A comprehensive guide comparing the ideal use cases and limitations of Google Cloud and third-party data analytics, processing, and database services.
Author: Michaël Bettan
Services Covered: 23
01
Data Warehousing & Data Lakes
BigQuery
Serverless (no-ops), highly-scalable and cost-effective multi-cloud Data Warehouse where you can run SQL queries over vast amounts of data
Ideal For
OLAP workloads
Data Warehousing
Structured data
Immediate consistency
Optimized for columnar storage and large-scale reads
Avoid
Low latency reqs.
Non-structured data
Non-relational SQL
Store large objects
Dataplex
Intelligent data fabric that helps organize, manage, and govern data across Google Cloud services.
Ideal For
Data discovery and metadata management
Data lineage tracking
Data quality monitoring
Data governance and access control
Avoid
Projects with limited data complexity
Lack of need for centralized data management
02
Data Processing & Integration
Dataflow
Apache Beam Managed Service for unified stream and batch data processing patterns. Automated infrastructure provisioning and cluster management with horizontal autoscaling.
Ideal For
Streaming Analytics
Real-time AI for Predictive Analytics or Fraud Detection
Sensor and log data process for System Health
ETL pipelines
Unified stream and batch processing
Avoid
Wrangling capability
Low Data Volume or Velocity
Spark-exclusive workloads
Legacy tooling/teams (against change)
Very low latency (< seconds)
Small datasets
Simpler Data Processing needs
Dataproc
Managed Apache Spark and Hadoop service. Fast and easy-to-use. Dataproc provisions big and small clusters rapidly, supports many popular job types, and is integrated with other Google Cloud services (e.g., GCS, Stackdriver)
Fully managed, cloud-native data integration service.
Ideal For
ETL/ELT processes
Data pipelines
Data transformation
Avoid
Simple data transformations
Limited budget
03
Messaging & Orchestration
Pub/Sub
Event-driven asynchronous messaging service that decouples senders (producing events) and receivers (processing events). Allows for secure and highly available communication between independently written applications. Unified messaging, global presence, push & pull-style subscriptions, replicated storage and guaranteed at-least-once message delivery, encryption of data at rest/transit, easy-to-use REST API
Ideal For
Data streaming from various processes or devices
Balancing workloads in network clusters
Queue can efficiently distribute tasks
Implementing asynchronous workflows
Reliability improvement- in case zone failure
Distributing event notifications
Refreshing distributed caches
Logging to multiple systems
Avoid
Kafka requirements
Budget constraint
Strict Ordering Requirements Across Multiple Publishers
Very large messages (prefer Cloud Storage for payload)
Synchronous request-response scenarios
Exactly-once delivery guarantees
Vendor-lock-in concerns
Rich Ecosystem and Integrations
High Throughput and Low Latency
Composer
Managed Apache Airflow service for orchestrating workflows.
Ideal For
Data pipelines
ETL processes
Workflow automation
Avoid
Simple, non-complex workflows
Real-time data processing
Datastream
Serverless change data capture and replication service that lets you synchronize data stored in supported sources to a variety of destinations. This enables real-time analytics, database migration, and event-driven architectures with minimal operational overhead.
Ideal For
Change Data Capture (CDC)
Database replication and synchronization
Populating data warehouses/lakes in near real-time
Avoid
Situations requiring complex transformations or joins before data lands in the destination
04
Relational Databases
Cloud SQL
Fully-managed relational database service supporting MySQL, PostgreSQL and MS SQL Server. Designed for relational data (record-based): tables, rows and columns, and super structured data. SQL compatible and can update fields.
Ideal For
SQL DB scaling vertically for structured data
Relational data (tables, rows, columns)
OLTP workloads
Structured data
MySQL, PostgreSQL, SQL Server compatibility
Managed database service
Avoid
RDBMS + large scale OLAP workloads
NoSQL data
Extremely high throughput
Low-latency transactions
Databases larger than 64TB
non-relational, highly nested, or unstructured (e.g., documents, graphs, key-value pairs).
Massive Global Scalability Needs
Strict Horizontal Scaling
Fully serverless database
Cloud Spanner
Real time transaction store, horizontally autoscale and always available.
Ideal For
Relational database
Structured data
Vertical + Horizontal scaling
Strong consistency
Transactional reads and writes → mission critical workloads
Avoid
Budget constraint
Data that is not relational or structured
Open-source RDBMS preference
When strong consistency and high availability are not critical
Small-Scale or Low-Traffic Applications
Vendor Lock-In Concerns
Lack of Need for Strong Consistency and ACID Transactions
AlloyDB for Postgres
A fully managed PostgreSQL-compatible database service for your most demanding enterprise workloads. AlloyDB combines the best of Google with PostgreSQL, for superior performance, scale, and availability.
Ideal For
PostgreSQL compatible
High performance OLTP
Automated scaling
Reduced operational overhead
Avoid
Non-relational data
Workloads not requiring high throughput
MariaDB
A community-developed, commercially supported fork of the MySQL relational database management system, intended to remain free and open-source software under the GNU General Public License
Ideal For
Community-developed and commercially supported
Open-source (GNU GPL)
Relational database with SQL support
ACID compliant
Avoid
Less scalable than some NoSQL solutions
Can be complex to manage for very large deployments
05
NoSQL Databases
Bigtable
A managed, scalable NoSQL, wide column database service
Ideal For
High-throughput, low-latency NoSQL workloads
Time-series & IoT data
Petabyte-scale operational data
HBase legacy migration
Real time data ingestion
Key-based reads
Low-latency reads and writes of individual records within massive datasets.
Avoid
Complex OLAP queries requiring SQL joins
Small datasets (< 1TB)
Relational data modeling
Replacement for MongoDB
Store large objects
Strong consistency requirements
Cloud Firestore
A scalable, fully-managed, NoSQL document database.
Document-oriented (suitable for various data types)
IoT data structures
low-latency reads
Avoid
Transactions requiring strong ACID properties
Complex joins
Cassandra
A highly scalable and distributed wide-column NoSQL database. It's designed to handle large volumes of data across multiple servers, providing high availability and fault tolerance. Cassandra prioritizes write performance and is well-suited for applications with massive data growth and unpredictable data structures.
Complex queries and joins across multiple partitions.
Strict ACID guarantees
CouchDB
A NoSQL, document-oriented database that stores data in JSON format, with a focus on scalability and availability. It uses Multi-Version Concurrency Control (MVCC) for consistency and offers a RESTful HTTP API to interact with the database.
Ideal For
Distributed environments: highly effective for distributed applications due to its built-in replication and synchronization across multiple nodes
Offline-first applications: CouchDB is ideal for applications that need to work offline, with automatic synchronization once connectivity is restored (e.g., mobile apps).
Document-based data (user profiles, product catalogs, or logs)
Avoid
High-performance transactional systems: eventual consistency model is not suited for applications that require strong ACID-compliant transactions, like banking or inventory systems.
Complex querying and analytics: If you require complex queries, joins, or analytical workloads, relational databases or specialized analytical databases
06
Storage & Caching
Cloud Storage
Global, secure and scalable object store. Fully-managed and highly-reliable object/blob storage.
Ideal For
Eventual consistency
Objects and blobs
Storing unstructured data (images, videos, text, logs, backups)
Data lake implementation
Cost-effective storage for large dataset
Serving static website content
Avoid
IoT
Spiky streaming data
Cloud Memorystore
A cache and better suited to storing key-value data for applications that need low latency access to data.
Ideal For
Caching
Session management
Real-time analytics
High-performance computing
Avoid
Data persistence as primary requirement
Large datasets that don't fit in memory
Redis
An open-source, in-memory data store. It's often called a "data structures server" because it allows you to store and manipulate data in various formats like strings, lists, sets, and hashes.