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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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Amazon Web Services (AWS) Certified Solutions Architect- Associate Amazon Web Services is one of the top cloud computing companies in recent times. They have achieved an impressive 43% growth over the last year. They are followed by Microsoft Azure and Google Cloud with a close lead. Amazon Web Services offer certifications at the foundation, associate, and professional levels. This prepares a candidate for developing and architecture roles and offers operational knowledge. The associate certification can be a steppingstone to a potential professional level certification which comes with veteran level jobs and authorizes years' worth of experience in the field of Cloud Computing architecture, and design.  Prerequisites: Amazon prefers hands-on experience in the fields of networking, database, computational, and storage AWS services with the ability to perfectly define requirements for an AWS application. They require critical thinking skills taking into view the AWS service format along with knowledge on building security services.  Exam Cost: It costs $150. A practice exam can be purchased for $20 USD. (Source).  The exam can be taken in English, Japanese, Korean, and Simplified Chinese languages. Exam Guide: Amazon offers a collection of hands-on training courses, videos, and much more to prepare for the exam. Self-evaluation methods include an exam guide and sample questions. 65 MCQ format questions.  Salary: The average Pay an AWS solution Architect can expect is around $121K (Source)  3. MCSE: Cloud Platform and Infrastructure (Microsoft)Microsoft is, again, one of the brands which have made a mark on the technology industry today. The MCSE: Cloud Platform and Infrastructure Course certifies a person's ability to effectively manage cloud data, shows their skill in managing virtual networks, storage management systems, and many more cloud technologies,  Prerequisites: One does not just take the MCSA (Microsoft Certified Solution Associate): Azure certification. They must also score a passing grade on an exam called the MSCE, which covers development, and Azure-based and related architecture solutions along with hybrid cloud operations and bits of big data analytics. An MCSE along with two or three pre-requisite exams need to be taken.   Exam Cost: The MCSE exam costs $165, (Source) while the pre-requisite exams cost $165 and $300 (MCSA and LFCS, respectively) Exam Guide/Courses: Microsoft Virtual Academy (MVA) offers free courses and reference matter relevant to Cloud professionals and cloud development. A program called Exam Reply is available that allows candidates to buy a slightly discounted exam, a practice attempt (which needs a slight upcharge), and a retake attempt as well.    Salary: The job title that can be earned after this certification is Microsoft Cloud Solution Architect, and this role can earn around $154133 per year. (Source)  4. Certified Cloud Security Professional (CCSP)Offered by the (ISC)^2 (International Information System Security Certification Consortium, the CCSP is a globally recognized certification. It validates a candidate's ability to work within a cloud architecture along with good abilities in the field of design, secure applications, along data and infrastructure. These are carried out under the protocols offered by (ISC)^2, which are a hallmark of security. It's ideal for those who want an enterprise architect role, and other roles include systems engineers, security administrator or a consultant in the field of security.  Prerequisites: The (ISC)^2 recommends around five years of experience in the field of IT, including three in Information security and one in any of the domains prescribed by CSSP Common Body of Knowledge.  Exam Cost: The exam is provided by Pearson VUE. The standard registration for the exam costs $600 (Source)  Exam Guide/Courses: The CCSP examination involves preparation in 6 different domains, as highlighted in the CCSP exam outline.  Salary: The job title earned is Cloud Security Professional, a job that can pay up to $138k per annum. (Source)  5. CompTIA Cloud+ An acronym for Computing Technology Industry Association, CompTIA is a non-profit. It serves the IT industry and is one of the global leaders in certifications like the ones you're looking for on the list. These are vendor-neutral, meaning you can apply to a broad range of jobs, and it means you're not restrained to any particular company. They cover certifications from novice to professional levels.  CompTIA Cloud+ acts as a foundation-level certification. Like its selling point, Cloud+ offers a piece of foundational knowledge in a broad domain in the Cloud market. It authorizes skills in the maintenance and optimization of cloud software. It shows that a candidate can demonstrate the ability to migrate data to cloud platforms, manage cloud resources and make appropriate modifications, perform automation tasks to improve performance, all the while focusing on security.  Prerequisites: It needs 2-3 years' worth of experience in system administration.  Exam Cost & Format: The exam includes 90 questions. Available in English and Japanese Costs $338 (Source), The certification expires in 3 years after launch Salary: As a cloud specialist an average pay that can be expected in the US market is around $80317 (Source). 6. VMware VCP7-CMA VMware is a company that is well known within the IT-sphere for its strong grasp of virtualization technologies. The VCP7- Cloud Management and Automation is the latest in a series of certifications the company has rolled out. The vRealise and the vSphere-based program are instrumental in certifying new as well as veteran IT professionals in the field of virtualization in the Cloud.  Prerequisites:  A prerequisite is to have a minimum of 6-month experience with the vSphere 6 and realized software.  One also needs to complete one of the training courses offered by VMware, which keeps updating on the current course list portion of the website.  Candidates can choose one out of 3 exams: vSphere 6 Foundations, vSphere 6.5 Foundations, or VMware Certified Professional Management and Automation exam.  Exam Cost: vSphere 6 and 6.5 cost $125, whereas the third exam costs $250 (Source). A VMWare candidate ID is needed to register.  Exam Guide: Exam Self-study material is available on the certification page.  Salary: As a VMWare Staff Engineer, the salary expected could be up to $188446 every year. (Source)  7. CCNP Cloud (Cisco)CCNP stands for Cisco Certified Network Professional. This is one of the more reputed certifications that allows a professional to validate their skills in the fields of data management, cloud architecture, and design and authorize their path as a cloud professional. Along with the Cloud, the CCNP is also available as a Collaboration, Service Provider, Data Centre, and many other fields in the collection of solutions. Be warned, though. Cisco focuses on the practical requirements as well, so their certification process is equally rigorous, with design, practical, architecture-based assessments to keep one on their toes. But in the end, this multidisciplinary approach proves itself. An understanding of Application Centric Infrastructure (ACI) is also vital. They provide a lot of resources to prepare as well, with assignments, discussion forums, self-assessments, and much more!  Training in the fields of CLDING, CLDDES, CLDAUT, CLACI, CLDINF is highly recommended. These cover information on Cisco cloud infrastructure, automation, infrastructure, and troubleshooting.  Prerequisites: There are four exams that need to be taken in each of the above fields. They are administered by Pearson VUE.  Exam Cost: Each exam costs $300, $1200 total. (Source)  Exam Guide: For the study material, Cisco has curated many resources like Learning Network games, self-assessment modules, seminars, videos, and much more. Textbooks and other materials are also available on the Cisco Marketplace Bookstore.  Salary: The typical job that can be obtained is Cisco Systems Cloud Engineer that pays around $158010 per annum.(Source)  Certification LevelsThese cloud certifications can be segregated into Professional and Associate levels, where various criteria are required to be fulfilled to be eligible to apply for the respective certification. As per the market trends and the demand, here is a detailed description of some of the most coveted certifications: Amazon Web Services - AWS 1. AWS Certified Solutions Architect - Professional This certification is for professionals who have experienced hands-on solutions architect roles. A candidate must have 2 or more years of experience in operating and managing the AWS operations. The exam costs 300 USD and is 180 minutes long. This course validates the following abilities:  Implementation of cost control strategies  Designing fault proof applications on AWS  Choosing appropriate AWS services for design and application   Migrating the complex applications on AWS  Exam criteria 2 or more years of experience in handling cloud architecture on AWS  One should have diverse knowledge of AWS CLI, AWS APIs, AWS CloudFormation templates, the AWS Billing Console, and the AWS Management Console  Detailed knowledge of the scripting language  Must have worked on Windows and Linux  Must be able to explain the five pillars of the AWS architecture Framework  Practical knowledge of the architectural design across multiple projects of the company.  2. AWS Certified Solutions Architect - Associate  This course is for professionals who have one year of experience in handling and designing fault free and scalable distributed systems on AWS.  This certificate validates the following abilities:  In depth knowledge of deploying the secure and powerful applications on AWS  Knowledge and application of customized architectural principles  Exam criteria The course requires a complete understanding of the AWS global infrastructure, network technologies, security features and tools related to AWS  Knowledge of how to build secure and reliable AWS applications  Experience of deployment and management of management services.  The exam duration is 130-minutes and the fee is $150. The above were some of the main certified courses of AWS. The other two Associate level courses are AWS SysOps Administrator Associate and the AWS Developer Associate. 3. The AWS Certified DevOps Engineer – ProfessionalThis exam is for professionals who have experience as a DevOps engineer and have experience in provisioning, operating, and managing AWS environments.  This course validates the following abilities: Management and implementation of delivery systems and methodology on AWS  Deploying and managing the logging, metrics, and monitoring system on AWS  Implementation and management of highly scalable, and self-healing systems on AWS.  Automation of security controls, government processes and compliance validation  Exam criteria  Knowledge and experience in administering operating systems and building highly automated infrastructure.  Knowledge of developing code in at least one high level programming language.  The cost of the exam is 300 USD and the duration is 180 minutes. There will be 75 questions.  Microsoft Web Service – Azure:  1. Azure Developer Associate AZ-204This course will provide you with the skill set to design, build, test and maintain cloud solutions from the start to the end.  You will master the basics of developing an app and all the other services Azure provides. This certification course will help you learn the actual syntax and programming languages that are used to integrate the application on Azure.  Exam criteria You are required to take an Exam AZ-204: Developing Solutions for Microsoft Azure ($165 USD) and must have at least 1-2 years’ of experience with development and azure development.  Having a good command in any of these languages like C#, PHP, Java, Python, or JavaScript would be a plus.  Getting certified with this course will set you ahead of your peers in the development sector.  2. Azure Data Scientist Associate DP-100Turning data and facts related to a business into useful and actionable insights is an art, and getting the Azure data scientist certification will prove that you have the required expertise in data and machine learning.  This course is for professionals who are currently working as a data scientist or are planning to become one soon. Exam: DP-100: Designing and Implementing a Data Science Solution on Azure ($165 USD)  Exam criteria You should have knowledge and experience in data science and in using Azure Machine Learning and Azure Databricks. This certification course can future-proof your career, as there is spectacular growth in internet use and the demand for job roles in this sector will continue to increase year on year.   Wondering where to start? Here are some pointers:Are you a Newbie?If you are a lost soul in the world of technology but want to learn, then the perfect way to start is the Azure Fundamentals Course. Any beginner can grasp the fundamentals and get started.Are you in the middle of the road?If you are someone who has average experience and has worked with hands-on AWS, GCP, or Azure then too we would recommend you start with the Fundamental course. Refresh your knowledge and make your basics stronger before you move on to the Administrator Associate certification, which can be very intimidating otherwise. Are you an Expert?  If you have had enough experience with cloud computing or have got serious geek vibes in you, then you can take up any speciality or professional certifications to add the missing edge to your expertise.  If you still need more clarity, you can explore our cloud certification category page for more details. Need more handholding? Contact our experts by using the Contact Learning Advisor button and fill up a small form. Let’s connect! Why be a Cloud Computing Professional? 1. A Growing Field As more and more of our lives are uploaded on the Cloud, the demand for professionals with the capabilities to handle cloud architecture is increasing by the day. Professionals with the right expertise are paid handsome salaries, and the investments made in certification repay themselves many times over. The demands for Cloud professionals outstrip the supply by a huge margin, making this an easy job for entry-level applicants.   2. A Good PayThe salary for a Cloud Engineer ranges from $117,892 to $229,000 (Source). This is a rewarding field, indeed! You can get onto the entry point of the ladder and work your way up, which is an easy journey if you earn a certification. It is one of the highest paying jobs that can be found in the IT sector.   Companies Hiring Certified Cloud Computing Professionals Some of the companies whose certifications we addressed above are also among the key employers in the Cloud Computing market. The key employers for these jobs are listed below.    Amazon They are the undoubted leaders in the fields of Cloud Computing and management. They are branching out in the fields of AI, the Internet of things, machine learning, and database management as well, and you can explore exciting new opportunities in any of these fields. As documented above, AWS has faced over 43% growth year after year for a sustained period. They are undoubtedly one of the largest hirers in the field as they need competent workforce for their expanding ventures.  Microsoft After the enormous success of the Office 365 platform, Cloud Computing was the next step forward for Microsoft, with the Azure platform. They are neck to neck with Amazon for the number 1 spot in the field of Cloud architecture and database management.   IBM The waning brand of IBM has now made a sudden resurgence to capitalize on the demand in the fields of AI, the Information Age, and the new Cloud phenomenon. They have recently acquired Red Hat and have entered the field of hybrid cloud development. They will surely be looking for professionals in the field to boost their chances. Dell Technologies (VMware) VMware, mentioned on the above lists, has partnered with Dell Technologies to form a robust cloud platform. A veteran player in the industry already, VMware has constantly evolved to adapt to advancements in the industry. They have partnerships with all the huge players like AWS, Microsoft Azure and Google Cloud as well.  ConclusionIt is quite evident that Cloud computing is one of the most exciting and lucrative fields one can be in, considering the investment to return ratio. These certifications offer incredibly excellent value for money and will lead to placements in leading companies, which is not easy via other paths.  There is a lot to learn in the field of Cloud Computing, and it is a highly adaptive job as well; that is why one needs to keep an eye on the newest software and architecture in the market. These certifications make sure that you can validate your experience and increase your employability. While there are many certifications available, only the ones from reputed institutions help to get a job. They show that you have the knowledge and expertise to make your mark in the industry.  It is never too late to start your learning journey, so grab that certification exam guide and start learning. Happy computing!  
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Top Cloud Certifications

What is Cloud Computing?Cloud is the new buzzword ... Read More

A Glimpse Of The Major Leading SAFe® Versions

A Quick view of SAFe® Agile has gained popularity in recent years, and with good reason. Teams love this approach that allows them to get a value to the customer faster while learning and adjusting to change as needed. But teams often don’t work in isolation. Many teams work in the context of larger organizations.  Often Agile doesn’t fit their needs. Some teams need an Agile approach that scales to larger projects that involve multiple teams.   It’s possible to do this. That’s where the Scaled Agile Framework, or SAFe®, can help.Why SAFe® is the best scalable framework?The Scaled Agile Framework is a structured Agile approach for large enterprises. It’s prescriptive and provides a path for interdependent teams to gain the benefits of using an Agile approach.Scaled Agile provides guidance not only at the team level but also at the Program and Portfolio levels. It also has built-in coordinated planning across related teams who are working in Release Trains.These planning increments allow teams to plan together to work with customers and release value frequently in a way that’s sustainable to teams.And it supports continuous improvement.It’s a great way for large companies to maintain structure and roll out Agile at a large scale.  What is SAFe® 4.5? Scaled Agile, otherwise known as SAFe®, was initially released in 2011 by Dean Leffingwell as a knowledge base for enterprises to adopt Agile. Over the years it has grown and evolved. SAFe® 4.5 was released on June 22, 2017, to accommodate improvements to the framework. Following are some of the key improvements in SAFe® 4.5:Essential SAFe® and ConfigurabilityInnovation with Lean Startup and Lean UXScalable DevOps and Continuous DeliveryImplementation roadmapBenefits of SAFe® 4.5 to companies:Organizations who adopt SAFe® 4.5 will be able to gain the following benefits:1) Test ideas more quickly. SAFe® 4.5 has a build-in iterative development and testing. This lets teams get faster feedback to learn and adjust more quickly.2) Deliver much faster. The changes to SAFe® 4.5 allow teams to move complex work through the pipeline and deliver value to the customer faster.3) Simplify governance and improve portfolio performance. Guidance and support have been added at the Portfolio level to guide organizations in addressing Portfolio-level concerns in a scaled agile context. SAFe® 4.5 - Key areas of improvements:A. Essential SAFe® and ConfigurabilityFour configurations of SAFe® that provide a more configurable and scalable approach:Essential SAFe®: The most basic level that teams can use. It contains just the essentials that a team needs to get the benefits of SAFe®.Portfolio SAFe®: For enterprises that implement multiple solutions that have portfolio responsibilities such as governance, strategy, and portfolio funding.Large Solution: Complex solutions that involve multiple Agile Release Trains. These initiatives don’t require Portfolio concerns, but only include the Large Solution and Essential SAFe® elements.  SAFe® Full SAFe®: The most comprehensive level that can be applied to huge enterprise initiatives requiring hundreds of people to complete.Because SAFe® is a framework, that provides the flexibility to choose the level of SAFe® that best fits your organization’s needs.B. Innovation with Lean Startup and Lean UXRather than creating an entire project plan up-front, SAFe® teams focus on features. They create a hypothesis about what a new feature will deliver and then use an iterative approach to develop and test their hypothesis along the way. As teams move forward through development, they perform this development and test approach repeatedly and adjust as needed, based on feedback. Teams also work closely with end users to identify the Minimum Viable Product (MVP) to focus on first. They identify what will be most valuable to the customer most immediately. Then they rely on feedback and learning as they develop the solution incrementally. They adjust as needed to incorporate what they’ve learned into the features. This collaboration and fast feedback and adjustment cycle result in a more successful product.  C. Scalable DevOps & Continuous DeliveryThe addition of a greater focus on DevOps allows teams to innovate faster. 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.  
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