AWS Big Data Blog

Category: Amazon Simple Storage Services (S3)

Extract Oracle OLTP data in real time with GoldenGate and query from Amazon Athena

This post describes how you can improve performance and reduce costs by offloading reporting workloads from an online transaction processing (OLTP) database to Amazon Athena and Amazon S3. The architecture described allows you to implement a reporting system and have an understanding of the data that you receive by being able to query it on arrival.

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How to export an Amazon DynamoDB table to Amazon S3 using AWS Step Functions and AWS Glue

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.

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Trigger cross-region replication of pre-existing objects using Amazon S3 inventory, Amazon EMR, and Amazon Athena

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 […]

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Improve Apache Spark write performance on Apache Parquet formats with the EMRFS S3-optimized committer

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 […]

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Our data lake story: How Woot.com built a serverless data lake on AWS

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 […]

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Migrate to Apache HBase on Amazon S3 on Amazon EMR: Guidelines and Best Practices

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.

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Connect to Amazon Athena with federated identities using temporary credentials

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.

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How to build a front-line concussion monitoring system using AWS IoT and serverless data lakes – Part 2

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 […]

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How to build a front-line concussion monitoring system using AWS IoT and serverless data lakes – Part 1

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.

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Build a Concurrent Data Orchestration Pipeline Using Amazon EMR and Apache Livy

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.

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