DigitalOcean vs. Google BigQuery

DigitalOcean is a powerful tool with respect to the services and pricing that it offers. It is easier than other products and also provides servers that are inexpensive with great performance. DigitalOcean also offers additional add-ons such as additional IP addresses, scheduling of backups, etc. One of the best advantages is that it is efficient and is open source. Although, it is suited for a firm that is looking to cut down cost. Also, it is not suited for an organization where the dev/platform/DBA team is less experienced.

Incentivized

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.

Incentivized

• Ease of use - You can get set up with a new server in a matter of minutes. It doesn't get any easier than that.
• Support - The public forums are incredibly helpful as are the official help articles. I've never needed to contact the support team because of this. All of the information is at my fingertips.
• Pricing - We're only paying $10/mo for a solution that gives our customers more confidence in us and is a selling point for us.

Incentivized

• 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.

Incentivized

• Some products/services available on other Cloud providers aren't available, but they seem to be catching up as they add new products like Managed SQL DBs.
• While they have FreeBSD droplets (VMs), support for *BSD OSs is limited. I.e. the new monitoring agent only works on Linux.
• There are no regions available on South America.
• They don't seem to offer enterprise-level products, even basic ones as Windows Server, MS SQL Server, Oracle products, etc.

Incentivized

• 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.

Incentivized

I've been very happy with it for my purposes and I plan to continue to use DigitalOcean for the foreseeable future!

Incentivized

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

With DigitalOcean it is very easy to start up a server/droplet. They have several templates and server images to select from, and they have good instructions on how to get a server set up and started. The monitoring tools in the dashboard look good and are easy to understand.

Incentivized

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

Have not found a single second of down time myself. Superior availability.

Incentivized

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

Very quick response and high performance, you have to fine tune configurations on your machines though.

Incentivized

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

They have always been fast, and the process has been straight-forward. I haven't had to use it enough to be frustrated with it, to be honest, and when I have an issue they fix it. As with all support, I wish it felt more human, but they are doing aces.

Incentivized

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

I chose DigitalOcean over Oracle Cloud because it's simpler, more cost-effective, and quicker to deploy. DigitalOcean’s intuitive interface allows me to manage servers easily, while Oracle Cloud is more complex and suited for larger enterprises. Also, DigitalOcean’s transparent pricing helps control costs, unlike Oracle’s more intricate and complex pricing model.

Incentivized

Google BigQuery of course collects a much much larger array of raw data and can handle (practically) an unlimited amount of data. For a large enterprise like ours that relies on large-scale analytics, this is absolutely imperative. Google BigQuery can also combine GA4 data with external sources (like CRM tools), so our analytics can be unified. Due to our heavy reliance on GA4, Google BigQuery is the natural choice since it is a Google product and has better integration.

Incentivized

Great scalability, you can start with small plans and move up to premium features at a very good price.

Incentivized

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

• DigitalOcean has very competitive egress pricing, which has been positive for reducing our costs when running services with a large amounts of data transfer
• DigitalOcean templates have helped us quickly launch services that would otherwise require a lot of configuration (saving time)
• We haven't had much in the way of negative ROI impacts using DigitalOcean as we don't use it extensively for our core product, but based on personal project experience it can require more engineering time to get up and running with than some other infrastructure services like Heroku. This has been one of the greatest barriers in pushing its adoption in our organization.

Incentivized

• In some places, Google BigQuery has helped us save some money by avoiding the need for expensive infrastructure and reducing some of the operational costs.
• Scalability is up-to-date and really helpful in multiple places.
• Knowledge transfer is easy as it is very user-friendly, so the learning curve has been reduced.
• Also, it gives us more insights from our data, helping us make smarter decisions for our business.

Incentivized