Google BigQuery seemlessly integrates with all the Google services. In Looker Studio you directly have a connector for Google BigQuery which can help to create dashboards in few clicks. For automating some stored procedures we have used Cloud Functions which are triggered by a …
I personally find it by far simpler than Amazon Redshift due it's onboarding seamlessness. For a quick start and simplify tye access to read the data big query provide better user experience and a smoother user interface. More importantly, the fact that Big Query can be easily …
Cost is the important factor for us compared with all of the other tools Google BigQuery stands top among all of them which charges very minimal charges for storage against all the apps that we have liked the most additionally, we can do query on our data, and can build …
BigQuery has a simpler and more intuitive user experience (as is the case with most of its products) compared to AWS, which has a more technical and complex profile, so it was the first tool we used. It's still my go-to option for handling SQL queries, though it doesn't detract …
We are heavily within the Google ecosystem and therefore didn't really consider alternatives to Google Data Studio since it met our somewhat limited needs at the time of implementation. For outside presentations, we would probably lean towards something that allows us to more …
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.
Does great at open canvas editing and letting you fully customize without the need for a grid. It is democratizing self-service no-code analytics. You do not need to be a data or analytics engineer to get started, and you can go very far based on how intuitive and straightforward the UI is. Some of the biggest challenges with Looker Studio relate to user management/security, embedding options, and issue support. For a long time, every user needed to have a Gmail to invite them to view a dashboard via login, not sure if that has been improved yet. You can let any user view without logging in, but that is not always recommended due to security reasons. In terms of embedding, you can only iframe dashboards. More sophisticated BI tools let you embed elements via API or Javascript. Iframing dashboards also make drill downs and dashboard to dashboard navigation tricky/near impossible. There is also no ability to contact Google for support when bugs or outages happen. They point everyone to the Data Studio community. There is some ability to get in contact with Google if you have an enterprise-level contract with Google Cloud, but the path for support is very ad hoc and not always fruitful.
Its serverless architecture and underlying Dremel technology are incredibly fast even on complex datasets. I can get answers to my questions almost instantly, without waiting hours for traditional data warehouses to churn through the data.
Previously, our data was scattered across various databases and spreadsheets and getting a holistic view was pretty difficult. Google BigQuery acts as a central repository and consolidates everything in one place to join data sets and find hidden patterns.
Running reports on our old systems used to take forever. Google BigQuery's crazy fast query speed lets us get insights from massive datasets in seconds.
It is challenging to predict costs due to BigQuery's pay-per-query pricing model. User-friendly cost estimation tools, along with improved budget alerting features, could help users better manage and predict expenses.
The BigQuery interface is less intuitive. A more user-friendly interface, enhanced documentation, and built-in tutorial systems could make BigQuery more accessible to a broader audience.
We have to use this product as its a 3rd party supplier choice to utilise this product for their data side backend so will not be likely we will move away from this product in the future unless the 3rd party supplier decides to change data vendors.
It is the simplest and least expensive way for us to automate our reporting at this time. I like the ability to customize literally everything about each report, and the ability to send out reports automatically in emails. The only issue we have been having recently is a technical glitch in the automatic email report. Sadly, there is almost no support for this tool from Google, but is also free, so that is important to take into consideration
web UI is easy and convenient. Many RDBMS clients such as aqua data studio, Dbeaver data grid, and others connect. Range of well-documented APIs available. The range of features keeps expanding, increasing similar features to traditional RDBMS such as Oracle and DB2
Google Data Studio has a clean interface that follows a lot of UX best practices. It is fairly easy to pick up the first time you use it, and there is a lot of documentation on line to help troubleshoot, if needed
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.
I give it a lower support rating because it seems like our Dev team hasn't gotten the support they need to set up our database to connect. Seems like we hit a roadblock and the project got put on pause for dev. That sucks for me because it is harder to get the dev team to focus on it if they don't get the help they need to set it up.
I have used Snowflake and DataGrip for data retrieval as well as Google BigQuery and can say that all these tools compete for head to head. It is very difficult to say which is better than the other but some features provided by Google BigQuery give it an edge over the others. For example, the reliability of Google is unmatchable by others. One thing that I really like is the ability to integrate Data Studio so easily with Google BigQuery.
Google Data Studio provides a great feature set considering its price point, especially when compared to commercial options from Microsoft and Tableau. While it may not be as versatile when it comes to working with and developing complex datasets, there is enough charm in its simple, easy-to-use UI to allow not-so-complex analytics to be conducted without having to hire a data analyst.
Google Support has kindly provide individual support and consultants to assist with the integration work. In the circumstance where the consultants are not present to support with the work, Google Support Helpline will always be available to answer to the queries without having to wait for more than 3 days.
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.
