AWS Big Data Blog

Guest post by Pablo Giner, Head of BI, Wind Mobility. Over the past few years, urban micro-mobility has become a trending topic. With the contamination indexes hitting historic highs, cities and companies worldwide have been introducing regulations and working on a wide spectrum of solutions to alleviate the situation. We at Wind Mobility strive to […]

Content, such as breaking news or sports scores, requires updates in near-real-time. To stay up to date, you may be constantly refreshing your browser or mobile app. Building APIs to deliver this content at speed and scale can be challenging. In this post, I present an alternative to an API-based approach. I outline the concept […]

We often have data processing requirements in which we need to merge multiple datasets with varying data ingestion frequencies. Some of these datasets are ingested one time in full, received infrequently, and always used in their entirety, whereas other datasets are incremental, received at certain intervals, and joined with the full datasets to generate output. To address this requirement, this post demonstrates how to build an extract, transform, and load (ETL) pipeline using AWS Glue.

In this post, we demonstrate how Moovit, with the support of AWS, implemented a lake house architecture by unloading data into Amazon Simple Storage Service (Amazon S3), instituting a hot/cold pattern using Amazon Redshift Spectrum — using AWS Glue to crawl and catalog the data — and querying data using Athena.

Amazon EMR web interfaces are hosted on the master node of an EMR cluster. When you launch an EMR cluster in a private subnet, the EMR master node doesn’t have a public DNS record. The web interfaces hosted in a private subnet aren’t easily accessible outside the subnet. You can use an Application Load Balancer (ALB), launched in a public subnet, as an HTTPS proxy to access EMR web interfaces over the internet without requiring SSH tunneling through a bastion host. This approach greatly simplifies accessing EMR web interfaces. This post outlines how to use an ALB to securely access EMR web interfaces over the internet for an EMR cluster launched in a private subnet.

This post discusses 10 best practices to help you maximize the benefits of Federated Query when you have large federated data sets, when your federated queries retrieve large volumes of data, or when you have many Redshift users accessing federated data sets. These techniques are not necessary for general usage of Federated Query. They are intended for advanced users who want to make the most of this exciting feature.

This post demonstrates how you can transfer Google Analytics data to Amazon S3 using Amazon AppFlow, and analyze it with Amazon Athena. You no longer need to build your own application to extract data from Google Analytics and other SaaS applications. Amazon AppFlow enables you to develop a fully automated data transfer and transformation workflow and an integrated query environment in one place.

This post demonstrates how to implement a simple SparkListener, monitor and observe Spark streaming applications, and set up some alerts. The post also shows how to use alerts to set up automatic scaling on Amazon EMR clusters, based on your CloudWatch custom metrics.

This post walks you through configuring ADFS 3.0 on a Windows Server 2012 R2 Amazon Elastic Compute Cloud (Amazon EC2) instance and setting up trust between ADFS 3.0 IdP and AWS through SAML 2.0. The post then demonstrates how to install the Athena OBDC driver on Amazon Linux EC2 instance (RHEL instance) and configure it to use ADFS for authentication.

This post details how we designed and implemented our data lake’s batch ETL pipeline to use Amazon EMR, and the numerous ways we iterated on its architecture to reduce Apache Spark runtimes from hours to minutes and save over 50% on operational costs.