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."
Ensure that your MSSQL SQL Server is accessible and that you have the necessary permissions to export the data. Verify the connectivity and gather the credentials required for accessing the database.
Use the SQL Server Management Studio (SSMS) or a similar tool to export data from your MSSQL database to a CSV file. You can accomplish this by using the "Export Data" wizard in SSMS:
- Right-click on the database, select "Tasks" > "Export Data".
- Choose "Flat File Destination" as the destination type.
- Specify the file path and options for CSV export.
- Select the tables or write a query to specify the data you want to export.
Before importing, ensure the CSV files are formatted correctly:
- Make sure there are no headers unless required.
- Use consistent delimiters and escape characters.
- Check for any special characters or null values that may need handling.
Ensure your Firebolt account is active and you have the necessary permissions to create tables and import data. If you haven't already, set up a database and schema in Firebolt using their console or CLI.
Use Firebolt's SQL interface to create tables that correspond to the structure of your MSSQL tables. Define the schema explicitly, taking care to match data types appropriately:
- Use Firebolt's supported data types and make adjustments where necessary.
- For example, adjust data types like `VARCHAR` in MSSQL to `TEXT` in Firebolt if needed.
Firebolt allows you to upload data directly from a CSV file using their SQL interface. Use the `COPY INTO` command to load the CSV file into your Firebolt tables:
- Ensure the CSV files are accessible from the Firebolt environment. If needed, upload them to a location that Firebolt can access, such as a cloud storage bucket.
- Use a command like:
```sql
COPY INTO my_table FROM 's3://my-bucket/my-file.csv'
CREDENTIALS=(...)
FILE_TYPE = 'CSV'
FIELD_DELIMITER = ',';
```
After uploading, it's essential to verify that the data in Firebolt matches the original data in MSSQL. Perform checks by running queries on both databases to ensure counts, sums, and other aggregate functions yield consistent results. Address any discrepancies by reviewing data types and handling any transformation issues during the CSV export or import 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.
Microsoft SQL Server Consultants help companies choose the best business software solutions for their needs. Microsoft SQL Server Consultants help businesses resolve questions and issues, provide businesses with reliable information resources, and, ultimately, make better decisions on the software most appropriate for their unique needs. Consultants are available to help on call and can connect remotely to businesses’ computers to upgrade outdated editions of SQL servers to bring functions up to date for improved productivity.
MSSQL - SQL Server provides access to a wide range of data types, including:
1. Relational data: This includes tables, views, and stored procedures that are used to store and manipulate data in a structured format.
2. Non-relational data: This includes data that is not stored in a structured format, such as XML documents, JSON objects, and binary data.
3. Spatial data: This includes data that is related to geographic locations, such as maps, coordinates, and spatial queries.
4. Time-series data: This includes data that is related to time, such as timestamps, dates, and time intervals.
5. Graph data: This includes data that is related to relationships between entities, such as social networks, supply chains, and organizational structures.
6. Machine learning data: This includes data that is used for training and testing machine learning models, such as feature vectors, labels, and performance metrics.
7. Streaming data: This includes data that is generated in real-time, such as sensor data, log files, and social media feeds.
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:
