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 has worked perfectly fine for our needs. It is easy to manager data and make reporting tables available to users throughout the company. We are able to create certified data sources and customize them to include exactly the needed information. Data refreshes fairly quickly do that we can keep all of our reporting up to date. Google BigQuery has enabled greater self service analytics capabilities within my company as now anyone can connect to the same certified data source.

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 big query is perfect for simple and fast use-cases where users need to access data quickly and and seamless. GCP IAM makes it easy to have a control on who can access the data and and provides services accounts to automate jobs. Which then makes it easy to have an overview on th data consumption.

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.

I would use Google BigQuery for storing data warehouse information, streaming from multiple sources, and displaying either in my application's dashboard, Looker Studio, or Grafana. It's very easy to stream data from Firebase to BQ, and very effective as well. It is hard to stream data from your main database, and requires some work, but I believe it is worth the effort.

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.

Google BigQuery integrated really when with a product that generates enormous amount of data, since appending data to BigQuery is much faster even in high frequency. They also offer a generous free tier which helps in determining its suitability and costs scale according the usage. If you need a really low latency query execution, this might not be what you are looking for.

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 handles big data sets really well and has a solid enginge to query and maniulapte the data. The syntax is easy to pick up if your use to other database languages like SQL server but there are some syntax differences. Once setup it is a simple product to use and utilise

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.

Managed service without any capital investment for users. New users must have knowledge of BigQuery and SQL in order to use it correctly and for its intended purpose. Also scales well and groups according to the size of the dataset and tasks.

Financial reporting and transactional reporting is suited well for Google BigQuery A lot of data like data streams are supported very well. Small scale usages are not adviced. The integration efforts are not marginal and should not be under estimated Also in case of data security concerns, I think Google is never a best practice to be used provider.

As previously mentioned Google BigQuery is perfect for storage of you have large data sets since they are charging very minimal charge for storage but they will charge for every single query that you run on Google BigQuery so if you have large data sets then go for it. If you want to do query on the data then Google BigQuery will already provide and you can also build the dashboard with your data on Google Data Studio.

Google BigQuery is great when you have a large body of information that needs to be analyzed. It provides an estimate of how much data is going to be queried which can help you identify if you need to optimize your code further before running.

BigQuery is great for organizing and preparing data for data analysis, reporting, and visualization. Using Standard SQL to query data within the data warehouse is a comprehensive and resource-rich language that is easy to use and robust. It is very helpful when multiple data sources must be strung together for analysis.

I would rate 9 out of 10. The platform's user-friendly interface and ease of learning make it accessible for various team members. Its exceptional capability to handle big data seamlessly aligns with our diverse analytics needs. The serverless architecture streamlines operations, enhancing overall efficiency. While there's room for slight improvements, Google BigQuery remains a valuable asset, significantly impacting our data analytics and decision-making processes.

- 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.