What users are saying about

Amazon Aurora

42 Ratings

Google BigQuery

64 Ratings

Amazon Aurora

42 Ratings
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Score 8.1 out of 101

Google BigQuery

64 Ratings
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Score 8.8 out of 101

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Likelihood to Recommend

Amazon Aurora

When already using a relational database, either MySQL or PostgreSQL, the change to Amazon Aurora should be very straightforward. The main benefits you get are cost efficiency and ease with regards to the storage, as it scales with you, and managing clusters including failovers are made very straightforward for you.If you are looking for a database which can scale up and down quickly with demand, Aurora may not be the best fit. However, there is now an Amazon Aurora Serverless service which attempts to address this requirement. I do not have any experience with it, so cannot comment further - but it is possible it will fit your use-case.
Andrew Raines profile photo

Google BigQuery

It can be an extremely good fit if:1. You have data in Google Cloud Storage2. You don't want to deal with the hassle of spinning up a Hadoop clusteror you have especially large dataset and you don't want to deal with scaling-out logic. Also, costs might be high.It's not good for you if you have some specific algorithm which cannot be phrased in the BogQuery SQL flavor.It maybe unnecessary if near-real-time results are not too important factor, and it doesn't matter if a query returns in 2-3 seconds or 20-30. If you already have some Hadoop infrastructure, HIVE or Spark, your existing solution might be cheaper.There are best practices which can decrease your costs a lot (for e.g. how many columns your query involves, how well do you filter your data in the query).
Csaba Toth profile photo

Feature Rating Comparison

Database-as-a-Service

Amazon Aurora
Google BigQuery
7.6
Automatic software patching
Amazon Aurora
Google BigQuery
10.0
Database scalability
Amazon Aurora
Google BigQuery
7.4
Automated backups
Amazon Aurora
Google BigQuery
7.7
Database security provisions
Amazon Aurora
Google BigQuery
8.2
Monitoring and metrics
Amazon Aurora
Google BigQuery
5.5
Automatic host deployment
Amazon Aurora
Google BigQuery
6.9

Pros

  • The MySQL compatibility meant we didn't have to change anything in our system which used to run on a MySQL database. It was a very simple configuration change to point at the new instance once set up
  • Much better performance than our previous MySQL database (hosted on AWS RDS) for lower costs due to the way storage is managed
  • Storage management is much more simple as it grows and shrinks with you without having to allocate and deallocate storage to the database
Andrew Raines profile photo
  • Processing of huge volumes of data enabled us to provide strategic insights by understanding the facts and realities.
  • Detailed Audience analysis enabled us to achieve better targeting for digital media and marketing campaigns
  • Personalization: We are able to achieve personalization by marrying, stitching, and processing huge volume of data.
Gaurav Gautam profile photo

Cons

  • Without direct access to the instances it isn't possible to do a few things you'd be able to do if you were running your own database server, but this is rarely an issue
Andrew Raines profile photo
  • SQL syntax is not exactly same as ANSI SQL so there is a learning curve. Traditional SQL queries cannot execute in BigQuery which limits portabiltiy of the code.
  • Limitation on visualization: We can improve visualization in data studio by bringing in the ability to support complex functions/formulas such as Tableau can do.
Gaurav Gautam profile photo

Alternatives Considered

Using cloud-based services such as RDS or Aurora take all the hassle out of managing database servers yourself. It also gives you the flexibility to easily spin up and down additional instances and as when required. Where Aurora outshines RDS is in terms of performance - we saw around 2-5x improvement in query read times across the board.
Andrew Raines profile photo
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.
Csaba Toth profile photo

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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  • By using BigQuery for visualization and personalization, we were able to achieve 5% higher conversion rate.
Gaurav Gautam profile photo

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