Category: AWS Lake Formation

In today’s data-driven world, organizations face unprecedented challenges in managing and extracting valuable insights from their ever-expanding data ecosystems. As the number of data assets and users grow, the traditional approaches to data management and governance are no longer sufficient. Customers are now building more advanced architectures to decentralize permissions management to allow for individual […]

Amazon EMR Studio is an integrated development environment (IDE) that makes it straightforward for data scientists and data engineers to develop, visualize, and debug data engineering and data science applications written in R, Python, Scala, and PySpark. EMR Studio provides fully managed Jupyter notebooks and tools such as Spark UI and YARN Timeline Server via […]

Data governance is the process of ensuring the integrity, availability, usability, and security of an organization’s data. Due to the volume, velocity, and variety of data being ingested in data lakes, it can get challenging to develop and maintain policies and procedures to ensure data governance at scale for your data lake. In this post, we showcase how to use AWS Glue with AWS Glue Data Quality, sensitive data detection transforms, and AWS Lake Formation tag-based access control to automate data governance.

Data lakes have been gaining popularity for storing vast amounts of data from diverse sources in a scalable and cost-effective way. As the number of data consumers grows, data lake administrators often need to implement fine-grained access controls for different user profiles. They might need to restrict access to certain tables or columns depending on […]

To ease the transition of data lake permissions from an IAM and S3 model to Lake Formation, we’re introducing a hybrid access mode for AWS Glue Data Catalog. This feature lets you secure and access the cataloged data using both Lake Formation permissions and IAM and S3 permissions. Hybrid access mode allows data administrators to onboard Lake Formation permissions selectively and incrementally, focusing on one data lake use case at a time. For example, say you have an existing extract, transform and load (ETL) data pipeline that uses the IAM and S3 policies to manage data access. Now you want to allow your data analysts to explore or query the same data using Amazon Athena. You can grant access to the data analysts using Lake Formation permissions, to include fine-grained controls as needed, without changing access for your ETL data pipelines.

Today’s modern data lakes span multiple accounts, AWS Regions, and lines of business in organizations. Companies also have employees and do business across multiple geographic regions and even around the world. It’s important that their data solution gives them the ability to share and access data securely and safely across Regions. The AWS Glue Data […]

The GDPR (General Data Protection Regulation) right to be forgotten, also known as the right to erasure, gives individuals the right to request the deletion of their personally identifiable information (PII) data held by organizations. This means that individuals can ask companies to erase their personal data from their systems and any third parties with […]

Data-driven organizations treat data as an asset and use it across different lines of business (LOBs) to drive timely insights and better business decisions. Many organizations have a distributed tools and infrastructure across various business units. This leads to having data across many instances of data warehouses and data lakes using a modern data architecture […]

Apache Hive is a SQL-based data warehouse system for processing highly distributed datasets on the Apache Hadoop platform. There are two key components to Apache Hive: the Hive SQL query engine and the Hive metastore (HMS). The Hive metastore is a repository of metadata about the SQL tables, such as database names, table names, schema, […]

Amazon Finance Automation (FinAuto) is the tech organization of Amazon Finance Operations (FinOps). Its mission is to enable FinOps to support the growth and expansion of Amazon businesses. It works as a force multiplier through automation and self-service, while providing accurate and on-time payments and collections. FinAuto has a unique position to look across FinOps […]