' button and selecting 'CSV' as the format.
Open the exported CSV file using a spreadsheet application like Excel or Google Sheets. Clean up any unnecessary columns and ensure that your data is structured correctly. Each row should represent a unique record, and each column should have a consistent data type (e.g., strings, integers).
If you haven't already, set up a Typesense instance. This involves installing Typesense on your server or using Docker for easier setup. Follow the official Typesense documentation to get your instance running. Ensure that you configure the server properly, choosing the relevant port and API keys for security.
Design the schema for your Typesense collection. This involves defining the fields that your data will contain, along with their data types, and specifying which fields should be searchable or indexed. Create a JSON schema that matches the structure of your CSV file.
Convert your cleaned CSV data into JSON format to match the Typesense schema. You can write a simple script in Python or use a tool like pandas to read the CSV and output a JSON file. Ensure that the JSON structure aligns with the schema you defined in the previous step.
Use the Typesense API to create a new collection with your defined schema. Then, use the API to import your JSON data into this collection. This can be done by sending HTTP POST requests with the JSON data to the Typesense server's import endpoint. Make sure to handle any API authentication required.
After importing, verify that your data has been correctly stored and is searchable. Use the Typesense API to perform a few test searches and ensure that the results are as expected. Check for data integrity by comparing a few records between your JSON file and the Typesense collection.
By following these steps, you will transfer your data from Looker to Typesense efficiently 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:
