I use the Collibra Lineage visualization features. The solution helps me understand data flows effectively. Collibra Lineage impacts my data analysis by helping to identify where data is used, making it significantly useful. The solution facilitates compliance by defining where sensitive data is located, helping to trace sensitive data.

One of the best features of Collibra Lineage is that it provides an end-to-end perspective of where data is used in the enterprise.

The positive benefit from using Collibra Lineage is that it provides an end-to-end perspective of data usage in the organization.

There is room for improvement in Collibra Lineage as it could incorporate automation features which would allow it to find data lineage across a data stack independently.

I have been working with Collibra Lineage for approximately four or five years.

I don't have much experience with Collibra Lineage, so I cannot comment on whether it is straightforward in terms of installation or if there are challenges when implementing.

I personally don't participate in the installation of Collibra Lineage.

Collibra Lineage is a stable solution.

Collibra Lineage is scalable.

I did not communicate with their support for Collibra Lineage, so I would not know if they were helpful or responsive.

When it comes to implementing Collibra Lineage, three or four engineers are involved.

Using Collibra Lineage is beneficial in terms of finance.

The metrics I track to measure efficiency improvement after using Collibra Lineage include integration and analytics. The time spent to identify data usage is reduced significantly.

Because I have not used Collibra Lineage extensively, I will not comment on future updates, improvements, or features I would like to see, such as more customization, more AI features, a refreshed interface, or a lower price. I rate Collibra Lineage 8 out of 10.

Implementing Collibra Governance is definitely something that all organizations having data need. It's the kind of tool especially for large enterprises where data is distributed across multiple data stacks, bringing rigor for both data management and data engineering.

We use Collibra Governance for metadata management and access restrictions, as well as lineage.

We use Collibra's Data Stewardship features. In terms of role assignment and accountability, there are three levels of users: people who are data users, then there are data stewards, who are users of data enforcing compliance and rigor around data, and finally, the data governor who sets up policies and principles.

We have utilized Collibra's Business Glossary. It is good for reference of the data, understanding what the data is about, which helps maintain data accuracy.

Collibra's integration capabilities benefit our organization, although compared to the rest of the market, it is a bit cumbersome, but it's acceptable.

My experience with Collibra's collaboration tools in improving data literacy has been quite good. I think it is one of the best for helping people understand and discuss certain data sets and manage workflows.

In future updates, I would like to see more AI-based capability in Collibra Governance, wherein the data definitions can be auto-generated and lineage can be tracked down easier.

I've been working with it for probably 12 years.

The installation process is quite straightforward, but for it to be adapted by the organization presents challenges based on the organization, so it's not a technical challenge.

For a basic installation, I spend just a few weeks, probably about two weeks on setup and configuration.

Three to four people from my side usually take part in the implementation.

There are some benefits in the long run in terms of return on investment. While there are no direct cost reductions, there is significant indirect cost reduction. If I consider putting data governance in place without a tool like this, it's difficult, resulting in a significant cost improvement. If something else can be used instead and it's more expensive, that is a consideration.

Regarding licensing, I find it is one of the more expensive options, so it is not particularly affordable.

The interface is very good, with no complaints.

I do work with other vendors as well. Some other vendors I work with include Informatica, Atlan, and Suchi.

On a scale of 1-10, I rate Collibra Governance a 9.

We have a data platform and we use Collibra Catalog as enterprise governance tools. We are using Collibra Catalog for basic functionality.

It has various features such as cataloging data products and data sets, and it also has a marketplace and edge connectors to scan metadata. All these are various features, and the data quality aspects are there as well.

Discoverability is one of the key things we are always looking into.

Lineage is also available, including both technical and metadata lineage.

One of the very key drawbacks is that automation for access provisioning is not available. If I discover a data set or data product in the marketplace and want to access the data, this feature doesn't exist at all.

This is the biggest problem because without access automation enabled, users cannot get access to the underlying data.

We have been using it for 4 and 1/2 years now.

Deployment issues exist; every time we do a deployment, it erases the data in its previous versions. We have highlighted this to Collibra Catalog and have raised a ticket. They are working on it.

We are not satisfied as it is not yet completed. We raised this issue a year ago, but there have not been any results yet.

Response time is the main issue.

The solution is scalable.

We have multiple cataloging systems similar to Collibra Catalog.

Pricing is not under my purview as I am an architect. The platform team handles the licensing aspects.

The comparison shows that each tool has its own pros and cons.

Gartner identifies Collibra Catalog as the leader, which aligns with our observations.

I have read reports about Collibra Governance and Microsoft Purview on the website. We are currently using Collibra Catalog. We also use Tableau. I work as a Collibra Catalog architect. The tech support rating is six out of ten. Overall, I rate this solution 8 out of 10.

I have been working with Collibra Lineage for more than five years now, overall almost five years. I am using the product myself for molecular consultants and resellers at a consulting firm.

The best features of Collibra Lineage are quite user-friendly, and business users can use it much better than other catalog tools such as Talend or challenges according to my team. The traceability diagrams are very complicated and not user-friendly at all.

The automatic data flow mapping is effective; some of the integrations pose issues in terms of the configuration of those data sources, but it works fine. Even transformation platforms such as DVD work really well.

The integration with other Collibra Lineage products is valuable in our data governance initiatives. I have worked on many migration projects where organizations are moving away from other catalogs such as Talend or Informatica Axon and Informatica EDC to Collibra Lineage. I have worked on projects where we are creating the catalog from scratch on both fronts.

The configuration issues I face include troubleshooting that is not very helpful. I keep struggling with that and usually have to go to the Citizen data platform and the marketplace to get the answers I am looking for.

The controlled logs and documentation need improvement, particularly regarding how to access class features that they do not control, and documentation in terms of customization and UI customization. Dashboarding needs significant improvement as the widgets are not very clear or usable, and there is not much documentation around it.

To make Collibra Lineage better, additional features should be added to help technical users. They have improved significantly in terms of university courses in the last five years, but aspects such as AI governance and data notebook are not being promoted adequately. Data notebook is a very good feature, but there is a mismatch in the courses' speed and how quickly they are giving people the ability to learn about it.

I have been working with Collibra Lineage for more than five years now.

The implementation becomes more complicated, especially in terms of integration if there is no Collibra Lineage native connector. If you have to build gateways using the APIs or communicate with the APIs, it becomes significantly more complicated.

I would rate their technical support a seven or eight, as it depends on the payment tier. If you have paid for support, they respond to every email, and you have a personal account manager who takes care of all issues. If you just purchased a license without a support system, it is hard to get a decent reply on tickets, but this is true for most other catalog tools as well.

I usually recommend products such as Purview instead of Collibra Lineage, which is a significant downgrade from Collibra Lineage in terms of experience and key features. I have seen people moving to in-house catalog tools as well.

The product is definitely expensive. I have seen many customers moving away from Collibra Lineage after using it for a couple of years because the cost-benefit analysis does not give them a favorable report. They are paying too much for features they are not able to utilize.

The pricing presents both advantages and challenges. Many clients do not anticipate the effort required to develop a catalog and fully utilize it for data consumption. When considering the time and effort required to build a catalog and utilize it effectively, combined with the prices, it often does not make financial sense.

I usually recommend products such as Purview instead of Collibra Lineage, which is a significant downgrade from Collibra Lineage in terms of experience and key features. I have seen people moving to in-house catalog tools as well.

I would still recommend Collibra Lineage to others, as I am a champion. I am a two-time Ranger certified person, and I speak about Collibra Lineage in a positive light when I get the chance. I rate this solution 8 out of 10.

The use case for Collibra Catalog is for metadata management.

The best features in Collibra Catalog are apparent when we do the integration and establish when we fetch the data into Collibra. We have an option to store everything into a particular domain under which all the data can reside. This feature stands out because when I worked with Alation and Microsoft Purview, they did not have these capabilities.

Using lineage and Collibra Catalog has helped me overall improve the trust and transparency regarding data origin and transformation.

The business glossary feature in Collibra Catalog contributes to consistent data terminology across my teams because it is easy to use. If you want to search any business term or business-related assets, you can go to the glossary and search for it directly. Instead of using global search, you can navigate directly to the glossary and find the asset more easily.

Collibra Catalog has improved my organization through data cataloging as we were able to find assets more easily. It was organized in a structured way so that everybody can understand where the assets reside and how to navigate them.

I have utilized the sophisticated search capability in Collibra Catalog, and it can be improved by implementing more natural language search capabilities. Currently, we need to enter the asset names or domain names as it checks for exact matches. If we could enter natural language search queries, it should fetch the relevant details.

In Collibra Catalog, the main area that has room for improvement is the search functionality. It should be more natural language oriented instead of searching for exact names.

I have been using Collibra Catalog for 3 years.

I would rate the stability of Collibra Catalog as seven.

For scalability, I would rate Collibra Catalog as eight.

I would rate support for Collibra Catalog a seven. There are some documentation materials which we were unable to understand clearly. More clear documentation on the Catalog site would be beneficial.

When comparing Collibra Catalog to other vendors' products in the market, I noticed that Alation has more features. In Alation, we have natural language search along with several filters which can be applied on the search itself. These features are not present in Collibra Catalog.

The deployment for Collibra Catalog took about 10 to 20 minutes to complete.

I haven't worked on GDPR and CCPA regulations, so I am not sure about their implementation.

The same number of users continue to work with Collibra Catalog.

I would recommend Collibra Catalog to other users if they implement changes such as natural text search. There are some small bugs that need fixing, but I would still recommend it. If they improve these aspects, it will be more beneficial for everyone.

I rate Collibra Catalog an eight out of ten.

The main use case for Collibra Lineage is focused on the data product integration. They want the data lineage, data product lineage, and data products. They have some AWS platform in their state, and they have implemented external layers on top of that, including iceberg tables. The data lineage and delta are used to write a model with data products in Snowflake. In the Snowflake layer, they have their reporting in Tableau.

They have implemented a model with data products in Snowflake, which helps them in reporting in Tableau.

The features of Collibra Lineage that I have found most valuable include its non-integrated, non-native nature, which is one of the unique features for customization. In my case, data engineers have created their approach with Snowflake. By using a customized approach, they develop simple solutions and integrate with Collibra. A custom lineage, technical lineage helps me to design or join all these multiple layers. For example, connecting three Oracle instances to AWS is not available out-of-the-box, so there is a function to customize.

The main benefits that Collibra Lineage provides include measurable advantages. Once I have Lineage implemented, I can implement any automation on top of that. When I classify the D's on the data product, I can transform that data word to all the related columns using automation and workflow. This functionality avoids needing to do everything manually, and business only has to handle it at one layer, which is the data product. The lineage connects backward, allowing me to push back all these D's across all connected columns and registers. It's useful for the technical team to understand how all the data businesses seem different.

When discussing potential areas of improvement for Collibra Lineage, there are several cases to consider. If we discuss Lineage, which is the major part I work with, solutions are implemented in PySpark, and Collibra does not have out-of-the-box features for that. If a developer implements ETL in Glue or PySpark, Collibra cannot capture lineage out of that. This is an area for improvement, alongside integration with Starburst, which is another area where lineage capability is lacking.

I have been working with Collibra Lineage for five years.

I rate the stability of Collibra Lineage as seven. This is a fair assessment compared to alternative tools such as IBM Cloud Pak for Data, which I've also worked with.

In terms of scalability, Collibra Lineage's performance is reliable, as the lineage harvest runs on the lineage server which operates on Collibra cloud. I have not faced any issues concerning scalability. Despite earlier concerns when it took eight hours for one data job, recent improvements have reduced this time to three to four hours, though it still depends on metadata volume.

For technical support, I would rate it as eight because with premium support, the service is excellent.

The initial setup of Collibra Lineage is very simple nowadays. You can drag lineage from the UI by selecting and dropping whatever configuration you want. For configuring Lineage capability for Snowflake, there are multiple options available. It's particularly efficient since last year when they moved lineage to UI.

I have not used data flow mapping with Collibra Lineage. I question the helpfulness of visualization features for Collibra, particularly lineage visualization.

I agree with the assessment that lineage visualization in Collibra is valuable.

Overall rating: 7 out of 10.

Regarding my most common use cases for Collibra Data Intelligence Platform, I can describe them clearly.

The platform provides me with data cataloging features, which is really helpful.

I can confirm that using the Collibra Data Intelligence Platform provides time savings, money savings, and other benefits that I have experienced.

Collibra Data Intelligence Platform impressively provides AI features.

I have noticed it works within one ecosystem where each tool is collaborating effectively.

In terms of improvements, I would highlight a few points that could be enhanced in the Collibra Data Intelligence Platform.

I am satisfied with the Collibra technical support team. If I ever contacted them, I might have had some issue or question for them.

If I had to rate them, I would definitely use a scale from one to ten to express my thoughts effectively.

I would give a 10 for the support team.

I have some experience deploying the Collibra Data Intelligence Platform, and the initial setup and configuration for the needs take a specific timeframe.

I may not recall exactly how long it took for my company or my clients to deploy the Collibra Data Intelligence Platform, but I can provide an estimate.

It is impressive to see how fast it can be set up, typically within a few days or working days, which I can confirm is really fast.

On our website, I did research on Data Governance tools, and I can share feedback on that type of tools.

I am representing PeerSpot, which is a review website, and I am collecting feedback on the tools to publish an article on our website.

For Data Governance tools, I have worked with Purview of Microsoft, though this was mainly for research and POC that I did for my clients.

I have been working with Collibra Data Intelligence Platform for a specific period.

On a scale of 1 to 10, I would rate Collibra Data Intelligence Platform a 10.

I work with Collibra Data Intelligence Platform. I am experienced in both platforms, but more experienced with Collibra Data Intelligence Platform. I have experience creating workflows, harvesting technical lineage, and working with Data Governance along with data quality.

The workflows in Collibra Data Intelligence Platform are unique as no other Data Governance tool has this feature. The data quality functionality provides cutting-edge, next-level insights when compared to other tools such as Alation and Microsoft Purview. In governance, Collibra Data Intelligence Platform has an upper hand.

The mapping features in Collibra Data Intelligence Platform allow users to import and export Excel files. While importing Excel files, users can map to numerous assets. We can build relations using these imports and mappings, and obtain lineage information. Additionally, there is integration between Data Quality and the data intelligence platform, which enables another type of mapping.

The workflows in Collibra Data Intelligence Platform save substantial time by allowing us to create and assign tasks to business stewards and stakeholders. Instead of sending emails each day, we can create tasks that automatically generate notifications to streamline the process.

The main advantage of Purview is its integration with Microsoft solutions. Data can be harvested from any solution within Azure, data fabric, or other platforms easily with Purview. With Collibra Data Intelligence Platform, users need to create and establish connections, which requires entering connection details. This process is more time-consuming in Collibra Data Intelligence Platform compared to Purview, where everything can be done in minutes. Additionally, Purview includes data products, which Collibra Data Intelligence Platform is currently introducing. For stakeholders, data products are crucial.

I have been using the solution for three years, precisely two years and ten months. This July it will be three years. I started as a fresher in Data Governance and have been working in this field since the beginning.

I have not experienced any downtime or stability issues until now.

I have not had any experience working with AI governance-related API. I am satisfied with their support team. I encountered some issues with data quality initially in Collibra Data Intelligence Platform, and they resolved those issues. I also faced challenges with establishing data quality rules in DQC aggregation files, which were resolved by the support team.

There were no blockers during setup. It was straightforward to create workflows and deploy them into Collibra Data Intelligence Platform by following the documentation.

Data quality in Collibra Data Intelligence Platform involves six dimensions: accuracy, completeness, integrity, and timeliness. The platform's Data Quality encompasses all these aspects.

Task completion time depends on the specific tasks in Collibra Data Intelligence Platform. For example, an approval task typically takes less than one hour.

Collibra Data Intelligence Platform introduced AI governance approximately two years ago. It features assessments that work in conjunction with AI governance, where users answer questions created by stakeholders or Data Owners.

I rate this solution 8 out of 10.