Google Cloud and SQL, MySQL & NoSQL?

Databases are an essential part of daily operations for today’s workforce. Whether for marketing, accounting, shipping, logistics, or virtually any other department, quality data architecture makes a huge difference in productivity and workload management.

If you’re interested in expanding your database capabilities using the dynamic power of Google Cloud Platform, here’s everything you need to know about Google Cloud and SQL, MySQL, and NoSQL.

What Are SQL, MySQL & NoSQL?

SQL stands for “structured query language.” It’s a programming language used to communicate with and manipulate data held in a relational database management system.

Simply put, SQL helps users to identify and retrieve specific query data points within a database using search functionality. SQL is incredibly versatile and relatively user-friendly, making it a great option for complex queries.

MySQL is an open-source relational database management system used to create and change data quickly. It allows users to modify, delete, and store data in an organized way whereas SQL is mostly used for queries and database operation.

NoSQL is a non-relational database that does not use SQL. In general, NoSQL databases tend to be snappy and responsive thanks to their simpler data structure. NoSQL databases also tend to be more scalable than their SQL counterparts.

Understanding the Differences Between SQL, MySQL, and NoSQL Databases

To get the most out of your data, it’s important to understand the differences between SQL, MySQL, and NoSQL databases. Let’s dig in a little deeper. 

SQL

• Query programming language that manages relational database management systems
• Used to query and operate databases
• Has a standard format and does not receive many updates
• Supports a single storage engine
• Vertically scalable, can increase load on a single server
• Table based design
• Not an open-source language, support available from reputable SQL vendors but not community support 
• Rigid, inserting a new column or field affects interface and design

MySQL

• Relational database management system that uses SQL
• Used to modify, store, and delete data in an organized way
• Has many variants and receives updates frequently
• Supports multiple storage engines and plug-in storage
• Vertically scalable, can increase load on a single server
• Table based design 
• Open-source platform with support available from a large community of users
• Rigid, inserting a new column or field affects interface and design

NoSQL

• Uses a dynamic schema for unstructured data, allowing data to be incorporated without a defined structure 
• Horizontally scalable, more traffic can be accommodated by adding more servers to the NoSQL database
• Has no standard query language
• Graph databases, key-value pairs, or document-oriented database design
• Support available from a smaller community of independent NoSQL developers
• Flexible, new columns and fields can be inserted without impacting interface and design

How to Monitor and Analyze Database Performance

Once you’ve decided which programming language is best for your database, you’ll need to spend some time implementing a dedicated database monitoring and analytics procedure.

Here are some proactive ways that you can identify any issues in your database before they develop into major obstructions to your workflow.

Monitor resource availability and consumption

Using a database performance analyzer, check that all databases are online and operational at regular intervals. A good database performance analyzer should automatically alert you of any outages.

You should also check for resource consumption of infrastructure-related resources like CPU, memory disk, and network. High availability of resources will generally translate to higher database performance.

Measure throughput

Establish baseline metrics for the amount of work your database is doing under normal conditions, such as completed transactions per second. Monitor your database performance and identify any deviations from average throughput.

Monitor slow-running queries 

Try to identify which database queries take the longest to run and analyze them for improvement. Tools like query analysis and tuning advisors can reveal long wait times, excess resource consumption, or missing indexes. Try auditing any unused large tables or bloated indexes to minimize the occurrence of slow queries.

Track changes

Any new version of a modern application might cause database objects to be dropped, modified, or added without your knowledge. These new data sources can radically alter the functionality of databases, and need to be monitored for impact. 

You can either initiate a throughput baseline after any changes and compare the database before and after, or you can monitor database schema changes as they unfold using database logs.

Monitor logs

By examining your database logs, you can learn all the queries running in the database and how long each query takes to complete. All logs from the database environment should be collected including system-generated logs, slow query logs, scheduled task logs, backup logs, and more. 

It’s no secret that Google Cloud Platform can skyrocket the efficiency of your databases. The power of services like Google Cloud Datastore serverless document database and Cloud Bigtable’s enterprise-grade NOSQL database, for example, give organizations the ability to build apps and manage data storage on their terms.  

In fact, some of the most prominent businesses in the world have enjoyed transformational success in their organizations using Google Cloud with SQL, MySQL, and NoSQL server databases.

Examples of successful cases where organizations have used Google Cloud with SQL, MySQL, and NoSQL databases include:

Wayfair

Online furniture retailer Wayfair leveraged the power of Google Cloud database services to quickly transition from their on-premises data centers running SQL to Google Cloud without interrupting their operations.

Using Google Cloud’s multiple database options, like Cloud SQL, Spanner, and PostgreSQL, Wayfair was able to give their developers and engineers the freedom to work they want in the context of Wayfair applications and infrastructure.

BBVA

Multinational financial services company Banco Bilbao Vizcaya Argentaria, S.A. (BBVA) is one the largest financial institutions in the world. Google Cloud services helped BBVA determine which database architecture options were best for their organization.

The relational database services of Cloud SQL gave BBVA the speed, ease of maintenance, and centralized control features they needed for their internal strategy. 

AutoTrader

Online automotive marketplace AutoTrader needed to transition away from on-premise infrastructure and onto the cloud. Google Cloud SQL alleviated the headaches of cloud database maintenance for AutoTrader, providing behind the scenes support for upgrades, backups, and patches, which enabled AutoTrader’s engineers to spend more time fine-tuning database performance. 

Trust Promevo

At Promevo, we help you harness the robust capabilities of Google to accelerate the growth of your company and give you the momentum you need to achieve your most ambitious business goals.

As your trusted service partner, Promevo supports your business with a comprehensive suite of services, including:

• Advanced Automation and Precision Control
• End-to-end Solutions Specific to Your Needs
• Advisory Workshops 
• Certifications and Google Expertise

With our expert consultation, comprehensive support, and exceptional service from end-to-end, you can drive productivity and accelerate the growth of your business.

Frequently Asked Questions

Can I use SQL in Google Cloud?

Google Cloud SQL is compatible with MySQL, PostgreSQL, or a standard SQL server.

Is Google Cloud SQL good?

Google Cloud SQL is a premier cloud database service that gives users high performance and scalability with an easy-to-use interface.