85: Arun Thulasidharan: Warehouse-native martech and an alternative pricing model

Summary: Arun clarifies 'warehouse-native' and 'connected' concepts, positioning Castled.io as a flexible solution that caters to specific customer needs. He addresses challenges in traditional martech, such as the disparity between customer base size and value derived, and presents Castled.io's unique solutions like an alternative pricing model and immediate data access. Arun navigates the issues of a warehouse-native approach, providing strategies for handling real-time data and minimizing compute charges. He cautions against seeing warehouse-native adoption as merely an escape from reverse ETL, emphasizing its potential to resolve existing martech problems and enhance functionalities. Arun encourages a positive attitude towards new, complex technologies, recognizing their transformative potential.

About Arun

• Arun is a data engineer by trade with over a decade of experience building and scaling systems in the startup ecosystem
• He started his career in software engineering roles at Applied Materials, an enterprise semiconductor manufacturer and later MiQ, a programmatic advertising media partner
• Arun then joined Flipkart, known today as India’s largest e-commerce marketplace with a whopping 150 million customers 
• He then moved to the startup world joining Hevo Data as one of the first tech hires, a No-code ETL Data Pipeline platform that enables companies to consolidate data from multiple software
• In 2021, Arun moved to San Francisco to co-found his first startup, Castled Data - A warehouse-native customer engagement platform that sits directly on top of cloud data warehouses
• Along with his team of founders Arun was selected by YC in the Winter 22 batch

From Open Source Reverse ETL Tool to Warehouse Native CEP

When asked about the transformational journey of Castled.io, Arun shed light on the genesis of the company's vision. It was a time when businesses wanted to move their data from warehouses to various tools, yet the market lacked the means to do this efficiently. Recognizing this gap, Arun embarked on the mission to develop an open source, reverse ETL solution. His concept was founded on the idea that no one-size-fits-all tool could cater to the wide range of companies' diverse requirements.

This venture brought Castled.io a fair amount of traction, with many companies employing their open source solution in-house, and a growing clientele availing of their cloud-based offering. However, around this time, a critical analysis of the martech landscape provoked a pivot. Arun realized the long-term sustainability of reverse ETL solutions was questionable, especially with the burgeoning concept of warehouse-native apps. Other companies were beginning to develop their own reverse ETL tools.

Arun observed that these ETL solutions were not truly designed for data teams but rather marketing growth teams, signaling a limitation in their scope. The need to constantly shift data to different platforms like Intercom was dwindling, given alternative and more efficient methods emerging in the martech ecosystem. In fact, he believed that the popularity of these reverse ETL solutions might begin to wane within a year.

The most crucial feedback that inspired the transformation of Castled.io came directly from its target audience – the marketers. They indicated that a reverse ETL solution did not fully resolve their challenges, especially in scenarios where handling large amounts of data became a bottleneck for their existing tools. It became clear that simply copying data from warehouses to another tool wasn't an effective solution.

Prompted by these revelations and the rising acceptance of the warehouse-native concept, Arun and his team decided to pivot. They transitioned from being an open-source reverse ETL tool provider to building Castled.io as a solution directly layered on top of data warehouses. This move allowed them to bypass data migration issues and directly cater to the marketers' needs.

Takeaway: The journey of Castled.io highlights the importance of remaining adaptable and receptive to market changes and customer feedback. This awareness allowed the company to evolve from being an open-source reverse ETL tool to a robust, warehouse-native solution, directly addressing marketers' challenges. The company's pivot is a testament to strategic foresight and innovation in the martech space.

The Similarities of Open vs Closed and Composable vs Packaged CDPs

In the fiery debate around composed versus packaged CDPs, Arun weighed in with his unique viewpoint. He likened the contrast between these two approaches to the difference between open source and closed source systems.

From Arun's perspective, the appeal of composable CDPs lies in the flexibility they offer. This format enables innovation on top of the data warehouse, unlike the constraints potentially imposed by a packaged system. If something isn't quite right, with a composable CDP, you're able to add more tables, create more transformations, and even integrate external tools.

Arun cited examples like Mutuality and Thing, tools that perform identity resolution on top of the data warehouse. These systems, instead of operating deterministically, utilize fuzzy resolution. They identify rows that may be the same and join them together - an innovative process executed directly within the data warehouse.

Such possibilities underscore the value of composable CDPs. Being locked into a closed system inhibits the ability to incorporate these innovations into one's data warehouse, a limitation he finds less appealing. Though there are countless other arguments surrounding this topic, Arun emphasizes this angle as one often overlooked in the broader conversation.

Takeaway: In the composable vs. packaged CDP debate, Arun highlights the flexibility and potential for innovation offered by composable CDPs. By likening them to open-source systems, he underscores the opportunities to customize and integrate additional tools directly on top of the data warehouse, an often overlooked yet crucial consideration in the martech space.

Unpacking the Definition of Warehouse-Native Martech

When asked about the varying definitions in the martech space, particularly 'warehouse-native' and 'connected', Arun addressed these terms with a refreshingly pragmatic viewpoint. He observed that while the industry is caught up in different terminologies, often what doesn't fit into these boxes is what the customer actually wants.

Arun described his understanding of warehouse-native as akin to the framework offered by Snowflake, where everything runs atop the data. A connected app, in his view, is one that separates compute and data - the data resides in a warehouse, not in the SaaS app, providing the flexibility we've discussed before. The actual computations happen on internal clusters, streamlining operations by removing the need for API integrations, enhancing consistency and security, and reducing data movement.

Yet, for Arun, the appeal of warehouse-native martech extends beyond these definitions. The true advantage lies in its potential to transform data into a goldmine of information that can fuel powerful reporting and analytics. The ability to write data back to the data warehouse creates a wealth of opportunities for customers, a feature he deems as a significant boon of connected apps and warehouse-native tech.

Despite these perspectives, Arun chooses not to classify Castled.io strictly as a warehouse-native or connected app. Instead, he emphasizes meeting customer needs. For some enterprise customers, the security of not moving data to an external system like Breeze or Iterable is paramount. Here, he sees val...