Catalog is primarily used for metadata cataloging and enriching it with additional information, such as adding business terms. Then, if lineage support is available in the tool, we can link business terms to technical terms.
Additionally, we can store sample data. If there's a client requirement, we can link physical metadata with reports or datasets. This allows users who need access to a particular dataset to obtain it by creating a dataset from the Catalog metadata.
For data discovery, we create datasets. The most frequently used datasets are featured on the dashboard. Users can create their own if their requirements aren't met by the most frequently used datasets. We also create data requests, and owners can approve these requests, which adds a process layer to accessing particular datasets.
So, it has been effective for data discovery in our company.
I use different functionalities within the tool. I use Collibra Catalog for metadata management or Collibra Lineage for data governance.
Once configured, data in the Catalog will be automatically updated, reducing the need for manual maintenance. So, the automation feature has positively impacted our data management tasks.
The data lineage capability is valuable as it shows how different sources are connected and how data flows, which is crucial for projects like migrations.
Moreover, data lineage visualization in Collibra Catalog aids our data governance initiatives. It's very important for identifying impacted reports during updates, making it crucial for decision-making due to its ability to link business and technical terms.
I'd like to see more integration with other reporting sources like Qlik Sense, beyond the currently supported ones like Tableau and Power BI.
I have been using it since 2019. It has been a long time.
Collibra updates the job server every quarter, so it's a stable solution that requires quarterly updates.
I would rate the stability a nine out of ten because it's quite good.
It's scalable. We create different configurations for each data source to minimize latency and ensure efficient data governance. This decentralized approach makes it highly scalable.
I would rate it a nine out of ten because it's quite good. It supports multiple authentication methods, not just username and password. It supports mechanisms like Windows authentication or any other supported source, so there's no issue.
We make it available to everyone in the company. Moreover, most users are on the client side during implementation.
The customer service and support have improved, and Collibra provides very good solutions now. They have a knowledge base that can answer our queries, suggesting solutions before we need to contact support.
The setup is quite easy and takes about 30 minutes to an hour if you have everything you need. Configuration isn't very difficult.
Deployment involves creating a job server in the client-hosted environment and configuring it to connect to all data tools, like MySQL, Oracle, or SQL Server. Then, we establish a connection between the job server and Collibra.
Data governance instances and the job server send requests to the actual data sources. Metadata gathered from the sources is passed to Collibra through this setup.
We're moving towards cloud solutions, so Collibra Data Intelligence is on the cloud. Collibra Edge can be server-based, but the job server can also be on-premises. It supports both setups, but the direction is towards cloud-only solutions.
One person can maintain the solution, and we need one service account for it.
Collibra offers a per-user licensing model.
Overall, I would rate the solution an eight out of ten because we can connect to multiple sources or create custom connectors, making it a good solution.
I highly recommend it.
