Google's BigQuery is part of the Google Cloud Platform, a database-as-a-service (DBaaS) supporting the querying and rapid analysis of enterprise data.
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Google BigQuery
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Chose Google BigQuery
is much better as it’s easily accessible provides velvet documentation and fulfils all our needs as well as easily integrated into clients, environment
Google BigQuery is simpler and I say it has simpler UI too. If you have a clear long term ask , mainly business intelligence needs then Google BigQuery offers you good. If you need too much of features under a single cloud and you are ok to be lil clumsy then you can check …
I have used most of the data analytics platforms. Based on my work, I have found that the user interface of Google BigQuery is simple to navigate. I like the front view - ease of joining tables, and integration with other platforms.
Compared to every other analytics DB solution I've used, Google BigQuery was by far the easiest to set up and maintain, and scale. The price was also much lower for our use case (internal data analysis).
For our usage, Google BigQuery is cheaper and more performant. The others have their place, but in certain scenarios, Google BigQuery is a better solution.
We actually use Snowflake and BigQuery in tandem because they both currently meet various needs. Redshift, however, has barely been used since our migration away from it. In the case of both Snowflake and BigQuery, they beat Redshift by a long shot. The main reasons are their …
I came to use BigQuery from a traditional system like MS SQL server, the features which are available in BigQuery as a cloud service far outweigh the features from SQL server. I have not used other similar tools like Amazon Redshift but Google BigQuery serves multiple use cases …
Google BigQuery is cheaper and much faster as compared to both. While as compared to Snowflake , we tested it was faster and cheaper by 30%, that is after Snowflake tweaked their environment, if not for that it would have been 90% cheaper than snowflake. Redshift is not easy …
In my opinion, Google BigQuery is custom made to be the best data lake system that is easy to use, scalas to fit any business size, has inbuilt security, as well as tools for data integrity. Although a few other tools have some of the same functionality, Google BigQuery is the …
It's easier to connect data between BigQuery and looker studio instead of connecting the data between BigQuery and tableau in terms of data explore or dashboard creating. Therefore we are considering migrating dashboards from tableau to looker studio for the whole company. On …
When comparing Google BigQuery and Databricks, both platforms are powerful tools for managing and analyzing large datasets. BQ is ideal for businesses requiring large-scale analytics, reporting, and dashboarding with minimal operational overhead. It’s also great for ad-hoc …
Google BigQuery's main advantage over its direct competitors (Amazon Redshift and Azure Synapse) is that it is widely supported by non-Google software, while the others rely heavily on their own cloud ecosystems.
I have used other data manipulation tools like SQL Server and Google BigQuery feels more intuitive, Google provides so much documentation and tutorials that getting to know the software is not only easy but even satisfactory, so I'd say Google BigQuery is very superior to that …
Amazon Redshift was a likely alternative we were considering , but it needs to be provisioned on cluster and nodes, which increases infrastructure management, whereas Google BigQuery is serverless, so no infra management :) Also, I remember when comparing them we did found out …
Google BigQuery as a platform allows for more integrations and customizability than many other offerings. Users mostly need to understand the basics of database and SQL programming in order to get the most from the product. However, other products like Hevo do have less of a …
There are some areas in which this product is better while there are some in which others do better. It's not like Google BigQuery surpasses them in every metric. For a holistic view, I will say we use this because of - scalability, performance, ease of use, and seamless …
The data performance of Google BigQuery is best as per other software. Limitations on Google BigQuery's data size are superior to those of Microsoft SQL. Obtaining real-time data from several IoT devices is another benefit.
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 …
BigQuery can automatically scale to accommodate the data and query load, providing potentially unlimited scalability. At the same time, Redshift requires manual scaling efforts to increase or decrease capacity, which might affect performance during scaling operations.
We focused more on data volume and less on full application capabilities. All in all, we found that the two solutions complement each other. For integration, some sources were better handled in SAP HANA, particularly other SAP systems where Google Big Query was more suitable …
SingleStore has a much lower query latency compared to BigQuery. Thus, we segregate faster tasks to SingleStore, and use BigQuery has our main database to store all historical data.
Google BigQuery i would say is better to use than AWS Redshift but not SQL products but this could be due to being more experience in Microsoft and AWS products. It would be really nice if it could use standard SQL server coding rather than having to learn another dialect of …
First and foremost, Google BigQuery's pricing structure, based on data processing and storage, is more cost-effective for our needs. Secondly, since we already use other Google Cloud services, its tight integration with them especially, with Cloud Storage and Dataflow was a big …
Google Cloud Storage feels much more intuitive to use versus Amazon S3. I really prefer Google's Node SDK and web interface, and find Google Cloud Platform's access/identity management much more straightforward.
This software is perfect for the organization where a huge amount of data are generated. The software has the perfect features by which the organization can store their information safely. The software has the features where one can share cloud with colleagues for project …
Google had the easiest and most secure file sharing abilities, and was a more trusted name for our clients to be assured that their files would remain private.
We have used Amazon S3 for similar needs, or for solutions that don't support Google Cloud Storage. For us, Google Cloud Storage works much better since it's integrated into Google Cloud Platform. Since we use Google Cloud Platform, Google Cloud Storage is part of our cloud …
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 …
Google Cloud Storage has been experiencing less technical issues and is more straight forward to use, for our technical team, it saves training time by having a straight forward help and FAQ section. We needed very little training, as opposed to previously used other tools …
Google Cloud offers Google Cloud Storage, while AWS offers Amazon Simple Storage Services. In Google Cloud services, data transmission is a fully encrypted format on the other hand, in AWS, data transmission is in the general format. Google Cloud volume size is 1 GB to 64 TB …
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 effectively outweighs Google Drive's limited capabilities. This software, being a paid version, allows for much room for extra storage space, faster loading times from my experience, and easy sharing features. Compared to Google Drive, Google Cloud Storage …
I chose Google Cloud Storage due to its implementation with Google Firestore. Both worked off each other and made our connection with Google Firestore a lot simpler.
Google Cloud Storage is the only right option for media and document files. Unless you need to work with structured information where Cloud SQL or Datastore may be better options, storing files in Cloud Storage is easy and right to the point. Also, prices are worth considering …
Google Cloud Storage compares very similar to Dropbox. The difference of using Googe Cloud Storage is that it is part of a big bundle of products that you are probably already using. If you are a big Google user then it would make sense to get Google Cloud Storage. This way you …
Google Cloud Storage is the only one I have tried. But when researching we looked at Google Cloud, as well as Microsoft and Amazon. Google was more expensive, but seemed to be more of what we were looking for. Again, not really my department.
If your budget is limited, or if you are just starting your business and you're looking for a cloud storage service, this is a good option which is reliable and cheap.
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 …
Guru and Google Cloud Storage are different in many ways. They are by no means the same product, but our team has begun to move away from Google for our documentation of processes. Guru has a superior system for organizing documents and articles.
I have used Microsoft One Drive, Dropbox, Box, Amazon Cloud Drive, and Google Cloud Storage. All of this cloud storage software do pretty much does the same thing, but the difference for me is in ease of use— for me the easiest to use is Google Drive. I will say this, I use …
Easier to set up and has a natural interface that is well known to users so it is easy for them to assimilate. Google Cloud Storage is faster than any other option we tried and works perfectly with our Chromebooks. From an ROI perspective, it is the best option for a medium …
We tend to only select Google Cloud Storage when we're using other Google products as it makes integration easier -- but we tend not to choose it for situations where we can use a competing offering from Azure or AWS. We tend to implement the most Azure and AWS by far, and …
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 …
We ended up with Google Cloud Storage most importantly because we found it far easier to set up, configure, and operate compared to Amazon's offerings. Amazon's many products make it difficult to find just the right one, and from there configuring is overly complicated. In …
Google BigQuery is great for being the central datastore and entry point of data if you're on GCP. It seamlessly integrates with other Google products, meaning you can ingest data from other Google products with ease and little technical knowledge, and all of it is near real-time. Being serverless, BigQuery will scale with you, which means you don't have to worry about contention or spikes in demand/storage. This can, however, mean your costs can run away quickly or mount up at short notice.
[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
Overall I say this product is awesome and very easy to use, and would highly recommend it to other business professionals. I feel that my documents and work product are safe and secure, and that I will find them easily when needed.
I have never had any significant issues with Google Big Query. It always seems to be up and running properly when I need it. I cannot recall any times where I received any kind of application errors or unplanned outages. If there were any they were resolved quickly by my IT team so I didn't notice them.
I think Google Big Query's performance is in the acceptable range. Sometimes larger datasets are somewhat sluggish to load but for most of our applications it performs at a reasonable speed. We do have some reports that include a lot of complex calculations and others that run on granular store level data that so sometimes take a bit longer to load which can be frustrating.
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
Google BigQuery of course collects a much much larger array of raw data and can handle (practically) an unlimited amount of data. For a large enterprise like ours that relies on large-scale analytics, this is absolutely imperative. Google BigQuery can also combine GA4 data with external sources (like CRM tools), so our analytics can be unified. Due to our heavy reliance on GA4, Google BigQuery is the natural choice since it is a Google product and has better integration.
Google Cloud Storage compares very similar to Dropbox. The difference of using Googe Cloud Storage is that it is part of a big bundle of products that you are probably already using. If you are a big Google user then it would make sense to get Google Cloud Storage. This way you can have all of the tools you need under one roof. I selected Google Cloud Storage because I was already using Google's other products and I was very impressed by those products so it was an easy sale.
We have continued to expand out use of Google Big Query over the years. I'd say its flexibility and scalability is actually quite good. It also integrates well with other tools like Tableau and Power BI. It has served the needs of multiple data sources across multiple departments within my company.
In some places, Google BigQuery has helped us save some money by avoiding the need for expensive infrastructure and reducing some of the operational costs.
Scalability is up-to-date and really helpful in multiple places.
Knowledge transfer is easy as it is very user-friendly, so the learning curve has been reduced.
Also, it gives us more insights from our data, helping us make smarter decisions for our business.