AWS Big Data Blog

Performance updates to Apache Spark in Amazon EMR 5.24 – Up to 13x better performance compared to Amazon EMR 5.16

Amazon EMR release 5.24.0 includes several optimizations in Spark that improve query performance. To evaluate the performance improvements, we used TPC-DS benchmark queries with 3-TB scale and ran them on a 6-node c4.8xlarge EMR cluster with data in Amazon S3. We observed up to 13X better query performance on EMR 5.24 compared to EMR 5.16 when operating with a similar configuration.

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Introducing Amazon QuickSight fine-grained access control over Amazon S3 and Amazon Athena

Today, AWS is excited to announce the availability of fine-grained access control for AWS Identity and Access Management (IAM)-permissioned resources in Amazon QuickSight. Fine-grained access control allows Amazon QuickSight account administrators to control authors’ default access to connected AWS resources. Fine-grained access control enables administrators to use IAM policies to scope down access permissions, limiting specific authors’ access to specific items within the AWS resources. Administrators can now apply this new level of access control to Amazon S3, Amazon Athena, and Amazon RDS/Redshift database discovery.

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How 3M Health Information Systems built a healthcare data reporting tool with Amazon Redshift

After reviewing many solutions, 3M HIS chose Amazon Redshift as the appropriate data warehouse solution. We concluded Amazon Redshift met our needs; a fast, fully managed, petabyte-scale data warehouse solution that uses columnar storage to minimize I/O, provides high data compression rates, and offers fast performance. We quickly spun up a cluster in our development environment, built out the dimensional model, loaded data, and made it available to perform benchmarking and testing of the user data. An extract, transform, load (ETL) tool was used to process and load the data from various sources into Amazon Redshift.

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Amazon QuickSight updates: Multiple sheets in dashboards, axis label orientation options, and more

Today, we are pleased to announce a set of updates to Amazon QuickSight: Richer dashboards with multiple sheets in your regular and embedded dashboards, multiple axis label orientation options for better readability of dashboards, more calculations such as standard deviation, variance and conditional string functions on SPICE, enhanced URL actions for supporting a broader set of interaction scenarios, and one-click duplication of visuals for faster authoring.

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Optimizing downstream data processing with Amazon Kinesis Data Firehose and Amazon EMR running Apache Spark

This blog post shows how to use Amazon Kinesis Data Firehose to merge many small messages into larger messages for delivery to Amazon S3, which results in faster processing with Amazon EMR running Spark. This post also shows how to read the compressed files using Apache Spark that are in Amazon S3, which does not have a proper file name extension and store back in Amazon S3 in parquet format.

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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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Easily query AWS service logs using Amazon Athena

In this post, we’re open-sourcing a Python library known as Athena Glue Service Logs (AGSlogger). This library has predefined templates for parsing and optimizing the most popular log formats. The library provides a mechanism for defining schemas, managing partitions, and transforming data within an extract, transform, load (ETL) job in AWS Glue. AWS Glue is a serverless data transformation and cataloging service. You can use this library in conjunction with AWS Glue ETL jobs to enable a common framework for processing log data.

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EMR Notebooks: A managed analytics environment based on Jupyter notebooks

Notebooks are increasingly becoming the standard tool for interactively developing big data applications. It’s easy to see why. Their flexible architecture allows you to experiment with data in multiple languages, test code interactively, and visualize large datasets. To help scientists and developers easily access notebook tools, we launched Amazon EMR Notebooks, a managed notebook environment […]

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