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 thoroughly understanding the structure and format of the data in your merge data source. Identify the types of data (e.g., CSV, JSON, SQL databases) and ensure you have the necessary permissions to access and extract this data. This foundational knowledge is crucial for planning the data transfer process effectively.
Export the data from your merge source into a format that is compatible with Databricks ingestion methods. Common formats include CSV, JSON, or Parquet files. Use built-in export functionalities provided by your data source to generate these files, ensuring that the export process covers all relevant data fields and respects any data formatting or transformation requirements.
Set up your Databricks environment by creating a new workspace or using an existing one. Ensure you have access to a Databricks Lakehouse and the necessary permissions to create databases and tables. Configure your cluster settings appropriately, taking into consideration the size and complexity of the data you will be importing.
Move the exported data files into a cloud storage solution that Databricks can access, such as AWS S3, Azure Blob Storage, or Google Cloud Storage. Use command-line tools or cloud service web interfaces to upload your files. Ensure that the files are organized and named clearly to facilitate easy access and management during the ingestion process.
In Databricks, use the Databricks File System (DBFS) to mount the cloud storage solution where your data files reside. This step involves writing a few lines of code in a Databricks notebook to establish a connection between your Databricks environment and the cloud storage. Verify that the mount is successful by listing the files to ensure they are accessible.
Define the schema for your data based on the structure of the exported files. Use Databricks SQL or PySpark to create tables within your Databricks Lakehouse. Ensure that the data types and structures match those of the source data to prevent issues during data import. This step sets up the necessary data structures to receive incoming data.
Use Databricks notebooks to read the data files from the mounted storage and load them into the tables you created in the Lakehouse. Employ Spark's powerful data processing capabilities to handle data transformations and load operations efficiently. Verify the loaded data for accuracy and completeness by running queries and checking against expected results.
By following these steps, you can successfully move data from a merge data source to a Databricks Lakehouse 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.
Merge is a puzzle game where players combine matching blocks to create new ones and clear the board. The game starts with simple blocks, but as players progress, they encounter more complex shapes and colors. The goal is to merge as many blocks as possible to earn points and advance to higher levels. The game also includes power-ups and special blocks that can help players clear the board more quickly. Merge is a fun and addictive game that challenges players to think strategically and quickly to achieve high scores.
Merge's API provides access to a wide range of healthcare data, including:
1. Patient Data: This includes demographic information, medical history, and clinical notes.
2. Imaging Data: This includes medical images such as X-rays, CT scans, and MRIs.
3. Clinical Trial Data: This includes information on clinical trials, including study design, patient enrollment, and outcomes.
4. Medical Device Data: This includes data from medical devices such as pacemakers, insulin pumps, and blood glucose monitors.
5. Electronic Health Record (EHR) Data: This includes data from EHR systems, such as medication lists, lab results, and vital signs.
6. Genomic Data: This includes genetic information, such as DNA sequencing data and gene expression data.
7. Research Data: This includes data from research studies, such as survey data and clinical trial data.
Overall, Merge's API provides access to a comprehensive set of healthcare data, enabling developers to build innovative applications and solutions that improve patient care and outcomes.
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:
