Google Cloud is a competitive platform that continues to expand its service catalog to meet enterprise and developer needs. Whether you are automating infrastructure provisioning, modernizing legacy applications, or building real-time data-driven apps, Google offers targeted tools for each use case. This article covers five Google Cloud tools worth evaluating for your DevOps and cloud engineering workflows.
1. Google Cloud Deployment Manager
Infrastructure as Code (IaC) is a cornerstone of modern DevOps practice. Google Cloud Deployment Manager is Google’s native IaC tool, allowing you to define and deploy GCP resources using declarative configuration files.
A Deployment Manager deployment can use up to three file types:
- Configuration file (YAML) — Required. This is the primary source file that describes the resources to deploy and any imports.
- Schema file — Optional. Defines the structure and constraints for templates.
- Template files (Python or Jinja2) — Optional. Allow reusable, parameterized resource definitions.
The configuration file is split into two sections: imports, which lists any external files or templates in use, and resources, which enumerates the GCP services to be provisioned. Deployment Manager reads these files and provisions infrastructure according to the declared state, making deployments repeatable and auditable.
Deployment Manager integrates with core GCP services including Compute Engine, BigQuery, and Cloud Storage. Not every GCP service is supported, so verify coverage before committing to it as your sole IaC tool. Deployment Manager itself is free to use; standard charges apply for any resources it provisions.
When to use it: If your workloads are primarily GCP-native and you want a managed IaC solution without introducing a third-party tool like Terraform, Deployment Manager is a natural starting point.
2. Google Cloud Anthos
Migrating legacy applications to the cloud is rarely straightforward. Simply lifting and shifting onto VMs sacrifices many cloud benefits — scalability, agility, and cost optimization among them. Google Cloud Anthos addresses this challenge by providing a cloud-agnostic container platform that bridges on-premises infrastructure and Google Cloud.
At its core, Anthos is built on:
- Google Kubernetes Engine (GKE) — Manages container clusters in Google Cloud.
- GKE On-Prem — Extends GKE to an organization’s existing on-premises hardware for hybrid architectures.
- Istio — Handles service mesh capabilities including traffic management, observability, and security between services.
Anthos also includes additional management services such as Anthos Config Management (for consistent policy enforcement across clusters), Traffic Director (a global traffic management plane), and Stackdriver (now part of Google Cloud’s operations suite) for monitoring and logging.
A key capability of Anthos is its ability to convert existing VM images into containers before deployment, helping teams modernize legacy applications without a full rewrite. This makes it practical for enterprises that cannot afford to rebuild applications from scratch.
When to use it: Anthos is best suited for enterprises running hybrid or multi-cloud architectures that need a consistent operational model across environments.
3. Google Access Transparency
Cloud customers regularly raise concerns about what cloud provider administrators can see and do within their environments. Google Access Transparency addresses this directly by giving customers near-real-time visibility into actions taken by Google personnel on their cloud resources.
Access Transparency logs record details such as:
- Which Google administrator accessed a resource
- What action was taken
- The justification provided for access
- The affected resource
At the time of the original article, Access Transparency worked with six GCP services: Compute Engine, App Engine, Cloud Storage, Persistent Disk, Cloud Key Management Service, and Cloud Identity and Access Management. Google has continued to expand integration over time — check current documentation to confirm which services are covered for your use case.
Beyond passive monitoring, Access Transparency logs can be fed into existing SIEM and event management tooling. This makes it possible to incorporate provider-side access events into your existing audit and compliance workflows, reducing the manual effort associated with audit preparation.
When to use it: Organizations in regulated industries — finance, healthcare, government — or those with strict compliance requirements will benefit most from enabling Access Transparency as part of their overall GCP security posture.
4. Firebase Realtime Database
Firebase Realtime Database is a cloud-hosted NoSQL database that synchronizes data in real time across all connected clients. Data is stored as a JSON tree and pushed to clients instantly whenever changes occur, making it well-suited for applications requiring live updates — chat applications, collaborative tools, and live dashboards, for example.
Firebase Realtime Database supports the following client environments:
- iOS (Swift/Objective-C SDK)
- Android SDK
- JavaScript SDK
- REST API
Its primary limitation is its flat JSON tree structure: it does not handle deeply hierarchical or highly relational data well, and it is not optimized for large volumes of data or complex querying. Firebase Realtime Database is also limited to the availability zones within a single region, which constrains its fault tolerance compared to multiregional alternatives.
The service includes an offline mode, allowing apps to read and write data locally when network connectivity is unavailable, with automatic synchronization when connectivity is restored.
5. Google Cloud Firestore
Cloud Firestore is Google’s more recent NoSQL document database, designed to overcome the limitations of Firebase Realtime Database for complex, scalable applications. Where Firebase stores data as a flat JSON tree, Firestore uses a document-collection model: data is stored in documents (each containing typed field-value pairs), and documents are organized into collections. Subcollections allow hierarchical data structures that Firebase Realtime Database cannot easily accommodate.
Firestore supports server-side development via SDKs for:
- Node.js
- Java
- Python
- Go
In addition to these server SDKs, Firestore also supports the same mobile and web client SDKs as Firebase, making it viable across the full application stack.
Firestore is a multiregional service, providing higher availability and resilience than Firebase Realtime Database. It supports up to 1 million concurrent connections and 10,000 writes per second per database (based on documented limits at the time of writing — verify current quotas in the GCP documentation).
Both Firebase Realtime Database and Cloud Firestore support offline data access. Each uses its own security rules language for access control, giving developers granular control over read and write permissions at the document or collection level.
Firebase vs. Firestore: How to Choose
| Criteria | Firebase Realtime Database | Cloud Firestore |
|---|---|---|
| Data model | JSON tree | Document-collection |
| Hierarchical data | Limited | Supported via subcollections |
| Availability | Single region | Multi-region |
| Server-side SDKs | Limited | Node.js, Java, Python, Go |
| Offline support | Yes | Yes |
| Best for | Simple real-time data, small datasets | Complex apps, large-scale production workloads |
If your project requirements make the choice difficult, Google allows both databases to coexist within the same Firebase project, enabling a gradual migration or hybrid approach.
Summary
Google Cloud offers a broad and growing set of tools for DevOps engineers and cloud architects. Cloud Deployment Manager streamlines infrastructure provisioning with native GCP IaC. Anthos brings Kubernetes-based hybrid and multi-cloud capability to enterprises modernizing legacy workloads. Access Transparency addresses the growing demand for provider-side auditability. And Firebase Realtime Database alongside Cloud Firestore cover a spectrum of NoSQL use cases from simple real-time sync to complex, globally distributed data storage.
Evaluating each tool against your specific reliability, scalability, and compliance requirements will help you make the right choices for your GCP environment.
Original source: searchcloudcomputing.techtarget.com