https://aws-dojo.com/excercises/ Recent content in Excercises on AWS Dojo Hugo -- gohugo.io en-us Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise1/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise1/ Microsoft Visual Studio is one of the most popular IDE for .NET framework based development. Amazon Web Services comes with AWS .NET SDK which allows the developers to work with the AWS Service using the languages like c#. This exercise will help you learn about configuring Microsoft Visual Studio to work with AWS .NET SDK. Step1: Pre-Requisite You need to have an AWS account with administrative access to complete the exercise. https://aws-dojo.com/excercises/excercise2/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise2/ AWS Cloud9 is a cloud-based integrated development environment (IDE) from Amazon Web Services. The Cloud9 IDE provides the software and tooling needed for dynamic programming with 40 languages including JavaScript, Python, PHP, Ruby, Go, and C++. Click on the link to learn about the language support. In this exercise, you learn about configuring AWS Cloud9 Environment for .NET Core based development. Step1: Pre-Requisite You need to have an AWS account with administrative access to complete the exercise. https://aws-dojo.com/excercises/excercise3/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise3/ AWS Cloud9 is a cloud-based integrated development environment (IDE) from Amazon Web Services. The Cloud9 IDE provides the software and tooling needed for dynamic programming with 40 languages including JavaScript, Python, PHP, Ruby, Go, and C++. You learn about configuring the IDE to be able to create a Docker image and publish the Docker image to the AWS Elastic Container Registry (ECR). Step1: Pre-Requisite You need to have an AWS account with administrative access to complete the exercise. https://aws-dojo.com/excercises/excercise4/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise4/ Amazon AppFlow is a fully managed integration service that enables secure transfer of data between Software-as-a-Service (SaaS) applications like Salesforce, Marketo, Slack, and ServiceNow, and AWS services like Amazon S3 and Amazon Redshift. It can also be used to perform such transfer between AWS Services like S3 and Redshift. AppFlow facilitates to run data flows at nearly any scale at the frequency of choice - Schedule, Event or On-demand. In this exercise, you learn to configure Amazon AppFlow to transfer data from S3 bucket to S3 bucket. https://aws-dojo.com/excercises/excercise5/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise5/ Amazon AppFlow is a fully managed integration service that enables secure transfer of data between Software-as-a-Service (SaaS) applications like Salesforce, Marketo, Slack, and ServiceNow, and AWS services like Amazon S3 and Amazon Redshift. AppFlow facilitates to run data flows at nearly any scale at the frequency of choice - Schedule, Event or On-demand. In this exercise, you learn to configure Amazon AppFlow to transfer data from Salesforce to S3 bucket. https://aws-dojo.com/excercises/excercise6/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise6/ AWS Cloud9 is a cloud-based integrated development environment (IDE) from Amazon Web Services. The Cloud9 IDE provides the software and tooling needed for dynamic programming with 40 languages including JavaScript, Python, PHP, Ruby, Go, and C++. You learn about configuring the IDE to be able to develop programming with the AWS Services using AWS SDK for Python Boto3. Step1: Pre-Requisite You need to have an AWS account with administrative access to complete the exercise. https://aws-dojo.com/excercises/excercise7/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise7/ Amazon Elastic File System (Amazon EFS) is a storage service which provides scalable and managed elastic NFS file system for use with AWS Cloud services and on-premises resources. On the other hand, AWS Lambda is a compute service which lets the users run code without provisioning or managing servers. In this exercise, you learn how to read-write data to Amazon EFS from AWS Lambda. Step1: Pre-Requisite You need to have an AWS account with administrative access to complete the exercise. https://aws-dojo.com/excercises/excercise8/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise8/ Amazon Athena is a serverless and interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Recently Amazon Athena introduced Federated Query which can be used to run SQL queries across data stored in relational, non-relational, object, and custom data sources. Athena uses data source connectors that run on AWS Lambda to execute federated queries. The data source connectors help connect with data sources like CloudWatch, DocumentDB, DynamoDB, HBase, JDBC data sources (like Redshift, MySql, SQL Server)etc. https://aws-dojo.com/excercises/excercise9/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise9/ AWS Lambda Destination provides visibility into Lambda function asynchronous execution by routing success/failure invocation results to different AWS services such as SNS, SQS etc. With Destinations, asynchronous function results are routed as an execution record to a destination resource without writing additional code. An execution record contains details about the request and response in JSON format including version, timestamp, request context, request payload, response context, and response payload. In this exercise, you create a Lambda function dojolambda which is invoked asynchronous from Amazon SNS by publishing a message to SNS topic dojotopic. https://aws-dojo.com/excercises/excercise10/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise10/ AWS Config enables to assess, audit, and evaluate the configurations of the AWS resources. It can help in evaluation and reporting of configuration drift from the desired configurations. Config can review changes in configurations and relationships between AWS resources, present detailed resource configuration histories, and determine the overall configuration compliance against the set guidelines. In this exercise, you will use AWS Config to keep check on AWS Lambda function configuration. You will also look into function configuration change history. https://aws-dojo.com/excercises/excercise11/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise11/ EC2 Image Builder helps in creating pipeline for Linux or Windows Server images for use with Amazon EC2 and on-premises. The pipeline takes care of all stages such as image creation, maintenance, validation, sharing, and deployment. In EC2 Image Builder pipeline, you start with a base Linux / Windows image and then customize the image during the pipeline to install / configure software or settings. The image is then validated and shared for the deployment. https://aws-dojo.com/excercises/excercise12/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise12/ Amazon Macie is a data security and data privacy service to enable discovery and protection of the sensitive data stored in Amazon S3. The service can automatically discover the sensitive data such as personally identifiable information (PII), financial information and intellectual property. It can publish discovery findings to AWS Services such as Event Bridge or Security Hub for action and remediation. Macie also enables to define custom detection rules that handle organization specific intellectual property, proprietary data, and particular scenarios. https://aws-dojo.com/excercises/excercise13/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise13/ AWS Systems Manager provides centralized and unified way to view operational data and automate operational tasks from across multiple AWS services and resources. Systems Manager simplifies resource and application management, shortens the time to detect and resolve operational problems, and makes it easy to operate and manage the infrastructure securely at scale. AWS Lambda is serverless compute service. Many times, you might want to create custom automation in AWS Systems Management to perform repeatable operational tasks on AWS resources at scale. https://aws-dojo.com/excercises/excercise14/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise14/ Amazon EventBridge is a serverless event bus to enable event publishing and rules based routing to the different destinations. Amazon EventBridge provides integration with custom applications, Software-as-a-Service (SaaS) applications, and AWS services. The routing rules can be based on facts like event source, AWS Service and also based on the content of the message. In this exercise, you create a custom event bus where you publish events using Python code. The events published are then routed based on source and content to the different AWS SQS Queues. https://aws-dojo.com/excercises/excercise15/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise15/ AWS Global Accelerator is a service that improves the availability and performance of the applications with local or global users. It provides static IP addresses that act as a fixed entry point to the application endpoints in a single or multiple AWS Regions. AWS Global Accelerator uses the AWS global network to optimize the path from the users to the applications, improving the performance of the traffic by as much as 60%. https://aws-dojo.com/excercises/excercise16/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise16/ Amazon CodeGuru is a developer tool powered by machine learning that provides intelligent recommendations for improving code quality and identifying an application’s most expensive lines of code. Amazon CodeGuru Reviewer uses machine learning to identify critical issues and hard-to-find bugs during application development to improve code quality. In this exercise, you will use CodeGuru to do code review of the Java code. The AWS Resource consumption for the exercise does not fall under AWS Free Tier. https://aws-dojo.com/excercises/excercise17/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise17/ AWS Lambda Layer works as common library which can be used by more than one Lambda functions. A layer is a ZIP archive that contains common libraries, a custom runtime, or other dependencies. Layers also help in keeping the deployment package small by keeping common libraries out of the Lambda function deployment. In this exercise, you will configure and use a Lambda Layer with the Lambda function. The AWS Resource consumption for the exercise falls under AWS Free Tier. https://aws-dojo.com/excercises/excercise18/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise18/ AWS App2Container (A2C) is a tool to modernize existing applications into containerized applications. A2C helps to standardize the deployment and operations through containers. It works for all types of deployments - virtual machines, on-premises or in the cloud. A2C currently supports .NET and Java Applications. In this exercise, you will containerize an ASP.NET web application deployed on Windows using A2C. The AWS Resource consumption for the exercise does not fall under AWS Free Tier. https://aws-dojo.com/excercises/excercise19/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise19/ Amazon Redshift can be accessed using the built-in Data API. Applications can use Redshift Data API to enable data access, ingest, and egress. The Data API takes care of managing database connections and returning data. The Data API stores the query results for 24 hours and is asynchronous so one can retrieve the results later. In this exercise, a Cloud9 based environment is used to talk to the Redshift Cluster using the Data API and Python as the programming language. https://aws-dojo.com/excercises/excercise20/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise20/ Amazon SageMaker notebook instance is a machine learning (ML) compute instance running the Jupyter Notebook App. Jupyter notebooks are used to prepare and process data, write code to train models, deploy models to SageMaker hosting, and test or validate the models. AWS CodeCommit is a fully-managed source control service that hosts secure Git-based repositories. It makes it easy for teams to collaborate on code in a secure and highly scalable ecosystem. https://aws-dojo.com/excercises/excercise21/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise21/ Session Manager is a AWS Systems Manager capability that allows to manage EC2 instances, on-premises instances, and virtual machines (VMs) through an interactive one-click browser-based shell or through the AWS CLI. Session Manager provides secure and auditable instance management without the need to open inbound ports, maintain bastion hosts, or manage SSH keys. In this exercise, you use session manager to create secure RDP session with a Windows server. For this access, you will not configure security group for RDP access and you will also not configure key-pair for the Windows EC2 instance. https://aws-dojo.com/excercises/excercise22/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise22/ AWS Copilot is a command line interface (CLI) that enables quick launch and easy manageability of containerized applications on AWS. Copilot automates each step in the application deployment lifecycle including pushing to a registry, creating a task definition, and creating a cluster. In this exercise, you learn to launch a containerized application using AWS CoPilot. Step1: Pre-Requisite You need to have an AWS account with administrative access to complete the exercise. https://aws-dojo.com/excercises/excercise23/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise23/ AWS CloudFormation Guard is an open-source command line interface (CLI) that helps enterprises keep AWS infrastructure and application resources in compliance with the company policy guidelines. It provides compliance administrators with a simple, policy-as-code language to define rules that can check for both required and prohibited resource configurations. It enables developers to validate their CloudFormation templates against those rules. In this exercise, you learn using AWS CloudFormation Guard. Step1: Pre-Requisite You need to have an AWS account with administrative access to complete the exercise. https://aws-dojo.com/excercises/excercise24/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise24/ Amazon Timestream is a serverless time series database service. Amazon Timestream manages time series data life cycle by keeping recent data in memory and moving historical data to a cost optimized storage tier. Amazon Timestream has built-in time series analytics functions, helping identify trends and patterns in the data in near real-time. Amazon Timestream is serverless and automatically scales up or down to adjust capacity and performance. In this exercise, you learn to use Python code to write data to the Amazon Timestream database table. https://aws-dojo.com/excercises/excercise25/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise25/ Amazon SQS FIFO (First-In-First-Out) queues are designed to enhance messaging between applications when the order of operations and events is critical, or where duplicates can’t be tolerated. FIFO queues also provide exactly-once processing but have a limited number of transactions per second (TPS). Amazon SNS FIFO Topic provides strict message ordering and de-duplicated message delivery to one or more subscribers. Amazon SQS FIFO Queue and Amazon SNS FIFO Topic can be used together to design fan out messages along with ordered processing. https://aws-dojo.com/excercises/excercise26/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise26/ AWS Glue uses classifiers to catalog the data. There are out of box classifiers available for XML, JSON, CSV, ORC, Parquet and Avro formats. But sometimes, the classifier is not able to catalog the data due to complex structure or hierarchy. In such cases, the custom classifiers are configured and used with the crawler. In this exercise, you configure a custom XML classifier to catalog XML data. Step1: Pre-Requisite You need to have an AWS account with administrative access to complete the exercise. https://aws-dojo.com/excercises/excercise27/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise27/ Amazon Redshift is the cloud data warehouse in AWS. Amazon Redshift provides seamless integration with other storages like Amazon S3. It enable a very cost effective data warehouse solution where the warm data can be kept in Amazon Redshift storage while the cold data can be kept in the S3 storage. The user can access the S3 data from Redshift in the same way, the data is accessed from the Redshift storage itself. https://aws-dojo.com/excercises/excercise28/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise28/ AWS Step Functions is a serverless orchestrator to create state machine workflows. AWS Step Functions can orchestrate calls to multiple AWS services to build business applications. Amazon API Gateway is a managed service to create, publish, maintain, monitor, and secure APIs. In this exercise, you learn how to call Amazon API Gateway APIs in AWS Step Functions state machine workflow. The AWS Resource consumption for the exercise falls under AWS Free Tier. https://aws-dojo.com/excercises/excercise29/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise29/ Amazon DynamoDB is a key-value and document database that delivers high performance at scale. Amazon DynamoDB supports PartiQL, a SQL-compatible query language, to select, insert, update, and delete data in Amazon DynamoDB. PartiQL operations provide the same availability, latency, and performance as the other DynamoDB data plane operations. In this exercise, you learn to use PartiQL with DynamoDB table. The AWS Resource consumption for the exercise falls under AWS Free Tier. https://aws-dojo.com/excercises/excercise30/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise30/ AWS Database Migration Service helps in migrating databases to AWS quickly and securely. The source database remains fully operational during the migration, minimizing downtime to applications that rely on the database. AWS Database Migration Service supports both homogeneous migrations such as Oracle to Oracle and heterogeneous migrations between different database platforms, such as Oracle or Microsoft SQL Server to Amazon Aurora. In this exercise, you learn how to migrate data from a relational database to S3 bucket. https://aws-dojo.com/excercises/excercise31/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise31/ Amazon Redshift supports user-defined function (UDF). The function is stored in the database and is available for any user with sufficient privileges to run. Amazon Redshift can use custom functions defined in AWS Lambda as part of SQL queries. Lambda UDFs are defined and managed in Lambda. One can control the access privileges to invoke these UDFs in Amazon Redshift. One can invoke multiple Lambda functions in the same query or invoke the same function multiple times. https://aws-dojo.com/excercises/excercise32/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise32/ Express Workflows are a new type of AWS Step Functions workflow which can run in synchronous manner. Express Workflows is suitable for high-volume event processing workloads such as IoT data ingestion, streaming data processing and transformation, and high-volume microservices orchestration. In this exercise, you create and run a Step Functions Express Workflow. The AWS Resource consumption for the exercise falls under AWS Free Tier. Step1: Pre-Requisite You need to have an AWS account with administrative access to complete the exercise. https://aws-dojo.com/excercises/excercise33/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise33/ The Lambda execution environment provides a file system for the code to use at /tmp. The file system is local to the Lambda function and can be used for read-write operations. This space has a fixed size of 512 MB. The same Lambda execution environment may be reused by multiple Lambda invocations to optimize performance. Step1: Pre-Requisite You need to have an AWS account with administrative access to complete the exercise. https://aws-dojo.com/excercises/excercise34/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise34/ AWS Data Wrangler is an open source initiative from AWS Professional Services. It extends the power of Pandas by allowing to work AWS data related services using Panda DataFrames. One can use Python Pandas and AWS Data Wrangler to build ETL with major services - Athena, Glue, Redshift, Timestream, QuickSight, CloudWatchLogs, DynamoDB, EMR, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON and EXCEL). In this exercise, you learn to use AWS Data Wrangler with AWS Lambda Function. https://aws-dojo.com/excercises/excercise35/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise35/ AWS Data Wrangler is an open source initiative from AWS Professional Services. It extends the power of Pandas by allowing to work AWS data related services using Panda DataFrames. One can use Python Pandas and AWS Data Wrangler to build ETL with major services - Athena, Glue, Redshift, Timestream, QuickSight, CloudWatchLogs, DynamoDB, EMR, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON and EXCEL). In this exercise, you learn to use AWS Data Wrangler with AWS Glue. https://aws-dojo.com/excercises/excercise36/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise36/ Amazon Neptune is a managed graph database service build and run applications that work with highly connected datasets. It is a purpose-built, high-performance graph database engine optimized for storing billions of relationships and querying the graph with milliseconds latency. The database supports query languages such as Apache TinkerPop Gremlin and SPARQL. In this exercise, you launch Amazon Neptune Database and learn to work with the data. Step1: Pre-Requisite You need to have an AWS account with administrative access to complete the exercise. https://aws-dojo.com/excercises/excercise37/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise37/ AWS Glue Workflow help create complex ETL activities involving multiple crawlers, jobs, and triggers. Each workflow manages the execution and monitoring of the components it orchestrates. The workflow records execution progress and status of its components, providing an overview of the larger task and the details of each step. The AWS Glue console also provides a visual representation of the workflow as a graph. In this exercise, you learn sharing state in Glue Workflow. https://aws-dojo.com/excercises/excercise38/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise38/ Amazon Redshift is the cloud data warehouse in AWS. Amazon Redshift provides seamless integration with other storages like Amazon S3. It enable a very cost effective data warehouse solution where the warm data can be kept in Amazon Redshift storage while the cold data can be kept in the S3 storage. The user can access the S3 data from Redshift in the same way, the data is accessed from the Redshift storage itself. https://aws-dojo.com/excercises/excercise39/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise39/ Amazon DynamoDB is a key-value and document database that delivers high performance at scale. AWS Data Wrangler is an open source initiative from AWS Professional Services. It extends the power of Pandas by allowing to work AWS data related services using Panda DataFrames. Once can use Python Panda and AWS Data Wrangler to build ETL with major services - Athena, Glue, Redshift, Timestream, QuickSight, CloudWatchLogs, DynamoDB, EMR, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON and EXCEL). https://aws-dojo.com/excercises/excercise40/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise40/ Amazon API Gateway allows to create an API directly in front of an AWS service API. This way the API works as proxy to the AWS Service. In this exercise, you learn to configure a Proxy API in API Gateway in front of a DynamoDB table. Step1: Pre-Requisite You need to have an AWS account with administrative access to complete the exercise. If you don’t have an AWS account, kindly use the link to create free trial account for AWS. https://aws-dojo.com/excercises/excercise41/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise41/ AWS Glue uses job bookmark to track processing of the data to ensure data processed in the previous job run does not get processed again. Job bookmarks help AWS Glue maintain state information and prevent the reprocessing of old data. In this exercise, you learn to configure job bookmark to avoid reprocessing of the data. Step1: Pre-Requisite You need to have an AWS account with administrative access to complete the exercise. https://aws-dojo.com/excercises/excercise42/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise42/ AWS X-Ray helps to analyze and debug applications by providing end-to-end view of requests as it travels through the application, and shows a map of the application’s underlying components. AWS X-Ray helps understand the application and its underlying services execution to identify and troubleshoot the root cause of performance issues and errors. Step1: Pre-Requisite You need to have an AWS account with administrative access to complete the exercise. If you don’t have an AWS account, kindly use the link to create free trial account for AWS. https://aws-dojo.com/excercises/excercise43/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise43/ Amazon DynamoDB is a key-value and document database that delivers high performance at scale. Amazon DynamoDB global tables provide a managed solution for deploying a multiregion and multi-active database. One can specify the AWS Regions for the global tables and DynamoDB takes care of all the necessary tasks to replicate ongoing data changes to all of them. In this exercise, you configure and use Global Tables in DynamoDB table. Step1: Pre-Requisite You need to have an AWS account with administrative access to complete the exercise. https://aws-dojo.com/excercises/excercise44/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise44/ AWS Glue Studio is a graphical interface to help create, run, and monitor extract, transform, and load (ETL) jobs in AWS Glue. One can visually compose data transformation workflows and seamlessly run them on AWS Glue’s Apache Spark-based serverless ETL engine. In this exercise, you configure and learn using Spark SQL, Catalog Target & Infer S3 Schema features in Glue Studio. Step1: Pre-Requisite You need to have an AWS account with administrative access to complete the exercise. https://aws-dojo.com/excercises/excercise45/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise45/ Amazon EMR is a big data platform for processing large scale data using open source tools such as Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi, and Presto. Amazon EMR is easy to set up, operate, and scale for the big data requirement by automating time-consuming tasks like provisioning capacity and tuning clusters. In this exercise, you launch an EMR Transient cluster along with a step. Step1: Pre-Requisite You need to have an AWS account with administrative access to complete the exercise. https://aws-dojo.com/excercises/excercise46/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise46/ AWS Glue Studio is GUI based service to create, run, and monitor extract, transform, and load (ETL) jobs in AWS Glue. It helps in visually composing data transformation workflows and run them on AWS Glue’s Apache Spark-based serverless ETL engine. AWS Glue Studio supports both tabular and semi-structured data. AWS Glue Studio also offers tools to monitor ETL workflows and validate that they are operating as intended. In this exercise, you create Glue Job in Glue Studio to predict missing values in the data using machine learning. https://aws-dojo.com/excercises/excercise47/ Wed, 06 May 2020 09:52:48 +0200 https://aws-dojo.com/excercises/excercise47/ AWS Lake Formation supports controlling access to data lake resources like databases, tables and columns using tags. Tag-based access control (TBAC) decouples policy creation from resource creation which helps in simplifying and scaling governance for the large number of databases, tables, and columns in the data lake. In this exercise, you use tags to control access to data lake resources like table. Step1: Pre-Requisite You need to have an AWS account with administrative access to complete the exercise.