BigQuery is better at storing and handling large amounts of data than Knime. Knime is locally run and does not have the ability to handle massive databases like BigQuery and importing from multiple sources for multiple teams would be impossible, that is not really the function …
We liked BQ because the cost of it is only dependent on the amount of data you store (and there are tiers of data access) and how much you search. For us, it is significantly less expensive to run BQ than an equivalent hosted RDBMS. Because most of our data pipelines are …
BigQuery by far the best solution in all angles compared to other ones: Especially scalability, ease of use, performance and there is no need to manage any cluster of servers. Also it's ABSOLUTELY pay as you go! No one in market currently provide such service that can compete …
Spinning up, provisioning, maintaining and debugging a Hadoop solution can be non-trivial, painful. I'm talking about both GCE based or HDInsight clusters. It requires expertise (+ employee hire, costs). With BigQuery if someone has a good SQL knowledge (and maybe a little …
The two services are very comparable, but we have many different services that all run on the Google Cloud Platform and therefore Google Cloud Storage made more sense as our storage solution rather than looking to an outside service like S3. Either one of these options would …
The big difference against other competitors that offer a Cloud storage solution, is the fact that Google Cloud Storage and other Google Cloud Platform products are billed in the local currency. This fact saves a lot of money because in some countries we have to pay for …
Aside from Google Cloud Storage, we've used AWS S3 and have found the two comparable. In fact, GCS is one of a large number of object storage systems that are compatible with S3 (including DigitalOcean Spaces, IBM Cloud Storage, and Azure Blog Storage). When it comes to these …
Google Cloud Storage is really a different application. It does not have the data parsing capabilities and it is simply a cold storage option in our use case. This means it is less flexible and has limited uses, but the uses it does have it, performs very well. There is a very …
We selected GCS vs. others because we decided to use other Google Cloud services. Since we integrated GCS into our tools, we're still using GCS today, even though we've largely transitioned away from Google Compute services. GCS is still a very solid choice, even if your server …
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 Cloud Storage is] great for storing and playing large video files, and even sharing them securely with others, whether or not they are part of your organization. No need to download video files before watching, and can also be used to store any other kinds of files.
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.
Really great, easy to use interface helps us manage files easily. Storage is fast and inexpensive, so we don't have to spin up storage instances locally
Great set of command-line tools to manage data and storage options via scripts and apps, as well as an SDK means we can build GCS into our orchestration and operations tools
Robust integration with other Google cloud tools means that we don't have to think too hard about using GCS for a variety of storage tasks as we interact with other Google services.
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.
after all of the investment made in the tool and considering how many teams use it I think we would not be likely to move away from this tool. A lot of our information including historical is already here and we are happy with the capabilities of the tool currently
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
Very easy to use. I love having my data backed up. I love that Google Cloud Storage provides me with the peace of mind that I no longer need to worry about my data being lost. I can now sleep better at night. Google Cloud Storage is very easy to use. Overall, you save time and have less stress by using Google Cloud Storage.
For performance i give Google Cloud Storage 10 of 10 on performance because even though there are other softwares that do exactly the same thing as Google Drive, it still works exceptionally well. It is very fast, and and far as integration, the only software I have used with it that integrated was Google Docs, and of course it integrates perfectly.
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.
We have never used official support from Google for our Google Cloud Storage, but there is plenty of documentation in place already. With a small amount of work, anybody should be able to get started. Once needs get more complicated, there is still documentation from Google, but also plenty of community support for common use cases around the internet.
overall I was not directly involved but hears the teams were satisfied with the implementation. the teams that used the tool did not encounter major issues, it was as expected with minor issues and bugs that were resolved later. The more significant learning curve was actually starting to use the tool
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.
We prefer Google Cloud Storage over Amazon Web Services because of the tools and code integration offered by Google Cloud Storage. We found the Google Cloud Storage toolset to be highly usable and give us the fine-grained control we need for managing digital assets. Ultimately, we chose Google Cloud Storage because we found the API and suitability for code integration with our Java codebase to be impeccable and because we had excellent direct support from the Google Cloud Storage team
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.
It has assisted greatly with our ability to share documents/information cross functionally. Especially within our advertising team, we store a large amount of information to assist new hires and refresh current employees.
Something that could improve is employees' understanding of how to best utilize Google Cloud Storage. This could improve by implementing a potential training video or tutorial.
Overall, Google Storage has been great. I have not used a similar storage product that had the same enterprise level capabilities.