VP- Cloud Data/ Solution Architect at a financial services firm with 10,001+ employees
Real User
Top 5
2023-10-09T14:32:27Z
Oct 9, 2023
It primarily focuses on managing data permissions and entitlements and plays a crucial role in data control and governance within AWS. Once our data is stored in the cloud, we grant access to various users and applications for various purposes, such as analytics, reporting, or other data-driven activities. To manage these access patterns, we define permissions either by giving direct access to users or by creating roles via AWS IAM which can be configured within AWS Lake Formation.
Head of Business Intelligence, Analytics and Big Data Service Line at NTT DATA
Real User
2020-11-02T17:15:00Z
Nov 2, 2020
In general, our clients use this as storage for raw data coming from systems. Our experience is that from the raw data you have to build up the data lake like a data warehouse. It's quite easy to pass from S3, for example, to Redshift. We used the data lake on AWS to store raw data and in some other cases to process advanced analytics. But the main use case here is storing raw data or archiving some data.
AWS Lake Formation is a service that makes it easy to set up a secure data lake in days. A data lake is a centralized, curated, and secured repository that stores all your data, both in its original form and prepared for analysis.
It primarily focuses on managing data permissions and entitlements and plays a crucial role in data control and governance within AWS. Once our data is stored in the cloud, we grant access to various users and applications for various purposes, such as analytics, reporting, or other data-driven activities. To manage these access patterns, we define permissions either by giving direct access to users or by creating roles via AWS IAM which can be configured within AWS Lake Formation.
We primarily use the solution in order to build infrastructure. It's basically for building network infrastructure.
In general, our clients use this as storage for raw data coming from systems. Our experience is that from the raw data you have to build up the data lake like a data warehouse. It's quite easy to pass from S3, for example, to Redshift. We used the data lake on AWS to store raw data and in some other cases to process advanced analytics. But the main use case here is storing raw data or archiving some data.
We primarily use the solution as a cloud data warehouse.