• Cloud Database Insider
  • Posts
  • Snowflake grows 30%đź“Š|Database automation insights✨|GCP AI BigQuery Integration🤖|Couchbase excels🧠|Datadog vs. Chronosphere 👀🔍

Snowflake grows 30%📊|Database automation insights✨|GCP AI BigQuery Integration🤖|Couchbase excels🧠|Datadog vs. Chronosphere 👀🔍

Let's talk about Data Observability...even more data stuff to know

Author

Gladstone Benjamin
August 04, 2025

In partnership with

What’s in today’s newsletter:

Also, check out the weekly Deep Dive - Data Observability in 2025, and Everything Else in Cloud Databases.

Unlock the Power of AI With the Complete Marketing Automation Playbook

Discover how to reclaim your time and scale smarter with AI-driven workflows that actually work. You’ll get frameworks, strategies, and templates you can put to use immediately to streamline and supercharge your marketing:

  • A detailed audit framework for your current marketing workflows

  • Step-by-step guidance for choosing the right AI-powered automations

  • Pro tips for improving personalization without losing the human touch

  • Tools and templates to speed up implementation

Built to help you automate the busywork and focus on work that actually makes an impact.

SNOWFLAKE

TL;DR: Snowflake’s AI-focused data platform drives a 30% revenue surge to $3.5B, attracting major clients with real-time analytics and signaling rising demand for cloud-based AI solutions in business.

  • Snowflake reported a 30% revenue increase, reaching $3.5 billion by focusing on AI-driven data solutions

  • The platform's integration with diverse data tools and real-time analytics attracts major corporate clients

  • Investments in AI functionalities enhance user experience and streamline data handling for organizations

  • Snowflake’s growth highlights rising demand for cloud-based analytics and AI in competitive business environments

Why this matters: Snowflake's 30% revenue growth underscores the critical role of AI-driven data solutions in transforming business intelligence. Its advanced cloud platform enables companies to harness real-time analytics, driving competitive advantage and signaling a shift toward greater AI adoption in enterprise data strategies.

DATABASE MANAGEMENT

TL;DR: The global database automation market is set to grow rapidly—projected to reach $7.39B by 2029—driven by AI integration, cloud adoption, and rising demand for scalable, real-time data systems.

  • The market is expected to grow from $4.70B in 2025 to $7.39B in 2029, at an 11.9% CAGR, signaling strong enterprise adoption of automation tools

  • Growth is driven by rising big data volumes, need for high availability, and demand for cost-efficient, automated database operations

  • Cloud, AI, machine learning, and serverless architectures are transforming how companies manage and deploy databases

  • Key players include Microsoft, Oracle, IBM, AWS, SAP, and Redgate, with North America leading and Asia-Pacific showing fastest growth

Why this matters: The shift toward database automation reflects a broader push for agility and intelligence in enterprise data strategies. With AI and cloud at the core, automated data infrastructure is becoming essential for modern business operations.

GCP

TL;DR: Google introduced new APIs and connectors to simplify AI agents' integration with BigQuery, enabling easier data querying and machine learning model use, boosting efficient data-driven decision-making for organizations.

  • Google introduced new tools to improve AI agents' integration with its BigQuery data analytics platform

  • The toolset offers APIs and connectors that simplify AI applications querying BigQuery without extensive coding

  • Features include automatic data formatting and easier machine learning model integration with BigQuery analytics 

  • Enhanced AI-BigQuery connections enable organizations to accelerate data-driven decisions and innovate across industries

Why this matters: Google's new toolset lowers barriers for companies to integrate AI with large datasets in BigQuery, accelerating advanced analytics and machine learning deployments. This fosters faster, more efficient data-driven decisions and innovation, helping businesses stay competitive in an AI-focused cloud environment.

TL;DR: Image embeddings convert visuals into vectors for advanced similarity searches, and BigQuery’s vector search enables efficient SQL-based retrieval, enhancing AI-powered image search across industries via accessible Google Cloud tools.

  • Image embeddings convert visual data into numerical vectors, enabling advanced AI-driven image search and analysis.  

  • Vector search in BigQuery allows efficient retrieval of visually similar images using SQL queries on large datasets.  

  • This technology enhances search accuracy beyond keywords, improving user experience in sectors like e-commerce and security.  

  • By simplifying complex ML implementations, Google Cloud democratizes access to powerful AI tools across various industries.

Why this matters: Image embeddings and vector search in BigQuery transform how visual data is analyzed and retrieved, enabling more precise and efficient searches. This advancement empowers businesses across sectors like e-commerce and security to enhance user experience and democratizes AI by simplifying complex machine learning implementations.

NOSQL

TL;DR: Couchbase excels with scalable NoSQL solutions blending JSON and SQL, showing strong revenue growth and cloud enhancements, competing closely with MongoDB in a dynamic market driving data agility and innovation.

  • Couchbase Inc. delivers scalable, high-performance NoSQL database solutions combining JSON and SQL capabilities.  

  • MongoDB emphasizes horizontal scalability and a strong ecosystem, positioning itself as a NoSQL leader.  

  • Couchbase's steady revenue growth and enhanced cloud services highlight its competitive enterprise appeal.  

  • The NoSQL database market competition drives innovation, customer focus, and rapid technological progress.

Why this matters: Couchbase’s growth and cloud advancements reflect the industry's shift toward flexible, scalable NoSQL solutions vital for modern applications. This intensifies competition with MongoDB, fueling innovation and improved services that shape the future of data management and enterprise technology strategies. 

DATA OBSERVABILITY

TL;DR: Datadog and Chronosphere compete for OpenAI's observability business, each enhancing AI monitoring tools for scalability and cloud-native agility, shaping future AI partnerships and emphasizing observability's industry importance.

  • Datadog and Chronosphere are competing to secure OpenAI's observability business amid growing observability demand.  

  • Datadog enhances features for large-scale applications, positioning itself as a strong contender for OpenAI.  

  • Chronosphere focuses on cloud-native environment management, offering agility for dynamic workloads and AI analytics.  

  • Winning this deal could influence future AI partnerships and emphasize the vital role of observability tools.

Why this matters: Securing OpenAI's observability business would validate the winning platform's approach to managing complex AI and cloud systems, influencing industry standards and driving innovation in observability tools essential for scaling reliable and efficient AI operations across the tech ecosystem.

EVERYTHING ELSE IN CLOUD DATABASES

DEEP DIVE
Data Observability in 2025

Not here to offend anyone, but sometimes I feel like a more “seasoned” gentlemen you may see in a park, or in a local shopping center, grouped together. I can only envision most of their sentences starting with “Remember when…”.

I “remember when”, back in the days of SQL Server 7, you had to do a lot of stuff to keep your databases running smoothly. With the advent of cloud databases, which most of them are managed, you had to keep a close watch on them, as a lot of data movement was manual, backups and restores were intricate, etc.

We now work in an era where a lot of human interaction is a thing of the past. The only good thing to come out keeping close watch of your databases is that you have greater understanding as to what is happening under the hood.

in 2025, we now have tools that facilitate Data Observability. What is Data Observability you ask?

Data observability is the ability to understand, monitor, and manage the health and quality of your data as it moves through various systems and pipelines.

It is a proactive approach to data management that provides visibility into the state of your data, allowing teams to quickly identify, troubleshoot, and resolve issues before they can impact business decisions or downstream applications like analytics and machine learning.

It is somewhat of an extrapolation of what I mentioned earlier about jobs, maintenance, etc.

The time is now 5 months shy of 2026, so now is the opportune time to talk about Data Observability. Data is proliferating, and we have to implement means to keep our eye on it, and understand the products and services that help with Data Observability.

Please, if you have a few minutes, take a look at my even deeper dive into the world of Data Observability, and the products that help with it.

Gladstone Benjamin