Data replication

Your complete Mode data platform

Unify your operations with the only connector you’ll ever need. Move large volumes of data with best-in-class replication, or build production-ready AI agents directly on your Mode data.

Try it freeTalk to sales

Setup in 3 easy steps

Start moving you Salesforce data securely and reliably to any destination

Setup Mode Source

Authenticate securely with your Mode instance using OAuth or API keys.

Choose Destination

Select from 50+ data warehouses, lakes, or databases to load your extracted data.

Configure Connection

Select streams, sync frequency, and sync modes (Full Refresh or Incremental).

Why Airbyte?

Airbyte is the only unified data movement platform built onthe open standard. It is uniquely positioned in terms ofdata sovereignty, connector extensibility, and support for AI workflows.

Connector Marketplace

Gen AI Workflows

Manage Pipelines

Ensure Data Security

Syncing data from Mode is only one of your 1,000 future data pipeline needs.

Leverage the largest Marketplace of 400+ pre-built and 10,000+ custom  structured and unstructured connectors. Join 2,000 + data engineers who built 7,000+ custom connectors in minutes with low-code/no-code Connector Builder or AI Assistant.

Connector Builder
Create context for AI agents by leveraging Airbyte's 600+ connectors.

Airbyte's pipelines transfer structured and unstructured data together for metadata preservation. With support for flexible destinations such as Iceberg, Airbyte is the ideal data movement solution for agentic application.

Any specific way you would like to sync data from Mode? Airbyte has you covered.

UI: Create connections and custom connectors in minutes.

API: Programmatic interactions, data syncing, and embedded connectors.

Terraform: Integration with CI/CD tools and rapid deployment with Infrastructure as Code.

PyAirbyte: Build LLM applications with Python libraries, SQL tools, and AI frameworks.

Flexible deployment options: self-hosted, cloud, and hybrid

Secure and compliant: ISO 27001, SOC 2, GDPR, HIPAA, data encryption, audit/monitoring, SSO, RBAC, and more. Centralized multi-tenant management with self-serve capabilities.

Trusted by AI and Data leaders

Raman Singh

Tech Lead at Symend

"With our legacy framework, if one of the pipelines fails for one client, it will stop everything for the rest of our clients. But with Airbyte, things are run in parallel because of the platform’s distributed nature, which means that we can process multiple clients at the same time without impacting performance."

75%
reduction in sync times
$ 900K
in annual savings
Learn more

Sean Carver

Director of Data at PetDesk

"The real ROI is in our ability to iterate quickly, especially at our increasing scale. At the end of the day, you want a tool like that to just work. We can forget about it and know that it's configured and it's connecting and it's working. That hands-free capability is a big appeal for the platform.”

20+
data sources integrated and growing
+1
FTE engineer in productivity efficiency
85%+
reduction in  data source integration time
Learn more

Mondor La Grange

Head of BI and Data Engineering

"Unlike Fivetran's credit-based system that created budget uncertainty, Airbyte's pricing model allows Kuda to forecast expenses accurately and avoid surprise bills."

20+
data sources integrated and growing
+1
FTE engineer in productivity efficiency
85%+
reduction in  data source integration time
Learn more

Amy Zhao

Senior Manager of Data Engineering

"What's different from Stitch Data or Informatica is the way that we can configure Airbyte connections and Airbyte entities through code. That's a huge plus to us as data engineers, because we are used to checking code and being able to manage changes from Github."

3 to 1
reduction in data integrations solutions for reduced TCO
1
week Shopify and Stripe integration with Airbyte
Learn more

Franziska Ibscher

Product Manager at Drivepoint

"Airbyte allows us to stay flexible while scaling from hundred-million to billion-dollar enterprise clients."

75%
of customers increased profitability
6.7%
EBITDA increase for customers
Learn more

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.

What is Mode?

What data can you extract from Mode?

Mode 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 data types, such as CSV, JSON, and Avro files.

2

Semi-structured data: This includes data that has some structure, but not necessarily a fixed schema, such as XML and JSON files.

3

Unstructured data: This includes data that has no predefined structure, such as text, images, and videos.

4

Time-series data: This includes data that is organized by time, such as stock prices, weather data, and sensor readings.

5

Geospatial data: This includes data that is related to geographic locations, such as maps, GPS coordinates, and spatial databases.

6

Machine learning data: This includes data that is used to train machine learning models, such as labeled datasets and feature vectors.

7

Streaming data: This includes data that is generated in real-time, such as social media feeds, IoT sensor data, and log files.

Overall, Mode's API provides access to a wide range of data types, making it a powerful tool for data analysis and machine learning.

How do I transfer data from Mode?

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps:

1

Set up Mode as a source connector (using Auth, or usually an API key)

2

Choose a destination (more than 50 available destination databases, data warehouses or lakes) to sync data too and set it up as a destination connector

3

Define which data you want to transfer from Mode and how frequently

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps:

What are top ETL tools to transfer data from Mode?

The most prominent ETL tools to transfer data to Mode include:

Airbyte

Fivetran

StitchData

Matillion

Talend Data Integration

These tools help in extracting data from various sources (APIs, databases, and more), transforming it efficiently, and loading it into Mode and other databases, data warehouses and data lakes, enhancing data management capabilities.

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.

Ready to connect your AI agents to Mode?

Get started in minutes with our open-source connector.

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