Category: Amazon Simple Storage Services (S3)

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 […]

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 simplest solution appeared to be a straightforward lift-and-shift migration from one relational database to another. However, we decided to step back and focus […]

This whitepaper walks you through the stages of a migration. It also helps you determine when to choose Apache HBase on Amazon S3 on Amazon EMR, plan for platform security, tune Apache HBase and EMRFS to support your application SLA, identify options to migrate and restore your data, and manage your cluster in production.

This post walks through three scenarios to enable trusted users to access Athena using temporary security credentials. First, we use SAML federation where user credentials were stored in Active Directory. Second, we use a custom credentials provider library to enable cross-account access. And third, we use an EC2 Instance Profile role to provide temporary credentials for users in our organization to access Athena.

In part 1 of this series, we demonstrated how to build a data pipeline in support of a data lake. We used key AWS services such as Amazon Kinesis Data Streams, Kinesis Data Analytics, Kinesis Data Firehose, and AWS Lambda. In part 2, we discuss how to process and visualize the data by creating a […]

In this two-part series, we show you how to build a data pipeline in support of a data lake. We use key AWS services such as Amazon Kinesis Data Streams, Kinesis Data Analytics, Kinesis Data Firehose, and AWS Lambda. In part 2, we focus on generating simple inferences from that data that can support RTP parameters.

In this post, we explore orchestrating a Spark data pipeline on Amazon EMR using Apache Livy and Apache Airflow, we create a simple Airflow DAG to demonstrate how to run spark jobs concurrently, and we see how Livy helps to hide the complexity to submit spark jobs via REST by using optimal EMR resources.

In this post, we show you how to export data from a DynamoDB table, convert it into a more efficient format with AWS Glue, and query the data with Athena. This approach gives you a way to pull insights from your data stored in DynamoDB.

Kinesis Data Firehose can now save data to Amazon S3 in Apache Parquet or Apache ORC format. These are optimized columnar formats that are highly recommended for best performance and cost-savings when querying data in S3. This feature directly benefits you if you use Amazon Athena, Amazon Redshift, AWS Glue, Amazon EMR, or any other big data tools that are available from the AWS Partner Network and through the open-source community.

I’ll describe how to read the DynamoDB backup file format in Data Pipeline, how to convert the objects in S3 to a CSV format that Amazon ML can read, and I’ll show you how to schedule regular exports and transformations using Data Pipeline.