Likelihood to Recommend 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.
Read full review Google BigQuery really shines in scenarios requiring real-time analytics on large data streams and predictive analytics with its machine learning integration. Teams have been using it extensively all over. However, it may not be the best fit for organizations dealing with small datasets because of the higher costs. And also, it might not be the best fit for highly complex data transformations, where simpler or more specialized solutions could be more appropriate.
Read full review Pros 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 Read full review 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 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. Read full review 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. Read full review Likelihood to Renew 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 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 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 Alternatives Considered 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.
Read full review I have used
Snowflake and
DataGrip for data retrieval as well as Google BigQuery and can say that all these tools compete for head to head. It is very difficult to say which is better than the other but some features provided by Google BigQuery give it an edge over the others. For example, the reliability of Google is unmatchable by others. One thing that I really like is the ability to integrate Data Studio so easily with Google BigQuery.
Read full review Contract Terms and Pricing Model None so far. Very satisfied with the transparency on contract terms and pricing model.
Read full review Professional Services 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 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 Read full review Pricing has been very reasonable for us. The first 10 GB of storage is free each month and costs start at 2 cents per GB per month after that. For example, if you store 1 terabyte (TB) for a month, then the cost would be $20. Streaming data inserts start at 1 cent per 200 megabytes (MBs). The first 1 TB of queries is free, with additional analysis at $5 per TB thereafter. Meta data operations are free. Big Query helps reduce the bar for data analytics, ML and AI. BQ takes care of mundane tasks and streamlines for easy data processing, consumption. The most impressive thing is the ML and AI integration as SQL functions, so the need for moving data around is minimized. The visuals of ML models is very helpful to fine tune training, model building and prediction, etc. Read full review ScreenShots Google BigQuery Screenshots