Tag: Amazon S3

In this post, we discuss how to leverage the automatic code generation process in AWS Glue ETL to simplify common data manipulation tasks, such as data type conversion and flattening complex structures. We also explore using AWS Glue Workflows to build and orchestrate data pipelines of varying complexity. Lastly, we look at how you can leverage the power of SQL, with the use of AWS Glue ETL and Glue Data Catalog, to query and transform your data.

Siemens is a global technology leader with more than 370,000 employees and 170 years of experience. To protect Siemens from cybercrime, the Siemens Cyber Defense Center (CDC) continuously monitors Siemens’ networks and assets. To handle the resulting enormous data load, the CDC built a next-generation threat detection and analysis platform called ARGOS. ARGOS is a […]

The AWS Cost & Usage Report (CUR) tracks your AWS usage and provides estimated charges associated with that usage. You can configure this report to present the data at hourly or daily intervals, and it is updated at least one time per day until it is finalized at the end of the billing period. The […]

This is a guest post by Floating Point Group. In their own words, “Floating Point Group is on a mission to bring institutional-grade trading services to the world of cryptocurrency.” The need and demand for financial infrastructure designed specifically for trading digital assets may not be obvious. There’s a rather pervasive narrative that these coins […]

This post outlines the approach taken by Intuit, though it is important to remember that there are many ways to build a data lake (for example, AWS Lake Formation). We’ll cover the technologies and processes involved in creating the Intuit Data Lake at a high level, including the overall structure and the automation used in provisioning accounts and resources. Watch this space in the future for more detailed blog posts on specific aspects of the system, from the other teams and engineers who worked together to build the Intuit Data Lake.

This post provides a detailed walkthrough of two new encryption options to help you secure your EMR cluster that handles sensitive data. The first option is native EBS encryption to encrypt volumes attached to EMR clusters. The second option is an Amazon S3 encryption that allows you to use different encryption modes and customer master keys (CMKs) for individual S3 buckets with Amazon EMR.

In this post, I show you how to use AWS Glue’s DynamoDB integration and AWS Step Functions to create a workflow to export your DynamoDB tables to S3 in Parquet. I also show how to create an Athena view for each table’s latest snapshot, giving you a consistent view of your DynamoDB table exports.

In Amazon Simple Storage Service (Amazon S3), you can use cross-region replication (CRR) to copy objects automatically and asynchronously across buckets in different AWS Regions. CRR is a bucket-level configuration, and it can help you meet compliance requirements and minimize latency by keeping copies of your data in different Regions. CRR replicates all objects in […]

The EMRFS S3-optimized committer is a new output committer available for use with Apache Spark jobs as of Amazon EMR 5.19.0. This committer improves performance when writing Apache Parquet files to Amazon S3 using the EMR File System (EMRFS). In this post, we run a performance benchmark to compare this new optimized committer with existing committer […]

February 9, 2024: Amazon Kinesis Data Firehose has been renamed to Amazon Data Firehose. Read the AWS What’s New post to learn more. In this post, we talk about designing a cloud-native data warehouse as a replacement for our legacy data warehouse built on a relational database. At the beginning of the design process, the […]