Recently, I have been working with Informatica, specifically with IDMS, data quality, and integration systems also of Informatica. Currently, I am not working with it, but I am in the process of getting it. Google Dataplex is one of our counterpart teams using it, but as a data leader, I am managing that. It is good, but Informatica is better because from the industry point of view, they are much better. Since I am managing this solution, I am using it internally in my company. For Google Cloud data, Google Dataplex's AI-driven data classification has really helped; the out-of-the-box capabilities are good, and they have good classifiers. But when I compare with the industry, they are evolving. The evolving part for me is the classifiers; if I look at Informatica, they have around 247 classifiers, but Google Cloud has limited. Regarding Google Dataplex's data lifecycle management, I have not used that capability, but I know that it is there. Lifecycle management is something I am building separately; actually, my team is building it separately. There is nothing that I would improve or enhance because that is secondary for me; for me, the main thing is Informatica. Google Dataplex is something which is a small use case for me, but my main focus is Informatica. Since I have less than 10% of the data based in Google Cloud, we are facing some challenges, which is why we used Google Dataplex, but that is not going to the core. Regarding any positive impact of Google Dataplex on my work, nothing major has come up as of today, other than the cataloging and lineage; at least with the limited usage we are doing, it is somewhat supporting us in the lineage part. My overall review rating for Google Dataplex is 5 out of 10.
Data Governance ensures reliable data across an organization by managing data availability, usability, and security. It sets policies and processes critical to the control of data quality, compliance, and accessibility.Enterprises must implement a solid Data Governance strategy to handle data growth and complexity. A well-defined framework helps ensure accurate reporting, improved regulatory compliance, and efficient data management. Users value solutions that offer robust policy frameworks,...
Recently, I have been working with Informatica, specifically with IDMS, data quality, and integration systems also of Informatica. Currently, I am not working with it, but I am in the process of getting it. Google Dataplex is one of our counterpart teams using it, but as a data leader, I am managing that. It is good, but Informatica is better because from the industry point of view, they are much better. Since I am managing this solution, I am using it internally in my company. For Google Cloud data, Google Dataplex's AI-driven data classification has really helped; the out-of-the-box capabilities are good, and they have good classifiers. But when I compare with the industry, they are evolving. The evolving part for me is the classifiers; if I look at Informatica, they have around 247 classifiers, but Google Cloud has limited. Regarding Google Dataplex's data lifecycle management, I have not used that capability, but I know that it is there. Lifecycle management is something I am building separately; actually, my team is building it separately. There is nothing that I would improve or enhance because that is secondary for me; for me, the main thing is Informatica. Google Dataplex is something which is a small use case for me, but my main focus is Informatica. Since I have less than 10% of the data based in Google Cloud, we are facing some challenges, which is why we used Google Dataplex, but that is not going to the core. Regarding any positive impact of Google Dataplex on my work, nothing major has come up as of today, other than the cataloging and lineage; at least with the limited usage we are doing, it is somewhat supporting us in the lineage part. My overall review rating for Google Dataplex is 5 out of 10.