How to load data from Parquet File to Databricks Lakehouse
Learn how to use Airbyte to synchronize your Parquet File 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: Prepare Your Environment
Ensure your Databricks environment is properly set up. This includes having a workspace and cluster ready. Log into your Databricks account, navigate to your workspace, and start a cluster if it's not already running. Ensure the cluster has appropriate permissions to access workspace files.
Step 2: Upload Parquet File to Databricks
Navigate to the "Data" tab in your Databricks workspace and click on "Add Data". Use the file browser to upload your Parquet file to the Databricks File System (DBFS). This makes the file accessible within your workspace.
Step 3: Create a New Notebook
In the workspace, create a new notebook where you'll write the code to load and process the Parquet data. Choose a programming language (typically Python or Scala) that you are comfortable with and that is supported by your cluster.
Step 4: Read Parquet File in Notebook
In your notebook, use Spark's built-in functionality to read the Parquet file. For example, in Python:
```python
df = spark.read.parquet("/dbfs/path/to/your/file.parquet")
```
Replace `"/dbfs/path/to/your/file.parquet"` with the actual path to your Parquet file in DBFS.
Step 5: Process or Transform Data
If necessary, perform any data transformations or processing using Spark DataFrame operations. This could include filtering, aggregating, or joining datasets as required by your use case.
Step 6: Write Data to Lakehouse Format
Using the DataFrame API, write the processed data to a Delta Lake format, which is a key component of the Databricks Lakehouse architecture. For example:
```python
df.write.format("delta").mode("overwrite").save("/delta/lakehouse/path")
```
Replace `"/delta/lakehouse/path"` with the desired path within your Databricks Lakehouse.
Step 7: Verify Data in Lakehouse
To ensure the data has been moved correctly, read the data back from the Delta Lake location and perform checks. You can query the data to verify its integrity:
```python
df_lakehouse = spark.read.format("delta").load("/delta/lakehouse/path")
df_lakehouse.show()
```
This step ensures that the data is correctly stored and accessible in the Lakehouse format.
Follow these steps to effectively move data from a Parquet file to a Databricks Lakehouse environment, leveraging the built-in capabilities of Databricks and Apache Spark.