Google BigQuery

Shortened time by 30% to draw out data insights

Increase in tech performance and speed by 20% due to correct data logging

Reduced cost of managing data across multiple databases

Has a bit of a learning curve.

Connectivity to other databases.

Support for Ad hoc queries.

With Google BigQuery, we're able to report on data we didn't have access to with our old reporting.

I've tried a few visualization/BI tools, but for our immediate needs (that are basic for now), the BQ warehouse with GDS as the visualization piece has been a good option.

It would have a positive ROI, the price is reasonable and machine learning always benefits your organization

Machine learning in general has profited the company extremely

Development of dashboards and reports at a very low cost

Execution of automatic rules in Firebase from queries in Bigquery

Reduced time to market of features compared to legacy warehouse

Improved engineer productivity

Simplified TCO

Improved data security

Reduced time to integrate from one components to another

Reduced the cost for cloud warehousing

Ease of use so reduced time to get to use on a daily basis

We are able to provide granular reports over trillion of data that we would not be able to provide otherwise

Positive - My organization has faced great profits and has trubleshooted many issues before hand because of the realtime data to work on. It keeps the data up to date.

Negative - As there are various Google BigQuer proficiency people in the organization. Some very experienced and some novice. It has a negative impact because people who are new write longer and inefficient codes so the company is charged more.

Integrate well with google data visualization tools that can save a lot of money on licensing of other tools.

Requires basic SQL skills which are transferrable and saves money spent on training.

Gives an all out data retrieval and storage service which makes it a lot easier for employees across organization to fetch data.

Once up and running, we no longer have to worry about scale and managing infrastructure. Instead of spending our time on our analytics and bringing that value to our customers.

We are fully data driven.

Productivity is increased.

Manageability is minimal.

Pricing is slightly high.

We are able to query our entire dataset without worrying about sharing.

BigQuery significantly cuts down query time.

It helped us define a structured process for data analytics.

Simplified the decision of where to store our data.

Big Query helps to analyze web traffic from different channels to optimize our message and conversion paths.

We use BigQuery Data Transfer Service for Google Ads to automatically schedule and manage recurring load jobs for Google Ads reporting data.

Being server-less, fully managed cloud server, Google BigQuery has a positive impact on the business in terms of amount of setup time and deployment resources needed to analyze a data set.

Positive impact on ROI due to reduction in CapEx and OpEx needed to provision a data warehouse upfront.

Positive impact on ROI due to no improvement in the speed of analyzing consumer data using Google BigQuery in real-time and proactively take action when/if needed based on the results.

We've seen a greatly reduced cost in data storage.

We have gained the ability to make more immediate data-driven decisions.

We have gained the ability to run many more reports and gain insight much faster.

BigQuery has been a great benefit for managing large datasets. We collect quite a bit of data in our line of work and it's difficult to find program that can easily manage that as well as have it accessible via cloud storage.

The cost is high, even though you only pay for what you use. Ideally the cost would be lower for certain usage ranges but for now, it's difficult for a small company to justify the cost even with such large scale data.

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.

Helped us focus on better locations for restaurants, which increased our conversion rate and quality of restaurants and locations, which we believe has led to more reservations.

Lowered our cost of restaurant acquisition with the sales team by about 20%.

It helps us to manage and understand the larger breadth of our Search Engine presence using data from our Search Console API. This helps us locate and track issues and improvements.

Using BgiQuery also improves the access to multiple types of data that we use for tracking performance and monitoring changes in our competitive position in the SERPs.

It allows us a faster, easier way to manage and store large databases without having to host them on a local machine or another cloud.

ROI is very small although it's positive. Still, I am not happy with this product as it took five to six days to understand the flow of the product and how to implement it into our project, whereas other database systems would have taken one to two days.

By using BigQuery for visualization and personalization, we were able to achieve 5% higher conversion rate.

Generating remarketing audience from Google Analytics data.

Reducing the cost for storage infrastructure

Comparability with different data sources and more depth analysis and efficient decision making

We were able to reduce our investment in self-hosted Postgres and move our bigger data assets to BigQuery. Overall, we saved hundreds per month, thousands per year.

We are able to search through very large datasets with BQ that were difficult to search with standard Postgres, even on very large servers. This gave us the ability to do ad-hoc data introspection easily.

Because we already used Google Storage, it was easy to integrate BQ into our environment.