IBM Cloudant vs. Google BigQuery

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
IBM Cloudant
Score 8.4 out of 10
N/A
Cloudant is an open source non-relational, distributed database service that requires zero-configuration. It's based on the Apache-backed CouchDB project and the creator of the open source BigCouch project. Cloudant's service provides integrated data management, search, and analytics engine designed for web applications. Cloudant scales your database on the CouchDB framework and provides hosting, administrative tools, analytics and commercial support for CouchDB and BigCouch. Cloudant is often…
$1
per month per GB of storage above the included 20 GB
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.
$6.25
per TiB (after the 1st 1 TiB per month, which is free)
Pricing
IBM CloudantGoogle BigQuery
Editions & Modules
Standard
$1
per month per GB of storage above the included 20 GB
Standard
$75
per month 100 reads/second ; 50 writes/second ; 5 global queries/second
Lite
Free
20 reads/second ; 10 writes/second ; 5 global queries / second ; 1 GB of storage capacity
Standard
Included
per month 20 GB of storage
Standard edition
$0.04 / slot hour
Enterprise edition
$0.06 / slot hour
Enterprise Plus edition
$0.10 / slot hour
Offerings
Pricing Offerings
IBM CloudantGoogle BigQuery
Free Trial
YesYes
Free/Freemium Version
YesYes
Premium Consulting/Integration Services
YesNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
IBM CloudantGoogle BigQuery
Considered Both Products
IBM Cloudant

No answer on this topic

Google BigQuery
Chose Google BigQuery
Google BigQuery is less expensive to run and offers free storage of up to the first 10 GB of data. Google BigQuery is also easier (and faster) to get up and running. Unlike Snowflake, Google BigQuery does not require any manual scaling or performance tuning. Scaling is …
Top Pros
Top Cons
Features
IBM CloudantGoogle BigQuery
NoSQL Databases
Comparison of NoSQL Databases features of Product A and Product B
IBM Cloudant
9.4
21 Ratings
7% above category average
Google BigQuery
-
Ratings
Performance9.821 Ratings00 Ratings
Availability8.121 Ratings00 Ratings
Concurrency9.921 Ratings00 Ratings
Security9.821 Ratings00 Ratings
Scalability9.121 Ratings00 Ratings
Data model flexibility9.921 Ratings00 Ratings
Deployment model flexibility9.121 Ratings00 Ratings
Database-as-a-Service
Comparison of Database-as-a-Service features of Product A and Product B
IBM Cloudant
-
Ratings
Google BigQuery
8.4
51 Ratings
4% below category average
Automatic software patching00 Ratings8.117 Ratings
Database scalability00 Ratings8.851 Ratings
Automated backups00 Ratings8.524 Ratings
Database security provisions00 Ratings8.744 Ratings
Monitoring and metrics00 Ratings8.446 Ratings
Automatic host deployment00 Ratings8.113 Ratings
Best Alternatives
IBM CloudantGoogle BigQuery
Small Businesses
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Score 9.0 out of 10
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Score 9.8 out of 10
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Score 9.0 out of 10
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Score 9.8 out of 10
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Score 9.0 out of 10
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User Ratings
IBM CloudantGoogle BigQuery
Likelihood to Recommend
8.1
(45 ratings)
8.6
(51 ratings)
Likelihood to Renew
7.3
(1 ratings)
7.0
(1 ratings)
Usability
7.7
(5 ratings)
9.4
(3 ratings)
Availability
8.2
(1 ratings)
-
(0 ratings)
Performance
8.2
(1 ratings)
-
(0 ratings)
Support Rating
8.6
(4 ratings)
10.0
(9 ratings)
Online Training
7.3
(2 ratings)
-
(0 ratings)
Implementation Rating
8.2
(4 ratings)
-
(0 ratings)
Configurability
8.5
(3 ratings)
-
(0 ratings)
Contract Terms and Pricing Model
-
(0 ratings)
10.0
(1 ratings)
Product Scalability
9.6
(23 ratings)
-
(0 ratings)
Professional Services
-
(0 ratings)
8.2
(2 ratings)
Vendor pre-sale
9.1
(1 ratings)
-
(0 ratings)
User Testimonials
IBM CloudantGoogle BigQuery
Likelihood to Recommend
IBM
Our organization found Cloudant most suitable if One, a fixed pricing structure would make the most sense, for example in a situation where the project Cloudant is being used in makes its revenue in procurement or fixed retainer — thus the predictability of costs is paramount; Two, where you need to frequently edit the data and/or share access to the query engine to non-engineers — this is where the GUI shines.
Read full review
Google
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
IBM
  • For us, performance and scalability is the key, and Cloudant DB backed by CouchDB is scalable and performant.
  • IBM Cloudant dB is very easy to provision for sandbox, development, QA as well as production.
  • Support for Java for CouchDB app server analytics enables a greater control for over developers.
  • Schema free oriented very easy to program and build applications on it.
  • We love it!!
Read full review
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.
Read full review
Cons
IBM
  • It was only after we went with the cloud-based solution that IBM rolled out an on-premise version.
  • We found that a 3rd-party ODBC driver was required for a few applications that needed to pull data out of Cloudant.
  • The sales process was difficult because the salesperson we used was not as versed on Cloudant as I had hoped.
Read full review
Google
  • 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
IBM
the flexibility of NoSQL allow us to modify and upgrade our apps very fast and in a convenient way. Having the solution hosted by IBM is also giving us the chance to focus on features and the improvement of our apps. It's one thing less to be worried about
Read full review
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.
Read full review
Usability
IBM
It's mostly just a straight forward API to a data store. I knock one off for the full text search thing, but I don't need it much anyways. Also, the dashboard UI they give is pretty nice to use. It provides syntax-highlighting for writing views and queries are easy to test. I wish other DBs had a UI like this.
Read full review
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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Reliability and Availability
IBM
it is a highly available solution in the IBM cloud portfolio and hence we have never had any issues with the data base being available - we also do continuous replication to be on the safer side just in case some thing goes awry. We also perform twice a year disaster recovery tests.
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Google
No answers on this topic
Performance
IBM
very easy to get started and is very developer friendly given that it uses couchDB analytics. It is a cloud based solution and hence there is no hardware investment in a server and staging the server to get started and the associated delays/bureaucracy involved to get started. Good documentation is also available.
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Google
No answers on this topic
Support Rating
IBM
Very happy by the commitment given by the team which has been really good over the last 7 years of usage.
Read full review
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.
Read full review
Online Training
IBM
online resources are good enough to understand but there is nothing like testing. In our case, we discovered some not documented behavior that we take in count now. Also, the experience in NodeJs is critical. Also, take in count that most of the "good practices" with cloudant are not in online courses but in blogs and pages from independent developers
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Google
No answers on this topic
Implementation Rating
IBM
  • Test the architecture on CouchDB helped us to address initial design flaws.
  • The migration to Cloudant as such was very painless.
  • We have migrate our replication system to Cloudant Android Sync for mobile devices.
  • We have regular informal contact with the Cloudant leadership to discuss our use cases and implementation strategies.
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Google
No answers on this topic
Alternatives Considered
IBM
The feature-set, including security, is very comparable. Overall, IBM's services added to the product are mature and stable, although product support and engineers could be a little better. Global availability is improving, and Disaster Recover Capabilities are great. Overall, it's very comparable to MongoDB as a DBaaS offer, available globally and with great documentation.
Read full review
Google
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
IBM
No answers on this topic
Google
None so far. Very satisfied with the transparency on contract terms and pricing model.
Read full review
Scalability
IBM
The service scales incredibly well. As you would expect from CloudDB and IBM combination. The only reason I wouldn't score it a 10 is the fact that document trees can get nested and nested very quickly if you are attempting to do very complex datasets. Which makes your code that much more complex to deal. Its very possible we could find a solution to this problem with better database planning to begin with, but one of the reasons we chose a service over a self-hosted solution was so we could set it up quick and forget about it. So we weren't going to dedicate a team to architecture optimization.
Read full review
Google
No answers on this topic
Professional Services
IBM
No answers on this topic
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.
Read full review
Return on Investment
IBM
  • IBM Cloudant is very secure and we never have to worry about losing data/unauthorized access
  • It is one of the best data backup system and works well
  • Global availability means it is easy to connect to the nearest data center and this reduces load time which is great.
Read full review
Google
  • 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

Screenshot of Migrating data warehouses to BigQuery - Features a streamlined migration path from Netezza, Oracle, Redshift, Teradata, or Snowflake to BigQuery using the fully managed BigQuery Migration Service.Screenshot of bringing any data into BigQuery - Data files can be uploaded from local sources, Google Drive, or Cloud Storage buckets, using BigQuery Data Transfer Service (DTS), Cloud Data Fusion plugins, by replicating data from relational databases with Datastream for BigQuery, or by leveraging Google's data integration partnerships.Screenshot of generative AI use cases with BigQuery and Gemini models - Data pipelines that blend structured data, unstructured data and generative AI models together can be built to create a new class of analytical applications. BigQuery integrates with Gemini 1.0 Pro using Vertex AI. The Gemini 1.0 Pro model is designed for higher input/output scale and better result quality across a wide range of tasks like text summarization and sentiment analysis. It can be accessed using simple SQL statements or BigQuery’s embedded DataFrame API from right inside the BigQuery console.Screenshot of insights derived from images, documents, and audio files, combined with structured data - Unstructured data represents a large portion of untapped enterprise data. However, it can be challenging to interpret, making it difficult to extract meaningful insights from it. Leveraging the power of BigLake, users can derive insights from images, documents, and audio files using a broad range of AI models including Vertex AI’s vision, document processing, and speech-to-text APIs, open-source TensorFlow Hub models, or custom models.Screenshot of event-driven analysis - Built-in streaming capabilities automatically ingest streaming data and make it immediately available to query. This allows users to make business decisions based on the freshest data. Or Dataflow can be used to enable simplified streaming data pipelines.Screenshot of predicting business outcomes AI/ML - Predictive analytics can be used to streamline operations, boost revenue, and mitigate risk. BigQuery ML democratizes the use of ML by empowering data analysts to build and run models using existing business intelligence tools and spreadsheets.