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.
$6.25
per TiB (after the 1st 1 TiB per month, which is free)
Jaspersoft Community Edition
Score 9.7 out of 10
N/A
Jaspersoft Community Edition is a free product that offers embeddable pixel-perfect reports for Java apps and small workgroups.
$0
per month
Snowflake
Score 8.7 out of 10
N/A
The Snowflake Cloud Data Platform is the eponymous data warehouse with, from the company in San Mateo, a cloud and SQL based DW that aims to allow users to unify, integrate, analyze, and share previously siloed data in secure, governed, and compliant ways. With it, users can securely access the Data Cloud to share live data with customers and business partners, and connect with other organizations doing business as data consumers, data providers, and data service providers.
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. …
Google BigQuery is less expensive to run and offers free storage of up to the first 10 GB of data. Google BigQuery is also easier (and faster) to get up and running. Unlike Snowflake, Google BigQuery does not require any manual scaling or performance tuning. Scaling is …
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 …
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 …
Fully serverless. We don’t manage clusters or warehouses. Requires us to manage virtual warehouses. BigQuery is cheaper for exploratory heavy queries; Snowflake is more predictable for sustained workloads. BigQuery is unbeatable if you’re deep in Google’s ecosystem; Snowflake …
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 …
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).
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 …
At my previous organization we used server based SQL server. There were days when the server was down and we couldn't work or access the data. This caused multiple reports and processes which were fed from the server to fail. Google BigQuery doesn't have such problems.
Both BigQuery and Redshift are two comparable fully managed petabyte-scale cloud data warehouses. They’re similar in many ways, but you should consider their unique features and how they can contribute to an organization’s data analytics infrastructure. When considering which …
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 …
We particularly liked Snowflake's security model as well as its unique storage (whereby everything is essentially a pointer to immutable micro-partitions, which is the key behind its zero-copy cloning, its secure sharing, its time travel, etc.). and also how it separates …
These are comparable products that can make sense depending on the specific needs of your organization. All are certainly serviceable and have varying pros and cons. Snowflake seems to provide the greatest degree of flexibility and easy scalability as new data gets brought into …
Snowflake has an attractive pricing model with auto-suspend and auto-resume and pay per use. AWS Redshift requires higher administrative efforts to maintain and scale the platform whereas with Snowflake those admin tasks are not needed or automatically taken care of.
Each of the other solutions were cloud vendor specific, Snowflake can ride on either Amazon Web Services, Microsoft Azure, or Google Cloud. The fact that they are ANSI-sql compliant and have an effective means of offloading data makes them portable and easy to sell to teams …
Snowflake has won the match because it is giving an excellent performance with its efficient features and reliable results. This is a totally secure program for our precious and important data.
In my experience running the data management practice at InterWorks, we believe that cloud data warehouse products will eventually serve the majority of data warehousing use cases and power data analytics at most companies. Of this cohort, we believe that Snowflake is the best …
Our issue with Redshift was that it was very expensive. On top of that, queries were still slow and if we used more of Redshift's memory, then it would have cost even more. Snowflake is not cheap, but less costly for us. Plus, the performance was much better. Also, we got to …
More flexible and faster compared to Redshift, more functionality compared to BigQuery e.g. - per minute billing, instant spin up of warehouse. Overall, the cost and time savings swayed us in favor of Snowflake.
Instant provisioning of computing resources and data sharing is something we have not seen with any other vendor. Being HIPAA compliant at the time of evaluation was a must for us. Other vendors were late on this. Onboarding on support during implementation was also excellent.
Event-based data can be captured seamlessly from our data layers (and exported to Google BigQuery). When events like page-views, clicks, add-to-cart are tracked, Google BigQuery can help efficiently with running queries to observe patterns in user behaviour. That intermediate step of trying to "untangle" event data is resolved by Google BigQuery. A scenario where it could possibly be less appropriate is when analysing "granular" details (like small changes to a database happening very frequently).
Any scenario where you need to reach back into TIBCO support is something I would highly avoid. Most responses at the second round get you pointed to engaging their professional services. The general mentality seems to be "since our product acquisition cost is low, why don't you engage PS to get the value you need" If you are looking at enterprise scenarios, I would highly recommend looking at other more established products.
Snowflake is well suited when you have to store your data and you want easy scalability and increase or decrease the storage per your requirement. You can also control the computing cost, and if your computing cost is less than or equal to 10% of your storage cost, then you don't have to pay for computing, which makes it cost-effective as well.
GSheet data can be linked to a BigQuery table and the data in that sheet is ingested in realtime into BigQuery. It's a live 'sync' which means it supports insertions, deletions, and alterations. The only limitation here is the schema'; this remains static once the table is created.
Seamless integration with other GCP products.
A simple pipeline might look like this:-
GForms -> GSheets -> BigQuery -> Looker
It all links up really well and with ease.
One instance holds many projects.
Separating data into datamarts or datameshes is really easy in BigQuery, since one BigQuery instance can hold multiple projects; which are isolated collections of datasets.
Very intuitive tool - An Advanced reporting user can figure out in a day on the end to end operations.
Easy customization - Simple changes in UI makes it less headache for the end user who maintains the tool. Being an open source product (offers Jasper web services) makes a lot developer friendly compared to SSRS, Cognos etc.
Easy Connectivity - Can connect to any data source (can connect to multiple data sources) for a single report - can be customized.
Community - A very strong community available online, videos - on basic how to questions, also provides assistance to use case specific scenarios.
Ease of Reporting - Can create a reports with in few clicks with out struggle.
Domain Designer - A very powerful tool you can create a dataset using clicks with out writing SQL code.
OnDemand Scheduling - A very good scheduling feature and a has got lot more simplistic compared to other popular tools like Cognos.
Jasper Studio - Very good programmatic tool (desktop tool) lets you customize the reports more company specific, can embed complex coding with 3rd party languages like Java, Groovy (scripting language).
Snowflake scales appropriately allowing you to manage expense for peak and off peak times for pulling and data retrieval and data centric processing jobs
Snowflake offers a marketplace solution that allows you to sell and subscribe to different data sources
Snowflake manages concurrency better in our trials than other premium competitors
Snowflake has little to no setup and ramp up time
Snowflake offers online training for various employee types
Please expand the availability of documentation, tutorials, and community forums to provide developers with comprehensive support and guidance on using Google BigQuery effectively for their projects.
If possible, simplify the pricing model and provide clearer cost breakdowns to help users understand and plan for expenses when using Google BigQuery. Also, some cost reduction is welcome.
It still misses the process of importing data into Google BigQuery. Probably, by improving compatibility with different data formats and sources and reducing the complexity of data ingestion workflows, it can be made to work.
Easier to customize the Jasper Server GUI. More flexibility with the input controls look and feel and maybe more input control types to select from.
Have browser event functionality in the reporting itself.
An easier way to iterate through an object. Maybe an option that tells the report Java run-time to stop following the report sequential flow and first integrate through this stated object many times.
An easier way to create a list and add values to this list.
Maybe one could stipulate the process flow of what needs to be computed first in a given field.
The crosstab is a bit buggy on the Jasper studio side, so if that could be improved.
Better and more examples of custom components
The jive functionality on the tables needs an update. It looks so old and outdated.
Do not force customers to renew for same or higher amount to avoid loosing unused credits. Already paid credits should not expire (at least within a reasonable time frame), independent of renewal deal size.
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.
Even though there are challenges with Architecture, sizing and other areas with Jaspersoft provided that Jaspersoft comes up with the right set of innovation, performance, scalability, and documentation in future it will be great expansion (win-win) situation for the clients and as well as Jaspersoft.
SnowFlake is very cost effective and we also like the fact we can stop, start and spin up additional processing engines as we need to. We also like the fact that it's easy to connect our SQL IDEs to Snowflake and write our queries in the environment that we are used to
I think overall it is easy to use. I haven't done anything from the development side but an more of an end user of reporting tables built in Google BigQuery. I connect data visualization tools like Tableau or Power BI to the BigQuery reporting tables to analyze trends and create complex dashboards.
The overall performance of this platform has been excellent. It is easy to work with the user interface. Creation of digital content and publishing on our social media platforms during marketing has been easy. Running our applications and upgrading our system to meet international standards has been successful. The user dashboard displays reports and performance of all departments effectively.
Because the fact that you can query tons of data in a few seconds is incredible, it also gives you a lot of functions to format and transform data right in your query, which is ideal when building data models in BI tools like Power BI, it is available as a connector in the most used BI tools worldwide.
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.
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 only had a couple of tickets to be open while using Jaspersoft. Each time we have a very prompte responses that would meet our expectations. We always felt that our issues were being address in a timely manner
We have had terrific experiences with Snowflake support. They have drilled into queries and given us tremendous detail and helpful answers. In one case they even figured out how a particular product was interacting with Snowflake, via its queries, and gave us detail to go back to that product's vendor because the Snowflake support team identified a fault in its operation. We got it solved without lots of back-and-forth or finger-pointing because the Snowflake team gave such detailed information.
While the training was somewhat beneficial, a lot of what was taught to us was what we managed to figure out in the weeks prior. It did provide some useful tips and reference material, but overall something more tailored to our requirements would've been better.
New releases can be very buggy, also lacking when it comes to standard time zones and formats that are outside the US. As a company we never feel comfortable implementing a new release for at least 3-6 months due to errors that are found
PowerBI can connect to GA4 for example but the data processing is more complicated and it takes longer to create dashboards. Azure is great once the data import has been configured but it's not an easy task for small businesses as it is with BigQuery.
Crystal reports is very expensive and does not integrate well with java based servers. Jasper also provides community edition, if we every want to downgrade the use, we always have the option of eliminating the server and using the reports as they are. Jasper also give you the opportunity to build BI on case by base basis including Dashboards.
I have had the experience of using one more database management system at my previous workplace. What Snowflake provides is better user-friendly consoles, suggestions while writing a query, ease of access to connect to various BI platforms to analyze, [and a] more robust system to store a large amount of data. All these functionalities give the better edge to Snowflake.
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.
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.
Previously, running complex queries on our on-premise data warehouse could take hours. Google BigQuery processes the same queries in minutes. We estimate it saves our team at least 25% of their time.
We can target our marketing campaigns very easily and understand our customer behaviour. It lets us personalize marketing campaigns and product recommendations and experience at least a 20% improvement in overall campaign performance.
Now, we only pay for the resources we use. Saved $1 million annually on data infrastructure and data storage costs compared to our previous solution.
Jaspersoft Community Edition has not had any significant impact on our ROI as we have purchased the commercial edition of the product.
The only impact on ROI is that when we bring on new developers, he or she can begin work using the community edition while awaiting the installation of the commercial edition.