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
"For TUI Musement, Airbyte cut development time in half and enabled dynamic customer experiences."
“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.”
"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 exporting the data you need from Dremio into a CSV format. Navigate to the Dremio UI, perform the necessary queries, and use the "Export" functionality to save the results as a CSV file. This is a straightforward way to extract data without additional tools.
Before importing data, ensure that you have a suitable table structure in MS SQL Server that matches the schema of your CSV data. Use SQL Server Management Studio (SSMS) to create a new table with appropriate columns and data types that align with your exported CSV data.
Move the CSV file to the server or local environment where your MS SQL Server instance is running. This can be done using secure copy (SCP), file transfer protocol (FTP), or by directly copying the file if you have access to the server.
Utilize the BULK INSERT command in SQL Server to import the CSV data into the destination table. Open SSMS, connect to your database, and execute a BULK INSERT command specifying the file path, table name, and field terminators that match your CSV format. For example:
```sql
BULK INSERT YourDatabase.dbo.YourTable
FROM 'C:\path\to\your\file.csv'
WITH (
FIELDTERMINATOR = ',',
ROWTERMINATOR = '\n',
FIRSTROW = 2
);
```
Ensure that the path is accessible by the SQL Server instance and that you have the necessary permissions.
Once the BULK INSERT operation is completed, verify the imported data. Run SELECT queries in SSMS to ensure that the data in the MS SQL Server table matches the original data from Dremio.
If there are discrepancies in the imported data, check for issues such as mismatched column types or missing data. Adjust the CSV file or the SQL Server table structure as necessary, and re-import the data if required.
For regular data transfers, automate the process using SQL Server Agent jobs or a PowerShell script. This can involve scheduling the data export from Dremio, transferring the CSV file, and executing the BULK INSERT command to keep your MS SQL Server database updated with the latest data from Dremio.
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:
