Google BigQuery

Google BigQuery is a fantastic tool for exporting and importing data from different programs. As organizations grow and utilize multiple different platforms, the ability to move large datasets between those platforms is incredibly valuable. Users capable of performing database queries can quickly access this data and use it in meaningful ways. However, users that don't understand the limitations of databases within programs that Google BigQuery can export information to will find themselves struggling to utilize it effectively. Many of our users heavily employ spreadsheets for business tracking, but as datasets become larger spreadsheets become cumbersome, and attempting to use spreadsheet formulas on database information does not translate well.

Google BigQuery really shines in scenarios requiring real-time analytics on large data streams and predictive analytics with its machine learning integration. Teams have been using it extensively all over.
However, it may not be the best fit for organizations dealing with small datasets because of the higher costs. And also, it might not be the best fit for highly complex data transformations, where simpler or more specialized solutions could be more appropriate.

Google BigQuery we have used while processing large amount of data when connected with Iot devices in automation factory which continuously give real time data and Google BigQuery can handle it very easily.

Sometime Small volume of data require same effort of writing query which is little bit hectic for users.

BigQuery has been a great product for getting information from many different sources. We can use BigQuery to connect/join other sources together and find ways to match the data together to have a master data source. There have been times when we have used it, though, when I do not think it was needed and it was probably more of a headache.

Google BigQuery is suitable for use cases where there is a need for continuous data collection and one would want to mine that data, derive trends and behavioral data based on set parameters.

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.

Good for large datasets where query performance is otherwise an issue. It is bad for diverse data sources that are not large enough to really benefit and are overkill. Similar to use cases where many users need to query infrequently, where the minor syntax differences between SQL and Google Big Query can be annoying.

Good for:
- Unlocking real-time user data
- Boosting data-processing power
- Performing more extensive business analytics

Not so good for:
- Transactional data
- Updating data

Google BigQuery is suited to easily sync/connect different Google products for analytics purposes. Google BigQuery is a great data warehouse if a business use Google Analytics. It also allows more autonomy to various end users with diverse technical knowledge to create dashboards independently in Google Data Studio (now Looker Studio).

If you've already invested in the Google Cloud ecosystem and since Google BigQuery is part of the Google Cloud Platform (GCP), it easily integrates with other GCP services like Cloud Storage for data storage and Cloud Data Studio for data visualization. We only pay for the resources we use, unlike traditional data warehouses with fixed costs regardless of usage, thanks to its pay-per-use pricing model with no upfront investment and ongoing maintenance.

Google BigQuery is well suited if you have TB or PBs of data which needs to be analyzed with accuracy and then you need to find trends or create dashboards as it has seemless integration with Looker.

Google BigQuery is not well suited if your Database is very small. As the Google BigQuery architecture take similar time in small database which is counter intuitive.

- If you are using Google Analytics and there is huge data that is getting streamed every day then you must have Big Query and use it for analysis. It is not only helpful for analysis but also for debugging your Google Analytics implementations.
- For analyzing a small dataset you don't need Big Query you can use normal MySQL on your own premises. Analyzing on Un-structured data is not possible with Big Query.

I found Google BigQuery very easy to use from the very beginning. Users do need a very good knowledge of SQL in order to write queries that are processed efficiently. Using Select * queries can bog down resources and drive up costs.

Google BigQuery is a great way to manage data across multiple databases within the organisation. The speed of querying makes it highly valuable. The graphs and charts helps analyse the draw insights from the data effectively. We also get a real time understanding of how much time it will take to run the query. We can choose a highly customisable plan as per the need of the organisation to effectively manage the licensing and costs.

Well; data warehousing transformation flexible ingestion

I would say that Google BigQuery are well suited for all scenarios, be it small scale projects or big projects where you have to maintain a huge chunk of data, you will find good budget to go with it. Easy to use for someone who is not well versed with cloud platform too.

Google BigQuery is suitable for scenarios where the dataset is large and needs to be analyzed based on real time data. Google BigQuer has been very useful when I was working on the inventory data for the bookstore. At the beginning of semester there was always high demand for school materials, this high demand caused a steady decline in the inventory. Getting updated real time helped us to restock the warehouse beforehand with products in higher demand and thereby led to higher sales.

If you are dealing with very large data sets that require analysis or other manipulation, BigQuery is usually well suited for the task. It also has some built-in ML capabilities that may be of use to some people. If your data set is not very large and is relational in nature, then a more traditional data store is probably all you need, which can likely be used at a lower cost.

We have several hundred terabytes of data and the size of our dataset is exponentially increasing. We needed a data warehouse that is highly scalable. We also serve a user base with several dashboards. BigQuery is great because it integrates nicely with Google Data Studio and other analytics products.

I use it primarily in place of Google Analytics in Tableau since we use Tableau for all of weekly and monthly reporting and dashboards. One of the many advantages is being able to access unfiltered data and unaggregated data. This allows us to accurately capture measures such as unique users across many different time frequencies, which you can only do at the yearly level with Google Analytics.

Google BigQuery is very well suited if your data is large and already in Google Cloud/GCP where the data itself is not simple structured data. It is less suited if you have well-defined data sets that may or may not exist in Google Cloud. Google BigQuery is also less suited if you have to analyze the data on a regular basis since the cost of accessing compute and storage adds up considerably.

BigQuery is unlike anything we've used as a big data tool. It is perfectly suited to query large data sets quickly and to store those large data sets for any time use. It's perfect for storing data and using it for reports. Logging data is the perfect application for BigQuery, but transactional data is possible as well.

BigQuery is a huge benefit to companies that work remotely, process large datasets, or need to easily manage those large datasets. It's a powerful tool with cloud storage and the ability to work with large scale datasets. It works well if your monthly usage varies because you can pay for the processing you do- not paying for a minimum that you don't meet. It's not going to be a great option for companies with smaller datasets or who could operate with a less powerful and cheaper system.

I recommend this platform for wide range of customers that have not super tight budget for their application hosting but want to stay away from bunch of low-level details of running and maintenance of application infrastructure. Google BigQuery is easy to use and its interface is very nice, it also has a wide range of servers, which makes its services are excellent. This software has allowed me to easily access my files and share them quickly and efficiently, it also allows other activities while loading and downloading files, therefore saving a lot of time compared to other similar applications.

Google BigQuery is well suited for millions of records as you can run a query in milliseconds. It is less appropriate for small scale organizations which are dealing with a smaller amount of data.