• Cloud Database Insider
  • Posts
  • Snowflake vs. Alphabet: The Battle for the Cloud Data Throne Just Got Personal❄️vs.🔤

Snowflake vs. Alphabet: The Battle for the Cloud Data Throne Just Got Personal❄️vs.🔤

Also in this issue, KurrentDB

Author

Gladstone Benjamin
December 22, 2025

In partnership with

What’s in today’s newsletter:

Also, check out the weekly Deep Dive - KurrentDB

Stop Drowning In AI Information Overload

Your inbox is flooded with newsletters. Your feed is chaos. Somewhere in that noise are the insights that could transform your work—but who has time to find them?

The Deep View solves this. We read everything, analyze what matters, and deliver only the intelligence you need. No duplicate stories, no filler content, no wasted time. Just the essential AI developments that impact your industry, explained clearly and concisely.

Replace hours of scattered reading with five focused minutes. While others scramble to keep up, you'll stay ahead of developments that matter. 600,000+ professionals at top companies have already made this switch.

CLOUD DATABASE

TL;DR: Snowflake leads in specialized data warehousing with strong growth, while Alphabet leverages AI and scale for broad cloud services, making their differing strategies key for investors in the competitive cloud market.

  • Snowflake excels with innovative data warehousing and collaboration platforms, driving strong revenue growth and strategic partnerships.

  • Alphabet leverages its vast ecosystem, AI, and machine learning to offer comprehensive and diversified cloud services.

  • Snowflake appeals to enterprises seeking easy-to-use, specialized cloud data solutions, suggesting growth potential in a niche market.

  • Alphabet's scale and infrastructure expansion position it to capture broader cloud market share over time.

Why this matters: Snowflake’s niche focus on data warehousing drives rapid growth and appeals to specialized enterprise needs, while Alphabet’s vast AI-powered cloud ecosystem and infrastructure expansion position it to dominate the broader market. Their distinct strategies reflect diverging paths in the competitive, evolving cloud data landscape.

AWS

TL;DR: AWS databases like DynamoDB, RDS, and Aurora are now accessible on Vercel's edge network, enabling faster, scalable web apps with reduced latency and simplified architecture for global users.

  • AWS databases like DynamoDB, RDS, and Aurora are now accessible on the Vercel edge network.

  • The integration reduces latency by allowing developers to connect databases closer to end users globally.

  • It supports multiple AWS database engines, offering flexibility for various application requirements.

  • This partnership simplifies architecture, enhancing application speed, scalability, and overall user experience.

Why this matters: Integrating AWS databases with Vercel's edge network drastically reduces latency, boosting application responsiveness for global users. This simplification of architecture accelerates development, offers flexibility across database engines, and enhances scalability, enabling businesses to deliver faster, more efficient web applications worldwide.

DATA ENGINEERING

TL;DR: The article reviews the top 10 cloud ETL tools for 2025, highlighting Apache Airflow, Fivetran, and AWS Glue for efficient, scalable data workflows that boost insights and reduce errors.

  • The article lists the top 10 cloud ETL tools for data engineers to use in 2025, focusing on efficiency and scalability.

  • Apache Airflow, Fivetran, and AWS Glue are highlighted for their flexibility, automation, and serverless capabilities.

  • Choosing the right ETL tools improves data workflows, reduces errors, and accelerates business insights.

  • The comparison of tools aids IT leaders in selecting solutions that optimize costs, integration, and operational efficiency.

Why this matters: Selecting the right cloud ETL tools in 2025 is vital for data engineers to manage growing data complexity efficiently. Tools like Apache Airflow, Fivetran, and AWS Glue drive automation, scalability, and flexibility, enabling faster insights, better governance, and cost-optimized operations—key for maintaining a competitive edge.

DATA ARCHITECTURE

Courtesy: Astera

TL;DR: Organizations struggle with non-standardized data, transformation errors, and security concerns when migrating operational data to warehouses and lakes, risking analytics reliability and compliance without scalable, well-governed strategies.

  • Organizations face difficulties migrating operational data into warehouses and lakes due to varied, non-standardized data formats.

  • Data transformation errors and inconsistencies reduce analytics reliability and complicate integration processes.

  • Security, compliance, and data governance challenges arise when protecting sensitive data during migration.

  • Effective data management and scalable migration strategies are crucial for competitive advantage and innovation.

Why this matters: Overcoming operational data migration challenges is vital for accurate analytics, regulatory compliance, and efficient operations. Organizations that invest in scalable, secure, and governed data integration gain a strategic edge, enabling advanced insights, innovation, and growth in an increasingly data-driven competitive landscape.

EVERYTHING ELSE IN CLOUD DATABASES

DEEP DIVE

KurrentDB

I like finding out about new databases. Possibly in the last 18 months, I have learned about literally over a hundred new databases. No other time have I seen such a dynamic database industry.

As I have mentioned several times before in the newsletter, AI gets all of the attention in the news media, and in our workplaces. Trust me, I am fully aware of the oxygen depleting nature of AI on the job.

But as I said, it always inspires me when I read about a new platform or company in the database realm.

So with that preamble about the robustness of the database market, let’s talk about KurrentDB.

KurrentDB is an event-native database designed specifically for event sourcing and real-time, append-only data workloads.

It is the core product of Kurrent (formerly Event Store), and it treats events—not rows or documents—as the primary unit of data.

What makes it different

  • Event-first storage model: Events are the system of record; state is rebuilt from event history.

  • Strong consistency per stream: Optimistic concurrency control ensures correctness in write-heavy domains.

  • Append-only, immutable log: Excellent auditability and temporal analysis.

  • Subscriptions & projections: Built-in mechanisms to react to events and materialize read models.

Core concepts

  • Streams: Ordered sequences of related events (e.g., Order-123).

  • Events: Immutable facts (e.g., OrderPlaced, PaymentCaptured).

  • Projections: Transform streams into queryable views.

  • Subscriptions: Push events to services in real time.

  • gRPC / HTTP APIs: Low-latency access patterns for modern services.

Typical use cases

  • Financial systems (trading, payments, ledgers)

  • Retail & logistics (orders, inventory, fulfillment)

  • IoT & telemetry (high-volume, time-ordered data)

  • Microservices needing event-driven orchestration

  • Audit-heavy domains where replayability matters

How it fits architecturally

KurrentDB is not a general OLTP replacement like PostgreSQL or MySQL, and it’s not a warehouse. It typically sits:

  • Upstream of analytical systems (Snowflake, Databricks, Fabric)

  • Alongside read stores (Postgres, Elastic, Redis) fed via projections

  • As the source of truth for business facts in event-driven architectures

If you got this far and want a full research report on KurrentDB? Read my research report here.

Gladstone Benjamin