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 8.7 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 Cloudant
Google 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
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 …
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.
Google BigQuery is great for being the central datastore and entry point of data if you're on GCP. It seamlessly integrates with other Google products, meaning you can ingest data from other Google products with ease and little technical knowledge, and all of it is near real-time. Being serverless, BigQuery will scale with you, which means you don't have to worry about contention or spikes in demand/storage. This can, however, mean your costs can run away quickly or mount up at short notice.
First and foremost - Google BigQuery is great at quickly analyzing large amounts of data, which helps us understand things like customer behavior or product performance without waiting for a long time.
It is very easy to use. Anyone in our team can easily ask questions about our data using simple language, like asking ChatGPT a question. This means everyone can find important information from our data without needing to be a data expert.
It plays nicely with other tools we use, so we can seamlessly connect it with things like Google Cloud Storage for storing data or Data Studio for creating visual reports. This makes our work smoother and helps us collaborate better across different tasks.
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.
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
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.
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.
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
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.
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.
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.
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
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.
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.
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.
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.
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.
