Google Cloud SQL Reviews

We use Google Cloud SQL for various use cases within our organization. It serves as a repository for organizational data, facilitating the management of multiple requests and ensuring the persistence of critical data. This operational data is then made available to users, allowing them to access specific orders and metrics, such as the number of orders processed yesterday and the proportion of those processed through Cloud services. Additionally, we leverage Google Cloud SQL to generate reports based on this operational data.

Google Cloud SQL provides SQL storage.

The most challenging part is dealing with legacy data from your old systems and migrating it into the new setup, but once you've completed the data migration, it becomes quite convenient to use.

I have been using Google Cloud SQL for two years.

Google Cloud SQL is a stable product, and it offers reliable support for Postgres databases.

The scalability of Google Cloud SQL is commendable. With the appropriate setup, we can efficiently manage operations, adjusting the number of read instances as needed. While write queries perform admirably, read queries may face slower performance due to the retrieval of large datasets. However, increasing the number of read instances gives us the necessary scalability for processing these queries.

There are 500 connections to the database shared between system users.

The initial setup is straightforward.

We are tracking because we have an integrated system. We have some integration with external third parties. We also track the data that we get from external sources. In Postgres, we can handle maybe ten million records per month. We use partitioning and healing features to manage this growth in data.

Google Cloud SQL provides fast and very short transactions when modifying our data, using Postgres as our database system across multiple ports. It performs queries for data and handles purchases based on those queries. The scalability with Postgres is impressive. Currently, we are in a beta phase, utilizing three instances. There were issues with write and read instances. Also, there were delays in data synchronization between the write and read columns. When data is written to the write instance, it's not always immediately available in the read instance, causing a delay of up to 20 seconds. 

We have the right systems to manage the necessary operations. Sometimes, we have one instance for handling requests; at other times, we might have more than two instances. Querying the correct data is fast. However, read queries can be slower due to the large amount of data they need to process. We use additional processing power from more instances to accommodate the required scale.

Google Cloud SQL is straightforward. The setup and configuration are easy, and managing the team is simple. We have one dedicated person per week to handle incidents or disturbances in the process and perform additional work. It's enough for maintenance and support.

As data accumulates, it's crucial to devise a strategy for managing its volume, which involves using historical tables, archiving, or partitioning. These methods are all useful. As the data volume increases, query execution times can slow down. Therefore, it's imperative to implement an effective indexing strategy. By optimizing indexes, partitioning, and other techniques, queries can be executed more efficiently, even when querying on multiple parameters.

There could be two or three read instances. The synchronization between the write and the read is very fast.

There are multiple advantages to Google Cloud SQL. Firstly, it operates in the cloud, making it accessible from anywhere. Unlike competitors, Google Cloud SQL is fully integrated into the cloud environment. You can quickly scale your resources up or down according to your needs, growing or decreasing. This flexibility allows you to stay aligned with current demands while optimizing costs. 

Overall, I rate the solution a nine out of ten.

We use it for our database applications.

What I like the most about Google Cloud SQL is that it handles the management, which allows us to concentrate on our applications.

We have not had any major issues with the solution. The only thing that could be better is the pricing. The best features of the solution are too expensive for our company's budget. Additionally, adding AI integration to Google Cloud SQL would be valuable, especially for security like fraud detection and monitoring login activities in the database. These features could boost data security.

I have been using Google Cloud SQL for a year.

The solution is stable.

Google Cloud SQL is scalable and user-friendly for scaling, thanks to its menu options and graphical interface. It is suitable for medium-sized businesses.

I have not had any reason to contact technical support so far.

We used Alibaba Cloud for SQL two years ago. We moved from Alibaba Cloud to Google Cloud SQL for better technology support. Hosting OpenShift was difficult on Alibaba, so we switched to Google Cloud.

It is very easy to install because it is only using the graphical user interface. The deployment only takes a few minutes. Maintenance is required mostly on the backup file, not the SQL instance itself.

When it comes to pricing and licensing for Google Cloud SQL we obtain licenses separately from third-party vendors for our application, while our database uses community-source licensing. The solution is paid for on a monthly basis.

My advice to people who are considering using Google Cloud SQl is to consider your budget and performance needs when picking Google Cloud services. If you have a big budget and need high performance, go for Google Cloud SQL. But if you're a small company with budget constraints, use VM instances with databases inside to save money. Overall, I would rate the solution an eight out of ten.

We are focusing on data pipeline creation and data migration. It involves continuous streaming of data from one place to Google Cloud to be analyzed immediately.

Knowledge graphs and data integration is very important for distribution for our business. To create a seamless data integration, the title integration of these databases with the data integration platforms is essential. This is what we would like to have in a future release.

We would also like to have artificial intelligence capabilities. We are exploring the leverage of Machine Learning Algorithms for workload prediction and AI capabilities would assist in this regard.

We have been using this solution for two years. 

This is a stable solution and offers good performance. 

As this product is cloud based, it is scalable. 

The technical support is good.

Positive

The initial setup is not that complex. 

From a financial perspective, Google Cloud SQL is on the cheaper side.

We did compare it with other solutions from Amazon and Microsoft. We received a special incentive from Google. That's why we went for Google Cloud SQL. 

I would rate this solution an eight out of ten. 

Our primary use case is for lift and shift migrations. For example, from on-prem MySQL to the cloud.

It supports different databases, like Postgres and MySQL. It allows you even to migrate data from one DB to another one, for example, from MySQL to Postgres, et cetera.

The solution is stable and reliable. 

It's a scalable product.

The solution has been issue-free.

We haven't had any issues and have not come across missing features. 

The monitoring part could be better. People who are not very, very technical and who may be more on the business side, find that it can be a little difficult to understand the metrics. That probably can be simplified.

We've only used the solution for four or five months. It hasn't been that long just yet. It's been much less than a year. 

It's quite stable. It's reliable. There are no bugs or glitches and it doesn't crash or freeze. The performance is good. 

We can easily scale the product as needed. It's not a problem.

We have eight to ten users at this time. 

We don't have any plans to scale at this time, however, it may be something we consider in the future. 

We've had no issues so far. We have not required technical support.

We previously used Postgres. Managing the DBs was a little tedious, so we migrated them to Cloud SQL.

There is no setup process per se. It's on the cloud and therefore there is nothing to upload or download.

We do have a technical team involved in the product, however, we don't have to worry about deployment or maintenance tasks. 

We have not needed any outside assistance. 

The is no license required. 

As a cloud product, we're always on the latest version of the solution. it self-updates. 

I'd recommend the solution. It is well suited for this lift and shift migration, especially if you are accessing everything within your VM. They have a Cloud SQL proxy and those things. It's easier to maintain and you don't really have to worry about the operation side of it.

I'd rate the solution eight out of ten.

Our primary use case involves deploying the platform within a private VPC environment, where we heavily rely on its managed services for product deployment and ongoing data management tasks.

The product’s automatic scaling feature is particularly advantageous for accommodating large volumes of data. Additionally, the query insights provide valuable analytics for optimizing performance and identifying potential issues.

They could improve documentation and dashboard stability for efficient user experience and database management.

We have been using Google Cloud SQL for approximately two years.

I would rate the product a seven in terms of stability, with occasional minor issues that are promptly addressed by Google's technical support.

The platform is highly scalable. It offers robust features. Our clients primarily consist of enterprise businesses, as it caters to their complex infrastructure and security needs. 

I rate the scalability an eight.

The technical support team offers responsive assistance, although there is room for improvement in certain areas.

Neutral

The setup process was straightforward, involving simple configuration steps within the Google Cloud environment.

Our deployments are typically within a private VPC environment, and the duration depends on the scale and architecture of the project, ranging from a few days to weeks for larger implementations.

The return on investment is significant for us, as Google Cloud SQL is integral to our product offerings and data management infrastructure.

While the platform’s pricing may be higher, it aligns with industry standards, considering the quality of service and features provided.

Google Cloud SQL enhances our AI-driven projects by providing features like query optimization and scalability for efficiently processing large datasets. Additionally, recent updates, such as the optimization feature for Germany, further contribute to improving performance and supporting our AI initiatives.

Despite minor areas for improvement, it offers robust features and scalability that make it a valuable asset for businesses seeking efficient data management solutions.

Overall, I rate it an eight.

As a product manager and managed service provider for the solution, I help build AI using Google Cloud SQL, focusing mainly on Compute Machine. My organization uses Google SQL to host our app because the main package service is on cloud servers.

It is not the cool features that I find valuable, it is the stability of Google Cloud Platform, which is why we chose the platform over hosting our own servers.

It is hard to do logging with the solution. It is difficult to take out the exact log from the servers.

I have been using Google SQL for about three years.

I rate the stability an eight out of ten since we still have to check on the storage.

We are hosting around 10,000 end users on our app.

According to our engineer, the initial setup was okay. We have ten engineers on our team who worked on deployment.

We need a maximum of two back-end engineers to maintain the solution.

The solution is affordable. I rate it a six out of ten. We have a monthly license.

I cannot say Google Cloud SQL is good or bad because they have a good ecosystem. Google Cloud SQL is a good choice if you are using other Google services, but it is okay not to use the solution if you are not using the Google Cloud Compute Engine.

It is good to use compared to other services, so we are sticking with it.

We use the solution due to the fact that our server instance, the data center instance, is in GCP.

The speed is very good. 

It's useful in terms of the simplicity of doing backups. 

You can have redundant databases for reports, for posts, for example. 

It's SQL. SQL is so easy if you know something about databases. It's easy to learn. 

I like the cloud aspect.

The pricing of the solution seems reasonable. 

The solution is stable. 

Google seems to be working on a data lake solution, and we are happy with that.

Other clouds, like Azure, have some specific products, and Google doesn't have something like that. For example, if you work in Google, you can use Dialogflow to make a chatbot. However, the Azure methodology or system is more powerful for those chatbots. 

In the case of Google, they need to work on a more easy interface for users.

Azure is easier to learn. I don't know why. I like the Azure system, even though I'm a user of Google. I don't like it when on the screen you can't follow the steps clearly. The user interface just isn't as intuitive. When it comes to Google Cloud, in order to do something, you need to read more maybe. It requires more research and learning. 

I've been using the solution for two to three years.

The product is stable, however, maybe they need support for more databases. I remember some problems with the PostgreSQL integration with BigQuery or something like that. I don't remember now, however, from what I recall, we needed more support for other databases in GCP SQL.

We have maybe 200 people using the solution right now in our organization.

We also use PostgreSQL in GCP and BigQuery too for extract information from Jira.

The licensing is charged yearly, however, I don't manage the cost of the cloud in my company. I only work with Atlassian products. We are users of GCP for our instance. When I work with data in Google Cloud, our data people say the cost is not a problem. I can make a dump of databases daily and store extractions in buckets, for example, every day, full data, all days. And it's not a problem, cost-wise.

We are a customer and an end-user.

I'd rate the solution an eight out of ten.

We use it for managing workflows, workflow data, and state management. We'll use it for tenant metadata information and keeping it. 

We have some BI use cases as well. We are in the automation industry, so, in general, whatever is required for automation, we keep all that information. 

We are using standard SQLs features only, so we don't use any very specific feature assets.

One feature we've found pretty interesting was their support, which is multi-region support. They have good multi-region failure support, and we can just set up there and read replicas directly and we can fall back. We are quite pleased with the results.

The initial setup is straightforward. 

It's stable. 

The solution can scale. 

The purging of the data could be better. We don't have a good mechanism to deal with the old data, especially when the data grows. We see latency issues, so we were forced to introduce an in-memory store. Otherwise, the solution is fine.

The enterprise edition is quite expensive. 

I've used the solution for a few months. 

The stability is good. There are no bugs or glitches. It doesn't crash or freeze. 

Currently, our DevOps team is using the product, and we have 20 to 30 people using it. We have plans to increase usage. 

While it is scalable, I don't yet have practical experience with the process. 

The solution is very easy to set up. It's not overly complex. 

The deployment is very fast. It was only a few minutes.

I'd rate the solution three and a half out of five in terms of ease of setup.

I'd rate it higher if there was general serverless support or good best practices by way of documentation for setting everything up. We are using the enterprise model. Having best practices and serverless support would make it a bit better.

Right now, we don't need much maintenance as we're still in the beginning stages and don't have much production workload.

We were able to handle the setup ourselves. The documentation was good enough to follow so that the setup was easy.

The pricing is interesting. For the enterprise edition, it was almost three times the cost of the standard edition. There should be some more clarity on that. It was not very clear why it's so much more or what the differences are. It requires better documentation. 

We're a customer and end-user.

We moved quite recently in production. In terms of the development run, we were on AWS. Now, some of the workloads we are moving to GCP even though we have been an Amazon shop for a long time. 

We are working on the latest version of the solution. 

I'd rate the solution eight out of ten.