How to load data from SalesLoft to BigQuery
Learn how to use Airbyte to synchronize your SalesLoft data into BigQuery within minutes.
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.
Building in-house pipelines
- Inconsistent and inaccurate data
- Laborious and expensive
- Brittle and inflexible
After Airbyte
- 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
Streamline AI workflows with Airbyte: load unstructured data into vector stores like Pinecone, Weaviate, and Milvus. Supports RAG transformations with LangChain chunking and embeddings from OpenAI, Cohere, etc., all in one operation.
Move Large Volumes, Fast
Quickly get up and running with a 5-minute setup that enables both incremental and full refreshes for databases of any size, seamlessly scaling to handle large data volumes. Our optimized architecture overcomes performance bottlenecks, ensuring efficient data synchronization even as your datasets grow from gigabytes to petabytes.
An Extensible Open-Source Standard
More than 1,000 developers contribute to Airbyte’s connectors, different interfaces (UI, API, Terraform Provider, Python Library), and integrations with the rest of the stack. Airbyte’s AI Connector Builder lets you edit or add new connectors in minutes.
Full Control & Security
Airbyte secures your data with cloud-hosted, self-hosted or hybrid deployment options. Single Sign-On (SSO) and Role-Based Access Control (RBAC) ensure only authorized users have access with the right permissions. Airbyte acts as a HIPAA conduit and supports compliance with CCPA, GDPR, and SOC2.
Fully Featured & Integrated
Airbyte automates schema evolution for seamless data flow, and utilizes efficient Change Data Capture (CDC) for real-time updates. Select only the columns you need, and leverage our dbt integration for powerful data transformations.
Enterprise Support with SLAs
Airbyte Self-Managed Enterprise comes with dedicated support and guaranteed service level agreements (SLAs), ensuring that your data movement infrastructure remains reliable and performant, and expert assistance is available when needed.
What our users say
Raman Singh
Predictable, straightforward pricing model that simplified budgeting and significantly reduced overall spend
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."
How to Sync to Manually
Step 1: Extract Data from Salesloft
Begin by exporting the required data from Salesloft in CSV format. Use Salesloft's built-in export functionality to download the data files directly onto your local machine. Ensure you export all necessary data fields you plan to move to BigQuery.
Step 2: Prepare Data for BigQuery
Before uploading to BigQuery, clean and format your CSV files to ensure compatibility. Check for data consistency, remove any unnecessary columns, and format the data types (e.g., dates, numbers) correctly. This step is crucial to prevent errors during the import process.
Step 3: Set Up Google Cloud Platform (GCP)
If you haven't already, set up a Google Cloud Platform account. Navigate to the Google Cloud Console and create a new project or select an existing one. Ensure that billing is enabled for the project to utilize BigQuery services.
Step 4: Create a Dataset in BigQuery
Within your project, navigate to BigQuery. Create a new dataset where you will store your Salesloft data. A dataset in BigQuery is a container for tables, and setting one up is necessary before importing data.
Step 5: Design Table Schema in BigQuery
Define the table schema that matches your Salesloft data structure. You need to manually specify column names, data types, and any other necessary configurations such as nullability. This step ensures that the imported data aligns correctly with your existing datasets.
Step 6: Upload Data to Google Cloud Storage (GCS)
Transfer your CSV files to Google Cloud Storage, which is a prerequisite for loading data into BigQuery. Use the Cloud Console or `gsutil` command-line tool to upload your files to a designated GCS bucket. Ensure the bucket is in the same region as your BigQuery dataset for optimal performance.
Step 7: Load Data into BigQuery
Use BigQuery's web interface or `bq` command-line tool to load the CSV data from Google Cloud Storage into your BigQuery table. Specify the GCS file path, the dataset, and the table name, and ensure the schema matches the CSV structure. Execute the load job and monitor it for successful completion. Once done, verify the data integrity by running queries in BigQuery.