Google BigQuery

Well; data warehousing transformation flexible ingestion

The web UI, general features, libraries, and integration with other products is continuously improving and already very strong

BigQuery is an extremely powerful tool to store granular data. We have tables with trillions of rows and BigQuery has proven to be extremely reliable over time. For organizations that require a very reliable datastore, BigQuery is an excellent choice. The price is also very reliable given the amount of data we store.

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 Support members are very helpful in resolving the issues and queries. Any questions or queries will be entertained at a timely manner with professionalism, as well as tips and improvement that can be done for the proposed solutioning. In the case that some functionalities are not present within Google BigQuery, they are more than happy to admit the limitation and would like feedback for improvement.

One of the most important aspects while working with data warehousing solutions and analytics is the ability to handle large datasets. Google BigQuery is the best in business for that particular aspect. It is ridiculously fast while handling large data sets. Another aspect where it is well suited is the ability to integrate it with data visualization tools like Data Studio. It is fast, easy to use, and very reliable. The only aspect where I feel it is less appropriate where you have to pay more of inefficient scripts and that can hamper the growth of the company a bit.

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.

Google BigQuery works well for enterprise organizations that have sufficient IT resources to implement its integration and data governance requirements. For example, if an organization is a billion-dollar food distributor, and it wants to run quick queries against large data warehouses to pull correlated sales and marketing reports. So, it can show return on investment driven by training initiatives and marketing campaigns to lift new customer acquisition rates and incremental case purchases. Google BigQuery is less appropriate to use in small businesses where data volume is low, and IT resources are not enough to maintain data quality or run SQL queries. Example: If a company requires to report eCommerce sales from digital-only marketing campaigns where audience size is a few hundred customers, Google BigQuery may not be needed. Instead, Google Analytics or Adobe Analytics will suffice.

I rated the overall support for Google BigQuery as a mediocre five because it has limited support from Google. Instead, it is heavily dependent on an organization's IT resources such as SQL analysts and Data Architects to run big data reports or maintain data quality. Additionally, if errors occur during a run of complex SQL queries or when sending data to Google BigQuery from other sources, Google provides basic email support which needs to be complemented with internal data warehouse support to fix the root cause of the database errors. Finally, due to constraints on the amount of data an analyst can query or pay the additional cost when exceeding the limit, basic Google support is not sufficient to meet data needs without interruption.

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.

We haven't needed support as of yet.

BigQuery is best if we need a fully managed data warehouse with the fastest querying support. The high price may be a point of concern for some.

We use to get issues on BigQuery even though its not frequent. Retrying works most of times

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 can be difficult to support because it is so solid as a product. Many of the issues you will see are related to your own data sets, however you may see issues importing data and managing jobs. If this occurs, it can be a challenge to get to speak to the correct person who can help you.

Google BigQuery is an especially powerful tool for teams running a business that collects data in multiple tools and want to have a place to centralize all of the data for process analysis. Teams that want to learn the insights of every aspect of their processes and their performance can store their processes data in Google BigQuery and then create queries and store them as needed then run experiments or test scenarios and measure outcomes easily.

It’s Google, they’re big and well organized, the documentation is abundant and the scalability is amazing. The UX is good too, considering it’s a professional tool expected to be used by people with a specific technical background. Overall, it makes me feels good and secure that we know where to store the data, how to use that data and that the data is handled with utmost security and performance practices.

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.

BigQuery has exceeded our cloud data warehouse needs with features such as it inherits security features of Google Cloud Platform, it encrypts data by default for added security and Google Cloud DLP service supports BigQuery. Also, BigQuery natively supports streaming data, which is available for real-time analysis within a few seconds of the first streaming insertion into a table.

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.

Suited to any company, small or large (as it's extremely scalable and low cost as it scales), that wants or needs to dive into data to make more data-driven decisions or back up decisions with user data. The team should have someone that is well versed in SQL though, as non-technical team members will be a bit lost.

BigQuery is well suited for organizations that use a lot of data across lots of teams or departments. It is perfect for those companies who need various data dumps or data storage areas for different parts of the company, where the data storage is flexible and easily accessible for everyone. It is also a cost-effective method from what I understand, so if your company needs to enable teams to have better access to larger amounts of data storage and databases BigQuery is a logical option.

BigQuery's main strength is its ability to process huge volumes of data with lightning speed, and also perform personal detailed analysis on web analytics data or continuous streams of piles of data and then link it directly to data studio.

BigQuery is best of OLAP. It's not a real-time system, so you shouldn't expect it to search through your billion records in 2 seconds. We use it to store raw, unaggregated data. For this use case, it's perfect, since the storage costs are low and the performance is more than good enough. BigQuery is also great for building data pipelines. It has convenient SDK to get data in and out of it, and SQL to marshall the data any way you want.

BigQuery is extremely well suited to being a general purpose analytics data warehouse, i.e. if you have large datasets that you wish to extract insights from, and are comfortable with SQL, then BigQuery should be the only place those data live. BigQuery is also extremely well suited to driving enterprise-level dashboards on your actual data, decreasing the deviation of the summarized data from the raw. BigQuery is not as well suited to cases where you hope to return very large datasets, as it is optimized for aggregations.

It works well for a big dataset starting from hundreds of GB. I wouldn't recommend using it for people with less than 100 GB in data – except when you expect to grow your dataset in the near future. It's also not really good to directly answer on live requests, it's much better to use it to pre-process some data, store it somewhere else, and serve it from there.

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.

Online documentation was readily available and it was easy to connected with the product management team for Google BigQuery during Google Cloud Next 2019 event. We didn't have to open a SR/ticket through the usual support channel to get our issues resolved!

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.

As with any large company, support isn't always easy to get to. There are plenty of online tutorials and they have online guides that aren't too difficult to find. For last minute issues, a call is the best option to figure out what your next step should be. it's not a terrible customer service system, especially if you spend an appropriate amount of time beforehand familiarizing yourself with the system. That'll expedite any further questions you have down the road and make the support a much bigger help.

It can be an extremely good fit if:
1. You have data in Google Cloud Storage
2. You don't want to deal with the hassle of spinning up a Hadoop cluster
or you have especially large dataset and you don't want to deal with scaling-out logic. Also, costs might be high.
It's not good for you if you have some specific algorithm which cannot be phrased in the BogQuery SQL flavor.
It maybe unnecessary if near-real-time results are not too important factor, and it doesn't matter if a query returns in 2-3 seconds or 20-30. If you already have some Hadoop infrastructure, HIVE or Spark, your existing solution might be cheaper.
There are best practices which can decrease your costs a lot (for e.g. how many columns your query involves, how well do you filter your data in the query).

It is well suited for generating reports quickly or doing interactive analytical queries over a large data set that contains hundreds of millions or billions of rows. The largest table we used in BigQuery has close to 30 billion rows. It is not suited for ETC processes or data pipeline.

BigQuery can be used for trillions of records as well as hundreds of thousands but not sure if it's useful for small set of data. Also in cases that query response needs to be on milliseconds that might be not right solution. Custom UDFs are also supported but very limited and have ittheirs own challenges since it's not SQL and need to write in JavaScript.

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.