Summarize
Trusted by data-driven companies
Building your pipeline or Using Airbyte
Airbyte is the only open source solution empowering data teams to meet all their growing custom business demands in the new AI era.
- Inconsistent and inaccurate data
- Laborious and expensive
- Brittle and inflexible
- Reliable and accurate
- Extensible and scalable for all your needs
- Deployed and governed your way
Start syncing with Airbyte in 3 easy steps within 10 minutes
Take a virtual tour
Demo video of Airbyte Cloud
Demo video of AI Connector Builder
Setup Complexities simplified!
Simple & Easy to use Interface
Airbyte is built to get out of your way. Our clean, modern interface walks you through setup, so you can go from zero to sync in minutes—without deep technical expertise.
Guided Tour: Assisting you in building connections
Whether you’re setting up your first connection or managing complex syncs, Airbyte’s UI and documentation help you move with confidence. No guesswork. Just clarity.
Airbyte AI Assistant that will act as your sidekick in building your data pipelines in Minutes
Airbyte’s built-in assistant helps you choose sources, set destinations, and configure syncs quickly. It’s like having a data engineer on call—without the overhead.
What sets Airbyte Apart
Modern GenAI Workflows
Move Large Volumes, Fast
An Extensible Open-Source Standard
Full Control & Security
Fully Featured & Integrated
Enterprise Support with SLAs
What our users say
Andre Exner
"For TUI Musement, Airbyte cut development time in half and enabled dynamic customer experiences."
Chase Zieman
“Airbyte helped us accelerate our progress by years, compared to our competitors. We don’t need to worry about connectors and focus on creating value for our users instead of building infrastructure. That’s priceless. The time and energy saved allows us to disrupt and grow faster.”
Rupak Patel
"With Airbyte, we could just push a few buttons, allow API access, and bring all the data into Google BigQuery. By blending all the different marketing data sources, we can gain valuable insights."
Begin by accessing Looker’s API. Looker provides a RESTful API that allows you to programmatically interact with your Looker instance. You will need to generate API credentials (client ID and client secret) through Looker’s admin panel under the API section. Ensure that your API user has the necessary permissions to access the data you want to export.
Use the Looker API to query the data you want to export. Construct an API request using the appropriate endpoint, such as `/queries/run/json`, to execute a saved Look or an Explore query. You can use tools like `curl`, or programming languages such as Python, to send HTTP requests. Make sure you handle authentication by including the API credentials in your request headers.
Once you receive the data from Looker in JSON format, parse it to extract the necessary information. This step involves converting the JSON response into a usable data structure in your programming environment. In Python, this can be done using the `json.loads()` function to transform the JSON string into a dictionary or list.
Transform the data into a format suitable for Redis. Redis typically stores data in key-value pairs. Depending on your data structure, you might convert each row of your Looker data into a separate Redis entry or aggregate data as needed. Decide on a consistent schema for storing the data in Redis (e.g., hash maps, lists, sets).
Establish a connection to your Redis instance. Use a Redis client library, such as `redis-py` for Python, to connect to Redis. Ensure you have the correct host, port, and authentication details for your Redis server. Initialize the Redis client in your script to prepare for data insertion.
Insert the transformed data into Redis. Use the Redis client library to push data into Redis using appropriate commands (e.g., `SET`, `HSET`, `LPUSH` for lists). Iterate over your parsed Looker data and load each entry into Redis, following the schema you defined in the transformation step.
After loading the data, confirm that the data transfer was successful. Query Redis to check the presence and accuracy of the newly inserted data. Verify that all entries are accounted for and that the data structure matches what you intended. You can use Redis CLI commands or the client library to perform these checks.
By following these steps, you can effectively move data from Looker to Redis without relying on third-party 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:
