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”
A. Use Salesforce Reports or Data Export Service
- Log in to your Salesforce account.
- Navigate to the “Reports” tab and create a report that includes all the data you want to transfer.
- Run the report and export the data to a CSV file.
- If you need to export large amounts of data or the entire database, consider using the Salesforce Data Export Service, which can be scheduled or run immediately.
B. Use Salesforce Data Loader for Custom Queries
- Install Salesforce Data Loader on your local machine.
- Log in to Data Loader using your Salesforce credentials.
- Select “Export” or “Export All” for including soft-deleted records.
- Write a SOQL query to specify the data you want to extract.
- Export the data to a CSV file.
- Review the CSV files and ensure that the data is clean and formatted correctly for import into Databricks.
- Remove any unnecessary columns or rows that are not needed in Databricks.
- If needed, split large CSV files into smaller chunks to facilitate easier uploading and processing.
- Log in to your Databricks workspace.
- Create a new cluster or start an existing cluster that you will use for data import.
- Install any necessary libraries that you might need for data processing.
- In Databricks, navigate to the “Data” tab.
- Click on “Add Data” and then choose “DBFS” to upload your files directly to DBFS using the Databricks UI.
- Alternatively, use the Databricks CLI to upload your CSV files to DBFS.
databricks fs cp <local_file_path.csv> dbfs:/<databricks_file_path.csv>
- Process and Transform Data (Optional)In Databricks, create a new notebook.
- Use the following code snippet to load the CSV data into a DataFrame:
file_location = "/<databricks_file_path.csv>"
file_type = "csv"
# CSV options
infer_schema = "true"
first_row_is_header = "true"
delimiter = ","
# Load the data into a DataFrame
df = spark.read.format(file_type) \
.option("inferSchema", infer_schema) \
.option("header", first_row_is_header) \
.option("sep", delimiter) \
.load(file_location)
# Show the DataFrame
df.show()
- Perform any transformations or processing required on the DataFrame within Databricks.
- Use Spark SQL or DataFrame API to manipulate the data as needed.
- Decide on the storage format (e.g., Delta Lake, Parquet) and the location where you want to store the data within Databricks.
- Use the DataFrame API to write the data to the Lakehouse.
data_location = "/mnt/<desired_path_in_databricks>"
df.write.format("delta").save(data_location)
- After the data has been loaded, run queries against the data to ensure it has been transferred correctly.
- Check for any discrepancies or data loss during the transfer process.
- To automate this process, you can create a job in Databricks that regularly runs a notebook or a script that performs these steps.
- Schedule the job according to your data refresh requirements.
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.
Salesforce is a cloud-based customer relationship management (CRM) platform providing business solutions software on a subscription basis. Salesforce is a huge force in the ecommerce world, helping businesses with marketing, commerce, service and sales, and enabling enterprises’ IT teams to collaborate easily from anywhere. Salesforces is the force behind many industries, offering healthcare, automotive, finance, media, communications, and manufacturing multichannel support. Its services are wide-ranging, with access to customer, partner, and developer communities as well as an app exchange marketplace.
Salesforce's API provides access to a wide range of data types, including:
1. Accounts: Information about customer accounts, including contact details, billing information, and purchase history.
2. Leads: Data on potential customers, including contact information, lead source, and lead status.
3. Opportunities: Information on potential sales deals, including deal size, stage, and probability of closing.
4. Contacts: Details on individual contacts associated with customer accounts, including contact information and activity history.
5. Cases: Information on customer service cases, including case details, status, and resolution.
6. Products: Data on products and services offered by the company, including pricing, availability, and product descriptions.
7. Campaigns: Information on marketing campaigns, including campaign details, status, and results.
8. Reports and Dashboards: Access to pre-built and custom reports and dashboards that provide insights into sales, marketing, and customer service performance.
9. Custom Objects: Ability to access and manipulate custom objects created by the organization to store specific types of data.
Overall, Salesforce's API provides access to a comprehensive set of data types that enable organizations to manage and analyze their customer relationships, sales processes, and marketing campaigns.
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:
Salesforce is a cloud-based customer relationship management (CRM) platform providing business solutions software on a subscription basis. Salesforce is a huge force in the ecommerce world, helping businesses with marketing, commerce, service and sales, and enabling enterprises’ IT teams to collaborate easily from anywhere. Salesforces is the force behind many industries, offering healthcare, automotive, finance, media, communications, and manufacturing multichannel support. Its services are wide-ranging, with access to customer, partner, and developer communities as well as an app exchange marketplace.
Databricks is an American enterprise software company founded by the creators of Apache Spark. Databricks combines data warehouses and data lakes into a lakehouse architecture.
1. Open the Airbyte platform and navigate to the "Sources" tab on the left-hand side of the screen.
2. Click on the "Salesforce" source connector and select "Create new connection."
3. Enter a name for your connection and click "Next."
4. Enter your Salesforce credentials, including your username, password, and security token.
5. Click "Test connection" to ensure that your credentials are correct and that Airbyte can connect to your Salesforce account.
6. Once the connection is successful, select the objects you want to replicate from Salesforce.
7. Choose the replication frequency and any other settings you want to apply to your connection.
8. Click "Create connection" to save your settings and start replicating data from Salesforce to Airbyte.
9. You can monitor the progress of your replication in the "Connections" tab and view the data in the "Dashboard" tab.
1. First, navigate to the Airbyte website and log in to your account.
2. Once you are logged in, click on the "Destinations" tab on the left-hand side of the screen.
3. Scroll down until you find the "Databricks Lakehouse" connector and click on it.
4. You will be prompted to enter your Databricks Lakehouse credentials, including your account name, personal access token, and workspace ID.
5. Once you have entered your credentials, click on the "Test" button to ensure that the connection is successful.
6. If the test is successful, click on the "Save" button to save your Databricks Lakehouse destination connector settings.
7. You can now use the Databricks Lakehouse connector to transfer data from your source connectors to your Databricks Lakehouse destination.
8. To set up a data transfer, navigate to the "Sources" tab and select the source connector that you want to use.
9. Follow the prompts to enter your source connector credentials and configure your data transfer settings.
10. Once you have configured your source connector, select the Databricks Lakehouse connector as your destination and follow the prompts to configure your data transfer settings.
11. Click on the "Run" button to initiate the data transfer.
With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.
We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
Ready to get started?
Frequently Asked Questions
Salesforce's API provides access to a wide range of data types, including:
1. Accounts: Information about customer accounts, including contact details, billing information, and purchase history.
2. Leads: Data on potential customers, including contact information, lead source, and lead status.
3. Opportunities: Information on potential sales deals, including deal size, stage, and probability of closing.
4. Contacts: Details on individual contacts associated with customer accounts, including contact information and activity history.
5. Cases: Information on customer service cases, including case details, status, and resolution.
6. Products: Data on products and services offered by the company, including pricing, availability, and product descriptions.
7. Campaigns: Information on marketing campaigns, including campaign details, status, and results.
8. Reports and Dashboards: Access to pre-built and custom reports and dashboards that provide insights into sales, marketing, and customer service performance.
9. Custom Objects: Ability to access and manipulate custom objects created by the organization to store specific types of data.
Overall, Salesforce's API provides access to a comprehensive set of data types that enable organizations to manage and analyze their customer relationships, sales processes, and marketing campaigns.