This month our Content Team did an amazing job at publishing and updating a ton of new content. Not only did our experts release the brand new AZ-303 and AZ-304 Certification Learning Paths, but they also created 16 new hands-on labs — and so much more!
New content on Cloud Academy
At any time, you can find all of our new releases by going to our Training Library and finding the section titled “New this month in our library.” You can also keep track of what new training is coming for the next 4-6 weeks with our Content Roadmap.
- Microsoft Azure
- Google Cloud Platform
- Data Science/Artificial Intelligence
This course is an introduction to the fundamental aspects of Alibaba’s Object Storage Service (OSS). It starts off by explaining the features and advantages of the service, before moving on to the concepts of OSS and security. You will then watch two demos that use real-life examples from the Alibaba Cloud platform to guide you through storage buckets and object operations.
This Learning Path is for beginners and is intended for those who are ready to begin their journey into the AWS cloud. It covers AWS and its foundational services of compute, storage, networking, and databases.
This is a short course that will introduce you to the Amazon Elastic Block Store (EBS) service, giving you an overview of what the service is and when you would use it as a storage option for your Amazon EC2 instances.
This course explores the cost metrics associated with the Amazon Relational Database Service, known as RDS. Minimizing cloud spend is always a priority when architecting and designing your cloud solutions, and care should be taken to understand where your costs come from and the steps you can take to reduce them.
This introductory course provides a solid foundation in monitoring Amazon RDS using AWS tools. It begins by getting you acquainted with monitoring databases hosted on the Amazon RDS service and then moves on to explore the available AWS tools that can be used for this purpose.
This course covers Amazon RDS, Amazon DynamoDB, Amazon ElastiCache, and Amazon Neptune. As well as getting a theoretical understanding of these, you will also watch guided demonstrations from the AWS platform showing you how to use each database service.
This is the second course in a two-part series on database fundamentals for AWS. This course explores four different AWS database services — Amazon Redshift, Amazon QLDB, Amazon DocumentDB, and Amazon Keyspaces — and the differences between them. As well as getting a theoretical understanding of these, you will also watch guided demonstrations from the AWS platform showing you how to use each database service.
Amazon Kinesis Agent is an application that continuously monitors files and sends data to an Amazon Kinesis Data Firehose Delivery Stream or a Kinesis Data Stream. The agent handles rotating files, checkpointing, and retrying upon a failure. In this lab, you will install and configure Kinesis Agent, use it to collect log entries, and query the log entries with Amazon Athena.
Kinesis Data Analytics allows you to make use of existing and familiar SQL skills. It also integrates with other AWS services. You can deliver your results to any destination supported by Kinesis Data Streams or Kinesis Firehose, and use a Lambda function to deliver to external or un-managed destinations. In this lab, you will learn how to use Kinesis Data Analytics to sessionize sample clickstream data and output it to DynamoDB using a Lambda.
The AZ-303 and AZ-304 exams replace the older AZ-300 and AZ-301 exams, which will retire on September 30, 2020.
This learning path is designed to help you prepare for the AZ-303 Microsoft Azure Architect Technologies exam. Even if you don’t plan to take the exam, these courses and hands-on labs will help you gain a solid understanding of how to architect a variety of Azure services.
The AZ-304 exam tests your knowledge of five subject areas. We’ll start with designing Azure monitoring. Next, we’ll show you how to design for identity and security using Azure Active Directory. After that, you’ll learn how to design a data storage solution. Then, you’ll find out how to design a business continuity strategy using services such as Azure Site Recovery. Finally, we’ll cover how to design infrastructure.
This learning path is designed to help you and your team prepare for the AZ-400 Microsoft Azure DevOps Solutions exam. Even if you don’t plan to take the exam, these courses and hands-on labs will help you get started on your way to becoming an Azure DevOps specialist.
In this course, we look at how Azure Resource Manager (ARM) templates can be built and deployed. We start with a simple template and move on to more complex examples to illustrate many of the useful features available when deploying resources with templates. This course contains plenty of demonstrations from the Azure platform so you can see exactly how to use Azure Resource Manager in practice.
Azure’s App Configuration Service allows you to manage access to settings data and then see how to use it within a .Net application. We will look at using Azure Key Vault in conjunction with App Configuration Service, and how to access Azure Key Vault directly from your application and from apps running in a container within a Kubernetes cluster. This course contains numerous demonstrations from the Azure platform so you can get a first-hand look at the topics we will be covering.
This course explores how to implement version control on Azure repos. It begins with an overview of what source control is and the different types of source control available. It then looks at the key elements of branching, different branching strategies, and how they impact the development process. You’ll move on to learn about pull requests and merging as repository functions, and the different merging scenarios available to you.
This course dives into creating a DevOps strategy for mobile applications using the Visual Studio App Center. The App Center gives us a centralized location where we can implement build services, carry out mobile UI testing with multiple devices sets, create public and private distribution groups, and perform release management for our distribution groups.
This course provides you with the foundational knowledge required to design an infrastructure and configuration management strategy. It starts by looking at hosting infrastructure — IaaS, PaaS, FaaS, and some modern native app options — before moving on to look at Infrastructure-as-Code. You’ll learn what Infrastructure-as-Code means and the tools and technologies that are used to deploy and manage it.
Automated ML is based on breakthrough research from the Microsoft Research division. Experiments can be processed on various different types of computing resources such as Virtual Machines and Kubernetes that can either be local or in the cloud. In this lab, you will create a predictive classification model with Azure’s Automated Machine Learning service to quickly discover the most optimal model for a dataset.
Google Cloud Platform
The Google Cloud Operations suite (formerly Stackdriver) includes a wide variety of tools to help you monitor and debug your GCP-hosted applications. This course will give you hands-on demonstrations of how to use the Monitoring, Logging, Error Reporting, Debugger, and Trace components of the Cloud Operations suite. You can follow along with your own GCP account to try these examples yourself.
In this lab, you will upload data to a Cloud Storage bucket, start an inspection of this data to understand whether it contains sensitive information, and de-identify sensitive information by using REST APIs.
The big advantage of using Compute Engine VMs is that when you can’t find a VM type that suits your needs, you can create one with custom CPUs and memory size. Compute Engine also allows you to create a group of VMs to meet the scaling requirements. In this lab, you will create a Linux compute engine VM, and you will SSH into the instance by using the browser-based connection.
In this lab, you will create a Windows compute engine VM, and you will connect to it through RDP using the Microsoft RDP client.
In this lab challenge, you will need to prove your knowledge of the Compute Engine service offered by Google Cloud. The objectives you will need to achieve represent essential skills that a Google Certified Associate Cloud Engineer needs to have. You’ll be given a desired end state and be required to reach it using your knowledge of the Google Compute Engine service.
Data Science/Artificial Intelligence
Data can come in all varieties which can be a major drawback of having a fixed schema. NoSQL allows for any type of data to be stored, allowing your schema to evolve. In today’s world with IoT and increasing data complexity, NoSQL is becoming a standard part of many technology stacks. This lab is aimed at students with a basic understanding of Python who want to learn about Amazon DynamoDB. Students will explore programmatically and using the DynamoDB Console interface to insert and scan data.
This lab is aimed at beginners who want to move beyond spreadsheets and migrate their data into a database. After completing this lab, students will be able to use a MySQL Database in Google Cloud SQL and populate it from a CSV file. Additionally, students will learn how to use basic queries against their data.
Amazon Quicksight can help you visualize, embed, and share data quickly. These valuable insights allow for users to look at quick summaries of aggregated information to make better business decisions. This lab is aimed at beginners who want to understand how to import and make their first insight.
The foundation of many applications involves Representational state transfer (REST) API, which is a way of structuring an API so that, through the use of HTTP verbs, one can get data. This lab will dive into understanding GET, POST, DELETE, and PATCH services. Students will learn how to use Python to interact with an API to view, create, update, and delete data.
Databases are extraordinarily powerful in managing sets of data, but what do we do when management wants to add/remove/modify something that code does? This lab is aimed at students with a moderate understanding of data engineering and Python who want to understand how to update schemas to handle new features. We will walk through the changing requirements of a bug tracking application and how to handle them.
This lab is aimed at students with a moderate understanding of data engineering and Python who want to understand how to perform aggregates on data such as COUNT(), SUM(), and AVG(). We will also look into advanced statements like CASE, and functions like DATEDIFF() and CONCAT(). We will walk through the changing requirements of a bug tracking application and how to handle them.
In today’s world, we have to deal with ever-growing datasets. These often require specialized computer solutions to handle the extremely large volume. Google BigQuery makes it easy to query, process, and visualize large datasets. In this lab, we will get started with BigQuery to analyze a large public dataset.
Machine learning is proving to be very powerful in gathering insights with your data. BigQuery ML allows the user to perform Machine Learning training, evaluation, and prediction on large sets of data. This lab will explain some of the basic concepts along with an example of training a linear regression and binary logistic regression model.
K-Means learning is a machine learning technique used to divide a dataset into clusters to analyze its results. This classification algorithm divides a large group of data into smaller groups to maximize the similarity between data points. We will walk through applying and analyzing the K-Means clustering algorithm on a set of data using the Python libraries: pandas, scikit-learn, and matplotlib.
This learning path will provide you with an introduction to the principles and practices that enable enterprises to reliably and economically scale critical services. Adopting SRE into your own organization requires re-alignment, with a new focus on engineering and automation, and the adoption of a range of new working paradigms. The SRE learning path consists of eight courses, each focusing on a particular aspect of SRE.
Helm is a package manager for Kubernetes, used to simplify and enhance the deployment experience for deploying resources into a Kubernetes cluster. This training course explores Helm 3, which is the latest version of Helm building upon the successes of Helm 2. During this course, you’ll learn the fundamentals of working with Helm3, its features, and the key differences between Helm 3 and Helm 2.
This is a technical lab that introduces the Python 3 programming language. Python has enjoyed popularity in a variety of fields for its large scope of application and its ease of use. With Python 2 having moved to end-of-life status on January 1, 2020, there are also large amounts of legacy code that need porting to Python 3.
Learn about the main challenges that companies face and how to overcome them with an efficient and business-oriented tech training program.
Are you running big data workloads on frameworks such as Spark or Hadoop? Join Chad Schmutzer, AWS Principal Developer Advocate, and Stuart Scott, Cloud Academy AWS Content & Security Lead, in this on-demand webinar as they explain how you can accelerate your big data application performance and lower costs by using Amazon EC2 Spot Instances with Amazon EMR.
Are you getting ready to take your first steps into the AWS cloud? Are you considering the AWS Solutions Architect Associate exam? Cloud Academy can help you. If you want your first steps to have impact and focus, watch this on-demand webinar with our experts, Stephen Cole — AWS Certification Specialist and Will Meadows — Senior AWS Trainer, to learn about AWS’s foundational services: Compute, Databases, and Storage.
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