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 ensuring that Dremio is installed and properly configured on your system. You can do this by downloading the latest version from the Dremio website and following the installation instructions specific to your operating system. Once installed, create a new data source connection to your database or data lake from which you want to extract data.
Write a SQL query in Dremio to extract the specific dataset you want to move to Redis. Use Dremio's SQL Editor to test and refine your query until it returns the exact data set required. Make sure to consider data transformations or aggregations needed for your use case.
Use Dremio's export functionality to save the query result to a CSV or JSON file. Navigate to the SQL Editor, execute your query, and select the export option. Choose the desired format (CSV or JSON) and download the file to your local system. This file will act as the intermediary data store between Dremio and Redis.
Ensure that Redis is installed and running on your local system or server. Download the Redis package from the official Redis website and follow the installation instructions. Start the Redis server using the command `redis-server` and check that it is running properly by connecting with the Redis CLI using `redis-cli`.
Create a Python script to read the exported CSV or JSON file and insert the data into Redis. Use the `csv` or `json` module to parse the data and the `redis-py` library to interact with Redis. Install `redis-py` using pip with the command `pip install redis`. Write the script to loop through the file and store each record in Redis as a hash or a set, depending on your data structure requirements.
Execute the Python script to ingest the data into Redis. Ensure that the script correctly processes each record and inserts it into Redis without errors. You can test the ingestion by querying Redis using `redis-cli` to verify that data has been stored as expected.
After data ingestion, verify the integrity and completeness of the data in Redis. Use Redis commands like `HGETALL` or `SMEMBERS` to fetch and check the stored data. If necessary, optimize the data model in Redis for faster access and retrieval, such as by creating indexes or restructuring data keys based on access patterns.
This step-by-step guide ensures a direct method to transfer data from Dremio to Redis without relying on third-party tools, providing control and flexibility over the data transfer process.
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.
Dremio is a data-as-a-service platform that enables businesses to access and analyze their data faster and more efficiently. It provides a self-service data platform that connects to various data sources, including cloud storage, databases, and data lakes, and allows users to query and analyze data using familiar tools like SQL and BI tools. Dremio's unique approach to data processing, called Data Reflections, accelerates query performance by automatically creating optimized copies of data in memory. This allows users to get insights from their data in real-time, without the need for complex data pipelines or data warehousing. Dremio also provides enterprise-grade security and governance features to ensure data privacy and compliance.
Dremio's API provides access to a wide range of data types, including:
1. Structured data: This includes data that is organized into tables with defined columns and rows, such as data from relational databases.
2. Semi-structured data: This includes data that has some structure, but is not organized into tables, such as JSON or XML data.
3. Unstructured data: This includes data that has no predefined structure, such as text documents, images, and videos.
4. Big data: This includes large volumes of data that cannot be processed using traditional data processing tools, such as Hadoop and Spark.
5. Streaming data: This includes real-time data that is generated continuously, such as data from IoT devices or social media feeds.
6. Cloud data: This includes data that is stored in cloud-based services, such as Amazon S3 or Microsoft Azure.
Overall, Dremio's API provides access to a wide range of data types, making it a powerful tool for data integration and analysis.
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:
