Google BigQuery vs. IBM Analytics Engine

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Google BigQuery
Score 8.6 out of 10
N/A
Google's BigQuery is part of the Google Cloud Platform, a database-as-a-service (DBaaS) supporting the querying and rapid analysis of enterprise data.
$0
per month
IBM Analytics Engine
Score 8.9 out of 10
N/A
IBM BigInsights is an analytics and data visualization tool leveraging hadoop.N/A
Pricing
Google BigQueryIBM Analytics Engine
Editions & Modules
Standard edition
$0.04 / slot hour
Enterprise edition
$0.06 / slot hour
Enterprise Plus edition
$0.10 / slot hour
No answers on this topic
Offerings
Pricing Offerings
Google BigQueryIBM Analytics Engine
Free Trial
YesNo
Free/Freemium Version
YesNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Google BigQueryIBM Analytics Engine
Considered Both Products
Google BigQuery

No answer on this topic

IBM Analytics Engine
Chose IBM Analytics Engine
We initially wanted to go with Google BigQuery, mainly for the name recognition. However, the pricing and support structure led us to seek alternatives, which pointed us to IBM. Apache Spark was also in the running, but here IBM's domination in the industry made the choice a …
Chose IBM Analytics Engine
We have tried the following solutions in the past and I must say they can't compare to IBM Analytics Engine:

Chose IBM Analytics Engine
IBM Analytics is a great tool and a welcome addition to your overall IBM strategy. I think in cases of tools like this, you either go with what your platform works best with or you go completely different with a 3rd party, like Snowflake. We are an Azure shop and just happened …
Top Pros
Top Cons
Features
Google BigQueryIBM Analytics Engine
Database-as-a-Service
Comparison of Database-as-a-Service features of Product A and Product B
Google BigQuery
8.5
51 Ratings
3% below category average
IBM Analytics Engine
-
Ratings
Automatic software patching8.117 Ratings00 Ratings
Database scalability8.851 Ratings00 Ratings
Automated backups8.524 Ratings00 Ratings
Database security provisions8.844 Ratings00 Ratings
Monitoring and metrics8.546 Ratings00 Ratings
Automatic host deployment8.113 Ratings00 Ratings
Best Alternatives
Google BigQueryIBM Analytics Engine
Small Businesses
SingleStore
SingleStore
Score 9.7 out of 10

No answers on this topic

Medium-sized Companies
SingleStore
SingleStore
Score 9.7 out of 10
Cloudera Manager
Cloudera Manager
Score 9.7 out of 10
Enterprises
SingleStore
SingleStore
Score 9.7 out of 10
Azure Data Lake Storage
Azure Data Lake Storage
Score 8.4 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Google BigQueryIBM Analytics Engine
Likelihood to Recommend
8.6
(51 ratings)
9.5
(9 ratings)
Likelihood to Renew
7.0
(1 ratings)
-
(0 ratings)
Usability
9.4
(3 ratings)
-
(0 ratings)
Support Rating
10.0
(9 ratings)
-
(0 ratings)
Contract Terms and Pricing Model
10.0
(1 ratings)
-
(0 ratings)
Professional Services
8.2
(2 ratings)
-
(0 ratings)
User Testimonials
Google BigQueryIBM Analytics Engine
Likelihood to Recommend
Google
For organizations looking to avoid the overhead of managing infrastructure, BigQuery's server-less architecture allows teams to focus on analyzing data without worrying about server maintenance or capacity planning. Small projects or startups with limited data analysis needs and tight budgets might find other solutions more cost-effective. Also, it is not suitable for OLTP systems.
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IBM
  • Well suited for my big data related project or a static data set analysis especially for uploading huge dataset to the cluster.
  • But had some issues with connecting IoT real-time data and feeding to Power BI. It might be my understanding please take it as a mere comment rather than a suggestion.
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Pros
Google
  • 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.
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IBM
  • Jobs with Spark, Hadoop, or Hive queries are rapidly attained
  • Can collect, organize and analyze your data accurately
  • You can customize, for example, Spark or Hadoop configuration settings, or Python, R, Scala, or Java libraries.
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Cons
Google
  • Can't use it out of Google's cloud platform which is a minus point if you want a local setup.
  • Can be a little expensive to manage.
  • A little difficult to manage someone with less technical expertise as it requires you to have SQL knowledge of joins, CTEs etc.
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IBM
  • Easier pricing and plug-and-play like you see with AWS and Azure, it would be nice from a budgeting and billing standpoint, as well as better support for the administration.
  • Bundling of the Cloud Object Storage should be included with the Analytics Engine.
  • The inability to add your own Hadoop stack components has made some transfers a little more complex.
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Likelihood to Renew
Google
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.
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IBM
No answers on this topic
Usability
Google
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
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IBM
No answers on this topic
Support Rating
Google
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.
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IBM
No answers on this topic
Alternatives Considered
Google
Google's Firebase isn't a competitor but we had to use Google's BigQuery because Google's Firebase's database is limited compared to Google's BigQuery. Linking your Firebase project to BigQuery lets you access your raw, unsampled event data along with all of your parameters and user properties. Highly recommend connecting the two if you have a mobile app.
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IBM
We initially wanted to go with Google BigQuery, mainly for the name recognition. However, the pricing and support structure led us to seek alternatives, which pointed us to IBM. Apache Spark was also in the running, but here IBM's domination in the industry made the choice a no-brainer. As previously stated, the support received was not quite what we expected, but was adequate.
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Contract Terms and Pricing Model
Google
None so far. Very satisfied with the transparency on contract terms and pricing model.
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IBM
No answers on this topic
Professional Services
Google
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.
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IBM
No answers on this topic
Return on Investment
Google
  • Google BigQuery has had enormous impact in terms of ROI to our business, as it has allowed us to ease our dependence on our physical servers, which we pay for monthly from another hosting service. We have been able to run multiple enterprise scale data processing applications with almost no investment
  • Since our business is highly client focused, Google Cloud Platform, and BigQuery specifically, has allowed us to get very granular in how our usage should be attributed to different projects, clients, and teams.
  • Plain and simple, I believe the meager investments that we have made in Google BigQuery have paid themselves back hundreds of times over.
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IBM
  • This product has allowed us to gather analytics data across multiple platforms so we can view and analyze the data from different workflows, all in one place.
  • IBM Analytics has allowed us to scale on demand which allows us to capture more and more data, thus increasing our ROI.
  • The convenience of the ability to access and administer the product via multiple interfaces has allowed our administrators to ensure that the application is making a positive ROI for our business users and partners.
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