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)
Jedox
Score 9.0 out of 10
N/A
Jedox is a Business Intelligence and Corporate Performance Management solution. According to the vendor, their solution’s unified planning, analysis and reporting empowers decision makers from finance, sales, purchasing and marketing. Additionally, the vendor says this solution helps business users work smarter, streamline business collaboration, and make insight-based decisions with confidence. The vendor also says 1,900 organizations in 127 countries are using Jedox for real-time planning…
N/A
Amazon Redshift
Score 8.9 out of 10
N/A
Amazon Redshift is a hosted data warehouse solution, from Amazon Web Services.
$0.24
per GB per month
Pricing
Google BigQuery
Jedox
Amazon Redshift
Editions & Modules
Standard edition
$0.04 / slot hour
Enterprise edition
$0.06 / slot hour
Enterprise Plus edition
$0.10 / slot hour
No answers on this topic
Redshift Managed Storage
$0.24
per GB per month
Current Generation
$0.25 - $13.04
per hour
Previous Generation
$0.25 - $4.08
per hour
Redshift Spectrum
$5.00
per terabyte of data scanned
Offerings
Pricing Offerings
Google BigQuery
Jedox
Amazon Redshift
Free Trial
Yes
Yes
No
Free/Freemium Version
Yes
Yes
No
Premium Consulting/Integration Services
No
Yes
No
Entry-level Setup Fee
No setup fee
No setup fee
No setup fee
Additional Details
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More Pricing Information
Community Pulse
Google BigQuery
Jedox
Amazon Redshift
Considered Multiple Products
Google BigQuery
Verified User
Analyst
Chose Google BigQuery
Google BigQuery needs minimal setup to get it up and running while Amazon Redshift and Oracle Analytics Cloud need moderate expertise and time to load a data set and run a query. Hadoop (open source) and its commercial version Cloudera do not provide a full out of the box …
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 …
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'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.
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).
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 …
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 …
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 …
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 …
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.
Google BigQuery is the best among the ones we evaluated. It works really well with the Google Cloud workloads and comes with exceptional security controls. It can be combined easily with lots of products that Google Cloud has. It is a real game-changer.
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 …
I was already familiar with the Google Cloud Platform environment, and I was better equipped with the standard SQL language. Some of the syntax does not translate well to Redshift. It also seemed like many data source integrations relevant to our business were easier and more …
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 …
Google BigQuery integrates seamlessly with Web Analytics data compared to the Azure cloud. Google BigQuery integrates natively with different digital media platforms compared to Azure and AWs.
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 …
Amazon Redshift, BigQuery, and Snowflake are all fully managed data warehouse services that are designed to handle large volumes of structured data and support business intelligence and analytics efforts. However, Amazon Redshift has the upper hand with its cost-effective …
Biggest advantage of Amazon Redshift is it's part of the aws ecosystem. When tuned well it is also very cheap compared to something like Snowflake. And compared to spark or databricks, Amazon Redshift is a solid warehouse that's well suited for tabular data. We use it for user …
We evaluated [Amazon] Redshift vs BigQuery vs Amazon EMR, back in 2014. Back then BigQuery cost was slightly higher than that of [Amazon] Redshift price structure. Amazon EMR, needs lots more management (Admin tasks) and EMR is designed to be ephemeral and not designed to be a …
Amazon Redshift supports multiple data formats including multiple structured data formats. And it is easy to implement a cluster if you do not have knowledge of data lake solution. Also when you do not need a lot of resources, you can just scale down so you do not have to spend …
As our applications are hosted on AWS service, Redshift is the best option for us. Also, it provide a near to real-time performance on limited datasets and less complex queries. High availability is the major concern for any growing business and AWS is the best option for this. …
Most of our stack is on AWS, so while Snowflake and BigQuery was a viable option from a performance perspective, it was easier to integrate with RedShift. We considered hosting SQL Server on AWS or using Amazon RDS (Postgres or MySQL), however, the self-service aspect of …
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).
Best suited for financial consolidation and / or as a highly customized and compact EPM / BI solution (up to 100 CCU) with individual workflows, planning and reporting functionalities, with moderate number of users (no restrictions for any industry, all industries are covered well). It also has advanced reporting & data analysis requirements and provides an integration and reporting layer of imported data from different external systems (via ETL). It can help with migrating your legacy Excel-based business models to the Web. It is not well suited for Enterprise BI applications with expecting >500 CCU (users at the same time working with the system) - this may cause serious performance issues, as all data is kept in RAM. Jedox is also less suited for applications with heavy document management requirements (document management is not an out of the box functionality in Jedox and rather requires custom development through custom widgets etc.).
If the number of connections is expected to be low, but the amounts of data are large or projected to grow it is a good solutions especially if there is previous exposure to PostgreSQL. Speaking of Postgres, Redshift is based on several versions old releases of PostgreSQL so the developers would not be able to take advantage of some of the newer SQL language features. The queries need some fine-tuning still, indexing is not provided, but playing with sorting keys becomes necessary. Lastly, there is no notion of the Primary Key in Redshift so the business must be prepared to explain why duplication occurred (must be vigilant for)
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.
[Amazon] Redshift has Distribution Keys. If you correctly define them on your tables, it improves Query performance. For instance, we can define Mapping/Meta-data tables with Distribution-All Key, so that it gets replicated across all the nodes, for fast joins and fast query results.
[Amazon] Redshift has Sort Keys. If you correctly define them on your tables along with above Distribution Keys, it further improves your Query performance. It also has Composite Sort Keys and Interleaved Sort Keys, to support various use cases
[Amazon] Redshift is forked out of PostgreSQL DB, and then AWS added "MPP" (Massively Parallel Processing) and "Column Oriented" concepts to it, to make it a powerful data store.
[Amazon] Redshift has "Analyze" operation that could be performed on tables, which will update the stats of the table in leader node. This is sort of a ledger about which data is stored in which node and which partition with in a node. Up to date stats improves Query performance.
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.
Diversity. Jedox can be applied to many different use cases from small to large deployments and from budgeting to enterprise class BI solutions. But rarely is one tool able to fulfill all of these requirements in one organisation. This value proposition can be complicated for prospective users.
Awareness. Jedox punches above its weight in capability and scalability, but not enough people have heard about it and therefore procurement processes can be drawn out as a result.
We've experienced some problems with hanging queries on Redshift Spectrum/external tables. We've had to roll back to and old version of Redshift while we wait for AWS to provide a patch.
Redshift's dialect is most similar to that of PostgreSQL 8. It lacks many modern features and data types.
Constraints are not enforced. We must rely on other means to verify the integrity of transformed tables.
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.
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.
To me Jedox deserves 10/10 because it is a consistent one-in-all platform with a modern look and feel. It is intuitive to use and allows you to make intuitive applications integrating traditional business intelligence with performance management functionality. It certainly has a short learning curve, especially for those that are familiar with MS Excel. An example: I've lost count but Jedox it is available in more than 25 languages. Another: Jedox does not require programming skills... it is developed to be used by the business.
Just very happy with the product, it fits our needs perfectly. Amazon pioneered the cloud and we have had a positive experience using RedShift. Really cool to be able to see your data housed and to be able to query and perform administrative tasks with ease.
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.
Jedox has very few bugs. Reports are available through an Excel add-in, the web and/or mobile device (IOS/Android). In my opinion, availability also means high performance, not having to wait for the system to give you the required reports, analysis, dashboards instantly.
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.
Jedox support in general is a professional and fast responding team. An easy-to-use ticketing system is in place. Bug-related questions are solved fast (responses come usually in a few hours after the question), but some questions / tickets, that are not Jedox-related bugs (for example some advanced questions about Jedox functionality), may be forwarded to Application Management team for further processing and then it may take several days or even weeks to get a response here -> there is room for improvement here.
The support was great and helped us in a timely fashion. We did use a lot of online forums as well, but the official documentation was an ongoing one, and it did take more time for us to look through it. We would have probably chosen a competitor product had it not been for the great support
The implementation of SSO, SAML Authentication, HTTPS, Server splitting (Frontend / Backend servers) could be more standardized and made more user friendly to set up (e.g. via setup guide). Otherwise the implementation of Jedox is quick and simple when compared to other similar technologies.
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.
Calumo is similar product to Jedox. I have used it extensively in my previous role. It was a major contender when we evaluated a BI platform for NIDA. Calumo is a great product as well and it was a very close call. Where we found Jedox to be a better fit for NIDA was the ability to prepare dynamic reports with ease without the need to learn MDX which was used extensively by Calumo to make dynamic reports which expand or shrink based on the underlying data. Another major benefit we saw in Jedox was the whole ETL process could be managed within Jedox instead of doing it in SQL server which negates having a dedicated SQL specialist role when the scale expands.
Than Vertica: Redshift is cheaper and AWS integrated (which was a plus because the whole company was on AWS). Than BigQuery: Redshift has a standard SQL interface, though recently I heard good things about BigQuery and would try it out again. Than Hive: Hive is great if you are in the PB+ range, but latencies tend to be much slower than Redshift and it is not suited for ad-hoc applications.
Redshift is relatively cheaper tool but since the pricing is dynamic, there is always a risk of exceeding the cost. Since most of our team is using it as self serve and there is no continuous tracking by a dedicated team, it really needs time & effort on analyst's side to know how much it is going to cost.
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.
Scalability is often another word for speed. Given enough data, enough users or enough calculations, the tool becomes slower and slower. You will find that Jedox has a very high performance that can even be increased by the use of grafical cards. Other thaen that it does not only offer BI (looking back based on historical ERP data) but also allows you to look forward through integrated budgetting, planning, forecasting, workflow and collaboration. Not easy to find a tool that can support so much business functionality. So, also pretty scalable in that respect.
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.
Financial budgeting and Forecasting are done in a centralized fashion in Jedox now instead of a decentralized excel based approach. A lot of cost savings and improved reliability
Easy to use self-help Dashboards and detailed reports
Our company is moving to the AWS infrastructure, and in this context moving the warehouse environments to Redshift sounds logical regardless of the cost.
Development organizations have to operate in the Dev/Ops mode where they build and support their apps at the same time.
Hard to estimate the overall ROI of moving to Redshift from my position. However, running Redshift seems to be inexpensive compared to all the licensing and hardware costs we had on our RDBMS platform before Redshift.
