Fivetran Accelerates Time to Market for Daydream: An IoT Case Study

• Functional Applications - Enterprise Resource Planning Systems (ERP)
• Platform as a Service (PaaS) - Application Development Platforms

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• Building Automation & Control
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• Cloud Planning, Design & Implementation Services
• System Integration

Daydream, an early-stage startup, provides financial insights to stakeholders across modern businesses. The company's business modeling and planning tool democratizes access to financial information by bringing together processes and data sources that are typically siloed. However, the Head of Engineering for Daydream, Shubham Sinha, faced a critical decision. The success of the startup hinged on its ability to move massive amounts of data from its customers’ cloud-based business systems, each with their own login credentials and access challenges, into the Daydream platform for analysis. The two options were to either ask its customers for login credentials to their business systems, posing a potential security risk, and use custom-built data pipelines to onboard data or to rely on Fivetran to broker the credential sharing exchange and onboard data using its pre-built data pipelines. Maintenance also posed a challenge as each cloud platform has its own APIs, processes, and data structures, many of them requiring custom integrations through scripting.

Daydream is an early-stage startup that provides stakeholders across modern businesses with the financial insights they need to make better decisions. The company's business modeling and planning tool democratizes access to financial information by bringing together processes and data sources that are typically siloed. This helps users to view data holistically, create insights, and make data-based, informed decisions that enable agility. The platform's simple-yet-powerful collaboration flows and AI-powered insights help teams keep tabs on their business health, understand trade-offs, spot opportunities, and double down on the growth initiatives that matter most.

To accelerate time to market, Shubham decided to rely on an existing data integration solution to handle data migrations and daily syncs. After investigating several solutions, Shubham signed up for a free trial of Fivetran. Within minutes he was experimenting with the hundreds of integrations already built into Fivetran’s connector library. Shubham and his team were able to build the Daydream platform directly on top of Fivetran’s data pipelines within weeks. Now, every time a new customer creates an account with Daydream, a new Fivetran account is automatically created as well. The system then takes the customer through a setup wizard that discovers the various platforms the customer uses for CRM, ERP, accounting, and other critical systems. Using REST APIs, Fivetran then pulls in and syncs data from SaaS platforms that the customer has granted Fivetran access to. Fivetran consolidates that information in Daydream’s platform, where it can be accessed and analyzed by its customers.

• Relying on Fivetran to pull data from dozens of SaaS platforms allows Shubham and the engineering team to focus on other aspects of building out the Daydream platform. This empowers a true customer-focused launch strategy based on discovering and solving businesses’ most pressing pain points. Not having to worry about data integrations, syncing, and cleanup enables innovation in other areas while accelerating time to market. Shubham also credits Fivetran for making the Daydream platform more scalable. As new CRM, ERP, accounting, and other business systems come to market, Daydream can trust that Fivetran will build out its connector library to include these new solutions and platforms. This allows the startup to expand its reach and provide additional coverage for customers without disrupting existing workflows.

• Fivetran cut at least a month off the launch timetable for Daydream
• Fivetran saves Daydream hundreds of engineering hours
• Fivetran’s pre-built dbt transformation packages relieve Daydream of having to design all of the transformations required for many of its data sources