Likelihood to Recommend Amazon Relational Database Service is a perfect fit for everyone who is seeking for an high-performance cloud-based database service. No matter if Postgres, Oracle, or any other type of relational database. Amazon RDS is our first choice for any kind of database requirement in the cloud. Especially I like the scalability.
Read full review Google BigQuery really shines in scenarios requiring real-time analytics on large data streams and predictive analytics with its machine learning integration. Teams have been using it extensively all over. However, it may not be the best fit for organizations dealing with small datasets because of the higher costs. And also, it might not be the best fit for highly complex data transformations, where simpler or more specialized solutions could be more appropriate.
Read full review Pros Automated Database Management: We use it for streamlining routine tasks like software patching and database backups. Scalability on Demand: we use it to handle traffic spikes, scaling both vertically and horizontally. Database Engine Compatibility: It works amazingly with multiple database engines used by different departments within our organization including MySQL, PostgreSQL, SQL Server, and Oracle. Monitoring: It covers our extensive monitoring and logging, and also has great compatibility with Amazon CloudWatch Read full review Its serverless architecture and underlying Dremel technology are incredibly fast even on complex datasets. I can get answers to my questions almost instantly, without waiting hours for traditional data warehouses to churn through the data. Previously, our data was scattered across various databases and spreadsheets and getting a holistic view was pretty difficult. Google BigQuery acts as a central repository and consolidates everything in one place to join data sets and find hidden patterns. Running reports on our old systems used to take forever. Google BigQuery's crazy fast query speed lets us get insights from massive datasets in seconds. Read full review Cons It is a little difficult to configure and connect to an RDS instance. The integration with ECS can be made more seamless. Exploring features within RDS is not very easy and intuitive. Either a human friendly documentation should be added or the User Interface be made intuitive so that people can explore and find features on their own. There should be tools to analyze cost and minimize it according to the usage. Read full review It is challenging to predict costs due to BigQuery's pay-per-query pricing model. User-friendly cost estimation tools, along with improved budget alerting features, could help users better manage and predict expenses. The BigQuery interface is less intuitive. A more user-friendly interface, enhanced documentation, and built-in tutorial systems could make BigQuery more accessible to a broader audience. Read full review Likelihood to Renew We do renew our use of Amazon Relational Database Service. We don't have any problems faced with RDS in place. RDS has taken away lot of overhead of hosting database, managing the database and keeping a team just to manage database. Even the backup, security and recovery another overhead that has been taken away by RDS. So, we will keep on using RDS.
Read full review We have to use this product as its a 3rd party supplier choice to utilise this product for their data side backend so will not be likely we will move away from this product in the future unless the 3rd party supplier decides to change data vendors.
Read full review Usability I've been using AWS Relational Database Services in several projects in different environments and from the AWS products, maybe this one together to EC2 are my favourite. They deliver what they promise. Reliable, fast, easy and with a fair price (in comparison to commercial products which have obscure license agreements).
Read full review web UI is easy and convenient. Many RDBMS clients such as aqua data studio, Dbeaver data grid, and others connect. Range of well-documented APIs available. The range of features keeps expanding, increasing similar features to traditional RDBMS such as Oracle and DB2
Read full review Support Rating I have only had good experiences in working with AWS support. I will admit that my experience comes from the benefit of having a premium tier of support but even working with free-tier accounts I have not had problems getting help with AWS products when needed. And most often, the docs do a pretty good job of explaining how to operate a service so a quick spin through the docs has been useful in solving problems.
Read full review BigQuery can be difficult to support because it is so solid as a product. Many of the issues you will see are related to your own data sets, however you may see issues importing data and managing jobs. If this occurs, it can be a challenge to get to speak to the correct person who can help you.
Read full review Online Training the online training & digital content available on the web from AWS was having sufficient information to deploy and run the service
Read full review Alternatives Considered In a few words, we are just to confortable working with oracle and sql server. Using RDS add another layer of distributed database in order to backup everything we have in case of a disaster and also complies with authorities locally and internacionally. All database we use, are local in custom servers that we maintain, but we agree to expand this.
Read full review I have used
Snowflake and
DataGrip for data retrieval as well as Google BigQuery and can say that all these tools compete for head to head. It is very difficult to say which is better than the other but some features provided by Google BigQuery give it an edge over the others. For example, the reliability of Google is unmatchable by others. One thing that I really like is the ability to integrate Data Studio so easily with Google BigQuery.
Read full review Contract Terms and Pricing Model None so far. Very satisfied with the transparency on contract terms and pricing model.
Read full review Professional Services Google Support has kindly provide individual support and consultants to assist with the integration work. In the circumstance where the consultants are not present to support with the work, Google Support Helpline will always be available to answer to the queries without having to wait for more than 3 days.
Read full review Return on Investment RDS is costly and thus small business should avoid it as it might not be worthful (in ROI perspective) Downtime is very low and there are automated backups thus we dont have to worry much about technical stuff and can focus more on marketing and sales Due to various automated features such as automated backup etc we dont need a huge technical team thus reducing the cost of maintaining a huge technical team , Read full review Pricing has been very reasonable for us. The first 10 GB of storage is free each month and costs start at 2 cents per GB per month after that. For example, if you store 1 terabyte (TB) for a month, then the cost would be $20. Streaming data inserts start at 1 cent per 200 megabytes (MBs). The first 1 TB of queries is free, with additional analysis at $5 per TB thereafter. Meta data operations are free. Big Query helps reduce the bar for data analytics, ML and AI. BQ takes care of mundane tasks and streamlines for easy data processing, consumption. The most impressive thing is the ML and AI integration as SQL functions, so the need for moving data around is minimized. The visuals of ML models is very helpful to fine tune training, model building and prediction, etc. Read full review ScreenShots Google BigQuery Screenshots