Deploying containerized applications on Amazon ECS using cross-account elastic container registries

This is an updated version of a post, originally published in October 2019. This post uses AWS CLI version 2 and contains updated versions of all Docker images.

Introduction

There are two scenarios I frequently encounter that require sharing Amazon Elastic Container Registry (ECR)-based Docker images across multiple AWS Accounts. In the first scenario, a vendor wants to share a Docker image with their customer, stored in the vendor’s private container registry. Many popular container security and observability solutions function in this manner.

Below, we see an example of an application consisting of three containers. Two of the container images originated…


Monitoring indoor air quality with LoRaWAN gateways and devices using the recently released AWS IoT Core for LoRaWAN

In the following post, we will learn how to monitor indoor air quality (IAQ) using a private LoRaWAN sensor device network. The devices transmit their sensor telemetry to AWS through a LoRaWAN gateway using the newly released AWS IoT Core for LoRaWAN service. We will then analyze and visualize the sensor data using AWS IoT Analytics and Amazon QuickSight.

Amazon QuickSight Dashboard showing IAQ sensor data

Introduction

On December 15, 2020, AWS announced support for Semtech’s low-power, long-range wide area network (LoRaWAN) connectivity. LoRaWAN devices and gateways can now connect to AWS IoT Core using AWS IoT Core for LoRaWAN. AWS IoT Core for LoRaWAN is a fully…


Exploring the convergence of IoT, Data Analytics, and Edge Computing with Grafana, Mosquitto, and TimescaleDB on ARM-based devices

In the following post, we explore the integration of several open source applications to build an IoT edge analytics stack designed to operate on low-cost, ARM-based edge nodes. We will use the stack to collect, analyze, and visualize IoT data without first shipping the data to the Cloud or other external systems.

GMT IoT Edge Analytics Stack architecture (Image by author)

This post is a revised version of an earlier post, featuring major version updates of TimescaleDB (v1.7.4-pg12 to v2.0.0-pg12), Grafana (v7.1.5 to v7.5.2), and Mosquitto (v1.6.12 to v2.0.9). All source code and SQL scripts are revised. …


Employing Amazon Macie to discover and protect sensitive data in your Amazon S3-based data lakes

Introduction

Working with Analytics customers, it is not uncommon to see data lakes with a dozen or more discrete data sources. Data typically originates from sources both internal and external to the customer. Internal data may come from multiple teams, departments, divisions, and enterprise systems. External data comes from vendors, partners, public sources, and subscriptions to licensed data sources. The volume, velocity, variety, veracity, and method of delivery vary across the data sources. All this data is being fed into data lakes for purposes such as analytics, business intelligence, and machine learning.

Given the growing volumes of incoming data and variations…


Managing QuickSight users with a third-party enterprise identity provider (IdP)

Introduction

As a Solutions Architect working with Analytics customers, I am often asked about integrating Amazon QuickSight with Active Directory or single sign-on with third-party identity providers for user management.

Amazon QuickSight

Amazon QuickSight, according to AWS, is a scalable, serverless, embeddable, machine learning-powered business intelligence (BI) service built for the cloud. QuickSight lets you easily create and publish interactive BI dashboards that include machine learning-powered insights. QuickSight dashboards can be accessed from any device and seamlessly embedded into your applications, portals, and websites.

Auth0

Auth0 is an easy-to-implement, adaptable authentication and authorization platform. Auth0’s identity and management platform, according to Auth0, provides greater…


Understanding Amazon MWAA’s Configuration Options

Introduction

For anyone new to Amazon Managed Workflows for Apache Airflow (Amazon MWAA), especially those used to managing their own Apache Airflow platform, Amazon MWAA’s configuration might appear to be a bit of a black box at first. This brief post will explore Amazon MWAA’s configuration — how to inspect it and how to modify it. We will use Airflow DAGs to review an MWAA environment’s airflow.cfg file, environment variables, and Python packages.

Amazon MWAA

Apache Airflow is a popular open-source platform designed to schedule and monitor workflows. According to Wikipedia, Airflow was created at Airbnb in 2014 to manage the company’s increasingly…


Using the new Amazon Managed Workflows for Apache Airflow (Amazon MWAA) Service on AWS

Introduction

In the first post of this series, we explored several ways to run PySpark applications on Amazon EMR using AWS services, including AWS CloudFormation, AWS Step Functions, and the AWS SDK for Python. This second post in the series will examine running Spark jobs on Amazon EMR using the recently announced Amazon Managed Workflows for Apache Airflow (Amazon MWAA) service.

Amazon EMR

According to AWS, Amazon Elastic MapReduce (Amazon EMR) is a Cloud-based big data platform for processing vast amounts of data using common open-source tools such as Apache Spark, Hive, HBase, Flink, Hudi, and Zeppelin, Jupyter, and Presto. Using Amazon EMR…


Add data exploration and visualization to your analytics cluster

Introduction

AWS provides nearly twenty-five different open-source data analytics applications that can be automatically installed and configured on Amazon Elastic MapReduce (Amazon EMR). Of all those options, EMR doesn’t offer a general-purpose data exploration and visualization tool. However, with EMR, you can automate the installation of additional software as part of the cluster creation process or post cluster creation. This brief post will explore how to install, configure, and access Apache Superset, the modern data exploration and visualization platform on Amazon EMR’s Master Node, as a post-cluster creation step. …


Methods for Interacting with PySpark on Amazon Elastic MapReduce

Introduction

According to AWS, Amazon Elastic MapReduce (Amazon EMR) is a Cloud-based big data platform for processing vast amounts of data using common open-source tools such as Apache Spark, Hive, HBase, Flink, Hudi, and Zeppelin, Jupyter, and Presto. Using Amazon EMR, data analysts, engineers, and scientists are free to explore, process, and visualize data. EMR takes care of provisioning, configuring, and tuning the underlying compute clusters, allowing you to focus on running analytics.

Amazon EMR Console’s Cluster Summary tab

Users interact with EMR in a variety of ways, depending on their specific requirements. For example, you might create a transient EMR cluster, execute a series of data…


Exploring IoT Data Analytics at the Edge with Grafana, Mosquitto, and TimescaleDB on ARM-based Architectures

In the following post, we will explore the integration of several open-source software applications to build an IoT edge analytics stack, designed to operate on ARM-based edge nodes. We will use the stack to collect, analyze, and visualize IoT data without first shipping the data to the Cloud or other external systems.

GMT IoT Edge Analytics Stack architecture (Image by author)

The Edge

Edge computing is a fast-growing technology trend, which involves pushing compute capabilities to the edge. Wikipedia describes edge computing as a distributed computing paradigm that brings computation and data storage closer to the location needed to improve response times and save bandwidth. …

Gary A. Stafford

AWS Solutions Architect | AWS Certified Pro | Polyglot Developer | Data Analytics | DataOps | DevOps

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