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
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
"The intake layer of Datadog’s self-serve analytics platform is largely built on Airbyte.Airbyte’s ease of use and extensibility allowed any team in the company to push their data into the platform - without assistance from the data team!"
“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.”
“We chose Airbyte for its ease of use, its pricing scalability and its absence of vendor lock-in. Having a lean team makes them our top criteria. The value of being able to scale and execute at a high level by maximizing resources is immense”
- Connect to your Oracle database using SQL*Plus or any other Oracle database client.
- Determine the data you want to export. You may want to export entire tables or just a subset of data, depending on your requirements.
- Export the data to a CSV file or another suitable format using Oracle’s export utilities like expdp or sqlplus. For example, you can use the following command in SQL*Plus to export a table to a CSV file:
SPOOL /path/to/your/outputfile.csv
SELECT /*csv*/ * FROM your_table;
SPOOL OFF
- Compress the file to reduce the size and transfer time, using a tool like gzip.
- Choose a cloud storage service compatible with Databricks, such as AWS S3, Azure Blob Storage, or Google Cloud Storage.
- Upload the exported file(s) to the chosen cloud storage. You can use the cloud provider’s web interface, CLI, or SDKs to upload the files.
- Log in to your Databricks workspace.
- Create a new cluster or use an existing cluster that meets your workload requirements.
- Install any necessary libraries on the cluster that may be required for reading from your cloud storage or processing the data.
- Mount the cloud storage to DBFS (Databricks File System) using Databricks’ built-in utilities. This will allow you to access the data as if it were a local file system. For example, to mount an S3 bucket, you can use the following command:
dbutils.fs.mount("s3a://your-bucket-name", "/mnt/your-mount-name")
- Read the data into a Spark DataFrame using the appropriate Spark APIs. For example, to read a CSV file:
df = spark.read.csv("/mnt/your-mount-name/path/to/your/outputfile.csv")
- Perform any necessary data transformations using Spark DataFrame transformations.
- Cleanse and prepare the data for storage in Databricks Lakehouse.
- Define the target location within Databricks Lakehouse where you want to store the data.
- Write the data from the Spark DataFrame to Databricks Lakehouse using DataFrame writer API. For example, to write data to Delta Lake format:
df.write.format("delta").save("/mnt/your-mount-name/delta/your-table")
- Verify the data has been transferred correctly by reading a sample of the data from Databricks Lakehouse and comparing it against the original data from Oracle.
- Perform any additional validation checks as necessary, such as row counts, data types, and integrity constraints.
- Un-mount the cloud storage if it is no longer needed.
- Delete any temporary files that were created during the process.
- Create a Databricks job to automate the data transfer process.
- Schedule the job to run at your desired frequency.
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.
Oracle DB is a fully scalable integrated cloud application and platform service; it is also referred to as a relational database architecture. It provides management and processing of data for both local and wide and networks. Offering software-as-a-service (SaaS), platform-as-a-service (PaaS), and infrastructure-as-a-service (IaaS), it sells a large variety of enterprise IT solutions that help companies streamline the business process, lower costs, and increase productivity.
Oracle DB provides access to a wide range of data types, including:
• Relational data: This includes tables, views, and indexes that are used to store and organize data in a structured manner.
• Spatial data: This includes data that is related to geographic locations, such as maps, satellite imagery, and GPS coordinates.
• Time-series data: This includes data that is related to time, such as stock prices, weather data, and sensor readings.
• Multimedia data: This includes data that is related to images, videos, and audio files.
• XML data: This includes data that is stored in XML format, such as web pages, documents, and other structured data.
• JSON data: This includes data that is stored in JSON format, such as web APIs, mobile apps, and other data sources.
• Graph data: This includes data that is related to relationships between entities, such as social networks, supply chains, and other complex systems.
Overall, Oracle DB's API provides access to a wide range of data types that can be used for a variety of applications, from business intelligence and analytics to machine learning and artificial intelligence.
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:
