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The Latest Monitoring and Observability Topics

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Simplified Time-Series Analytics Using the time_bucket() Function
Turn raw data into fixed time intervals for metric analysis and visualization.
August 9, 2019
by Diana Hsieh
· 18,043 Views · 3 Likes
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Implementing Serverless Microservices Architecture on AWS
In this article, we will explain how enterprises can implement serverless microservices architectures using AWS Cloud.
August 5, 2019
by Aarti Parikh
· 21,840 Views · 11 Likes
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Collecting Prometheus Metrics With Azure Monitor
Learn more about collecting Prometheus metrics with Azure Monitor.
August 5, 2019
by Sam Cogan
· 10,851 Views · 1 Like
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Debug and Execute Code From FLASH on the Seeed Arch Mix NXP i.MX RT1052 Board
Learn more about debugging and executing code from FLASH.
July 30, 2019
by Erich Styger
· 8,195 Views · 2 Likes
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How to Build a Distributed Load Testing Infrastructure with AWS, Docker, and JMeter
A tutorial on how to build a distributed load testing infrastructure with AWS, Docker, and JMeter.
Updated July 29, 2019
by Dragos Campean
· 19,957 Views · 4 Likes
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Serverless Multi-Tier Architecture on AWS
AWS's serverless tools enable developers to create a connected multi-tier architecture for easily maintainable and scaleable applications.
July 24, 2019
by Aarti Parikh
· 14,160 Views · 7 Likes
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Moving to Observability Driven Development
An outline of how to move to observability driven development, and the different components thereof.
July 23, 2019
by Bharath Kasimani
· 18,339 Views · 11 Likes
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Business Intelligence in Microservices: Improving Performance
An in-depth review of the different ways to improve your microservices.
July 22, 2019
by Artem Barmin
· 25,761 Views · 11 Likes
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Microservices Observability (Part 1)
This is a demonstration of how to observe, trace, and monitor microservices on Java applications in an Openshift environment.
July 18, 2019
by Raphael Abreu
· 41,714 Views · 24 Likes
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Remote Debugging Java Applications With JDWP
Take a look at this simple breakdown of how to use the JDWP to debug Java applications.
July 16, 2019
by Mahmoud Anouti
· 30,611 Views · 1 Like
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Migrating Spring Java Applications to Azure App Service (Part 2 - Logging and Monitoring)
After having migrated your legacy Java application, integrate the Applications Insight plugin for monitoring and logging.
July 16, 2019
by Elena Neroslavskaya
· 10,221 Views · 5 Likes
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How to Upload and Serve Data Using Amazon CloudFront and Amazon S3 in Node.js
Upload and serve data faster and more efficiently.
July 15, 2019
by Swathi Prasad
· 11,700 Views · 4 Likes
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Dialogflow vs. Lex vs. Watson vs. Wit vs. Azure Bot
See a comparison of Dialogflow, Lex, IBM Watson, Wit, and Azure bot.
July 12, 2019
by Devashish Mamgain
· 22,025 Views · 5 Likes
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Import Local SQL Server Database to Azure: Pick the Best Method
In this post, we will discuss two different approaches for the migration from on-premises SQL Server to Azure SQL database.
Updated July 10, 2019
by Andrew Jackson
· 20,721 Views · 2 Likes
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Tips to Debug Jenkins v2.x Pipeline Script(s), Part 2
The second part of this series takes a more in-depth look at how to debug a Jenkins Pipeline.
Updated July 10, 2019
by Ramanathan M
· 14,424 Views · 2 Likes
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How to Upload and Serve Data Using Amazon CloudFront and Amazon S3 in Node.js
In this post, we'll learn how to upload files to an S3 bucket and serve those files through CloudFront in Node.js.
July 10, 2019
by Swathi Prasad
· 18,701 Views · 3 Likes
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DBMS_JOB — Watching for Failures
If you rely on jobs submitted via DBMS_JOB to fail, then read on.
June 28, 2019
by Connor McDonald
· 12,455 Views · 2 Likes
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AWS Workflow With PyCharm
Avoid some of the snafus of creating a workflow using AWS Toolkits with this tutorial.
June 26, 2019
by Marco Christiani
· 11,252 Views · 3 Likes
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Oracle vs. Snowflake
Take a look at Oracle vs. Snowflake from someone who has worked for both.
Updated June 25, 2019
by John Ryan
· 20,710 Views · 5 Likes
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The 4 AWS Pricing Principles with a Critical Eye
Amazon Web Services advertises four principles that guide their pricing strategy - pay as you go, pay less by using more, save when you reserve, and their free tier. These principles provide immense benefits and efficiencies for thousands of organizations, which has driven AWS’s stellar growth. But there are downsides as well. In this article we'll review the five principles and provide a grain of salt you should consider before hooking into the Amazon machine. Principle #1: Pay as You Go The Principle: This is the main idea behind AWS - instead of buying or building costly infrastructure, rent it. AWS is dedicated to turning your CapEx expenses into OpEx. It also provides extreme flexibility - you can order 1,000 machines for an hour and then stop them and pay only for those 1,000 machine hours. The Good: It’s why organizations started moving to the cloud. Pay per use is great because it eliminates overcapacity and wasted computing resources. The Bad: Flexibility comes at a cost. If you want to really “pay as you go”, you’ll have to settle for Amazon’s On-Demand Pricing, which becomes quite expensive even for small workloads, when used for ongoing server deployments. Truth be told, most servers don’t have huge peaks or troughs in their usage - and running them on dedicated hosting or on-prem will be a lot cheaper than on Amazon. Principle #2: Pay Less by Using More The Principle: AWS provides volume discounts. Amazon S3 and many other services offer tiered pricing, and Amazon EC2 offers volume discounts for users who spend more than $500,000 in upfront costs. Amazon also provides a plethora of services and options for most use cases, allowing you to switch to a service that meets your need at a lower cost. For example, there are several AWS backup options including the AWS Backup service and storage services like S3, Glacier, EBS, EFS, etc. Organizations can move data between these storage services to gain efficiencies. The Good: Sophisticated users of AWS can save a lot by dynamically moving workloads between services and creating economies of scale. The Bad: This principle is also one of the hidden reasons for Amazon’s enormous complexity. True - you can create a tiered storage strategy and save 90% or more in many cases. But do your engineers or IT staff know the intricacies of each data service, and have the know-how to detect the relevant events and store data selectively into different data stores? Amazon provides the tools to do all this. But it requires time and expertise which by itself costs organizations serious money. Principle #3: Save When You Reserve The Principle: At the core of AWS is its compute service, Amazon EC2. EC2 machine instances are substantially discounted (on the order of 30-50%) if you reserve an instance for 1-3 years in advance. Another option is to use “spot instances” - machine instances that happen to be available at a given time, and will be taken away from you when another user demands them. Switching loads dynamically between spot instances, and helping Amazon manage their demand, can give you even bigger discounts. The Good: Amazon provides a lot of price flexibility. You can significantly cut costs by committing to 1 years or more - it’s possible to do this selectively for some workloads, while using others on demand. The spot instances option is a creative one, which lets anyone with expertise, and the time to architect a spot instances solution, shave 60% off costs. The Bad: Committing to a machine instance for 1 to 3 years on the cloud might sound like an oxymoron. Organizations are moving to the cloud to get computing resources on demand. A long-term financial commitment flies in the face of this flexibility. Many AWS users take on-demand prices as a given, and pay the price of flexibility. Principle #4: Free Usage Tier The Principle: Amazon grants 1 year of free usage with generous quotas for many of its services, to reduce risk and encourage cloud adoption. This was a primary way AWS gained its initial market share in the early years. The Free Tier Grants (as of the time of this writing) 1 year of usage with 750 hours of EC2 instances, 750 hours of RDS usage (can run managed databases like MySQL), 5 GB on S3, 1 million requests on the cool serverless delivery platform Lambda, 50 GB storage on CloudFront (delivery network), 5GB on EFS (file storage), 30GB on EBS (block storage), 750 hours of ElasticSearch, and more. The Bad: The free tier has helped many organizations and technologists get “hooked” on Amazon’s offerings - it is a showcase of the astounding depth, breadth and technical excellence of their service profile. Amazon provides - and encourages - an enormous amount of sophistication within its ecosystem. It provides power, but power brings with it responsibility, overhead and a high cost of skills. Very often, organizations select AWS by default because it is a market leader and the option most well known by their teams. I can’t say the free tier is bad, but it has creatd an unfair advantage vs. other cloud offerings, which have their own strengths. Wrap Up AWS is great, but it is also a business and has established pricing that safeguards its interests. Carefully consider the benefits and tradeoffs of the Amazon pricing philosophy before entering a large-scale engagement. If you're already heavily engaged, plan your cloud consumption 1-2 years ahead and see if other cloud platforms - such as Azure or Google Cloud Platform - can give Amazon a run for its money.
Updated June 25, 2019
by Gilad David Maayan
· 23,193 Views · 2 Likes
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