Best Practices for Selecting a Database on Google Cloud Platform

By Dishani Sen

In our previous blog, we gently introduced GCP for data engineering and shared a lite edition of best practices, when it comes to data storage, security and machine learning. In this one, we will dive deep into choosing a database on GCP.

The importance of a database in data engineering cannot be overstated. Data engineering is the backbone of any data-driven organisation, responsible for designing, building, and maintaining the infrastructure and systems that enable efficient and effective data processing and analysis. A well-chosen and well-designed database plays a central role in the success of data engineering efforts

Migrating to Google Cloud Platform (GCP) can bring numerous benefits to an organisation, including improved scalability, agility, and cost-effectiveness. However, the success of a migration largely depends on choosing the correct database for your specific needs.

How to choose your database on GCP?

Google Cloud Platform (GCP) offers a wide array of database services, each tailored to specific use cases. In this blog, we'll explore the best practices for selecting the ideal database on GCP to meet the needs of your technical projects. Let's jump in!

Understand Your Data and Use Case:

Before making any decisions, take the time to understand your data and the requirements of your application. Consider the volume of data, the read and write patterns, the required data structure, and any specific performance or compliance needs. Different databases excel in distinct scenarios, so a thorough assessment of your use case is the first step to a successful choice.

Choose Between Relational and NoSQL Databases:

GCP provides both relational and NoSQL database options. Relational databases, like Cloud SQL (MySQL, PostgreSQL) and Cloud Spanner, are suitable for structured data with complex relationships, ensuring ACID (Atomicity, Consistency, Isolation, Durability) transactions and strong consistency. NoSQL databases, such as Cloud Firestore, Cloud Datastore, and Bigtable, are best suited for large-scale, unstructured or semi-structured data with high scalability and eventual consistency.

Consider Scalability:

Scalability is a critical factor when choosing a database on GCP. Assess whether your application requires horizontal or vertical scalability. NoSQL databases generally offer better horizontal scalability, allowing you to add more nodes to handle increased traffic. On the other hand, some relational databases can be vertically scaled by upgrading the instance's hardware, but this approach has its limits.

Evaluate Performance Requirements:

Performance is another crucial aspect. Depending on your application, you might need high-throughput reads and writes or low-latency queries. For demanding workloads, consider databases with in-memory caching capabilities like Cloud Memorystore or high-speed read and write access like Cloud Bigtable.

Factor in Geographic Distribution:

If your application needs to serve users across the globe, consider a globally distributed database. Google Cloud offers features like multi-region and regional configurations, which can help you achieve low-latency access and better disaster recovery strategies.

Security and Compliance:

Ensure that the chosen database meets your security and compliance needs. GCP provides robust security features, such as encryption at rest and in transit, IAM (Identity and Access Management) controls, and audit logging. If your application handles sensitive data, compliance with industry standards like GDPR or HIPAA might be mandatory.

Cost Optimisation:

Cost is a significant factor when operating databases in the cloud. Review the pricing models for different database options and identify the most cost-effective solution that meets your requirements. Additionally, consider automated scaling options that can help you optimise costs based on usage patterns.

Evaluate Database Management and Operations:

Consider the level of control and management you require. Fully managed databases like Cloud SQL and Cloud Firestore handle most administrative tasks, allowing your team to focus on application development. However, if you need more control and customisation, self-managed options like Compute Engine instances running databases might be more suitable.

Commonly Used Databases on GCP

Here's an overview on some of the commonly used databases on GCP:

  1. Cloud SQL: Cloud SQL is a fully managed relational database service that supports popular database engines like MySQL, PostgreSQL, and SQL Server. It is an excellent choice for traditional database applications and workloads that require ACID compliance and strong consistency. Cloud SQL offers automated backups, scaling options, and seamless integration with other GCP services.
  2. BigQuery: BigQuery is a serverless, fully managed data warehouse and analytics platform. It is designed to handle large-scale, columnar datasets and allows users to perform ad-hoc SQL queries for analytics purposes. With its blazing-fast performance and automatic scaling, BigQuery is a go-to solution for data analysts and data engineers dealing with big data and complex analytical workloads.
  3. Cloud Spanner: Cloud Spanner is a globally-distributed, horizontally-scalable database service that provides both relational and NoSQL capabilities. It combines the benefits of traditional relational databases with the scalability of NoSQL databases. Cloud Spanner offers strong consistency and global ACID transactions, making it an excellent choice for mission-critical, globally-distributed applications.
  4. Cloud Firestore: Cloud Firestore is a fully managed NoSQL database that allows developers to build real-time applications with ease. It offers seamless synchronization across devices and platforms, making it ideal for mobile and web applications. Cloud Firestore's real-time data syncing and automatic scaling make it a popular choice for applications requiring low-latency data access.
  5. Cloud Datastore: Cloud Datastore is a NoSQL document database that provides horizontal scalability and automatic replication. It is suitable for semi-structured data and offers ACID transactions at the entity level. Cloud Datastore is a versatile database that can handle various use cases, from web applications to gaming backends.
  6. Cloud Bigtable: Cloud Bigtable is a high-performance, NoSQL wide-column store database. It is designed to handle massive amounts of data with low-latency access, making it ideal for time-series data, IoT applications, and analytical workloads. Cloud Bigtable's scalability and performance make it a preferred choice for applications that require large-scale data storage and processing.


The following table provides a high-level comparison of the major features of each database service:

FeatureCloud SQLBigQueryCloud SpannerBigtableCloud DatastoreFirestore
Database TypeRelationalData WarehouseRelational & NoSQLNoSQLNoSQLNoSQL
Use CaseTraditional DB AppsAnalytics, OLAPGlobally-distributed, mission-critical appsTime-series DataWeb & Mobile AppsReal-time Apps
ScalingVertical, ManualHorizontal, AutoHorizontal, AutoHorizontal, AutoHorizontal, AutoHorizontal, Auto
Data Size LimitUp to 30TBPetabytesPetabytesPetabytesNot SpecifiedNot Specified
Data ModelStructuredColumnar, Semi-structuredStructured & Semi-structuredWide-columnSemi-structuredSemi-structured
Consistency ModelACIDEventually ConsistentStrongly ConsistentEventual ConsistencyEventual ConsistencyStrongly Consistent
Query LanguageSQLSQLSQLNoSQL APIGQL (Datastore Query Language)GQL (Firestore Query Language)
Indexing SupportYesYesAutomatic & ManualYesAutomaticAutomatic
Cost ModelProvisionedOn-DemandOn-DemandOn-DemandOn-DemandOn-Demand
Real-time AnalyticsLimitedYesLimitedNoLimitedYes
Geospatial SupportLimitedLimitedYesNoYesYes
Native IntegrationsSome GCP ServicesMany GCP ServicesMany GCP ServicesLimitedMany GCP ServicesMany GCP Services
Use of PartitioningYesYesYesYesYesYes
Secondary IndexesYesNoYesYesYesYes

Choosing the right database on Google Cloud Platform is a critical decision that can significantly impact the performance, scalability, and cost-effectiveness of your application. By understanding your data, evaluating performance needs, and considering factors like scalability, security, and compliance, you can make an informed decision that aligns with your technical project's objectives. It is crucial to always keep in mind that each database option has its strengths and weaknesses, so take the time to select the one that best suits your unique use case.

Happy data managing!