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
"For TUI Musement, Airbyte cut development time in half and enabled dynamic customer experiences."
“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.”
"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."
First, configure K6 to output its test results in a JSON format. You can do this by creating a local script that includes specifying the output in K6's configuration. For example, use the command: `k6 run --out json=output.json your_test_script.js`. This will generate a JSON file (`output.json`) containing the test results.
Execute your K6 test script locally with the configured output settings. Ensure that the test results are written to the specified JSON file. This step will effectively capture the data from K6 Cloud into a format that can be processed further.
Once the test is complete, parse the JSON data to extract the necessary information that needs to be transferred to Elasticsearch. You can use a programming language like Python or JavaScript to read the JSON file and transform the data as needed. Focus on extracting relevant fields such as timestamps, metrics, and labels.
Before sending data to Elasticsearch, create an appropriate index that will receive the K6 data. Use the Elasticsearch API or Kibana to define the index mapping that corresponds to the structure of your parsed K6 data. Make sure that the index can handle the data types you intend to store, such as numbers, strings, or dates.
Transform the parsed data into a format suitable for Elasticsearch's Bulk API. This typically involves creating a newline-delimited JSON formatted file where each line contains an action-and-meta-data line followed by the source line. For example, the first line might be `{"index": {"_index": "k6_results"}}`, followed by the actual data line.
Use a tool like `curl` or a programming language with HTTP client capabilities to send the formatted data to Elasticsearch. The Bulk API endpoint is typically `http://localhost:9200/_bulk` if running Elasticsearch locally. Ensure that you handle any errors or responses from Elasticsearch to confirm that the data has been successfully ingested.
After the data is sent, verify that it has been correctly indexed in Elasticsearch. Use Kibana or an Elasticsearch query to check the index and review the data. Verify the accuracy and completeness of the data, ensuring it matches what was outputted by the K6 test. If any discrepancies are found, revisit previous steps to troubleshoot and resolve them.
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.
k6 Cloud is a commercial SaaS product that we designed to be the perfect companion to k6 OSS. It brings ease of use, team management, and continuous testing capabilities to your performance testing projects. k6 Cloud Docs assist you to learn and use k6 Cloud features and functionality. The k6 Cloud is a fully-managed load testing service that complements k6 to accelerate your performance testing. k6 is an open-source load testing tool and cloud service for developers, DevOps, QA, and SRE teams.
K6 Cloud's API provides access to various types of data related to performance testing and monitoring. The following are the categories of data that can be accessed through the API:
1. Test Results: This category includes data related to the results of performance tests, such as response times, error rates, and throughput.
2. Metrics: This category includes data related to various performance metrics, such as CPU usage, memory usage, and network traffic.
3. User Behavior: This category includes data related to user behavior during performance tests, such as the number of users, their actions, and their locations.
4. Environment: This category includes data related to the environment in which the performance tests are conducted, such as the hardware and software configurations.
5. Alerts: This category includes data related to alerts generated during performance tests, such as threshold breaches and error notifications.
6. Reports: This category includes data related to performance test reports, such as summary reports, detailed reports, and trend analysis reports.
Overall, K6 Cloud's API provides a comprehensive set of data that can be used to analyze and optimize the performance of web applications and services.
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:
