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By Shiva Mogili
Amazon’s recent pivot on the Selling Partner API is an encouraging reminder that developer pushback can breach walled gardens.
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By David Millman
Five paths give Fivetran the flexibility to connect virtually any data source, from SaaS applications and flat files to application databases, proprietary APIs, and real-time event streams.
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By Shounak Vijay
Centralizing data builds the foundation, but activating trusted data across operational systems will drive the next wave of growth in APAC.
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By Fivetran
Learn what Debezium is, how its CDC architecture and connectors support MySQL and SQL Server, and which of the three deployment modes fits your pipeline.
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By Shiva Mogili
As software vendors place more controls around data access, enterprises must decide whether their future AI capabilities will be defined by their strategy or their vendors' policies.
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By Jamie Cole
You will still need to solve many last-mile problems to make them production-ready. We can help.
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By Natalie Waller
Explore the architecture, open standards, and semantic layer that give AI agents access to accurate, reusable business context.
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By Andrew Madson
A practical guide to building a governed foundation that can support diverse workloads without copying data into every tool.
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By Fivetran
As Superhuman expanded its AI platform across Grammarly, Coda, Superhuman Mail, and Superhuman Go, more of the business began to rely on timely data from Salesforce, Outreach, Pardot, Stripe, Zendesk, Qualtrics, and other third-party systems. The challenge went far beyond moving data into Databricks. Go-to-market, finance, and customer teams needed faster, reliable access to trusted data without turning every new data request into weeks of custom engineering.
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By Fivetran
As AI accelerates the pace of change, demanding fresher data, diverse formats, and support across multiple engines, many teams discover their infrastructure was built for reporting, not real-time AI at scale. Open Data Infrastructure is redefining how organizations design for analytics, operations, and AI. By leveraging Fivetran as an interoperable data foundation, organizations can embrace open standards, separate storage from compute, and keep data portable across clouds and engines, preserving adaptability while scaling AI and operational workloads with Databricks.
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By Fivetran
Healthcare organizations operate some of the most complex data environments, spanning thousands of systems across clinical, financial, and operational domains. At Inova Health, this complexity created an opportunity to rethink how data could better support analytics and AI at scale.
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By Fivetran
Learn how Fivetran activates data and delivers it into business applications for analytics, insights, and customer segmentation.
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By Fivetran
Learn how Fivetran enables forward and reverse data replication. In this demo, you will see data sync from Salesforce to Snowflake with Fivetran and back to Salesforce with a lead score derived from both Salesforce and warehouse data fields showcasing the power of the combined Fivetran and Census platform for marketing use cases.
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Fivetran fully automated connectors sync data from cloud applications, databases, event logs and more into your data warehouse. Our integrations are built for analysts who need data centralized but don’t want to spend time maintaining their own pipelines or ETL systems.
Focus on analytics, not engineering. Our prebuilt connectors deliver analysis-ready schemas and adapt to source changes automatically.
Keep your team focused on analysis:
- Prebuilt connectors: Centralize your operational data in minutes with 150+ zero-configuration connectors.
- Ready-to-query schemas: Use thoughtful, research-driven schemas and ERDs for all your sources.
- Automated schema migrations: Save resources with connectors that automatically adapt to schema and API changes.
- Fully managed data integration: Reduce technical debt with scalable connectors managed from source to destination.
- SQL-based transformations: Model your business logic in any destination using SQL, the industry standard.
- Incremental batch updates: Change data capture delivers incremental updates for all your sources.
Simple, reliable data integration for analytics teams.