Denodo is the eponymous data integration platform from the global company headquartered in Silicon Valley.
N/A
Google BigQuery
Score 8.8 out of 10
N/A
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)
TIBCO Data Virtualization
Score 8.6 out of 10
N/A
TIBCO Data Virtualization is an enterprise data virtualization solution that orchestrates access to multiple and varied data sources and delivers the datasets and IT-curated data services foundation for nearly any solution.
In its tool selection process, Cloetta used a Value for Money model on Excel, scoring the functions, performance, vendor qualifications, support, and consultancy required against the total cost of ownership for the first five years of usage. The company went with TIBCO® Data …
Denodo allows us to create and combine new views to create a virtual repository and APIs without a single line of code. It is excellent because it can present connectors with a view format for downstream consumers by flattening a JSON file. Reading or connecting to various sources and displaying a tabular view is an excellent feature. The product's technical data catalog is well-organized.
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).
TIBCO Data Virtualization is well suited for customers who are challenged to deal with extracting data from dozens of different sources and systems, and do not have the time and liberty to hire data engineers and/or ETL developers to write dozens or hundreds of complex ETLs. However, there are situations where TIBCO Data Virtualization severely underperforms, and those are where we are dealing with large volumes of data, in tera bytes or peta byte scale system. For example, a messaging queue which sends 200 million messages every hour will choke TIBCO Data Virtualization if the technology is chosen to route the data.
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.
Caching - but I am sure it will be improved by now. There were times when we expected the cache to be refreshed but it was stale.
Schema generation of endpoints from API response was sometimes incomplete as not all API calls returned all the fields. Will be good to have an ability to load the schema itself (XSD/JSON/Soap XML etc).
Denodo exposed web services were in preliminary stage when we used; I'm sure it will be improved by now.
Export/Import deployment, while it was helpful, there were unexpected issues without any errors during deployment. Issues were only identified during testing. Some views were not created properly and did not work. If it was working in the environment from where it was exported from, it should work in the environment where it is imported.
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.
Performance of TDV repository database is rather poor for larger numbers of objects .(Note: We have approx. 9tsd objects introspected in TDV and approx. 20tsd objects generated in upper DV layers.)
Propagation of privileges to parent/child dependencies does not work when applying recursively on a folder. (It's a huge setback when working with large number of objects organized semantically into subfolders.)
Lack of command line client interface for scripting at the time of version 8.4 (I had to write my own CLI.)
TDV Studio does an absolutely horrible job with its own code editors when indentation is in place. Also, the editor is brutally slow and feature-poor.
Tracking privileges on the level of table/view columns causes occasional problems when regranting.
TDV's stored programs ("SQL scripts" in their own terminology) compiler leaves out many syntactic and semantic checks, making them hugely prone to run-time errors.
TDV Server's REST API is a very poor (in terms of features) and flawed cousin to its SOAP API (at the time of version 8.4).
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.
TDV's interface is a bit dated and not entirely intuitive. Would recommend some UX design review as the interface leaves a bit to be better understood to be used by users without inherent knowledge of Tibco. Overall I'd suggest more improvement here to ensure usability by a lesser tech audience.
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.
Denodo is a tool to rapidly mash data sources together and create meaningful datasets. It does have its downfalls though. When you create larger, more complex datasets, you will most likely need to cache your datasets, regardless of how proper your joins are set up. Since DV takes data from multiple environments, you are taxing the corporate network, so you need to be conscious of how much data you are sending through the network and truly understand how and when to join datasets due to this.
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.
This product's performance is very consistent. It is extremely rare for templates to fail. I've been using this software for 5 years and find it to be both simple and powerful. The impact within the company has been very positive as different processes in different areas, such as data analysis, development, and integrations, have been improved, and, best of all, it has not affected the users. Various systems with which it is connected in order to obtain information.
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.
On a few occasions I have asked TIBCO technical support for help because I have adapted perfectly to their tools, but in those few that I have communicated with their technical team I have received personalized, attentive, responsible attention and I am always assisted by an expert staff the topic. A TIBCO technical support technician spent more than an hour helping me to solve a problem in the initial stage of implementation in my department and this is something that I always appreciate.
The training was helpful. I was able to understand how to use TIBCO for the data load process that we implemented and how to perform various troubleshooting steps based on the training I received. The technician was thorough and took the time to answer any questions. Once we were shown how to use TIBCO in the test environment, we were able to configure the production environment ourselves.
Other vendors have clearer, more visual implementation documentation. We also did not have our data architect and and server administrator available full-time for implementation. In the future, we will secure the necessary internal resources.
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.
We did not need to evaluate another technology in the same category for data virtualization, since we are 100% sure of the capabilities and benefits that we would have with TIBCO Data Virtualization, both for market positioning as well as success stories from other companies. great renown worldwide. From the first day of use, it meets our needs to provide the expected solutions.
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.
