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What are the Benefits of Amazon EMR? What are the EMR use Cases?

Amazon EMR(Elastic MapReduce) is a cloud-based big data platform that allows the team to quickly process large amounts of data at an effective cost. For this, they use open source tools like Apache Hive, Apache Spark, Apache Flink, Apache HBase, and Presto. With the help of Amazon S3’s scalable storage and Amazon EC2’s dynamic stability, EMR provides the elasticity and engines for running Petabyte-scale analysis. The cost of this is just a fraction of the traditional on-premise clusters’ cost. For iterative collaboration, development, and data access across data products like Amazon DynamoDB, Amazon S3, and Amazon Redshift, you can use Jupyter-based EMR Notebooks. It helps in reducing time for insight and operationalizing analytics quickly. Several customers use EMR for reliably and securely handling the big data use cases like machine learning, deep learning, bioinformatics, financial and scientific stimulation, log analysis, and data transformations (ETL). With EMR, the team has the flexibility of running use cases on short lived, single-purpose clusters or highly available, long running clusters. Here are some other benefits of using EMR:1. Easy to useSince clusters are launched in minutes by EMR, you don’t have to worry about infrastructure setup, node provisioning, cluster tuning, and Hadoop configuration. All these tasks are taken care of by EMR so that you can concentrate on analysis. Data engineers, data scientists, and data analysts can use the EMR notebooks for launching a serverless Jupyter notebook within a matter of seconds. This also allows the team and individuals to interactively explore, visualize and process the data. 2. Low costThe pricing of EMR is simple as well as predictable. There is a one-minute minimum charge and the rest is paid according to per-instance rate for every second. You can use applications like Apache Hive and Apache Spark for launching a 10-node EMR cluster for a low cost of $0.15 per hour. Also, EMR has native support for Reserved instances and Amazon EC2 spot, which can help you save on the cost of the underlying instances by about 50-80%. The pricing of Amazon EMR depends on the number of deployed EC2 instances, the type of the instance and the region where you are launching your cluster. Since it is on-demand pricing, you can expect low rates. But for reducing the cost even further, you can purchase Spot instances or reserved instances. The cost of spot instanced is about one-tenth less than the on-demand pricing. Remember that if you are using services like Amazon D3, DynamoDB or Amazon Kinesis along with your EMR cluster, they will be charged separately from the usage for Amazon EMR. 3. ElasticityEMR allows provisioning of not one but thousands of compute instances for processing data at any scale. All these instances’ numbers can be decreased or increased manually or automatically with the help of Auto Scaling which can manage the size of clusters on the basis of utilization. This allows you to pay only for what you use. Also, unlike the on-premise clusters of the rigid infrastructure, EMR decouples persistent storage and compute which gives you the ability of scaling every one of them independently. 4. ReliabilityThanks to EMR, you can now spend less time monitoring and tuning your cluster. Tuned for the cloud, EMT monitors your cluster constantly. They retry failed tasks and replace poorly performed instances automatically. Also, you don’t have to manage bug fixes and updates as EMR provides the latest stable releases of open source software. This results in lesser efforts and fewer issues in maintaining the environment. With the help of multiple master nodes, clusters are not only highly available, but also failover in case of a node failure automatically. With Amazon EMR, you have a configuration option for controlling the termination of your cluster, whether you do it manually or automatically. If you go for the option of automatic termination, the cluster will be terminated once the steps are completed. This is known as a transient cluster. However, if you go for the manual option, the cluster will continue to run even after the processing is completed. You will have to manually terminate it when you no longer need it. The other option is creating a cluster, interacting directly with the installed applications, and then manually terminating the cluster. These are known as long-running clusters. Also, there is an option of configuring the termination protection for preventing the clusters’ instances from being terminated due to issues and errors during processing. This allows recovery of instances’ data before they are terminated. These options’ default settings depend on whether you launched your cluster with the console, API or CLI. 5. SecurityEMR is responsible for automatically configuring the firewall settings of EC2. these setting control and instances’ network access and launches the clusters in an Amazon VPC. For all the objects residing in S3, client-side or server-side encryption is used along with EMRFS, which is an object store on S3 for Hadoop. To achieve this, you can either use your own customer-managed keys or the AWS Key Management Service. With the help of EMR, you can easily enable other encryption options like at-rest and in-transit encryption. Amazon EMR can leverage AWS services like Amazon VPC and IAM and features like Amazon EC2 key pairs for securing the cluster and data. Let’s go through these leverages one by one: IAM When integrated with IAM, Amazon EMR allows managing of permissions. You can use the IAM policies for defining the permissions which are then attached to the IAM groups or IAM users. The defined permissions determine the actions the members of the group or the users can perform and accessible resources. Apart from this, IAM roles are used by the Amazon EMR for Amazon EMR service itself as well as EC2 instance profile. These roles can grant permissions for accessing other AWS services. There is a default role for EC2 instance profile and Amazon EMR service. The AWS managed policies are used by the default role which is automatically created when you launch the EMR cluster from the console for the first time and select default permissions. You can use the AWS CLI for creating default IAM roles. For managing permissions, you can select custom roles for instance and the service profile. Security Groups Security groups are used by Amazon EMR for controlling outbound and inbound traffic to the EC2 instances. When you are launching the cluster, a security group for master instance and to be shared by the task/core instance is used. The security group rules are configured by the Amazon EMR for ensuring communication between the instances. Apart from this, there is an option for configuring additional security groups and assigning them to the master as well as task/core instances for advanced rules. Encryption The Amazon S3 client-side and server-side encryption along with EMRFS is supported by the Amazon EMR, this allows protecting the data stored in Amazon S3. The server-side encryption allows encrypting the data after you have uploaded it. The client-side encryption allows encrypting the decrypting on the EMR cluster in the EMRFS client. You can use the AWS Key Management Service for managing the master key for the client-side encryption. Amazon VPC You can launch clusters in a Virtual Private Cloud (VPC). A VPC is a virtual network isolated in the AWS providing the ability to control network access and configuration’s advanced aspects. AWS CloudTrail When integrated with CloudTrail, Amazon EMR allows logging information regarding request made by the AWS account. You can use this information to track who is accessing the cluster and when and can even determine the IP address that made the request. Amazon EC2 Key Pairs A secure connection needs to be formed between the master node and your remote computer for monitoring and interacting with the cluster. For the connection, you can use the Secure Shell (SSH) network and for authentication, you can use Kerberos. An Amazon EC2 key pair will be required, if you are using SSH. 6. FlexibilityEMR allows you to have complete control over the cluster. This involves easy installation of additional applications, having root access to every instance, and customizing every cluster with bootstrap actions. Also, you won’t have to re-launch the cluster for reconfiguring the running clusters on the fly or using the custom Amazon Linux AMIs for launching EMR clusters. Also, you have the option of scaling up or down your clusters according to your computing needs. You can remove instances for controlling costs when peak workloads subside or add instances for peak overloads by resizing your clusters. Amazon EMR also allows running multiple instance groups so that on-demand instanced can be used in a single group for processing power with spot instances in other group. This helps faster completion of jobs at a lower price. You can even take advantage of low price on one spot instance type over another by mixing different types of instances together. Amazon EMR offers the flexibility of using different file systems for your input, intermediate and output data. For example: Hadoop Distributed File System (HDFS) for running the core and master nodes of your cluster to process that is not required after the lifecycle of the cluster. EMR File System (EMRFS) for using Amazon S3 as a data layer to run applications on the cluster for separating the storage and compute, and persist data after the lifecycle of the cluster. It also allows independent scaling up and down of your storage and compute needs. Scaling of the compute needs can also be done by using Amazon S3 or resizing your cluster. 7. AWS IntegrationIntegrating Amazon EMR with other services offered by the AWS can help in providing functionalities and capabilities of networking, security, storage and many more. Here are some of the examples of such integration: For the instances comprising the nodes in the cluster, Amazon EC2 For configuring the virtual network in which you will be launching your instances, Amazon Virtual Private Cloud (VPC) For storing input as well as output data, Amazon S3 For configuring alarms and monitoring cluster performance, Amazon CloudWatch For configuring permissions, AWS Identity and Access Management (IAM) For auditing requests made to the service, AWS CloudTrail For scheduling and starting your clusters, AWS Data Pipeline 8. DeploymentThe EMR clusters have EC2 instances which are responsible for performing the work that you are submitting to the cluster. When you are launching the cluster, the instances with the applications like Apache Spark or Apache Hadoop are configured by the Amazon EMR. You need to select the type and size of the instance that suits the cluster’s processing needs including streaming data, batch processing, large data storage, and low-latency queries. There are different ways of configuring the software on your cluster provided by the Amazon EMR. For example: Installation of an Amazon EMR release with applications that can include applications like Spark, Pig or Apache and versatile frameworks like Hadoop. Installation of several MapR distributions. Amazon Linus is used for the manual installation of the software on the cluster. For this, the yum package manager can be used. 9. MonitoringTroubleshooting of Cluster issues like errors or failures can be done by using the log files and Amazon EMR Management Interface. You will have the capability of archiving log files in Amazon S3 for storing log and troubleshoot issues even after the cluster has been terminated. There is also an optional debugging tool available in the Amazon EMR console that can be used for browsing log files based on tasks, jobs and steps. CloudWatch is integrated with Amazon EMR for tracking performance metrics for the cluster as well as the jobs within the cluster. Configuration of alarms is done based on metrics like what is the percentage of used storage or if the cluster is idle or not. 10. Management InterfacesThere are different ways for interacting with the Amazon EMR including the following: Console This is a graphical user interface that can be used for launching and managing clusters. You need to specify the details of the cluster to be launched and check out the details of the existing clusters, terminated clusters and debug by filling out web forms. It is the easiest way to start working with Amazon EMR as no programming knowledge is required. You can get the console online from here. AWS Command Line Interface (AWS CLI) This is a client application that you can run on your local machine for connecting to the Amazon EMR and creating and managing clusters. There is a set of commands available in the AWS CLI for the Amazon EMR, you can use this for writing scripts that can automate the launch and management of the cluster.  Software Development Kit (SDK) There are functions available in the SDKs that can call Amazon EMR for creating and managing clusters. You can even write applications for automating this process. It is the best way of extending and customizing the Amazon EMR’s functionality. The available SDKs for the Amazon EMR are Java, Go, PHP, Python, .NET, Ruby, and Node.js. Web Service API This is a low-level interface that uses JSON for calling the Amazon EMR directly. This can be used for creating a customized SDK that calls the web service. Now that we have discussed the benefits of EMR, let’s move on to the EMR use cases: Use Cases of EMR  1. Machine LearningEMR provides built-in machine learning tools for scalable machine learning algorithms like TensorFLow, Apache Spark MLib, and Apache MXNet. Also you can easily use Bootstrap Actions and Custom AMIs for easily adding the preferred tools and libraries for creating your very own predictive analytics toolset. 2. Extract Transform Load (ETL)For cost-effective and quick performance of data transformation workloads (ETL) like sort, join and aggregate on large datasets, you can use EMR. 3. Clickstream analysisWith EMR, along with Apache Hive and Apache Spark, you can segment users, deliver effective ads by understanding the user preferences. All this can be achieved by analyzing the clickstream data from Amazon S3. 4. Real-time streamingWith EMR and Amazon Spark Streaming, analyzing events from Amazon Kinesis, Amazon Kafka or any other streaming data source is possible. This helps in creating highly available, long running, and fault-tolerant streaming data pipelines. Persist transformed insights to Amazon Elasticsearch and datasets to HDFS or Amazon S3. 5. Interactive AnalyticsWith EMR Notebooks, you will be provided with an open-source Jupyter based, managed analytic environment. This will allow data analysts, developers and scientists in preparing and visualizing data, collaborating with peers, building applications, and performing interactive analysis. 6. GenomicsEMR can also be used for quickly and efficiently processing large amounts of genomic data or any other large, scientific dataset. Genomic data hosted on AWS can be accessed by researchers for free. In this article, you got a quick introduction to Amazon EMR and how it has different log files’ types. Also, you got to understand the benefits of Elastic MapReduce. To become an expert in AWS services, enroll in the AWS certification course offered by KnowledgeHut. 

What are the Benefits of Amazon EMR? What are the EMR use Cases?

11K
  • by Joydip Kumar
  • 30th Sep, 2019
  • Last updated on 11th Mar, 2021
  • 8 mins read
What are the Benefits of Amazon EMR? What are the EMR use Cases?

Amazon EMR(Elastic MapReduce) is a cloud-based big data platform that allows the team to quickly process large amounts of data at an effective cost. For this, they use open source tools like Apache Hive, Apache Spark, Apache Flink, Apache HBase, and Presto. With the help of Amazon S3’s scalable storage and Amazon EC2’s dynamic stability, EMR provides the elasticity and engines for running Petabyte-scale analysis. The cost of this is just a fraction of the traditional on-premise clusters’ cost. For iterative collaboration, development, and data access across data products like Amazon DynamoDB, Amazon S3, and Amazon Redshift, you can use Jupyter-based EMR Notebooks. It helps in reducing time for insight and operationalizing analytics quickly. 

Several customers use EMR for reliably and securely handling the big data use cases like machine learning, deep learning, bioinformatics, financial and scientific stimulation, log analysis, and data transformations (ETL). With EMR, the team has the flexibility of running use cases on short lived, single-purpose clusters or highly available, long running clusters. 

Here are some other benefits of using EMR:

Benefits of using EMR

1. Easy to use

Since clusters are launched in minutes by EMR, you don’t have to worry about infrastructure setup, node provisioning, cluster tuning, and Hadoop configuration. All these tasks are taken care of by EMR so that you can concentrate on analysis. Data engineers, data scientists, and data analysts can use the EMR notebooks for launching a serverless Jupyter notebook within a matter of seconds. This also allows the team and individuals to interactively explore, visualize and process the data. 

2. Low cost

The pricing of EMR is simple as well as predictable. There is a one-minute minimum charge and the rest is paid according to per-instance rate for every second. You can use applications like Apache Hive and Apache Spark for launching a 10-node EMR cluster for a low cost of $0.15 per hour. Also, EMR has native support for Reserved instances and Amazon EC2 spot, which can help you save on the cost of the underlying instances by about 50-80%. The pricing of Amazon EMR depends on the number of deployed EC2 instances, the type of the instance and the region where you are launching your cluster. Since it is on-demand pricing, you can expect low rates. But for reducing the cost even further, you can purchase Spot instances or reserved instances. The cost of spot instanced is about one-tenth less than the on-demand pricing. Remember that if you are using services like Amazon D3, DynamoDB or Amazon Kinesis along with your EMR cluster, they will be charged separately from the usage for Amazon EMR. 

3. Elasticity

EMR allows provisioning of not one but thousands of compute instances for processing data at any scale. All these instances’ numbers can be decreased or increased manually or automatically with the help of Auto Scaling which can manage the size of clusters on the basis of utilization. This allows you to pay only for what you use. Also, unlike the on-premise clusters of the rigid infrastructure, EMR decouples persistent storage and compute which gives you the ability of scaling every one of them independently. 

4. Reliability

Thanks to EMR, you can now spend less time monitoring and tuning your cluster. Tuned for the cloud, EMT monitors your cluster constantly. They retry failed tasks and replace poorly performed instances automatically. Also, you don’t have to manage bug fixes and updates as EMR provides the latest stable releases of open source software. This results in lesser efforts and fewer issues in maintaining the environment. With the help of multiple master nodes, clusters are not only highly available, but also failover in case of a node failure automatically. 

With Amazon EMR, you have a configuration option for controlling the termination of your cluster, whether you do it manually or automatically. If you go for the option of automatic termination, the cluster will be terminated once the steps are completed. This is known as a transient cluster. However, if you go for the manual option, the cluster will continue to run even after the processing is completed. You will have to manually terminate it when you no longer need it. The other option is creating a cluster, interacting directly with the installed applications, and then manually terminating the cluster. These are known as long-running clusters. 

Also, there is an option of configuring the termination protection for preventing the clusters’ instances from being terminated due to issues and errors during processing. This allows recovery of instances’ data before they are terminated. These options’ default settings depend on whether you launched your cluster with the console, API or CLI. 

5. Security

EMR is responsible for automatically configuring the firewall settings of EC2. these setting control and instances’ network access and launches the clusters in an Amazon VPC. For all the objects residing in S3, client-side or server-side encryption is used along with EMRFS, which is an object store on S3 for Hadoop. To achieve this, you can either use your own customer-managed keys or the AWS Key Management Service. With the help of EMR, you can easily enable other encryption options like at-rest and in-transit encryption. 

Amazon EMR can leverage AWS services like Amazon VPC and IAM and features like Amazon EC2 key pairs for securing the cluster and data. Let’s go through these leverages one by one: 

  • IAM 

When integrated with IAM, Amazon EMR allows managing of permissions. You can use the IAM policies for defining the permissions which are then attached to the IAM groups or IAM users. The defined permissions determine the actions the members of the group or the users can perform and accessible resources. 

Apart from this, IAM roles are used by the Amazon EMR for Amazon EMR service itself as well as EC2 instance profile. These roles can grant permissions for accessing other AWS services. There is a default role for EC2 instance profile and Amazon EMR service. The AWS managed policies are used by the default role which is automatically created when you launch the EMR cluster from the console for the first time and select default permissions. You can use the AWS CLI for creating default IAM roles. For managing permissions, you can select custom roles for instance and the service profile. 

  • Security Groups 

Security groups are used by Amazon EMR for controlling outbound and inbound traffic to the EC2 instances. When you are launching the cluster, a security group for master instance and to be shared by the task/core instance is used. The security group rules are configured by the Amazon EMR for ensuring communication between the instances. Apart from this, there is an option for configuring additional security groups and assigning them to the master as well as task/core instances for advanced rules. 

  • Encryption 

The Amazon S3 client-side and server-side encryption along with EMRFS is supported by the Amazon EMR, this allows protecting the data stored in Amazon S3. The server-side encryption allows encrypting the data after you have uploaded it. The client-side encryption allows encrypting the decrypting on the EMR cluster in the EMRFS client. You can use the AWS Key Management Service for managing the master key for the client-side encryption. 

  • Amazon VPC 

You can launch clusters in a Virtual Private Cloud (VPC). A VPC is a virtual network isolated in the AWS providing the ability to control network access and configuration’s advanced aspects. 

  • AWS CloudTrail 

When integrated with CloudTrail, Amazon EMR allows logging information regarding request made by the AWS account. You can use this information to track who is accessing the cluster and when and can even determine the IP address that made the request. 

  • Amazon EC2 Key Pairs 

A secure connection needs to be formed between the master node and your remote computer for monitoring and interacting with the cluster. For the connection, you can use the Secure Shell (SSH) network and for authentication, you can use Kerberos. An Amazon EC2 key pair will be required, if you are using SSH. 

6. Flexibility

EMR allows you to have complete control over the cluster. This involves easy installation of additional applications, having root access to every instance, and customizing every cluster with bootstrap actions. Also, you won’t have to re-launch the cluster for reconfiguring the running clusters on the fly or using the custom Amazon Linux AMIs for launching EMR clusters. 

Also, you have the option of scaling up or down your clusters according to your computing needs. You can remove instances for controlling costs when peak workloads subside or add instances for peak overloads by resizing your clusters. 

Amazon EMR also allows running multiple instance groups so that on-demand instanced can be used in a single group for processing power with spot instances in other group. This helps faster completion of jobs at a lower price. You can even take advantage of low price on one spot instance type over another by mixing different types of instances together. 

Amazon EMR offers the flexibility of using different file systems for your input, intermediate and output data. For example: 

  • Hadoop Distributed File System (HDFS) for running the core and master nodes of your cluster to process that is not required after the lifecycle of the cluster. 
  • EMR File System (EMRFS) for using Amazon S3 as a data layer to run applications on the cluster for separating the storage and compute, and persist data after the lifecycle of the cluster. It also allows independent scaling up and down of your storage and compute needs. Scaling of the compute needs can also be done by using Amazon S3 or resizing your cluster. 

7. AWS Integration

Integrating Amazon EMR with other services offered by the AWS can help in providing functionalities and capabilities of networking, security, storage and many more. Here are some of the examples of such integration: 

  • For the instances comprising the nodes in the cluster, Amazon EC2 
  • For configuring the virtual network in which you will be launching your instances, Amazon Virtual Private Cloud (VPC) 
  • For storing input as well as output data, Amazon S3 
  • For configuring alarms and monitoring cluster performance, Amazon CloudWatch 
  • For configuring permissions, AWS Identity and Access Management (IAM) 
  • For auditing requests made to the service, AWS CloudTrail 
  • For scheduling and starting your clusters, AWS Data Pipeline 

8. Deployment

The EMR clusters have EC2 instances which are responsible for performing the work that you are submitting to the cluster. When you are launching the cluster, the instances with the applications like Apache Spark or Apache Hadoop are configured by the Amazon EMR. You need to select the type and size of the instance that suits the cluster’s processing needs including streaming data, batch processing, large data storage, and low-latency queries. There are different ways of configuring the software on your cluster provided by the Amazon EMR. For example: 

  • Installation of an Amazon EMR release with applications that can include applications like Spark, Pig or Apache and versatile frameworks like Hadoop. 
  • Installation of several MapR distributions. Amazon Linus is used for the manual installation of the software on the cluster. For this, the yum package manager can be used. 

9. Monitoring

Troubleshooting of Cluster issues like errors or failures can be done by using the log files and Amazon EMR Management Interface. You will have the capability of archiving log files in Amazon S3 for storing log and troubleshoot issues even after the cluster has been terminated. There is also an optional debugging tool available in the Amazon EMR console that can be used for browsing log files based on tasks, jobs and steps. 

CloudWatch is integrated with Amazon EMR for tracking performance metrics for the cluster as well as the jobs within the cluster. Configuration of alarms is done based on metrics like what is the percentage of used storage or if the cluster is idle or not. 

10. Management Interfaces

There are different ways for interacting with the Amazon EMR including the following: 

  • Console 

This is a graphical user interface that can be used for launching and managing clusters. You need to specify the details of the cluster to be launched and check out the details of the existing clusters, terminated clusters and debug by filling out web forms. It is the easiest way to start working with Amazon EMR as no programming knowledge is required. You can get the console online from here

  • AWS Command Line Interface (AWS CLI) 

This is a client application that you can run on your local machine for connecting to the Amazon EMR and creating and managing clusters. There is a set of commands available in the AWS CLI for the Amazon EMR, you can use this for writing scripts that can automate the launch and management of the cluster.  

  • Software Development Kit (SDK) 

There are functions available in the SDKs that can call Amazon EMR for creating and managing clusters. You can even write applications for automating this process. It is the best way of extending and customizing the Amazon EMR’s functionality. The available SDKs for the Amazon EMR are Java, Go, PHP, Python, .NET, Ruby, and Node.js. 

  • Web Service API 

This is a low-level interface that uses JSON for calling the Amazon EMR directly. This can be used for creating a customized SDK that calls the web service. Now that we have discussed the benefits of EMR, let’s move on to the EMR use cases: 

Use Cases of EMR Use Cases of EMR

 1. Machine Learning

EMR provides built-in machine learning tools for scalable machine learning algorithms like TensorFLow, Apache Spark MLib, and Apache MXNet. Also you can easily use Bootstrap Actions and Custom AMIs for easily adding the preferred tools and libraries for creating your very own predictive analytics toolset. 

2. Extract Transform Load (ETL)

For cost-effective and quick performance of data transformation workloads (ETL) like sort, join and aggregate on large datasets, you can use EMR. 

3. Clickstream analysis

With EMR, along with Apache Hive and Apache Spark, you can segment users, deliver effective ads by understanding the user preferences. All this can be achieved by analyzing the clickstream data from Amazon S3. 

4. Real-time streaming

With EMR and Amazon Spark Streaming, analyzing events from Amazon Kinesis, Amazon Kafka or any other streaming data source is possible. This helps in creating highly available, long running, and fault-tolerant streaming data pipelines. Persist transformed insights to Amazon Elasticsearch and datasets to HDFS or Amazon S3. 

5. Interactive Analytics

With EMR Notebooks, you will be provided with an open-source Jupyter based, managed analytic environment. This will allow data analysts, developers and scientists in preparing and visualizing data, collaborating with peers, building applications, and performing interactive analysis. 

6. Genomics

EMR can also be used for quickly and efficiently processing large amounts of genomic data or any other large, scientific dataset. Genomic data hosted on AWS can be accessed by researchers for free. 

In this article, you got a quick introduction to Amazon EMR and how it has different log files’ types. Also, you got to understand the benefits of Elastic MapReduce. To become an expert in AWS services, enroll in the AWS certification course offered by KnowledgeHut. 

Joydip

Joydip Kumar

Solution Architect

Joydip is passionate about building cloud-based applications and has been providing solutions to various multinational clients. Being a java programmer and an AWS certified cloud architect, he loves to design, develop, and integrate solutions. Amidst his busy work schedule, Joydip loves to spend time on writing blogs and contributing to the opensource community.


Website : https://geeks18.com/

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Like Agile, DevOps is a mindset. And like Agile, it allows teams to learn, adjust, and deliver value to users incrementally. The continuous delivery pipeline allows teams to move value through the pipeline faster through continuous exploration, continuous integration, continuous deployment, and released on demand. DevOps breaks down silos and supports Agile teams to work together more seamlessly. This results in more efficient delivery of value to the end users faster. It’s a perfect complement to Scaled Agile.D. Implementation RoadmapSAFe® now offers a suggested roadmap to SAFe® adoption. While change can be challenging, the implementation roadmap provides guidance that can help with that organizational change.Critical Role of the SAFe® Program ConsultantSAFe® Program Consultants, or SPCs, are critical change agents in the transition to Scaled Agile.Because of the depth of knowledge required to gain SPC certification, they’re perfectly positioned to help the organization move through challenges of change.They can train and coach all levels of SAFe® participants, from team members to executive leaders. They can also train the Scrum Master, Product Owners, and Agile Release Train Engineers, which are critical roles in SAFe®.The SPC can also train teams and help them launch their Agile Release Trains (ARTs).And they can support teams on the path to continued improvement as they continue to learn and grow.The SPC can also help identify value streams in the organization that may be ready to launch Agile Release Trains.The can also help develop rollout plans for SAFe® in the enterprise.Along with this, they can provide important communications that help the enterprise understand the drivers and value behind the SAFe® transition.       How SAFe® 4.5 is backward compatible with SAFe® 4.0?Even if your organization has already adopted SAFe® 4.0, SAFe® 4.5 has been developed in a way that can be easily adopted without disruption. Your organization can adopt the changes at the pace that works best.Few Updates in the new courseware The courseware for SAFe® 4.5 has incorporated changes to support the changes in SAFe® 4.5.They include Implementing SAFe®, Leading SAFe®, and SAFe® for Teams.Some of the changes you’ll see are as follows:Two new lessons for Leading SAFe®Student workbookTrainer GuideNew look and feelUpdated LPM contentSmoother lesson flowNEW Course Delivery Enablement (CDE) Changes were made to improve alignment between SAFe® and Scrum:Iteration Review: Increments previously known as Sprints now have reviews added. This allows more opportunities for teams to incorporate improvements. Additionally, a Team Demo has been added in each iteration review. This provides more opportunity for transparency, sharing, and feedback.Development Team: The Development team was specifically identified at the team level in SAFe® 4.5. The development team is made up of three to nine people who can move an element of work from development through the test. This development team contains software developers, testers, and engineers, and does not include the Product Owner and Scrum Master. Each of those roles is shown separately at the team level in SAFe® 4.5.Scrum events: The list of scrum events are shown next to the ScrumXP icon and include Plan, Execute, Review, and Retro (for a retrospective.)Combined SAFe® Foundation Elements SAFe® 4.0 had the foundational elements of Core Values, Lean-Agile Mindset, SAFe® Principles, and Implementing SAFe® at a basic level.SAFe® 4.5 adds to the foundation elements by also including Lean-Agile Leaders, the Implementation Roadmap, and the support of the SPC in the successful implementation of SAFe®.Additional changes include: Communities of Practice: This was moved to the spanning palette to show support at all levels: team, program, large solution, and portfolio.Lean-Agile Leaders: This role is now included in the foundational level. Supportive leadership is critical to a successful SAFe® adoption.SAFe® Program Consultant: This role was added to the Foundational Layer. The SPC can play a key leadership role in a successful transition to Scaled Agile.Implementation Roadmap: The implementation roadmap replaces the basic implementation information in SAFe® 4.0. It provides more in-depth information on the elements to a successful enterprise transition to SAFe®.Benefits of upgrading to SAFe® 4.5With the addition of Lean Startup approaches, along with a deeper focus on DevOps and Continuous Delivery, teams will be situated to deliver quality and value to users more quickly.With improvements at the Portfolio level, teams get more guidance on Portfolio governance and other portfolio levels concerns, such as budgeting and compliance.  Reasons to Upgrade to SAFe® 4.5 Enterprises who’ve been using SAFe® 4.0 will find greater flexibility with the added levels in SAFe® 4.5. Smaller groups in the enterprise can use the team level, while groups working on more complex initiatives can create Agile Release Trains with many teams.Your teams can innovate faster by using the Lean Startup Approach. Work with end users to identify the Minimum Viable Product (MVP), then iterate as you get fast feedback and adjust. This also makes your customer more of a partner in development, resulting in better collaboration and a better end product.Get features and value to your user community faster with DevOps and the Continuous Delivery pipeline. Your teams can continuously hypothesize, build, measure, and learn to continuously release value. This also allows large organizations to innovate more quickly.Most Recent Changes in SAFe® series - SAFe® 4.6Because Scaled Agile continues to improve, new changes have been incorporated with SAFe® 4.6. with the addition of five core competencies that enable enterprises to respond to technology and market changes.Lean Portfolio Management: The information needed for how to use a Lean-Agile approach to portfolio strategy, funding, and governance.Business Solutions and Lean Systems: Optimizing activities to Implement large, complex initiatives using a Scaled Agile approach while still addressing the necessary activities such as designing, testing, deployment, and even retiring old solutions.DevOps and Release on Demand: The skills needed to release value as needed through a continuous delivery pipeline.Team and Technical Agility: The skills needed to establish successful teams who consistently deliver value and quality to meet customer needs.Lean-Agile Leadership: How leadership enables a successful agile transformation by supporting empowered teams in implementing agile practices. Leaders carry out the Agile principles and practices and ensure teams have the support they need to succeedSAFe® Agilist (SA) Certification exam: The SAFe® Agilist certification is for the change leaders in an organization to learn about the SAFe® practices to support change at all levels: team, program, and portfolio levels. These change agents can play a positive role in an enterprise transition to SAFe®.In order to become certified as a SAFe® Agilist (SA), you must first take the Leading SAFe® class and pass the SAFe® certification exam. To learn more about this, see this article on How To Pass Leading SAFe® 4.5 Exam.SAFe® Certification Exam: KnowledgeHut provides Leading SAFe® training in multiple locations. Check the site for locations and dates.SAFe® Agile Certification Cost: Check KnowledgeHut’s scheduled training offerings to see the course cost. Each course includes the opportunity to sit for the exam included in the cost.Scaled Agile Framework Certification Cost: There are multiple levels of SAFe® certification, including Scrum Master, Release Train Engineer, and Product Owner. Courses range in cost, but each includes the chance to sit for the corresponding SAFe® certification.SAFe® Classes: SAFe® classes are offered by various organizations. To see if KnowledgeHut is offering SAFe® Training near you, check the SAFe® training schedule on our website.TrainingKnowledgeHut provides multiple Scaled Agile courses to give both leaders and team members in your organization the information they need to for a successful transition to Scaled Agile. Check the site for the list of classes to find those that are right for your organization as you make the journey.All course fees cover examination costs for certification.SAFe® 4.5 Scrum Master with SSM Certification TrainingLearn the core competencies of implementing Agile across the enterprise, along with how to lead high-performing teams to deliver successful solutions. You’ll also learn how to implement DevOps practices. Completion of this course will prepare you for obtaining your SAFe® 4 Scrum Master certificate.SAFe® 4 Advanced Scrum Master (SASM)This two-day course teaches you to how to apply Scrum at the enterprise level and prepares you to lead high-performing teams in a Scaled Agile environment. At course completion, you’ll be prepared to manage interactions not only on your team but also across teams and with stakeholders. You’ll also be prepared to take the SAFe® Advanced Scrum Master exam.Leading SAFe®4.5 Training Course (SA)This two-day Leading SAFe® class prepares you to become a Certified SAFe® 4 Agilist, ready to lead the agile transformation in your enterprise.  By the end of this course, you’ll be able to take the SAFe® Agilist (SA) certification exam.SAFe® 4.5 for Teams (SP) This two-day course teaches Scrum fundamentals, principles tools, and processes. You’ll learn about software engineering practices needed to scale agile and deliver quality solutions in a Scaled Agile environment. Teams new to Scaled Agile will find value in going through this course. Attending the class prepares you for the certification exam to become a certified SAFe® 4 Practitioner (SP). DevOps Foundation Certification trainingThis course teaches you the DevOps framework, along with the practices to prepare you to apply the principles in your work environment. Completion of this course will prepare you also to take the DevOps Foundation exam for certification.
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A Glimpse Of The Major Leading SAFe® Versions

A Quick view of SAFe® Agile has gained popularit... Read More

How Start Ups Can Benefit From Cloud Computing?

From nebulous beginnings, the cloud has grown to a platform that has gained universal acceptance and is transforming businesses across industries. Companies that have adopted cloud technology have seen significant payoffs, with cloud based tools redefining their data storage, data sharing, marketing and project management capabilities. The easy availability of affordable cloud infrastructure has made it so easy to set up new businesses that the economy is all set for a start up boom which has its head, so to speak, in the cloud! With the advent of this new technology, complete newbie’s in the market are able to hold their own against established market players—by achieving an amazing quantum of work using skeleton manpower resources. Recently, a popular ad doing the rounds on TV showed a long haired youth conducting business from a cafe on his HP Pavilion laptop, where he is ridiculed by some well heeled middle aged businessmen on their coffee break. Back at their office, they find that this youngster is the new investor that their boss has been heaping accolades on. “Where’s your office?” one of them asks the young man…to be laughingly told that he carries his entire office in his laptop! And that, typically, is how the new-age start up business looks. We have heard many stories of how a clever idea has turned a tidy profit for a smart entrepreneur working out of his laptop. While cloud computing is pushing the boundaries of science and innovation into a new realm, it is also laying the foundation for a new wave of business start ups. New ventures in general suffer from a lack of infrastructure, manpower and funding…and all these three concerns are categorically addressed by the cloud. Moving to the cloud minimizes the need of huge capital investments to set up expensive infrastructure. For nascent entrepreneurs, physical hardware and server costs used to be formidable given the limited budgets at their disposal. Seed money was also required to hire office space, promote the business and hire workers. Today, thanks to cloud technology, getting a new business off the ground and running costs virtually nothing. Most of the resources and tools that new ventures need are available on the cloud at minimal costs, in fact quite often at zero costs, making this a powerful value proposition for small businesses. A cloud hosting provider such as AWS can enable you to go live immediately, and will even scale up to your requirement once your business expands. Small businesses can think and dream big with the cloud. When it comes to manpower resources, it takes just a handful of people to work wonders using the online resources that are at their disposal. If you have a brilliant idea and have a workable plan for execution, you can comfortably compete neck to neck with market leaders. The messaging sensation WhatsApp was started in 2009 by just two former Yahoo employees who leveraged the power of the internet – and this goes to show that clever use of technology can completely eliminate the need for a sizeable manpower pool. Start ups have always been more agile than their large scale counterparts, and the cloud helps them take this a step further. Resources can be scaled up or down in no time, whereas in traditional environments it would have taken many days, considerable planning and funds to add hardware and software. Cloud computing also helps improve collaboration across teams, often across geographies. Data sharing is instantaneous, and teams can work on a task together in real time regardless of their location. Powered by the cloud, small businesses operate with shoestring budgets and key players in different continents. All their accounting, client data, marketing and other business critical files can be stored online and are accessible from anywhere. These online tools can be accessed and utilised instantly, and underpin all the crucial processes on which these businesses thrive. Strategic financial decisions are made after garnering insights from cloud-based accounting software. E-invoicing helps settle bills in a fraction of the time of traditional billing systems, and client queries are answered quickly through cloud-based management systems—saving precious time and increasing customer satisfaction levels to an all-time high. Whether at home, on vacation or on the phone, businesses can oversee sales, replenish products and plan new sales strategies. That’s a whole new way of doing business, and seems to be very successful! An estimate by Cloudworks has put the anticipated cloud computing market at over $200 billion by the year 2018. As Jeff Weiner, CEO of LinkedIn, succinctly put it, the cloud “makes it easier and cheaper than ever for anyone anywhere to be an entrepreneur and to have access to all the best infrastructure of innovation.” With cloud technology rapidly levelling the playing field between nascent and established businesses, it is anybody’s guess as to just how many new start ups will burst into the scene in the next few years. Hoping that the blog has helped you gain a clear understanding of the importance of Cloud Computing.  To gain more knowledge on what cloud computing has to offer, take a look at other blogs as well as the AWS certifications that we have to offer or enrol yourself for the AWS Certification Training course by KnowledgeHut.  
How Start Ups Can Benefit From Cloud Computing?

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Business Transformation through Enterprise Cloud Computing

The Cloud Best Practices Network is an industry solutions groups and best practices catalogue of how-to information for Cloud Computing. While we cover all aspects of the technology our primary goal is to explain the enabling relationship between this new IT trend and business transformation, where our materials include: Core Competencies – The mix of new skills and technologies required to successfully implement new Cloud-based IT applications. Reference Documents – The core articles that define what Cloud Computing is and what the best practices are for implementation, predominately referring to the NIST schedule of information. Case studies – Best practices derived from analysis of pioneer adopters, such as the State of Michigan and their ‘MiCloud‘ framework . Read this article ‘Make MiCloud Your Cloud‘ as an introduction to the Cloud & business transformation capability. e-Guides – These package up collections of best practice resources directed towards a particular topic or industry. For example our GovCloud.info site specializes in Cloud Computing for the public sector. White papers – Educational documents from vendors and other experts, such as the IT Value mapping paper from VMware. Core competencies The mix of new skills and technologies required to successfully implement new Cloud-based IT applications, and also the new capabilities that these platforms make possible: Virtualization Cloud Identity and Security – Cloud Privacy Cloud 2.0 Cloud Configuration Management Cloud Migration Management DevOps Cloud BCP ITaaS Procurement Cloud Identity and Security Cloud Identity and Security best practices (CloudIDSec) provides a comprehensive framework for ensuring the safe and compliant use of Cloud systems. This is achieved through combining a focus on the core references for Cloud Security, the Cloud Security Alliance, with those of Cloud Identity best practices: IDaaS – Identity Management 2.0 Federated Identity Ecosystems Cloud Privacy A common critcal focus area for Cloud computing is data privacy, particularly with regards to the international aspects of Cloud hosting. Cloud Privacy refers to the combination of technologies and legal frameworks to ensure privacy of personal information held in Cloud systems, and a ‘Cloud Privacy-by-Design’ process can then be used to identify the local legislated privacy requirements of information. Tools for designing these types of privacy controls have been developed by global privacy experts, such as Ann Cavoukian, the current Privacy Commissioner for Ontario, who provides tools to design and build these federated privacy systems. The Privacy by Design Cloud Computing Architecture (26-page PDF) document provides a base reference for how to combine traditional PIAs (Privacy Impact Assessments) with Cloud Computing. As this Privacy Framework presentation then explains these regulatory mechanisms that Kantara enables can then provide the foundations for securing the information in a manner that encompasses all the legacy, privacy and technical requirements needed to ensure it is suitable for e-Government scenarios. This then enables it to achieve compliance with the Cloud Privacy recommendations put forward by global privacy experts, such as Ann Cavoukian, the current Privacy Commissioner for Ontario, who stipulates a range of ‘Cloud Privacy By Design‘ best practices Cloud 2.0 Cloud is as much a business model as it is a technology, and this model is best described through the term ‘Cloud 2.0′. As the saying goes a picture tells a thousand words, and as described by this one Cloud 2.0 represents the intersection between social media, Cloud computing and Crowdsourcing. The Social Cloud In short it marries the emergent new online world of Twitter, Linkedin et al, and the technologies that are powering them, with the traditional, back-end world of mainframe systems, mini-computers and all other shapes and sizes of legacy data-centre. “Socializing” these applications means moving them ‘into the Cloud’, in the sense of connecting them into this social data world, as much as it does means virtualizing the application to run on new hardware. This a simple but really powerful mix, that can act as a catalyst for an exciting new level of business process capability. It can provide a platform for modernizing business processes in a significant and highly innovative manner, a breath of fresh air that many government agency programs are crying out for. Government agencies operate many older technology platforms for many of their services, making it difficult to amend them for new ways of working and in particular connecting them to the web for self-service options. Crowdsourcing Social media encourages better collaboration between users and information, and tools for open data and back-end legacy integrations can pull the transactional systems informtion needed to make this functional and valuable. Crowdsourcing is: a distributed problem-solving and production process that involves outsourcing tasks to a network of people, also known as the crowd. Although not a component of the technologies of Cloud Computing, Crowdsourcing is a fundamental concept inherent to the success of the Cloud 2.0 model. The commercial success of migration to Cloud Computing will be amplified when there is a strong focus on the new Web 2.0 type business models that the technology is ideal for enabling. Case study – Peer to Patent One such example is the Whitehouse project the Peer to the Patent portal, a headline example of Open Government, led by one its keynote experts Beth Noveck. This project illustrates the huge potential for business transformation that Cloud 2.0 offers. It’s not just about migrating data-center apps into a Cloud provider, connecting an existing IT system to a web interface or just publishing Open Data reporting data online, but rather utilizing the nature of the web to entirely re-invent the core process itself. It’s about moving the process into the Cloud. In this 40 page Harvard white paper Beth describes how the US Patent Office was building up a huge backlog of over one million patent applications due to a ‘closed’ approach where only staff from the USPTO could review, contribute and decide upon applications. To address this bottleneck she migrated the process to an online, Open version where contributors from across multiple organizations could help move an application through the process via open participation web site features. Peer to Patent is a headline example of the power of Open Government, because it demonstrates its about far more than simply publishing reporting information online in an open manner, so that they public can inspect data like procurement spending numbers. Rather it’s about changing the core decision-making processes entirely, reinventing how Government itself works from the inside out, reinventing it from a centralized hierarchical monolith to an agile, distributed peer to peer network. In essence it transforms the process from ‘closed’ to ‘open’, in terms of who and how others can participate, utilizing the best practice of ‘Open Innovation‘ to break the gridlock that had occured due the constraints caused by private, traditional ways of working. Open Grantmaking – Sharing Cloud Best Practices Beth has subsequently advised further on how these principles can be applied in general across Government. For example in this article on her own blog she describes ‘Open Grantmaking‘ – How the Peer To Patent crowdsourcing model might be applied to the workflows for government grant applications. She touches on what is the important factor about these new models, their ability to accelerate continual improvement within organizations through repeatedly sharing and refining best practices: “In practice, this means that if a community college wins a grant to create a videogame to teach how to install solar panels, everyone will have the benefit of that knowledge. They will be able to play the game for free. In addition, anyone can translate it into Spanish or Russian or use it as the basis to create a new game to teach how to do a home energy retrofit.” Beth describes how Open Grantmaking might be utilized to improve community investing in another blog, describing how OG would enable more transparency and related improvements. Cloud 2.0 As the underlying technology Cloud 2.0 caters for both the hosting of the software and also the social media 2.0 features that enable the cross-enterprise collaboration that Beth describes. Cloud Configuration Management CCM is the best practice for change and configuration management within Cloud environments, illustrated through vendors such as Evolven. Problem Statement One of the key goals and perceived benefits of Cloud computing is a simplified IT environment, a reduction of complexity through virtualizing applications into a single overall environment. However complexity actually increases.  Virtual Machines (VMs) encapsulate application and infrastructure configurations, they package up a combination of applications and their settings, obscuring this data from traditional configuration management tools. Furthermore the ease of self-service creation of VMs results in their widespread proliferation, and so actually the adoption of Cloud technologies creates a need for a new, extra dimension of systems management. This is called CCM, and incorporates: Release & Incident Management The increased complexity therefore increases the difficulties in trouble-shooting technical problems, and thus requires an updated set of tools and also updates to best practices like the use of ITIL procedures. ‘Release into Production’ is a particularly sensitive process within software teams, as major upgrades and patches are transitioned from test to live environments. Any number of configuration-related errors could cause the move to fail, and so CCM software delivers the core competency of being better able to respond quicker to identify and resolve these issues, reducing the MTTR significantly. DevOps DevOps is a set of principles, methods and practices for communication, collaboration and integration between software development and IT operations. Through the implementation of a shared Lean adoption program and QMS (Quality Management System) the two groups can better work together to minimize downtimes while improving the speed and quality of software development. It’s therefore directly linked to Business Agility. The higher the value of speed and quality = a faster ability to react to market changes, deploy new products and processes and in general adapt the organization, achieved through increasing the frequency of ‘Release Events’: It’s therefore directly linked to Business Agility. The higher the value of speed and quality = a faster ability to react to market changes, deploy new products and processes and in general adapt the organization, achieved through increasing the frequency of ‘Release Events’: ITaaS Procurement The fundamental shift that Cloud Computing represents is illustrated in one key implementation area:   Procurement. Moving to Cloud services means changing from a financial model for technology where you buy your own hardware and software, and pay for it up front, to an approach where instead you access it as a rental, utility service where you “PAYG – Pay As You Go”. To encompass all the different ‘as a Service’ models this is known at an overall level as ‘ITaaS’ – IT as a Service. Any type of IT can be virtualized and delivered via this Service model. Towards the end, I hope that you have gained a clear understanding of How Business Transforms Through Enterprise Cloud Computing. If this article has helped you clear your fundamentals and if you wish to learn more about Cloud computing by getting certified, then you can undertake the AWS certification course offered by KnowledgeHut.
Business Transformation through Enterprise Cloud C...

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