The evolution of data platforms is currently at a historic turning point, where Data Engineering and Artificial Intelligence are no longer silos but move forward hand in hand. While the previous focus was on transitioning from Data Warehouses to Data Lakehouses to support BI and Data Science, today’s market demands architectures capable of supporting Generative IA (LLMs), RAG (Retrieval-Augmented Generation), and autonomous Agents.
Based on a recent technical meeting among specialists, we analyze how the market’s leading players, Databricks, Snowflake, and ClickHouse, are shaping their tools to respond to these new societal and corporate needs.
The new paradigm in the evolution of data platforms
It is no secret that data platforms needed to evolve to integrate diverse sources and make data available for analysis. However, the paradigm has shifted. Today, to implement an effective RAG solution, companies require components that were not native to traditional architectures:
- Vector Databases (Vector Stores): To store and search data by semantic similarity, which is essential for feeding LLMs with context.
- API-based Availability: Data is no longer just queried by an analyst, it must be consumed via API by AI agents.
- Environments for Agents: Platforms where it is possible to build, populate the vector store, and interact with the end user autonomously.
This shift in requirements redefines how we think about the evolution of data platforms.
Databricks: governance and natural language as pillars
Databricks has been moving aggressively to unify data and AI. At the heart of this strategy is the expansion of the Unity Catalog. Governance has become crucial in the evolution of data platforms, not just for security, but to provide context to LLMs. Without clear metadata and governed KPI definitions, an LLM cannot distinguish one “ID” column from another, making results irrelevant.
Highlights of Databricks in the evolution of data platforms:
- Mosaic AI: The result of a recent acquisition, this framework allows developers to build, deploy, and evaluate their own AI agents and RAG systems with ease.
- Genie: A natural language interface (Natural Language Processing, NLP) that allows users to “talk” to their data, going far beyond what static dashboards can answer.
- Lakeflow & Lakebase: The platform expands into low-code pipelines triggered by natural language (Lakeflow) and introduces a native transactional database (Lakebase), similar to PostgreSQL, to close the ecosystem.
Snowflake: simplifying AI via SQL
Snowflake’s strategy for the evolution of data platforms focuses on democratization and simplicity, allowing analysts to use AI directly via SQL without the complex need for data scientists or infrastructure management.
How Snowflake is approaching the evolution of data platforms:
- Cortex SQL: Introduces native functions such as summarize (text summary), sentiment analysis, and translation directly within the SQL layer. The idea is simple: the data stays in Snowflake, and the AI comes to it via simple queries.
- Snowflake Intelligence: Similar to Databricks’ Genie, it is a conversational natural language interface for end users to interact with data.
- Snowpark & Transactional Workloads: The Snowpark library demonstrates incredible performance (e.g., processing 800 million rows in 10 seconds). Parallel to this, with the acquisition of PiraDyna, Snowflake is moving toward supporting native transactional workloads.
ClickHouse: speed and efficient vectors
ClickHouse’s historical focus has always been on ingestion and query speed. In the evolution of data platforms for the AI era, they maintain their performance DNA, focusing on cost optimization and open architecture.
Highlights of ClickHouse in the evolution of data platforms:
- K-Beat & Vector Search: A technical innovation that uses quantization to drastically reduce memory and disk usage when storing vectors. Users can dynamically adjust the balance between speed and accuracy in queries without needing to reindex data.
- Managed MCP Server: Facilitates the direct connection of AI agents to the database, allowing LLMs to explore schemas and execute queries at scale without complex engineering.
- Langfuse: A tool for observability and tracking the entire LLM process, which is crucial for production monitoring.
How to decide which data platform to use?
While all platforms are converging to include AI, each has a different architectural “fit,” as discussed in our technical meeting:
- Choose Databricks if: You need deep model customization (fine-tuning), have a strong team in Spark/Python, and prioritize a unified data lake and AI system.
- Choose Snowflake if: Your team is focused on SQL/BI and you seek simplicity (“zero infra”) with plug-and-play AI features and high processing speed via Snowpark.
- Choose ClickHouse if: Query latency and cost efficiency are the absolute priorities, especially for high-performance vector search.
Engineering in the Evolution of Data Platforms
The evolution of data platforms redefines the role of the Data Engineer. It is no longer enough to deliver clean data; one must deliver context. The concept of “Context Engineering” emerges, where the engineer adjusts the data, metadata, and governance that will feed AI agents.
Governance, often neglected, becomes the foundation for ensuring LLMs do not hallucinate. As consultants and engineers, we must be attentive to these changes and market tools to propose architectures that do not just serve yesterday’s BI, but tomorrow’s AI agents.
Connect With Our Specialists
The transition toward AI Agents and RAG systems requires a sophisticated approach to Data Engineering. Our team at BIX Tecnologia provides the strategic oversight and technical expertise necessary to prepare your infrastructure for this evolution.
