Category: Analytics

Today, we’re introducing the Amazon EMR Migrations Guide (first published June 2019.) This paper is a comprehensive guide to offer sound technical advice to help customers in planning how to move from on-premises big data deployments to EMR.

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.

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.

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 […]

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.

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 […]

In this blog post, we introduce Deequ, an open source tool developed and used at Amazon. Deequ allows you to calculate data quality metrics on your dataset, define and verify data quality constraints, and be informed about changes in the data distribution. Instead of implementing checks and verification algorithms on your own, you can focus on describing how your data should look.

Many customers use Amazon EMR to run big data workloads, such as Apache Spark and Apache Hive queries, in their development environment. Data analysts and data scientists frequently use these types of clusters, known as analytics EMR clusters. Users often forget to terminate the clusters after their work is done. This leads to idle running […]

Data warehousing is a critical component for analyzing and extracting actionable insights from your data. Amazon Redshift is a fast, scalable data warehouse that makes it cost-effective to analyze all of your data across your data warehouse and data lake. The Amazon Redshift console recently launched the Query Editor. The Query Editor is an in-browser […]