Likelihood to Recommend For organizations looking to avoid the overhead of managing infrastructure, BigQuery's server-less architecture allows teams to focus on analyzing data without worrying about server maintenance or capacity planning. Small projects or startups with limited data analysis needs and tight budgets might find other solutions more cost-effective. Also, it is not suitable for OLTP systems.
Read full review Well suited for my big data related project or a static data set analysis especially for uploading huge dataset to the cluster. But had some issues with connecting IoT real-time data and feeding to Power BI. It might be my understanding please take it as a mere comment rather than a suggestion. Read full review Pros 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 Jobs with Spark, Hadoop, or Hive queries are rapidly attained Can collect, organize and analyze your data accurately You can customize, for example, Spark or Hadoop configuration settings, or Python, R, Scala, or Java libraries. Read full review Cons Can't use it out of Google's cloud platform which is a minus point if you want a local setup. Can be a little expensive to manage. A little difficult to manage someone with less technical expertise as it requires you to have SQL knowledge of joins, CTEs etc. Read full review Easier pricing and plug-and-play like you see with AWS and Azure, it would be nice from a budgeting and billing standpoint, as well as better support for the administration. Bundling of the Cloud Object Storage should be included with the Analytics Engine. The inability to add your own Hadoop stack components has made some transfers a little more complex. 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 Google's
Firebase isn't a competitor but we had to use Google's BigQuery because Google's
Firebase 's database is limited compared to Google's BigQuery. Linking your
Firebase project to BigQuery lets you access your raw, unsampled event data along with all of your parameters and user properties. Highly recommend connecting the two if you have a mobile app.
Read full review We initially wanted to go with
Google BigQuery , mainly for the name recognition. However, the pricing and support structure led us to seek alternatives, which pointed us to IBM.
Apache Spark was also in the running, but here IBM's domination in the industry made the choice a no-brainer. As previously stated, the support received was not quite what we expected, but was adequate.
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 Google BigQuery has had enormous impact in terms of ROI to our business, as it has allowed us to ease our dependence on our physical servers, which we pay for monthly from another hosting service. We have been able to run multiple enterprise scale data processing applications with almost no investment Since our business is highly client focused, Google Cloud Platform, and BigQuery specifically, has allowed us to get very granular in how our usage should be attributed to different projects, clients, and teams. Plain and simple, I believe the meager investments that we have made in Google BigQuery have paid themselves back hundreds of times over. Read full review This product has allowed us to gather analytics data across multiple platforms so we can view and analyze the data from different workflows, all in one place. IBM Analytics has allowed us to scale on demand which allows us to capture more and more data, thus increasing our ROI. The convenience of the ability to access and administer the product via multiple interfaces has allowed our administrators to ensure that the application is making a positive ROI for our business users and partners. Read full review ScreenShots