Full Data Engineer at a financial services firm with 10,001+ employees
Real User
Top 20
2025-08-29T11:50:23Z
Aug 29, 2025
In my experience, I have mostly worked with AWS, but I did have a little experience with Google Cloud, although I didn't develop on Google Cloud; I had some limited contact. In my experience, I have worked primarily with AWS Lake Formation for data warehousing, as I had a significant contract with AWS while working in my last corporation, Natura Pay, followed by my time at Itaú Bank, which had a major contract with Amazon, making AWS the dominant experience for me.
My use cases include real-time healthcare analysis at Kaiser, where we build real-time streaming pipelines using Kafka and Apache Flink to process patient monitoring data. Data from electronic healthcare records is ingested through Kafka. It is processed in Flink with event time aggregation and stored in Delta Lakes for downstream analysis. We use this to reduce latency from minutes to seconds, as we aim for real-time visibility into patient healthcare monitoring. The other use case involves using AWS and Azure together, specifically in stream and batch processing pipelines for IoT devices in drilling equipment at Kaiser, where we use Spark on Hadoop for large-scale data processing and Kafka and Azure Event Hubs for real-time ingestion. We also enable detection of models, such as predicting equipment failure, which reduces downtime by 20% and cuts infrastructure costs by 40% through migration to Azure.
I am an end user of AWS Lake Formation. We use AWS Lake Formation to control all our data lake at the UOL company. We use AWS Lake Formation to control privacy and build privacy controls. We split information based on user roles, and AWS Lake Formation ensures that users can access only specific data. Beyond that, we use AWS Lake Formation to control our schemas. By controlling schemas, we define the types of data we input. For instance, we have schema control that determines which field is char and which field is int. We use AWS Lake Formation to control that and input descriptions in these fields. For instance, if we have a table called sales with a field ID, our team inputs what this field means in the application. ID means the number of identification of our student or client. We use AWS Lake Formation to manage permissions and governance. In our company, we use AWS Lake Formation just to manage data. We use other solutions to transform data.
AWS Data Engineer at a healthcare company with 10,001+ employees
Real User
Top 20
2025-08-05T15:31:00Z
Aug 5, 2025
We are the review platform and informational platform that works with different vendors to collect thoughts and opinions on products. We have partnered with AWS and are helping them collect feedback regarding AWS Lake Formation solutions. In my recent projects, I have over seven years of experience working as an AWS data engineer. I have used all AWS services throughout my projects according to requirements. I have used AWS Glue to build robust pipelines and Lambda to trigger the pipelines when an event occurs. I have also used Apache Airflow to automate all pipelines. Additionally, I have utilized SNS (Simple Notification Services), Amazon QuickSight, and Oozie. All these services have been implemented in my project according to their specific uses.
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.
In my experience, I have mostly worked with AWS, but I did have a little experience with Google Cloud, although I didn't develop on Google Cloud; I had some limited contact. In my experience, I have worked primarily with AWS Lake Formation for data warehousing, as I had a significant contract with AWS while working in my last corporation, Natura Pay, followed by my time at Itaú Bank, which had a major contract with Amazon, making AWS the dominant experience for me.
My use cases include real-time healthcare analysis at Kaiser, where we build real-time streaming pipelines using Kafka and Apache Flink to process patient monitoring data. Data from electronic healthcare records is ingested through Kafka. It is processed in Flink with event time aggregation and stored in Delta Lakes for downstream analysis. We use this to reduce latency from minutes to seconds, as we aim for real-time visibility into patient healthcare monitoring. The other use case involves using AWS and Azure together, specifically in stream and batch processing pipelines for IoT devices in drilling equipment at Kaiser, where we use Spark on Hadoop for large-scale data processing and Kafka and Azure Event Hubs for real-time ingestion. We also enable detection of models, such as predicting equipment failure, which reduces downtime by 20% and cuts infrastructure costs by 40% through migration to Azure.
I am an end user of AWS Lake Formation. We use AWS Lake Formation to control all our data lake at the UOL company. We use AWS Lake Formation to control privacy and build privacy controls. We split information based on user roles, and AWS Lake Formation ensures that users can access only specific data. Beyond that, we use AWS Lake Formation to control our schemas. By controlling schemas, we define the types of data we input. For instance, we have schema control that determines which field is char and which field is int. We use AWS Lake Formation to control that and input descriptions in these fields. For instance, if we have a table called sales with a field ID, our team inputs what this field means in the application. ID means the number of identification of our student or client. We use AWS Lake Formation to manage permissions and governance. In our company, we use AWS Lake Formation just to manage data. We use other solutions to transform data.
We are the review platform and informational platform that works with different vendors to collect thoughts and opinions on products. We have partnered with AWS and are helping them collect feedback regarding AWS Lake Formation solutions. In my recent projects, I have over seven years of experience working as an AWS data engineer. I have used all AWS services throughout my projects according to requirements. I have used AWS Glue to build robust pipelines and Lambda to trigger the pipelines when an event occurs. I have also used Apache Airflow to automate all pipelines. Additionally, I have utilized SNS (Simple Notification Services), Amazon QuickSight, and Oozie. All these services have been implemented in my project according to their specific uses.
I use AWS Lake Formation primarily for data lakes.
Our data team uses the solution for ETL jobs.
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.