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75 Ratings
69 Ratings
Top Rated
75 Ratings
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Score 8.4 out of 101
69 Ratings
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Score 8.6 out of 101

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Likelihood to Recommend

Amazon RDS

Amazon Relational Database Service is great when you need a Database solution that can work for you very fast. It is also a great solution when you need a system that can scale to handle very large amounts of data. You can get set up with a complex, large database eco-system in minutes/hours, and have the best security and backups scheduled. The alternative to doing this would typically be hiring one or more very experienced database professionals for not just the implementation, but also on staff to handle the periodic maintenance necessary.
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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 RDS
Google BigQuery
7.7
Automatic software patching
Amazon RDS
Google BigQuery
10.0
Database scalability
Amazon RDS
Google BigQuery
7.5
Automated backups
Amazon RDS
Google BigQuery
7.6
Database security provisions
Amazon RDS
Google BigQuery
9.1
Monitoring and metrics
Amazon RDS
Google BigQuery
5.3
Automatic host deployment
Amazon RDS
Google BigQuery
6.5

Pros

  • Getting the data in and out of our databases, especially with the close integration RDS has with S3 buckets.
  • Less overall management of the database servers. Our DBAs are now do more DBA work than server admin work, which moves them higher on the value chain.
  • Performance and Scalability.
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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

  • RDS is much more expensive than MySQL+EC2.
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  • BigQuery does impose quite a few limits on the higher end queries, although they are entirely understandable. For example, very large 'GROUP BY' clauses can sometimes fail with a "Resources Exceeded" error, as the distributed computational nature of BigQuery forces all of that data to be compiled on a single machine, and when that machine runs out of memory it throws the aforementioned error. You can increase your Billing Tier to complete these queries, though.
  • When getting data out of BigQuery, there are also quite a few limits. For example, if you are returning a large result set, you are essentially forced to write the results to a table and then export that table to Google Cloud Storage to then be downloaded. However, during the export process, if the table is large, Google will split that table into many smaller blocks that need to be reassembled.
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Alternatives Considered

Automated snap-shotting every 24 hours is, again something that I could just set up in minutes with a few clicks, though we also backup on cron jobs to elsewhere, and, because of our industry we have a HUGE "forensic logs" that initially live in the database but get archived off every seven days. (We literally log every single API call between our front-end and our application servers, and the data structures that the APIs return.
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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

  • Combined with metabase, RDS has enabled numerous non-technical members of our team to run and share sophisticated queries.
  • The engineering time spent on worrying about RDS has been minimal. We set it up once over a year ago and haven't touched it since.
  • We're relying on metabase and RDS more for operations-related dashboards, because it's generally faster to iterate on a simple query than a complex web application.
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  • If BigQuery fits your business needs (see best scenarios) it can yield great ROI.
  • You maybe able to answer questions with ease which would take much more effort with other big data query technologies (HIVE, Spark, ...). You miay pay some costs.
  • Following some best practices (how to construct and limit your queries) can decrease your costs.
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Pricing Details

Amazon RDS

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