Google BigQuery Reviews and Ratings

Use Cases and Deployment Scope

We use Google BigQuery as the company's data warehouse. We also have some stored queries that levarage the scheduling feature of Google BigQuery. And we use it to connect to some google sheets files we have online so that we can make queries over them using SQL and perform som data analysis

Likelihood to Recommend

overall, Google BigQuery is a powerful tool for large scale data analysis and warehouse management, specially when you use a lot of products from google cloud's products. However, it is not well suited when it comes to real time processing or transactional workloads. In such cases, firebase or cloud sql might be more appropriate

Use Cases and Deployment Scope

we are using Store all data that we have. It has proven to be a very good product where we have been easily able to migrate to from our Excel sheets and create a combined database where we store all our historical data across clients and different teams and provide a place where they can access the data easily.

Likelihood to Recommend

if the data is not huge and we don’t need a lot of cloud storage features such as high processing, immediate retrieval of data, et cetera. Then they might be the solutions which are better

Use Cases and Deployment Scope

We have UI survey reporting database in Google BigQuery.

It is meant to give insights of how the users or sales people like the user experience.

We recieve files which finally gets loaded in gcp env.

Querying tables In Google BigQuery gives fast insights with comparatively less time than other cloud dbs.

Likelihood to Recommend

I feel like Choosing it when we have Streaming data with pub sub playing a big role.

Though streaming analytics can be a lil.challenging when you have real time insights needed fast.

Much suited for micro batches or batch data.

You can create a big data warehouse store history.

Batch is where I would prefer

Use Cases and Deployment Scope

I use Google BigQuery to run codes on Telecom customers to understand their connect and disconnect behavior. Some of the key business problems that I have solved using Google BigQuery is mapping geospatial data with zip/market zones to enhance our product sales by reaching the correct/targeted audience. Apart from this I use it to understand churn trends - why customers are moving/disconnecting, what's working, how to streamlines offers etc.

I also utilize it to understand demand and what is driving customers to take up an offer.

Likelihood to Recommend

1. Complex Query Optimization Issues: Sometimes optimizing very complex queries involving multiple joins and subqueries becomes difficult and ends up taking time to process

2. Outside source data integration that works: Working on in-built libraries or data has helped me improve working, especially Zillow

Use Cases and Deployment Scope

We use Google BigQuery as our main analytics database, as a data warehouse. It addresses our need for having all our data, from different sources, be available for analysis and flexible querying.

Likelihood to Recommend

I would 10/10 use Google BigQuery as an analytics DB again and again. It's perfect for it, really easy to use both for sending and ingesting data and also for retrieving and querying the data. It also has lots of integrations in 3rd party products, ranging from visualization to AI, as it is a widespread solution.

I would not use it for a production database, as it has relatively high latency and is priced per query, so it doesn't really work in that scenario.

Use Cases and Deployment Scope

For data analysis by our enterprise data team, primarily. Most activity gets dumped into Google BigQuery in one form or another to support enterprise reporting. We also use it for creating Pub/Sub ledgers, using the Google BigQuery Pub/Sub subscription type. This allows us to debug and research data that has flowed through a topic.

Likelihood to Recommend

Where low cost, low frequency of access is needed for working with large datasets.

Use Cases and Deployment Scope

BigQuery fits naturally into the GCP estate, seamlessly integrating with GSheets and allowing users to ingest data into BigQuery with little to no technical knowledge. This is beneficial for GForm data, which is naturally presented via GSheet. With the real-time integration GSheets has with BigQuery, this form of data can then be in your data stack in mere seconds. This is really helpful when we want to collect feedback data or review a process using GForms as the method of data collection.

Likelihood to Recommend

Google BigQuery is great for being the central datastore and entry point of data if you're on GCP. It seamlessly integrates with other Google products, meaning you can ingest data from other Google products with ease and little technical knowledge, and all of it is near real-time. Being serverless, BigQuery will scale with you, which means you don't have to worry about contention or spikes in demand/storage. This can, however, mean your costs can run away quickly or mount up at short notice.

Use Cases and Deployment Scope

We use BigQuery to analyze marketing campaign performance, track customer engagement, and optimize marketing strategies for over 40 clients. This involves processing large volumes of data from various marketing channels. BigQuery supports real-time data ingestion and analysis, enabling us to find problems and make quick and informed business decisions. BigQuery’s flexible pricing allows us to manage costs effectively based on client budgets.

Likelihood to Recommend

I have a big e-commerce company that needs to analyze transaction data from millions of customers. Thanks to BigQuery, I do that quite well. I analyze customer behavior to understand purchasing patterns, preferences, and trends. Managing inventory levels in real-time to ensure products are always in stock. Providing personalized product recommendations

Use Cases and Deployment Scope

We use BigQuery to store and analyze data for identifying long term trends. Data is ingested through pipelines on Airflow. Since our other logging solutions were only storing information for less than 2 months it was very difficult to identify the overall trend and seasonality. Using BigQuery, we are not only able to visualize the trends and make better decisions, using BigQuery eliminates the overhead of maintaining the underlying infrastructure or add more resources for servers/databases. The ability of using petabyte size data is pretty useful and is cost effective with pay per use features.

Likelihood to Recommend

We store GCP BigQuery to store long term data where personal information is redacted and another Splunk for checking day to day log or for troubleshooting by operations team. Since cost is associated with each query we have not found BigQuery to be useful in both of these use cases. In addition costs can quickly escalate when large datasets are involved. Similar use case is observable in transaction base data or data which needs to be frequently queried and the output is small in size, Minimum bytes charged is 10 MB.

Use Cases and Deployment Scope

Google BigQuery is helping us with data preparation and storage. It is also helping with performing ML or Machine Learning operations natively. Support comes as Google BigQuery ML and AutoML which is easy to use and work with as the data is lies within Google BigQuery itself. The storage cost is pretty low for upto 1TB. There are multiple public data sets that helps build use cases easily.

Likelihood to Recommend

It is well suited for analytical and ML use cases on large data sets with ease. Enabling external data integration and allowing queries with various data sources without having to move the data to Google BigQuery, thereby saving cost and increased efficiency of the analysis being done. The Google BigQuery ML and AutoML integration has helped a lot by which the capability to create model, train, Forecast and evaluate can be done in the same platform or interface without having to leave the Google BigQuery environment. The capability to create visualizations via Google BigQuery on Looker Studio takes away the need for starting from scratch and helps immediate analysis of the data being rendered in the query.