Overview
ProductRatingMost Used ByProduct SummaryStarting Price
CouchDB
Score 6.2 out of 10
N/A
Apache CouchDB is an HTTP + JSON document database with Map Reduce views and bi-directional replication. The Couch Replication Protocol is implemented in a variety of projects and products that span computing environments from globally distributed server-clusters, over mobile phones to web browsers.N/A
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
Apache CouchDBGoogle BigQuery
Editions & Modules
No answers on this topic
Standard edition
$0.04 / slot hour
Enterprise edition
$0.06 / slot hour
Enterprise Plus edition
$0.10 / slot hour
Offerings
Pricing Offerings
CouchDBGoogle BigQuery
Free Trial
NoYes
Free/Freemium Version
NoYes
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Apache CouchDBGoogle BigQuery
Top Pros
Top Cons
Features
Apache CouchDBGoogle BigQuery
NoSQL Databases
Comparison of NoSQL Databases features of Product A and Product B
Apache CouchDB
7.9
2 Ratings
11% below category average
Google BigQuery
-
Ratings
Performance8.02 Ratings00 Ratings
Availability8.52 Ratings00 Ratings
Concurrency8.52 Ratings00 Ratings
Security6.02 Ratings00 Ratings
Scalability8.02 Ratings00 Ratings
Data model flexibility7.02 Ratings00 Ratings
Deployment model flexibility9.02 Ratings00 Ratings
Database-as-a-Service
Comparison of Database-as-a-Service features of Product A and Product B
Apache CouchDB
-
Ratings
Google BigQuery
8.4
50 Ratings
4% below category average
Automatic software patching00 Ratings8.117 Ratings
Database scalability00 Ratings8.850 Ratings
Automated backups00 Ratings8.524 Ratings
Database security provisions00 Ratings8.743 Ratings
Monitoring and metrics00 Ratings8.445 Ratings
Automatic host deployment00 Ratings8.113 Ratings
Best Alternatives
Apache CouchDBGoogle BigQuery
Small Businesses
IBM Cloudant
IBM Cloudant
Score 8.4 out of 10
SingleStore
SingleStore
Score 9.8 out of 10
Medium-sized Companies
IBM Cloudant
IBM Cloudant
Score 8.4 out of 10
SingleStore
SingleStore
Score 9.8 out of 10
Enterprises
IBM Cloudant
IBM Cloudant
Score 8.4 out of 10
SingleStore
SingleStore
Score 9.8 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Apache CouchDBGoogle BigQuery
Likelihood to Recommend
9.0
(10 ratings)
8.6
(50 ratings)
Likelihood to Renew
9.0
(9 ratings)
7.0
(1 ratings)
Usability
8.0
(1 ratings)
9.4
(3 ratings)
Support Rating
-
(0 ratings)
10.0
(9 ratings)
Implementation Rating
9.0
(1 ratings)
-
(0 ratings)
Contract Terms and Pricing Model
-
(0 ratings)
10.0
(1 ratings)
Professional Services
-
(0 ratings)
8.2
(2 ratings)
User Testimonials
Apache CouchDBGoogle BigQuery
Likelihood to Recommend
Apache
Great for REST API development, if you want a small, fast server that will send and receive JSON structures, CouchDB is hard to beat. Not great for enterprise-level relational database querying (no kidding). While by definition, document-oriented databases are not relational, porting or migrating from relational, and using CouchDB as a backend is probably not a wise move as it's reliable, but It may not always be highly available.
Read full review
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.
Read full review
Pros
Apache
  • It can replicate and sync with web browsers via PouchDB. This lets you keep a synced copy of your database on the client-side, which offers much faster data access than continuous HTTP requests would allow, and enables offline usage.
  • Simple Map/Reduce support. The M/R system lets you process terabytes of documents in parallel, save the results, and only need to reprocess documents that have changed on subsequent updates. While not as powerful as Hadoop, it is an easy to use query system that's hard to screw up.
  • Sharding and Clustering support. As of CouchDB 2.0, it supports clustering and sharding of documents between instances without needing a load balancer to determine where requests should go.
  • Master to Master replication lets you clone, continuously backup, and listen for changes through the replication protocol, even over unreliable WAN links.
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
Apache
  • NoSQL DB can become a challenge for seasoned RDBMS users.
  • The map-reduce paradigm can be very demanding for first-time users.
  • JSON format documents with Key-Value pairs are somewhat verbose and consume more storage.
Read full review
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.
Read full review
Likelihood to Renew
Apache
Because our current solution S3 is working great and CouchDB was a nightmare. The worst is that at first, it seemed fine until we filled it with tons of data and then started to create views and actually delete.
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
Apache
Couchdb is very simple to use and the features are also reduced but well implemented. In order to use it the way its designed, the ui is adequate and easy. Of course, there are some other task that can't be performed through the admin ui but the minimalistic design allows you to use external libraries to develop custom scripts
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
Read full review
Support Rating
Apache
No answers on this topic
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
Implementation Rating
Apache
it support is minimal also hw requirements. Also for development, we can have databases replicated everywhere and the replication is automagical. once you set up the security and the rules for replication, you are ready to go. The absence of a model let you build your app the way you want it
Read full review
Google
No answers on this topic
Alternatives Considered
Apache
It has been 5+ years since we chose CouchDB. We looked an MongoDB, Cassandra, and probably some others. At the end of the day, the performance, power potential, and simplicity of CouchDB made it a simple choice for our needs. No one should use just because we did. As I said early, make sure you understand your problems, and find the right solution. Some random reading that might be useful: http://www.julianbrowne.com/article/viewer/brewers-cap-theorem https://www.couchbase.com/nosql-resources/why-nosql\ https://www.infoq.com/articles/cap-twelve-years-later-how-the-rules-have-changed
Read full review
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.
Read full review
Contract Terms and Pricing Model
Apache
No answers on this topic
Google
None so far. Very satisfied with the transparency on contract terms and pricing model.
Read full review
Professional Services
Apache
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
Apache
  • It has saved us hours and hours of coding.
  • It is has taught us a new way to look at things.
  • It has taught us patience as the first few weeks with CouchDB were not pleasant. It was not easy to pick up like MongoDB.
Read full review
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.
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.