Google BigQuery

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!

Both BigQuery and Redshift are two comparable fully managed petabyte-scale cloud data warehouses. They’re similar in many ways, but you should consider their unique features and how they can contribute to an organization’s data analytics infrastructure. When considering which one to use, it is best to take advantage their free trial periods to run your tests. This way you'll be able to use your own results as well as compare them to third party benchmarks that closely make your own business, in order to determine the best cloud data warehouse.

Google BigQuery needs minimal setup to get it up and running while Amazon Redshift and Oracle Analytics Cloud need moderate expertise and time to load a data set and run a query. Hadoop (open source) and its commercial version Cloudera do not provide a full out of the box solution for data warehousing and need additional components and installs. Databricks is a smaller vendor and does not come into picture if you are already an Oracle or a Google shop (=using their cloud, DB, et al.)

BigQuery is the first product we've seen of it's kind. Nothing seems to compare to the speed we get and the amount of data that we can fit into BigQuery. Data views are possible and recommended with basic data visualisation tools, but you may also choose to use something with a data layer for visualisation to make the most of your data.

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.

Firebase is not a competitor, necessarily, of BigQuery, but its integration with it allows for a greater deep dive into our Firebase data. The only reason we needed to start using BigQuery was that Firebase didn't give us the locational data that we need. Because of the easy integration with Firebase, we didn't look at any other services.

BigQuery is better at storing and handling large amounts of data than Knime. Knime is locally run and does not have the ability to handle massive databases like BigQuery and importing from multiple sources for multiple teams would be impossible, that is not really the function of Knime anyhow. Knime is far better at manipulating data and creating reports. I use Knime with BigQuery to create reports, and do many data tasks like Keyword Selection, analysis and other related things.

It is much faster than MySQL so it is responsible for handling our log data which have millions of records.

Google BigQuery integrates seamlessly with Web Analytics data compared to the Azure cloud.
Google BigQuery integrates natively with different digital media platforms compared to Azure and AWs.

We liked BQ because the cost of it is only dependent on the amount of data you store (and there are tiers of data access) and how much you search. For us, it is significantly less expensive to run BQ than an equivalent hosted RDBMS. Because most of our data pipelines are automated, and, we only need to do ad-hoc queries irregularly, BQ fit our criteria very well.

Other locally hosted solutions are capable of providing the required level of performance, but the administration requirements are significantly more involved than with BigQuery. Additionally, there are capacity and availability concerns with locally hosted platforms that are a concern when working with data used for digital marketing analysis and ad performance optimization.

BigQuery by far the best solution in all angles compared to other ones: Especially scalability, ease of use, performance and there is no need to manage any cluster of servers. Also it's ABSOLUTELY pay as you go! No one in market currently provide such service that can compete with Google BigQuery. The closest was Snowflake and in some cases like supporting ANSI SQL it even better than BigQuery. But at end of the day, BigQuery wins on cost and performance.

Spinning up, provisioning, maintaining and debugging a Hadoop solution can be non-trivial, painful. I'm talking about both GCE based or HDInsight clusters. It requires expertise (+ employee hire, costs). With BigQuery if someone has a good SQL knowledge (and maybe a little programming), can already start to test and develop. All of the infrastructure and platform services are taken care of. Google BigQuery is a magnitudes simpler to use than Hadoop, but you have to evaluate the costs. BigQuery billing is dependent on your data size and how much data your query touches.

Comparing to competitors, Google BigQuery has the lowest cost and most flexible pricing model. Definitely higher ROI.