DigitalOcean Droplets vs. Google BigQuery

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
DigitalOcean Droplets
Score 9.0 out of 10
N/A
DigitalOcean's Droplets is designed to help the user spin up a virtual machine in just 55 seconds. Standard, General Purpose, CPU-Optimized, or Memory-Optimized configurations provide flexibility to build, test, and grow an app from startup to scale.N/A
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
DigitalOcean DropletsGoogle 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
DigitalOcean DropletsGoogle 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
DigitalOcean DropletsGoogle BigQuery
Top Pros
Top Cons
Features
DigitalOcean DropletsGoogle BigQuery
Database-as-a-Service
Comparison of Database-as-a-Service features of Product A and Product B
DigitalOcean Droplets
-
Ratings
Google BigQuery
8.4
71 Ratings
4% below category average
Automatic software patching00 Ratings8.017 Ratings
Database scalability00 Ratings9.070 Ratings
Automated backups00 Ratings8.524 Ratings
Database security provisions00 Ratings8.864 Ratings
Monitoring and metrics00 Ratings8.266 Ratings
Automatic host deployment00 Ratings8.013 Ratings
Best Alternatives
DigitalOcean DropletsGoogle BigQuery
Small Businesses
Amazon Elastic Compute Cloud (EC2)
Amazon Elastic Compute Cloud (EC2)
Score 8.8 out of 10
IBM Cloudant
IBM Cloudant
Score 7.7 out of 10
Medium-sized Companies
SAP on IBM Cloud
SAP on IBM Cloud
Score 9.6 out of 10
IBM Cloudant
IBM Cloudant
Score 7.7 out of 10
Enterprises
SAP on IBM Cloud
SAP on IBM Cloud
Score 9.6 out of 10
IBM Cloudant
IBM Cloudant
Score 7.7 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
DigitalOcean DropletsGoogle BigQuery
Likelihood to Recommend
9.9
(7 ratings)
8.8
(71 ratings)
Likelihood to Renew
-
(0 ratings)
7.8
(3 ratings)
Usability
-
(0 ratings)
7.7
(5 ratings)
Support Rating
-
(0 ratings)
7.7
(10 ratings)
Contract Terms and Pricing Model
-
(0 ratings)
10.0
(1 ratings)
Professional Services
-
(0 ratings)
8.2
(2 ratings)
User Testimonials
DigitalOcean DropletsGoogle BigQuery
Likelihood to Recommend
DigitalOcean
DigitalOcean Droplets are the best choice for developers teams that need reliable Linux servers to deploy their projects, the ability to create a droplet for testing purposes then destroy it, and only get charged for the few hours used makes the chances of messing up very slim. DigitalOcean Droplets is a great solution because the servers are scalable and the process of adding more resources like CPU or RAM to an existing droplet takes only a few minutes and once a server is scaled up it can also be scaled down if necessary which is perfect for supporting a temporary peak in traffic for example.
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Google
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.
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Pros
DigitalOcean
  • Simplicity to scale services--the interface is very quick and effective to use
  • Reliability--this is key for us, as any downtime effects our reputation
  • Keeps the costs down--hosting our own equivalent infrastructure would cost a lot more
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Google
  • 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.
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Cons
DigitalOcean
  • In terms of an availability zone, they have limitations not available in most of the geographical locations.
  • No live support is available which can cause problem if you have outage.
  • Number of service is quite limited.
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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.
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Likelihood to Renew
DigitalOcean
No answers on this topic
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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Usability
DigitalOcean
No answers on this topic
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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Support Rating
DigitalOcean
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.
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Alternatives Considered
DigitalOcean
DigitalOcean Droplets is continuously evolving to be more and more powerful. It has great features and has low cost options, which is really great for developers. Its CDN, Loadbalancer, etc. make it a good place to host a high-traffic application. Moroever, DigitalOcean Droplets has a nonprofit program that helps nonprofit sites to run their infrastructure, which is tremendous and no competitor of DigitalOcean Droplets does that.
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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.
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Contract Terms and Pricing Model
DigitalOcean
No answers on this topic
Google
None so far. Very satisfied with the transparency on contract terms and pricing model.
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Professional Services
DigitalOcean
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.
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Return on Investment
DigitalOcean
  • Digital Ocean has been great helping us move web apps to the cloud
  • Digital Ocean has been really helpful when hiring contractors
  • The interface could use some work, but overall its not terrible
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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.
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.