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Score 8.3 out of 101
69 Ratings
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Score 8.6 out of 101

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Likelihood to Recommend

Amazon Aurora

Amazon Aurora should be considered for those who need a fast and reliable cloud database service. It includes the main features of a MySQL database, using the latest trends in architectural principals. It ensures out-of-the-box fault-tolerance and high scalability. It is also very important for those who don’t want to worry about features and security updates. As the price is higher than a standard MySQL for instance, it should only be considered for higher applications where performance and scalability is really important.
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Google BigQuery

- If you are using Google Analytics and there is huge data that is getting streamed every day then you must have Big Query and use it for analysis. It is not only helpful for analysis but also for debugging your Google Analytics implementations.- For analyzing a small dataset you don't need Big Query you can use normal MySQL on your own premises. Analyzing on Un-structured data is not possible with Big Query.
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Feature Rating Comparison

Database-as-a-Service

Amazon Aurora
Google BigQuery
7.7
Automatic software patching
Amazon Aurora
Google BigQuery
10.0
Database scalability
Amazon Aurora
Google BigQuery
7.5
Automated backups
Amazon Aurora
Google BigQuery
7.6
Database security provisions
Amazon Aurora
Google BigQuery
9.1
Monitoring and metrics
Amazon Aurora
Google BigQuery
5.3
Automatic host deployment
Amazon Aurora
Google BigQuery
6.6

Pros

  • Amazon Aurora has high availability, since the customer started to use it, the database never had to be left out of service.
  • Amazon Aurora provides frequent and automated upgrades, which makes our database system always up to date on the latest features and security practices
  • Since Amazon Aurora uses MySQL as its core database, it is very easy to find specialized people to work. Amazon’s relational database management system also makes it very easy to expand and create new databases
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  • Big Query is fast and based on the cloud you can run your query on a huge dataset. Huge means data in TB's. This also reduces the company cost to build that kind of infrastructure to store data.
  • Not specific to Google Analytics but you can import data from different sources for analysis purpose and use the power of the cloud to run the query.
  • Not much time to learn - You don't need any special skills, just SQL and you can use Big Query for your use. Learning SQL is not a big task you can learn it in a week.
  • Big Query refrence schema and different sample query are available to practice on queries.
  • Google also provide sample dataset to use then purchase Big Query.
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Cons

  • The cost of Amazon Aurora when compared to a simple MySQL instance is considerably higher, so we really need to look at and run some performance tests to compare if the performance improvements are worth the extra cost.
  • Although backup restores are a rare feature to use, when we need them it is always painful to restore our data. We are always searching for a database service to provide new and innovating features in terms of data recovery. For instance, being able to search on backup information to see if the needed data is there. It is a very common need to compare the hot data with the backup data, for example to fix some database data that a malfunction application wrongly updated.
  • Since aurora is an Amazon relational database service there is no way to run a dev database on a local storage for tests and development.
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  • If accidentally the return dataset would be humongous (you forget to LIMIT), you cannot really stop a running query, and it'll probably be billed
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Alternatives Considered

In the end, we went with Amazon Aurora due to the decent performance and cost. Cost was determined in two ways for us: 1) no additional license is required (such as using SQL Server or Oracle) and 2) the ability to cut down on needed resources to maintain the system from the maintenance perspective due to the built-in maintenance capabilities. Amazon Aurora is also based on MySQL (soon to include PostgreSQL ) which allowed my team to quickly and easily move our existing MySQL servers to a faster system.
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Spinning up, provisioning, maintaining and debugging a Hadoop solution can be non-trivial, painful. I'm talking about both GCE based or HDInsight clusters. It requires expertise (+ employee hire, costs). With BigQuery if someone has a good SQL knowledge (and maybe a little programming), can already start to test and develop. All of the infrastructure and platform services are taken care of. Google BigQuery is a magnitudes simpler to use than Hadoop, but you have to evaluate the costs. BigQuery billing is dependent on your data size and how much data your query touches.
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Return on Investment

  • The main positive for my team is the time that has been freed up from the tasks of managing updates and fixing replication issues.
  • A negative for myself as a database administrator is removal of features that were available in Mysql. Examples include 1) the use of the storage engines other than InnoDB (such as the Federated Storage Engine), 2) certain administrative privileges such as ability to export to csv file and easy ability to kill processes. I seem to also forget they removed the built-in kill ability and you must use their own provided kill functionality.
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  • Increased employee efficiency: interactive analytical queries can run much faster on BigQuery, problems are discovered/identified quicker.
  • KPI reports are delivered to management much quicker.
  • Overall reduced cost for BlueCava.
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Pricing Details

Amazon Aurora

General
Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No
Additional Pricing Details

Google BigQuery

General
Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No
Additional Pricing Details