How to load data from Adjust to Databricks Lakehouse
Learn how to use Airbyte to synchronize your Adjust data into Databricks Lakehouse within minutes.
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.
Building in-house pipelines
- Inconsistent and inaccurate data
- Laborious and expensive
- Brittle and inflexible
After Airbyte
- 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
Streamline AI workflows with Airbyte: load unstructured data into vector stores like Pinecone, Weaviate, and Milvus. Supports RAG transformations with LangChain chunking and embeddings from OpenAI, Cohere, etc., all in one operation.
Move Large Volumes, Fast
Quickly get up and running with a 5-minute setup that enables both incremental and full refreshes for databases of any size, seamlessly scaling to handle large data volumes. Our optimized architecture overcomes performance bottlenecks, ensuring efficient data synchronization even as your datasets grow from gigabytes to petabytes.
An Extensible Open-Source Standard
More than 1,000 developers contribute to Airbyte’s connectors, different interfaces (UI, API, Terraform Provider, Python Library), and integrations with the rest of the stack. Airbyte’s AI Connector Builder lets you edit or add new connectors in minutes.
Full Control & Security
Airbyte secures your data with cloud-hosted, self-hosted or hybrid deployment options. Single Sign-On (SSO) and Role-Based Access Control (RBAC) ensure only authorized users have access with the right permissions. Airbyte acts as a HIPAA conduit and supports compliance with CCPA, GDPR, and SOC2.
Fully Featured & Integrated
Airbyte automates schema evolution for seamless data flow, and utilizes efficient Change Data Capture (CDC) for real-time updates. Select only the columns you need, and leverage our dbt integration for powerful data transformations.
Enterprise Support with SLAs
Airbyte Self-Managed Enterprise comes with dedicated support and guaranteed service level agreements (SLAs), ensuring that your data movement infrastructure remains reliable and performant, and expert assistance is available when needed.
What our users say
Raman Singh
Predictable, straightforward pricing model that simplified budgeting and significantly reduced overall spend
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."
How to Sync to Manually
Step 1: Export Data from Adjust
Begin by logging into your Adjust account. Navigate to the data export section, where you can manually export data. Choose the desired data set and export it in a CSV or JSON format, which is suitable for further processing and transfer.
Step 2: Prepare an S3 Bucket on AWS
Set up an AWS S3 bucket as a temporary storage location for your exported data. This step involves creating a new S3 bucket through the AWS Management Console, ensuring that the bucket is configured with appropriate permissions for data uploading.
Step 3: Upload Data to S3 Bucket
Use the AWS Management Console or the AWS CLI to upload the exported data files from Adjust into the S3 bucket. Ensure the data files are correctly named and organized in the bucket for easy retrieval.
Step 4: Configure Databricks Workspace
Access your Databricks Workspace and ensure it is connected to the same AWS account where your S3 bucket is located. Verify that the necessary IAM roles and permissions are set up to allow Databricks to access data in your S3 bucket.
Step 5: Create a Databricks Cluster
Within the Databricks Workspace, create a new cluster. Specify the cluster configuration based on your processing needs (e.g., choosing the appropriate instance types and cluster size). Ensure the cluster is running before proceeding.
Step 6: Use Databricks to Load Data from S3
In a Databricks notebook, write a script to load data from your S3 bucket. Use PySpark or Scala to read the data files into a DataFrame. For example, you can use the `spark.read.csv` or `spark.read.json` functions, specifying the S3 path where your files are located.
Step 7: Store Data in Databricks Lakehouse
Once the data is loaded into a DataFrame, transform and process it as necessary. Finally, write the DataFrame to your Databricks Lakehouse. Use the `write` method to store the data in Delta Lake format, which is optimized for performance and reliability. Specify the destination path within your Databricks file system.
By following these steps, you can effectively move data from Adjust to the Databricks Lakehouse without utilizing third-party connectors or integrations.