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‍ For many enterprises, their legacy systems are becoming more of a liability than an asset, slow to change, expensive to maintain, and risky in today’s security climate. But the rise of cloud infrastructure isn’t just hype: 94% of enterprises report using cloud services in some form by 2025. Moreover, the cloud‐first mindset has gone mainstream. According to Gartner, 85% of organizations will embrace a cloud‐first strategy by 2025.

An iPaaS, or integration platform as a service is a cloud-based integration platform that acts as a central hub to seamlessly connect and manage different applications, data sources, GenAI models, knowledge bases, and AI agents wherever they are deployed—cloud-based, on-premises, or hybrid.

Fivetran is recognized as a Leader in Data Integration & Modeling in Snowflake’s Modern Marketing Data Stack 2026 report.

Fivetran Managed Data Lake Service solves complexity, vendor lock-in, rising compute costs, and long-term data retention.

Trinny London, Nando’s, and Katkin unified data to cut costs, enhance customer experience, and drive smarter decisions.

Connect data from across all sources to see, understand, and predict every aspect of manufacturing operations and optimize decision making.

Fivetran and Databricks combine to help financial services teams unify data, accelerate AI adoption, and drive real-time, personalized insights at scale.

Integrate.io offers comprehensive Oracle database integration capabilities with 200+ pre-built integrations, supporting ETL and ELT patterns with enterprise-grade security and near real-time processing. This guide explores the top 15 Oracle connectors available on the platform, covering scenarios where Oracle serves as both data source and destination for enterprise data workflows.

MongoDB is one of the most popular databases for modern applications. Its flexible, document-oriented model makes it easy for developers to store and query everything from user activity logs and product usage events to application telemetry. But while MongoDB is great for storing and organizing data, it’s not always easy for business teams to use it to answer questions or track key metrics. The data often lives in a raw format that requires technical skills to interpret.