Warehouses and Lakes
Engineering Analytics

How to load data from K6 Cloud to AWS Datalake

Learn how to use Airbyte to synchronize your K6 Cloud data into AWS Datalake within minutes.

TL;DR

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps:

  1. set up K6 Cloud as a source connector (using Auth, or usually an API key)
  2. set up AWS Datalake as a destination connector
  3. define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is K6 Cloud

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.

What is AWS Datalake

An AWS Data Lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. It is designed to handle massive amounts of data from various sources, such as databases, applications, IoT devices, and more. With AWS Data Lake, you can easily ingest, store, catalog, process, and analyze data using a wide range of AWS services like Amazon S3, Amazon Athena, AWS Glue, and Amazon EMR. This allows you to build data lakes for machine learning, big data analytics, and data warehousing workloads. AWS Data Lake provides a secure, scalable, and cost-effective solution for managing your organization's data.

Integrate K6 Cloud with AWS Datalake in minutes

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Prerequisites

  1. A K6 Cloud account to transfer your customer data automatically from.
  2. A AWS Datalake account.
  3. An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.

Airbyte is an open-source data integration platform that consolidates and streamlines the process of extracting and loading data from multiple data sources to data warehouses. It offers pre-built connectors, including K6 Cloud and AWS Datalake, for seamless data migration.

When using Airbyte to move data from K6 Cloud to AWS Datalake, it extracts data from K6 Cloud using the source connector, converts it into a format AWS Datalake can ingest using the provided schema, and then loads it into AWS Datalake via the destination connector. This allows businesses to leverage their K6 Cloud data for advanced analytics and insights within AWS Datalake, simplifying the ETL process and saving significant time and resources.

Step 1: Set up K6 Cloud as a source connector

1. First, navigate to the K6 Cloud source connector page on Airbyte.com.
2. Click on the "Create a new connection" button.
3. In the "Connection Configuration" section, enter a name for your connection.
4. In the "Source Type" dropdown menu, select "K6 Cloud".
5. In the "API Token" field, enter your K6 Cloud API token.
6. In the "Organization ID" field, enter your K6 Cloud organization ID.
7. In the "Test Configuration" section, click on the "Test Connection" button to ensure that your credentials are correct and that Airbyte can connect to your K6 Cloud account.
8. If the test is successful, click on the "Create Connection" button to save your configuration.
9. You can now use this connection to create a K6 Cloud source in Airbyte and start syncing your data.

Step 2: Set up AWS Datalake as a destination connector

1. Log in to your AWS account and navigate to the AWS Management Console.
2. Click on the S3 service and create a new bucket where you will store your data.
3. Create an IAM user with the necessary permissions to access the S3 bucket. Make sure to save the access key and secret key.
4. Open Airbyte and navigate to the Destinations tab.
5. Select the AWS Datalake destination connector and click on "Create new connection".
6. Enter a name for your connection and paste the access key and secret key you saved earlier.
7. Enter the name of the S3 bucket you created in step 2 and select the region where it is located.
8. Choose the format in which you want your data to be stored in the S3 bucket (e.g. CSV, JSON, Parquet).
9. Configure any additional settings, such as compression or encryption, if necessary.
10. Test the connection to make sure it is working properly.
11. Save the connection and start syncing your data to the AWS Datalake.

Step 3: Set up a connection to sync your K6 Cloud data to AWS Datalake

Once you've successfully connected K6 Cloud as a data source and AWS Datalake as a destination in Airbyte, you can set up a data pipeline between them with the following steps:

  1. Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
  2. Choose your source: Select K6 Cloud from the dropdown list of your configured sources.
  3. Select your destination: Choose AWS Datalake from the dropdown list of your configured destinations.
  4. Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
  5. Select the data to sync: Choose the specific K6 Cloud objects you want to import data from towards AWS Datalake. You can sync all data or select specific tables and fields.
  6. Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
  7. Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
  8. Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from K6 Cloud to AWS Datalake according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your AWS Datalake data warehouse is always up-to-date with your K6 Cloud data.

Use Cases to transfer your K6 Cloud data to AWS Datalake

Integrating data from K6 Cloud to AWS Datalake provides several benefits. Here are a few use cases:

  1. Advanced Analytics: AWS Datalake’s powerful data processing capabilities enable you to perform complex queries and data analysis on your K6 Cloud data, extracting insights that wouldn't be possible within K6 Cloud alone.
  2. Data Consolidation: If you're using multiple other sources along with K6 Cloud, syncing to AWS Datalake allows you to centralize your data for a holistic view of your operations, and to set up a change data capture process so you never have any discrepancies in your data again.
  3. Historical Data Analysis: K6 Cloud has limits on historical data. Syncing data to AWS Datalake allows for long-term data retention and analysis of historical trends over time.
  4. Data Security and Compliance: AWS Datalake provides robust data security features. Syncing K6 Cloud data to AWS Datalake ensures your data is secured and allows for advanced data governance and compliance management.
  5. Scalability: AWS Datalake can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding K6 Cloud data.
  6. Data Science and Machine Learning: By having K6 Cloud data in AWS Datalake, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
  7. Reporting and Visualization: While K6 Cloud provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to AWS Datalake, providing more advanced business intelligence options. If you have a K6 Cloud table that needs to be converted to a AWS Datalake table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

  1. Configure a K6 Cloud account as an Airbyte data source connector.
  2. Configure AWS Datalake as a data destination connector.
  3. Create an Airbyte data pipeline that will automatically be moving data directly from K6 Cloud to AWS Datalake after you set a schedule

With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.

We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!

What should you do next?

Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:

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Connectors Used

What should you do next?

Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:

flag icon
Easily address your data movement needs with Airbyte Cloud
Take the first step towards extensible data movement infrastructure that will give a ton of time back to your data team. 
Get started with Airbyte for free
high five icon
Talk to a data infrastructure expert
Get a free consultation with an Airbyte expert to significantly improve your data movement infrastructure. 
Talk to sales
stars sparkling
Improve your data infrastructure knowledge
Subscribe to our monthly newsletter and get the community’s new enlightening content along with Airbyte’s progress in their mission to solve data integration once and for all.
Subscribe to newsletter

Connectors Used

Frequently Asked Questions

What data can you extract from K6 Cloud?

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 data can you transfer to AWS Datalake?

You can transfer a wide variety of data to AWS Datalake. This usually includes structured, semi-structured, and unstructured data like transaction records, log files, JSON data, CSV files, and more, allowing robust, scalable data integration and analysis.

What are top ETL tools to transfer data from K6 Cloud to AWS Datalake?

The most prominent ETL tools to transfer data from K6 Cloud to AWS Datalake include:

  • Airbyte
  • Fivetran
  • Stitch
  • Matillion
  • Talend Data Integration

These tools help in extracting data from K6 Cloud and various sources (APIs, databases, and more), transforming it efficiently, and loading it into AWS Datalake and other databases, data warehouses and data lakes, enhancing data management capabilities.