Google BigQuery

The data performance of Google BigQuery is best as per other software. Limitations on Google BigQuery's data size are superior to those of Microsoft SQL. Obtaining real-time data from several IoT devices is another benefit.

I've used Domo while working for an advertising agency and the functionality was way worse and the user interface was not near as good.

I personally find it by far simpler than Amazon Redshift due it's onboarding seamlessness. For a quick start and simplify tye access to read the data big query provide better user experience and a smoother user interface. More importantly, the fact that Big Query can be easily integrated with Looker Studio for visualisations. Query data and visualizing has never been easier.

Compared to SingleStore, BigQuery has a big advantage of being completely serverless, and without practical limitations.

Compared to RedShift, we found the cost model to be more fitted to our needs.

BigQuery also has better integration with Google products, such as Firebase, Google Ads, and Google Analytics, which results in lower TCO for those needs.

BigQuery can automatically scale to accommodate the data and query load, providing potentially unlimited scalability. At the same time, Redshift requires manual scaling efforts to increase or decrease capacity, which might affect performance during scaling operations.

We focused more on data volume and less on full application capabilities. All in all, we found that the two solutions complement each other. For integration, some sources were better handled in SAP HANA, particularly other SAP systems where Google Big Query was more suitable for various R&D data, such as time series.

SingleStore has a much lower query latency compared to BigQuery. Thus, we segregate faster tasks to SingleStore, and use BigQuery has our main database to store all historical data.

Google BigQuery is the best among the ones we evaluated. It works really well with the Google Cloud workloads and comes with exceptional security controls. It can be combined easily with lots of products that Google Cloud has. It is a real game-changer.

Google BigQuery i would say is better to use than AWS Redshift but not SQL products but this could be due to being more experience in Microsoft and AWS products. It would be really nice if it could use standard SQL server coding rather than having to learn another dialect of SQL server. It was choosen due to the 3rd parties choice of software.

First and foremost, Google BigQuery's pricing structure, based on data processing and storage, is more cost-effective for our needs. Secondly, since we already use other Google Cloud services, its tight integration with them especially, with Cloud Storage and Dataflow was a big plus and streamlined data transfer and simplified our workflows. Apart from that, as my team deals with large datasets and complex queries, we need a serverless architecture technology that has an edge in terms of query speed and scalability for our specific needs.

Google BigQuery seemlessly integrates with all the Google services. In Looker Studio you directly have a connector for Google BigQuery which can help to create dashboards in few clicks.
For automating some stored procedures we have used Cloud Functions which are triggered by a PubSub topic when an event happens.

Suits well for Business Intellegence and vizualization with Looker. Cloud storage options and seamless integration with Google online products.

Google BigQuery is a fully managed, serverless data warehouse offered by Google Cloud Platform. It stands out for its scalability, performance, and ease of use compared to other data warehouse solutions. Here's how it stacks up against others. Google BigQuery is designed to handle massive datasets, allowing you to store and query petabytes of data without worrying about infrastructure management or performance degradation.

Cost is the important factor for us compared with all of the other tools Google BigQuery stands top among all of them which charges very minimal charges for storage against all the apps that we have liked the most additionally, we can do query on our data, and can build dashboard on Google data Studio so we that's why we have preferred Google BigQuery against all of the other apps.

I was already familiar with the Google Cloud Platform environment, and I was better equipped with the standard SQL language. Some of the syntax does not translate well to Redshift. It also seemed like many data source integrations relevant to our business were easier and more readily available for BigQuery versus Redshift.

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 completely automatic.

Google BigQuery manages data like no one else. The light speed of running queries makes it a one stop solution. The editor and query builder also have a highly intuitive interface that makes it easy to build new queries fast. Google BigQuery can easily be integrated with other google products like gmail and drive allowing the team to get real time updates and actionables.

Treasure Data is more for the marketer rather than a developer audience, so depending on who your main users will be for the machine learning you can decide which tool is better. In our case we went with Treasure Data because it was more for a marketer and less for the developer side.

BigQuery has a simpler and more intuitive user experience (as is the case with most of its products) compared to AWS, which has a more technical and complex profile, so it was the first tool we used. It's still my go-to option for handling SQL queries, though it doesn't detract from Athenas if the rest of the infrastructure is on AWS.

We based our analysis primarily on [BigQuery vs. Redshift vs. Athena] and BigQuery proved to be the best solution for us.

At my previous organization we used server based SQL server. There were days when the server was down and we couldn't work or access the data. This caused multiple reports and processes which were fed from the server to fail. Google BigQuery doesn't have such problems.

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.

We selected BigQuery since we were already making use of many other offerings within the Google Cloud Platform and it made sense to stay within that eco-system. Of course, we made sure it met our needs and was cost-effective, and when it did we didn't seriously consider an alternative from another vendor.

Google Cloud BigQuery was our first and last choice for a data warehouse. It serves all of our needs!