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By Coral Trivedi
Data lakes can save your enterprise money but come with unique challenges. These tips will help you overcome three of the biggest issues.
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By Kelly Kohlleffel
Exploring fraud detection with automated data integration in a cloud data warehouse: part two of two.
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By Kelly Kohlleffel
Learn how CHS Inc., a Fortune 500 secondary cooperative, leverages Fivetran, Snowflake and dbt to improve supply chain efficiency and power its billion-dollar operation.
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By Charles Wang
Why data lakes are the essential centerpiece of modern data architectures and an indispensable prerequisite for AI.
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By Jalene Jizdeortega
This marks an incredible three-peat win, solidifying our ongoing commitment to innovation and collaboration within the Google Cloud ecosystem.
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By Cat Origitano
From new data sources to cloud regions and destinations, Fivetran continues to expand what it means to be the leader in data movement.
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By Angel Hernandez
The Fivetran REST API makes it a breeze to automate the deployment, configuration and management of connectors, saving you time and effort.
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By Kelly Kohlleffel
Fivetran and Google Cloud synergize incredibly well, bringing generative AI to your fingertips.
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By Tina Wang
We tested a product-led go-to-market motion last year. Here are the exciting results and learnings.
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By Fivetran
George Fraser, CEO and co-founder of Fivetran, and Ali Ghodsi, CEO and co-founder of Databricks, are building products that power the modern data stack. They offer an insider’s perspective on the hardest parts of building and deploying generative AI in the enterprise.
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By Fivetran
The key to breaking down data silos and fostering innovation goes well beyond having the right technology. It’s the people and processes that truly drive change. Ajay Bidani, Data and Insights Manager at Powell Industries, shares his perspective on how a strong, inclusive data culture is fueling the manufacturer’s global success.
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By Fivetran
George Fraser, CEO and co-founder of Fivetran, and Ali Ghodsi, CEO and co-founder of Databricks, are building products that power the modern data stack. They offer an insider’s perspective on the hardest parts of building and deploying generative AI in the enterprise.
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By Fivetran
Lakshmi Ramesh, Vice President of Data Services at Tinuiti, which services brands like Rite Aid, Nestle, and Instacart, joins us to talk about her work at the intersection of tech, data and marketing. We discuss how the company manages data from hundreds of platforms to serve both clients and internal teams.
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By Fivetran
In this episode of Data Drip, Aarthi Sridharan, VP of Data Insights and Analytics at BODi, examines her experience leading a complex data migration project to achieve customer 360 in a rapidly evolving fitness industry. She reflects on the challenges of migrating from multiple on-premises data warehouses to a unified cloud-based system and highlights the most important lessons she learned about planning, adapting, and managing a major multi-year project.
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By Fivetran
Learn how Fivetran’s automated data movement platform allows you to accelerate building Gen AI applications for customer service in Google Cloud with BigQuery and Vertex AI. Kelly Kohlleffel steps you through creating four connectors to BigQuery, including a relational database connector plus Jira, Slack, and Zendesk connectors. Then you’ll see how easy it is to quickly build two Gen AI apps, one for search and one for chat, using Vertex AI and the new customer service datasets in BigQuery.
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By Fivetran
Generative AI has everyone talking, but has that buzz overshadowed the potential of predictive AI? We talked with Parag Shah, Senior Director of Data and Analytics at Rocket Software, to explore the hype and hope around both generative and predictive AI.
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By Fivetran
Sorenson Communications is a leading provider of captioning and interpretation services for the hard-of-hearing and deaf, with the mission to make communication accessible and clear regardless of signed or spoken language. Automated, real-time translation of all kinds depends heavily on natural language processing and the data used to train it.
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By Fivetran
Learn how Fivetran’s automated data movement platform allows you to quickly set up a relational database connector to the Databricks Lakehouse to move a wine quality dataset over to the lakehouse and ensure that it’s ML-ready. Then you’ll see how to use Databricks and AutoML to run classification experiments on the dataset to generate models for wine quality predictions based on a variety of parameters, including citric acid, ph, residual sugar, and sulphates. An extra bonus is that you don’t have to be a data engineer, ML engineer, or a wine expert to deliver quick value with this tech stack and approach.
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By Fivetran
Learn how Fivetran accelerates and automates data movement for Workday HCM data to the Snowflake Data Cloud. Using Fivetran’s fully automated and fully managed data movement service, you can achieve self-service data integration for all Snowflake data workloads quickly and securely and always be 100% automated. Kelly Kohlleffel steps you through creating a Workday to Snowflake connector with Fivetran for both the initial sync and ongoing incremental change data capture while creating a data app-ready dataset in Snowflake that is immediately useable, trusted, organized, and understandable by Streamlit.
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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.