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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    CouchDB

    Score6 out of 10
    N/AApache 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

    Score8.8 out of 10
    N/AGoogle'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
    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
    CouchDBGoogle BigQuery
    Considered Both Products
    Apache
    No answer on this topic
    Google
    No answer on this topic
    Key User Insights
    Would buy again
    No answers on this topic
    98%
    Would buy again
    64 Answers
    Delivers good value for the price
    No answers on this topic
    97%
    Delivers good value for the price
    56 Answers
    Happy with the feature set
    No answers on this topic
    97%
    Happy with the feature set
    63 Answers
    Lived up to sales and marketing promises
    No answers on this topic
    100%
    Lived up to sales and marketing promises
    42 Answers
    Implementation went as expected
    No answers on this topic
    100%
    Implementation went as expected
    59 Answers
    Features
    CouchDBGoogle BigQuery
    NoSQL Databases
    Comparison of NoSQL Databases features of Apache CouchDB and Google BigQuery
    Feature
    Apache CouchDB
    7.9
    2 Ratings
    8% 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 Apache CouchDB and Google BigQuery
    Feature
    Apache CouchDB
    -
    Ratings
    Google BigQuery
    8.5
    80 Ratings
    1% above category average
    Automatic software patching00 Ratings8.017 Ratings
    Database scalability00 Ratings9.079 Ratings
    Automated backups00 Ratings8.524 Ratings
    Database security provisions00 Ratings8.873 Ratings
    Monitoring and metrics00 Ratings8.675 Ratings
    Automatic host deployment00 Ratings8.013 Ratings
    Best Alternatives
    CouchDBGoogle BigQuery
    Small Businesses
    IBM Cloudant
    Score7.4 out of 10
    MongoDB Atlas
    Score7.8 out of 10
    Medium-sized Companies
    IBM Cloudant
    Score7.4 out of 10
    Azure Database
    Score8.8 out of 10
    Enterprises
    IBM Cloudant
    Score7.4 out of 10
    Google Cloud SQL
    Score8.7 out of 10
    All AlternativesView all alternativesView all alternatives
    User Ratings
    CouchDBGoogle BigQuery
    Likelihood to Recommend
    9.0
    (10 ratings)
    9.0
    (79 ratings)
    Likelihood to Renew
    9.0
    (9 ratings)
    8.1
    (5 ratings)
    Usability
    8.0
    (1 ratings)
    6.6
    (6 ratings)
    Availability
    -
    (0 ratings)
    7.3
    (1 ratings)
    Performance
    -
    (0 ratings)
    6.4
    (1 ratings)
    Support Rating
    -
    (0 ratings)
    4.8
    (11 ratings)
    Implementation Rating
    9.0
    (1 ratings)
    -
    (0 ratings)
    Configurability
    -
    (0 ratings)
    6.4
    (1 ratings)
    Contract Terms and Pricing Model
    -
    (0 ratings)
    10.0
    (1 ratings)
    Ease of integration
    -
    (0 ratings)
    7.3
    (1 ratings)
    Product Scalability
    -
    (0 ratings)
    7.3
    (1 ratings)
    Professional Services
    -
    (0 ratings)
    8.2
    (2 ratings)
    User Testimonials
    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.
    Incentivized
    Read full review
    Google
    Event-based data can be captured seamlessly from our data layers (and exported to Google BigQuery). When events like page-views, clicks, add-to-cart are tracked, Google BigQuery can help efficiently with running queries to observe patterns in user behaviour. That intermediate step of trying to "untangle" event data is resolved by Google BigQuery. A scenario where it could possibly be less appropriate is when analysing "granular" details (like small changes to a database happening very frequently).
    Incentivized
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    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.
    Incentivized
    Read full review
    Google
    • Realtime integration with Google Sheets.
    • GSheet data can be linked to a BigQuery table and the data in that sheet is ingested in realtime into BigQuery. It's a live 'sync' which means it supports insertions, deletions, and alterations. The only limitation here is the schema'; this remains static once the table is created.
    • Seamless integration with other GCP products.
    • A simple pipeline might look like this:-
    • GForms -> GSheets -> BigQuery -> Looker
    • It all links up really well and with ease.
    • One instance holds many projects.
    • Separating data into datamarts or datameshes is really easy in BigQuery, since one BigQuery instance can hold multiple projects; which are isolated collections of datasets.
    Incentivized
    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.
    Incentivized
    Read full review
    Google
    • Please expand the availability of documentation, tutorials, and community forums to provide developers with comprehensive support and guidance on using Google BigQuery effectively for their projects.
    • If possible, simplify the pricing model and provide clearer cost breakdowns to help users understand and plan for expenses when using Google BigQuery. Also, some cost reduction is welcome.
    • It still misses the process of importing data into Google BigQuery. Probably, by improving compatibility with different data formats and sources and reducing the complexity of data ingestion workflows, it can be made to work.
    Incentivized
    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.
    Incentivized
    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.
    Incentivized
    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
    Incentivized
    Read full review
    Google
    I think overall it is easy to use. I haven't done anything from the development side but an more of an end user of reporting tables built in Google BigQuery. I connect data visualization tools like Tableau or Power BI to the BigQuery reporting tables to analyze trends and create complex dashboards.
    Incentivized
    Read full review
    Reliability and Availability
    Apache
    No answers on this topic
    Google
    I have never had any significant issues with Google Big Query. It always seems to be up and running properly when I need it. I cannot recall any times where I received any kind of application errors or unplanned outages. If there were any they were resolved quickly by my IT team so I didn't notice them.
    Incentivized
    Read full review
    Performance
    Apache
    No answers on this topic
    Google
    I think Google Big Query's performance is in the acceptable range. Sometimes larger datasets are somewhat sluggish to load but for most of our applications it performs at a reasonable speed. We do have some reports that include a lot of complex calculations and others that run on granular store level data that so sometimes take a bit longer to load which can be frustrating.
    Incentivized
    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.
    Incentivized
    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
    Incentivized
    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
    Incentivized
    Read full review
    Google
    PowerBI can connect to GA4 for example but the data processing is more complicated and it takes longer to create dashboards. Azure is great once the data import has been configured but it's not an easy task for small businesses as it is with BigQuery.
    Incentivized
    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
    Scalability
    Apache
    No answers on this topic
    Google
    We have continued to expand out use of Google Big Query over the years. I'd say its flexibility and scalability is actually quite good. It also integrates well with other tools like Tableau and Power BI. It has served the needs of multiple data sources across multiple departments within my company.
    Incentivized
    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.
    Incentivized
    Read full review
    Google
    • Previously, running complex queries on our on-premise data warehouse could take hours. Google BigQuery processes the same queries in minutes. We estimate it saves our team at least 25% of their time.
    • We can target our marketing campaigns very easily and understand our customer behaviour. It lets us personalize marketing campaigns and product recommendations and experience at least a 20% improvement in overall campaign performance.
    • Now, we only pay for the resources we use. Saved $1 million annually on data infrastructure and data storage costs compared to our previous solution.
    Incentivized
    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.