Why Snowflake and Databricks Can’t Replace Adobe Experience Platform: Data Unification is More Than Just Storing and Analyzing Data

The hype around cloud data platforms like Snowflake and Databricks is deafening. They’re hailed as the Swiss Army knives of data management—capable of storing, analyzing, and transforming massive datasets with ease. But let’s cut through the noise: Snowflake and Databricks are not data unification platforms. They’re tools for specific jobs, and data unification isn’t one of them. If you’re banking on these platforms to deliver the trusted, real-time, cross-domain data profiles that power modern AI and customer experiences, you’re betting on the wrong horse. Here’s why Adobe Experience Platform (AEP) remains unmatched—and why global enterprises are doubling down on it.

1. Storage and Analytics Aren’t Unification

Snowflake and Databricks excel at ingesting and storing data. They’re the go-to for building data lakes or warehouses and running complex analytics. But unification? That’s a different beast. Data unification isn’t about dumping everything into a single repository—it’s about resolving duplicates, creating trusted golden records, and building real-time, relationship-rich customer profiles that fuel personalized experiences and AI-driven decisions.

Take a real-world example: a global retailer with millions of customers across online, in-store, and mobile channels. Snowflake can store their purchase histories, and Databricks can analyze trends in that data. But what happens when a customer uses different emails for online and in-store purchases? Or when their loyalty program data lives in a separate CRM? Snowflake and Databricks don’t natively resolve these inconsistencies—they leave you to stitch together fragmented identities. AEP, by contrast, uses its Identity Service to map disparate data points into a single, trusted customer profile in real time. This isn’t just a nice-to-have—it’s the backbone of delivering seamless, personalized experiences.

“Data unification is about creating a single source of truth for each customer, no matter how they interact with your brand. Without it, you’re just guessing.”
— Adobe

2. Batch Analytics Don’t Cut It for Operational Trust

Snowflake and Databricks are built for batch analytics—perfect for dashboards and quarterly reports. But when your AI agent needs to make a split-second decision—like offering a personalized discount during a customer’s checkout—batch processing is a liability. Enterprises need real-time, governed data they can trust for mission-critical operations.

Consider a financial services company deploying an AI-powered chatbot to handle customer inquiries. If the chatbot pulls from a Snowflake warehouse with ungoverned, inconsistent data, it might offer a loan to a customer who’s already been declined, eroding trust and risking compliance violations. AEP’s Real-Time Customer Data Platform ensures data is clean, governed, and instantly accessible, enabling AI systems to act with confidence. As McKinsey notes, “Real-time data activation is critical for 71% of enterprises adopting AI, yet only 20% have the infrastructure to support it” 

3. General-Purpose Tools Require Custom-Built Solutions

Snowflake, Redshift, and Databricks are the ultimate DIY platforms—flexible, powerful, and endlessly customizable. But that flexibility is a double-edged sword. Want governance? Build it. Need master data logic? Code it. Want native identity matching? Good luck. These platforms give you raw materials, but you’re the one hammering nails.

AEP, by contrast, is purpose-built for data unification. It comes with pre-configured governance frameworks, identity resolution, and master data management out of the box.

For example, a major airline using AEP can instantly unify passenger data from booking systems, loyalty programs, and in-flight purchases without writing a single line of code. Snowflake and Databricks would require months of custom development to achieve the same result—time most businesses don’t have in today’s fast-moving markets.

4. SQL Isn’t the Language of Business

Snowflake and Databricks speak SQL fluently, but most business users don’t. If your marketing team needs to ask, “Who are our high-value customers in Europe?” and the answer requires a 50-line SQL query, you’re not running a data platform—you’re running a technical debt factory. AEP’s user-friendly interface empowers non-technical users to explore data, generate insights, and act on them without a data scientist on speed dial. This democratization of data is critical: Gartner predicts that by 2026, 75% of enterprises will prioritize platforms that enable business users to engage directly with data.

5. Relational Logic Can’t Handle Modern Complexity

Snowflake and Databricks are rooted in relational logic, which works well for structured data but buckles under the complexity of today’s enterprises. Customers don’t live in neat rows and columns—they exist in a web of relationships, contexts, and interactions. A graph-native architecture, like the one AEP leverages, is designed to handle this complexity. It maps relationships across domains—say, connecting a customer’s social media activity to their purchase history and support tickets—to uncover insights that relational databases miss.

For instance, a telecom company using AEP can identify churn risks by analyzing not just billing data but also customer sentiment on social media and support call patterns. This interconnected view is impossible to achieve efficiently with Snowflake or Databricks alone, which lack native graph capabilities.

6. They’re Complements, Not Competitors

Here’s the kicker: Snowflake, Redshift and Databricks aren’t rivals to AEP—they’re partners. AEP acts as the central nervous system, unifying and enriching data before feeding it into Snowflake or Databricks for storage. A major CPG brand, for example, might use AEP to create unified customer profiles, then push those profiles to Snowflake for long-term storage. Without AEP’s unification layer, the data flowing into these platforms is fragmented and unreliable—garbage in, garbage out.

“AEP doesn’t replace your data warehouse—it makes it better by ensuring the data you’re analyzing is accurate and complete.”
— Adobe

The Stakes Are Higher Than Ever

In the era of agentic AI, where autonomous systems make decisions in milliseconds, trusted, contextual data is non-negotiable. Snowflake, Redshift and Databricks are critical components of the data ecosystem, but they’re not the foundation. Adobe Experience Platform is. It’s the platform that global leaders like Coca-Cola, T-Mobile, and Unilever rely on to unify their data and power real-time, personalized experiences.

Don’t settle for platforms that leave you building from scratch.