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What Is a Data Pipeline? What Are the Properties and Types of Data Pipeline Solutions

A data pipeline is a set of actions that extracts data from numerous sources. It is a computerized process where the system takes columns from the database and merges them with other columns from this API. It also combines subset rows and corresponding values, alternates NAs with the median and loads them in this other database. This is known as a “job”, and pipelines are made of many jobs. Generally, the endpoint for a data pipeline is a data lake, such as Hadoop, S3, or a relational database. An ideal data pipeline should have the following properties:Low Occurrence Inactivity: Data scientists should have accessibility to the data. Users should be able to raise a query to recover the recent event data in the pipeline. Usually, this happens in minutes or seconds of the event being directed to the data collection endpoint. Scalability: A data pipeline should be able to gauge billions of data points, and product sales. Collaborative Querying: A highly operational data pipeline should support both long-running batch queries and minor interactive queries that allow data scientists to discover tables and comprehend the data scheme.Versioning: You should be able to edit and customize your data pipeline and event definitions without damaging the framework Monitoring: Data tracking and monitoring are important to check if the data is dispatched properly. In case of a failure, immediate alerts should be generated through tools such as PagerDuty.Testing: You should be able to test your data pipeline with test events that do not end up in your data lake or database, but that do test components in the pipeline.Do You want to Get AWS Certified? Learn about various AWS Certification in detailData Pipeline- UsageHere are a few things you can do with Data Pipeline.Convert received data to a common format.Prepare data for investigation and imagining.Travel between databases.Share data processing logic across web apps, batch jobs, and APIs.Power your data ingestion and integration tools.Input large XML, CSV, and fixed-width files.Substitute batch jobs with real-time dataNote that the Data Pipeline does not levy a specific structure on your data. All the data flowing through your pipelines can follow the same plan or an alternative NoSQL approach. The NoSQL feature offers a diverse structure to the data that can be altered at any point in your pipeline.What are the Types of DataData is typically defined with the following labels:Raw Data: This is on processed data stored in the message encoding format which is used to send tracking events, such as JSON. Processed Data: Processed data is raw data that has been deciphered into event-specific formats, with an applied plan.Cooked Data: Processed data that has been amassed or abridged is referred to as cooked data.The Evolution of Data PipelinesOver the past two decades the framework for accumulating and analyzing data been drastically changed. Earlier users would store data locally through log files, today we have modern systems that can trace data activity and use machine learning for real-time solutions. There are four different approaches to pipelines:Flat File Era: Data is saved locally on game serversDatabase Era: Data is staged in flat files and then loaded into a databaseData Lake Era: Data is stored in Hadoop/S3 and then loaded into a DBServerless Era: Managed services are used for storage and queryingEach of the steps supports the grouping of greater data sets. But it ultimately depends on the goal of the company to decide how the data is to be utilized and distributed.Application of Data PipelinesMetadata: Data Pipeline lets users connect metadata to each separate record or field.Data processing: Dataflows when processed and broken into smaller units, are easier to work with. It also quickens the process and saves on memory.Adapting to Apps: Data Pipeline adjusts to your applications and services. It occupies a small footprint of less than 20 MB on disk and in RAM. Flexible Data Components: Data Pipeline comes with readers and writers integrated to stream the inflow or outflow of data. There are also stream operators for controlling this data flow.Data Pipeline TechnologiesSome examples of products used in building data pipelines. These tools are used by engineers to find competent results and enhance the system’s performance and reach; Data warehousesETL toolsData Prep toolsLuigi: a workflow timetable that can be used to manage jobs and processes in Hadoop and similar systems.Python / Java / Ruby: programming languages used to transcribe processes in many of these systems.AWS Data Pipelines: another workflow management service that charts and implements data movement and processesKafka: a real-time streaming platform that allows you to move data between systems and applications, can also transform or react to these data streams.Types of data pipeline solutionsThe following list shows the most popular types of pipelines available:Batch: Batch processing is most valuable of all as it lets you move huge volumes of data at a steady interval.Real-time: These tools are improved to develop data in real time. Cloud native: These tools are optimized to work with cloud-based data, such as data from AWS buckets. These tools are hosted in the cloud, and are a cost effective and quick technique to enhance the infrastructure.Open source: These tools are a cheaper alternative to a vendor. Open source tools are often inexpensive but require technical know-how on the part of the user. The platform is open for all to optimise and edit any way they want. AWS Data PipelineAWS Data Pipeline is a web service that supports dependable process and transfer data between a diverse range of AWS services, as well as on-premises data sources. With the AWS Data Pipeline, you can frequently keep in contact with the data and back where it’s deposited. Developers can also customize the data, convert and modify it at scale, and resourcefully allocate the results to other AWS services such as Amazon S3, Amazon RDS, Amazon DynamoDB, and Amazon EMR.AWS Data Pipeline aids you in creating an intricate data processing network. It takes care of all the data monitoring, tracking and optimization tasks. AWS Data Pipeline also allows you to change the data that was previously protected in the on-site data storage facility.Decoding Data Pipelines Let’s look into the process of assigning, transferring, altering and storing data via pipelines; Sources: First and foremost, we decide where we get the data from. Data can be accessed from different sources and in different formats. RDBMS, Application APIs, Hadoop, NoSQL, cloud sources, are a few primary sources. After the data is retrieved, it has to pass through the security controls and follow set protocols. next, the data schema and statistics are gathered about the source to simplify pipeline design.List of common terms related to data science Joins: It is common for data to be shared from different sources as part of a data pipeline.Extraction: Some separate data elements may be implanted in bigger fields. In some cases numerous values are clustered together. Or, distinct values may need to be removed- data pipelines allow all that. Standardization: Data needs to be consistent. It should follow a unit of measure, dates, attributes such as color or size, and codes related to industry standards.Correction: Data, especially raw data can contain a lot of errors. Some common errors are- invalid fields that are not present or abbreviations that need to be extended. There may also be corrupt records that need to be detached or studied in an isolated process.Loads: Once the data is ready, it needs to be loaded into a system for scrutiny. The endpoint is generally an RDBMS, a data warehouse, or Hadoop. Each destination has its own set of regulations and restrictions that need to be followed. Automation: Data pipelines are usually completed many times, and characteristically set on a schedule. This simplifies the error detection process and aids monitoring by sending regular reports to the system.Moving Data Pipelines Many corporations have hundreds or thousands of data pipelines. Companies shape each pipeline with one or more technologies, and each pipeline might follow a different approach. Datasets often start with an establishment’s customer base. But there are cases where they will also initiate with their assumed departments within the organization itself. Thinking of data as events simplifies the process. Events are logged in, integrated and then transmuted across the pipeline. The data is then changed and altered to suit the systems that they are moved to. Moving data from place to place means that different end users can use it more methodically and accurately. Users can now access the data from one place rather than refer to multiple sources. Good data pipeline architecture will be able to provide justification for all sources of events. It would also have an explanation or reason to support the setups and schemes caring for these datasets. Event frameworks help you get hold of events from your applications a lot faster. This is achieved by making an event log that can then be processed for use.ConclusionA career in data science is a very profitable decision considering the revolutionary discoveries made in the field each day. We hope that this information was useful in helping the reader understand all about data pipelines and why they are important.
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What Is a Data Pipeline? What Are the Properties and Types of Data Pipeline Solutions

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  • by Joydip Kumar
  • 06th Sep, 2019
  • Last updated on 02nd Jan, 2020
  • 11 mins read
What Is a Data Pipeline? What Are the Properties and Types of Data Pipeline Solutions

A data pipeline is a set of actions that extracts data from numerous sources. It is a computerized process where the system takes columns from the database and merges them with other columns from this API. It also combines subset rows and corresponding values, alternates NAs with the median and loads them in this other database. This is known as a “job”, and pipelines are made of many jobs. Generally, the endpoint for a data pipeline is a data lake, such as Hadoop, S3, or a relational database. An ideal data pipeline should have the following properties:

  • Low Occurrence Inactivity: Data scientists should have accessibility to the data. Users should be able to raise a query to recover the recent event data in the pipeline. Usually, this happens in minutes or seconds of the event being directed to the data collection endpoint. 
  • Scalability: A data pipeline should be able to gauge billions of data points, and product sales. 
  • Collaborative Querying: A highly operational data pipeline should support both long-running batch queries and minor interactive queries that allow data scientists to discover tables and comprehend the data scheme.
  • Versioning: You should be able to edit and customize your data pipeline and event definitions without damaging the framework 
  • Monitoring: Data tracking and monitoring are important to check if the data is dispatched properly. In case of a failure, immediate alerts should be generated through tools such as PagerDuty.
  • Testing: You should be able to test your data pipeline with test events that do not end up in your data lake or database, but that do test components in the pipeline.

Do You want to Get AWS Certified? Learn about various AWS Certification in detail

Data Pipeline- Usage

Here are a few things you can do with Data Pipeline.

  • Convert received data to a common format.
  • Prepare data for investigation and imagining.
  • Travel between databases.
  • Share data processing logic across web apps, batch jobs, and APIs.
  • Power your data ingestion and integration tools.
  • Input large XML, CSV, and fixed-width files.
  • Substitute batch jobs with real-time data

Note that the Data Pipeline does not levy a specific structure on your data. All the data flowing through your pipelines can follow the same plan or an alternative NoSQL approach. The NoSQL feature offers a diverse structure to the data that can be altered at any point in your pipeline.

What are the Types of Data

Types of Data in AWS Data Pipeline

Data is typically defined with the following labels:

  • Raw Data: This is on processed data stored in the message encoding format which is used to send tracking events, such as JSON. 
  • Processed Data: Processed data is raw data that has been deciphered into event-specific formats, with an applied plan.
  • Cooked Data: Processed data that has been amassed or abridged is referred to as cooked data.

The Evolution of Data Pipelines

Over the past two decades the framework for accumulating and analyzing data been drastically changed. Earlier users would store data locally through log files, today we have modern systems that can trace data activity and use machine learning for real-time solutions. There are four different approaches to pipelines:

  • Flat File Era: Data is saved locally on game servers
  • Database Era: Data is staged in flat files and then loaded into a database
  • Data Lake Era: Data is stored in Hadoop/S3 and then loaded into a DB
  • Serverless Era: Managed services are used for storage and querying

Each of the steps supports the grouping of greater data sets. But it ultimately depends on the goal of the company to decide how the data is to be utilized and distributed.

Application of Data PipelinesApplication of Data Pipelines in AWS

  • Metadata: Data Pipeline lets users connect metadata to each separate record or field.
  • Data processing: Dataflows when processed and broken into smaller units, are easier to work with. It also quickens the process and saves on memory.
  • Adapting to Apps: Data Pipeline adjusts to your applications and services. It occupies a small footprint of less than 20 MB on disk and in RAM. 
  • Flexible Data Components: Data Pipeline comes with readers and writers integrated to stream the inflow or outflow of data. There are also stream operators for controlling this data flow.

Data Pipeline Technologies

Some examples of products used in building data pipelines. These tools are used by engineers to find competent results and enhance the system’s performance and reach; 

  • Data warehouses
  • ETL tools
  • Data Prep tools
  • Luigi: a workflow timetable that can be used to manage jobs and processes in Hadoop and similar systems.
  • Python / Java / Ruby: programming languages used to transcribe processes in many of these systems.
  • AWS Data Pipelines: another workflow management service that charts and implements data movement and processes
  • Kafka: a real-time streaming platform that allows you to move data between systems and applications, can also transform or react to these data streams.

Types of data pipeline solutions

The following list shows the most popular types of pipelines available:

  • Batch: Batch processing is most valuable of all as it lets you move huge volumes of data at a steady interval.
  • Real-time: These tools are improved to develop data in real time. 
  • Cloud native: These tools are optimized to work with cloud-based data, such as data from AWS buckets. These tools are hosted in the cloud, and are a cost effective and quick technique to enhance the infrastructure.
  • Open source: These tools are a cheaper alternative to a vendor. Open source tools are often inexpensive but require technical know-how on the part of the user. The platform is open for all to optimise and edit any way they want. 

AWS Data Pipeline

AWS Data Pipeline is a web service that supports dependable process and transfer data between a diverse range of AWS services, as well as on-premises data sources. With the AWS Data Pipeline, you can frequently keep in contact with the data and back where it’s deposited. Developers can also customize the data, convert and modify it at scale, and resourcefully allocate the results to other AWS services such as Amazon S3, Amazon RDS, Amazon DynamoDB, and Amazon EMR.

AWS Data Pipeline aids you in creating an intricate data processing network. It takes care of all the data monitoring, tracking and optimization tasks. AWS Data Pipeline also allows you to change the data that was previously protected in the on-site data storage facility.

Decoding Data Pipelines 

Let’s look into the process of assigning, transferring, altering and storing data via pipelines; 

Sources: First and foremost, we decide where we get the data from. Data can be accessed from different sources and in different formats. RDBMS, Application APIs, Hadoop, NoSQL, cloud sources, are a few primary sources. After the data is retrieved, it has to pass through the security controls and follow set protocols. next, the data schema and statistics are gathered about the source to simplify pipeline design.

List of common terms related to data science 

  • Joins: It is common for data to be shared from different sources as part of a data pipeline.
  • Extraction: Some separate data elements may be implanted in bigger fields. In some cases numerous values are clustered together. Or, distinct values may need to be removed- data pipelines allow all that. 
  • Standardization: Data needs to be consistent. It should follow a unit of measure, dates, attributes such as color or size, and codes related to industry standards.
  • Correction: Data, especially raw data can contain a lot of errors. Some common errors are- invalid fields that are not present or abbreviations that need to be extended. There may also be corrupt records that need to be detached or studied in an isolated process.
  • Loads: Once the data is ready, it needs to be loaded into a system for scrutiny. The endpoint is generally an RDBMS, a data warehouse, or Hadoop. Each destination has its own set of regulations and restrictions that need to be followed. 
  • Automation: Data pipelines are usually completed many times, and characteristically set on a schedule. This simplifies the error detection process and aids monitoring by sending regular reports to the system.

Moving Data Pipelines 

Many corporations have hundreds or thousands of data pipelines. Companies shape each pipeline with one or more technologies, and each pipeline might follow a different approach. Datasets often start with an establishment’s customer base. But there are cases where they will also initiate with their assumed departments within the organization itself. Thinking of data as events simplifies the process. Events are logged in, integrated and then transmuted across the pipeline. The data is then changed and altered to suit the systems that they are moved to. 

Moving data from place to place means that different end users can use it more methodically and accurately. Users can now access the data from one place rather than refer to multiple sources. Good data pipeline architecture will be able to provide justification for all sources of events. It would also have an explanation or reason to support the setups and schemes caring for these datasets. 

Event frameworks help you get hold of events from your applications a lot faster. This is achieved by making an event log that can then be processed for use.

Conclusion

A career in data science is a very profitable decision considering the revolutionary discoveries made in the field each day. We hope that this information was useful in helping the reader understand all about data pipelines and why they are important.

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 : http://geeks18.com/

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Apart from this, you must also have knowledge of key AWS services like databases, change management services, workflow services, notifications, and storage services.One must satisfy certain eligibility criteria to take the AWS Certified Developer – Associate exam:Knowledge of the AWS architecture, services offered by the AWS and their uses. Proficiency in using the AWS platform for designing, building, and deploying cloud-based applications as well as applications built for Amazon SNS, SQS, SWS, DynamoDB, S3, CloudFormation, and Elastic Beanstalk. Knowledge of at least one high-level programming language. At least 1 year of working experience in designing and maintaining AWS-based cloud applicationsAn AWS Certified Developer- Associate’s average annual income is $130, 272.4. AWS Certified SysOps Administrator – AssociateThe SysOps Administrator certification exam is the only exam offered by AWS that is completely for system administrators.  To pass this exam, a candidate must have conceptual knowledge as well as technical expertise in operational aspects of the AWS. During the exam, you skills in using the AWS platform for deploying applications, transferring data between the AWS and the data centers, meeting the needs of an organization by selecting the right AWS service, knowledge of how to provision, secure, and manage systems deployed in an AWS environment will be tested.To be eligible for the AWS Certified Developer – Associate exam, the following are required: One or more years of working experience in operating and managing applications deployed on the AWS platform. Must be able to provide guidance on how to deploy and operate applications on AWS, define and identify the best practices available on the AWS for the complete project’s lifecycle as well as the solutions for the applications based on AWS, and understand how to operate, provision and maintain AWS based systems.Every year, an AWS Certified SysOps Administrator – Associate makes about $130,6105. AWS Certified Solutions Architect – ProfessionalA professional AWS architect is responsible for evaluating the requirements of the organization and then making architectural recommendations in order to implement and deploy AWS-based applications. To get this certification, a candidate must have experience and technical skills required to design applications on the AWS platform.The exam for the AWS Solutions Architect – Professional certification will include practices implemented for architecting the AWS-based applications. The candidate must have knowledge of the strategies used for cost optimizations. Also, they must know how to fulfill the application’s requirements by choosing the correct AWS service and how to migrate different, complex applications system to the AWS platform.To be eligible for this certification exam, a candidate must have:At least 2 years of working experience in designing and deploying AWS-based cloud architecture. Skills to recommend services that can be used to design, provision, and deploy AWS-based applications Familiarity with the practices involved in implementing the AWS application’s architecture. Expertise in high-level programming language.As an AWS Certified Solutions Architect – Professional, you will be able to earn $167,500 per year.6. AWS Certified DevOps Engineer – ProfessionalThis certification validates your skills to provision, operate and manage AWS-based applications. The focus of the exam is on the fundamental concepts of the DevOps movement – automation of processes and continuous delivery.During the certification exam, the candidate will be tested on concepts like the modern continuous delivery methodologies and their implementation in the CD systems, setting up, logging, and monitoring systems on AWS, implementation of scalable and highly available systems on AWS, and designing and managing tools required for enabling the automation of production operations.As a DevOps Engineer, a candidate must fulfill certain requirements to be eligible for the AWS Certified DevOps – Professional exam. This includes:Two or more years of working experience in provisioning, managing, and operating applications deployed in the AWS environmentExperience in developing code in a high-level programming languageKnowledge of automation and testing using scripting and programming languages as well as other development processes and methodologies like Agile.The average annual salary of an AWS Certified DevOps Engineer – Professional is $137,724.7. AWS Certified Big Data – SpecialtyThis specialty certification from AWS is for a candidate working in the field of data analytics and have worked with AWS services to design and architect solutions for big data. The certification exam validates the candidate’s skills to use the AWS services to extract the value from data.The areas covered in the exam include implementation of big data services of AWS through best architectural practices, automating the data analysis process using the AWS tools, providing best security practices for big data solutions, knowing how to design and maintain big data, and other AWS services like Athena, Kinesis, Rekognition, and Quicksight.To be eligible for the AWS Certified Big Data – Specialty exam, a candidate must satisfy certain requirements:At least 5 years of experience working in the field of data analytics. Experience in designing and developing robust, scalable, and cost-effective architecture for data processing. Understanding of how to define and architect big data services of AWS and how they exist in the lifecycle of data this includes how to collect, ingest, store, process or visualize.An AWS Certified Big Data – Specialty professional can earn up to $99,909 per year.8. AWS Certified Advanced Networking – SpecialtyThis certification will validate your skills of using the hybrid IT networking architecture and the AWS platform to perform complex tasks related to networking. To ace this exam, one must have experience in implementing and architecting network solutions and knowledge of using the AWS for networking.The areas covered during this exam includes how to design, develop, and deploy AWS-based cloud solutions, using the best architectural practices to implement core services, troubleshooting, optimizing network, implementing compliance and security design, automating the tasks of AWS for network deployments. Also, they must know how to design and maintain network architecture for the AWS platform and leverage analysis and automation tools used for networking tasks on the AWS platform.One must fulfill certain requirements to be eligible for the AWS Certified Advanced Networking – Specialty exam. They must have: Minimum 5 years of working experience in architecting and implementing network solutions. Knowledge and understanding of concepts and technologies used in AWS networking.An AWS Certified Networking specialist can earn up to $113,065 per year.9. AWS Certified Security - SpecialtyFor this certification, you will have to master the fundamentals of the security, the best practices used and have a deep understanding of the key security services on the AWS platform. You will be tested on topics like encryption, data protection, identity and access management, logging, infrastructure security, monitoring, and incident response.The certification exam will cover topics like how to use AWS services to get the desired security level depending on the deployment method and data sensitivity. This also includes using the best data protection techniques like encryption mechanisms, monitoring solutions and implementing logging for analyzing and detecting weaknesses and vulnerabilities in the infrastructure’s security.The eligibility requirements for the AWS Certified Security – Specialty exam are:At least 5 years of experience as an IT security personnel on designing and implementing security solutions. 2 or more years of working experience in securing AWS workloads and knowledge of using security controls for AWS workloads.The annual remuneration of an AWS Certified Security – Specialty professional is $122,155.10. AWS Certified Alexa Skill Builder – SpecialtyThis certification will help you demonstrate your skill in creating, deploying, and testing Amazon Alexa. If you are currently working as an Alexa skill builder, this exam is for you.The exam will cover concepts like the value of the voice, Alexa developer console, implementing security measures by following Alexa and AWS practices, and user experience design.Anyone wanting to get AWS Certified Alexa Skill Builder – Specialty must have:More than 6 months of working experience in a programming knowledge as well as using Alexa Skills Kit to build Alexa skills11. AWS Certified Machine Learning SpecialtyThis certification exam will validate your skills in creating, implementing, and maintaining machine learning solutions for different business problems.During the certification exam, the covered areas will be selecting the best machine learning approach for a given problem, designing and implementing machine learning solutions that are secure, scalable, cost-optimized, and reliable, and identifying the right AWS solution for creating and deploying ML solutions.The eligibility requirement for this certification is:1 to 2 years of working experience in using the AWS cloud for implementing concepts of Machine Learning as well as deep learning. Background in developing and data science.AWS Services have become a major player in the internet infrastructure industry. With the AWS certifications in your hand, you will be able to beat the crowd for the best opportunities. Knowing the eligibility requirements for AWS will help you select which AWS certification is the best for you. This will allow you to prepare for the exam accordingly. All the figures mentioned above are accurate as of August 2019 and are sourced from online job portals such as Indeed.com, Salary.com, Glassdoor.com, etc.
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How Much Salary do Top AWS Certified Professionals earn?

Since the advent of Amazon Web Services, the landscape of the internet’s infrastructure has vastly changed. The services are becoming quite popular because of the ease and scalability they provide in a number of processes. Getting an AWS certification today will help you stay ahead of the crowd. As per a recent salary survey from Global Knowledge, the average yearly salary of an AWS Amazon Certifications holder is $113,932. Salaries have gone up by 10% in just one year and with AWS being the primary computing platform in hundreds of enterprises, there is quite a good chance that they will continue growing.Getting an AWS certification will help you land a better, and more lucrative job. The certification will also open doors for jobs like:Operational Support EngineerThe job of an operational support engineer includes monitoring and resolving operational issues that are reported. Also, they assist in any environment upgrade. Their average annual salary is between $59,000 and $92,000.Cloud Software EngineerA cloud software engineer designs and implements new systems and software services in a high-level programming language like C++, JavaScript, Python, Ruby, etc. They also mentor new junior employees and explain complex processes to non-technical members of the team. They can earn anything between $63,000 and $93,000 per year.System Integrator – CloudFor this job, you need to have a thorough understanding of information systems and cloud computing. They work with the team to help troubleshoot and support complex development processes. A System Integrator can have an average annual income of $81,000.Cloud DeveloperAs a cloud developer, you will be responsible for developing enterprise-level applications and software services. You must have knowledge of common cloud orchestration tools as well as working experience as a software developer to get a high-paying job as a Cloud Developer. The average annual salary of a cloud developer is $95,000.DevOps EngineerA DevOps Engineer is responsible for designing AWS Cloud Solutions that can significantly impact and improve your business. Also, they implement patching or debugging and perform the required server maintenance. Depending on their experience and the company they are working for, the average annual income of a DevOps Engineer varies between $93,000 and $144,000.AWS Solutions ArchitectThe job of an AWS Solutions architect is to design, build, and maintain scalable, cost-efficient, and highly available AWS cloud environments. They keep up with the latest updates in the field of cloud computing and based on this knowledge, make recommendations regarding the AWS toolsets. The average annual income of an AWS Solutions architect ranges between $98,000 and $150,000.AWS SysOps AdministratorTo effectively provision, install, configure, operate and maintain software, virtual systems, and infrastructure, you need an AWS SysOps Administrator. They are also responsible for building dashboards for reporting and maintaining analytics software. Their average salary varies between $111,000 and $160,000 per year.Senior AWS Cloud ArchitectAs a senior AWS cloud architect, they work with engineers and customers. So, they are the technical leader as well as interface with the stakeholders from the client-side. Their responsibilities include leading the implementation process, delivering technical architectures, and successfully integrating the customer environments with new technologies. The average yearly remuneration of a senior AWS Cloud Architect is $165,000.According to Forrester, by 2020, the market for the Amazon cloud will reach $236B. With more and more companies adopting cloud services, the shortage of AWS Certified Professionals continues, driving up salaries and incentives. The AWS provides associate and professional level certification for developers, solutions architects, and system operations administrators to help bridge this gap. Here, we have explained in detail all the different certifications offered by1. AWS Cloud Practitioner – FundamentalThis certification is for managers, sales, C-level executives, and marketing associates who need to have a basic knowledge of the AWS cloud. To prepare for this certification, there is a digital course offered by AWS for free. The average annual salary of a professional holding an AWS Cloud Practitioner certification is $113,932.2. AWS Certified Solutions ArchitectThis certification is offered at two levels; the Associate level and the Professional level. To advance to the professional level, you must first earn an associate-level certification.Solutions Architect AssociateAWS Certified Solutions Architect Associate one of the most popular AWS certifications, it is mostly preferred by professionals who are just entering the arena of cloud architecting and want to use the AWS platform for designing distributed applications. This certification will help you demonstrate your skill in designing and developing efficient and effective solutions on the platform of Amazon Web Services. Before taking the exam, you must know how to:  Use the AWS platform to deploy on-premise apps  Design and deploy highly available and scalable systems on AWS  Select the AWS service according to your requirementsIn the United States and Canada, the average annual income of an IT professional with AWS Certified Solutions Architect – Associate Level is $130,883.Solutions Architect ProfessionalThis AWS certification will display your advanced expert skills in designing the applications and distributed systems on the AWS platform. You need to have the Associate level certification to take up the exam. Before you take the exam, you must have the following skills:2+years of working experience in designing and deploying cloud architecture on the AWS platform.Using the AWS for migrating complex, multi-tier applications.An IT associate with the AWS Certified Solutions Architect – Professional can earn an average income of $148,456 per year.3. AWS Certified DeveloperAWS Developer Certification will allow you to demonstrate your skills in developing and maintaining AWS applications. To achieve this certification, you must know how to:  Select the right AWS service according to the application  Write optimized code in a high-level programming language  Interact with other services from the application using software development kits (SDKs)  Maintain application security at the code levelThe average salary of an IT professional with the AWS Certified Developer certificate is about $130,272 in North America.4. AWS Certified SysOps AdministratorSysOps Administrator certification from the Amazon Web Services will demonstrate how well you can deploy, scale, migrate, and manage systems on the AWS platform. To ace this certification, you must have the following skills:  Managing the cloud applications deployed on the AWS platform  Controlling and implementing the data flow to and from AWS  Identifying the cost control mechanism of the operations  Migrating the on-premises apps to the AWS platformIn North America, the average salary of an AWS Certified SysOps Administrator is about $130,610.5. AWS Certified DevOps EngineerThis certification validates your skills to provision, manage, secure, and operate distributed application systems and the AWS solutions. For obtaining this certification, you must have professional-level certification for SysOps Administrator as well as Developer. Candidates planning to take up this exam must have a thorough understanding of the following:  How to provision and manage AWS environments  Implement and manage continuous delivery methodologies and systems on the AWS platform  Governance processes and security controls  Automating the operational process by maintaining the tools required to do soIn the United States and Canada, the average annual earnings of an AWS Certified DevOps Engineer is about $137,724.6. AWS Certified Big Data – SpecialtyAs an individual with the AWS Big Data certification, you will be able to display your skills of designing and managing AWS solutions of organizations that use business data for extracting actionable intelligence and valuable insights. All candidates must have at least one AWS associate-level certification. Also, 5+years of working experience in data analytics is recommended. With this certification, an IT professional can bag an annual pay of more than $130,000.7. AWS Certified Advanced Networking – SpecialtyThis AWS certification validates your skills in designing and deploying AWS to scale as part of an IT network hybrid architecture. To get this certification, you must have one of the AWS Associate credentials. Apart from that, +5 years of working experience in managing and architecting network solutions for the enterprise is recommended.8. AWS Certified Security – SpecialtyThis certification validates your skills to use advanced methods to secure the AWS platform. This includes data protection by using encryption techniques. For this certification, you must have any one of the Associate certifications or an AWS Cloud Practitioner certification. Also, +5 years of working experience in securing the AWS platform is a must.9. AWS Certified Alexa Skill Builder – SpecialityWith this certification, you will be able to create, test, and deploy Amazon’s Alexa. To be eligible for this certification, you must be an expert in a high-level programming language and have at least 6 months of experience working with Alexa skills kit.10. AWS Certified Machine Learning – SpecialtyYou must take this certification for demonstrating your skills of designing, implementing, and maintaining the machine learning solutions of the organization. For this, you must have a data science and development background as well as 1-2 years of working experience with machine learning and deep learning. Since all the AWS services are continuously updated, you need to take the recertification exam every three years. The market for cloud services is rapidly evolving. AWS is releasing new services and products to stay in the competition. New certifications are launched along with new solutions to help validate the skills of an IT professional. The importance and need for validating the AWS skills with the certifications are only going to grow in the future. There are so many people working in the IT industry. You need something that will help you outshine the others. AWS provides all the resources that you will need to get the certification including hands-on practice labs and questionnaires. Apart from this, there are exam readiness workshops and authorized training courses that will help you learn the skills and focus on the exam.All the figures mentioned above are accurate as of August 2019 and are sourced from online job portals such as Indeed.com, Salary.com, Glassdoor.com, etc. 
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How Much Salary do Top AWS Certified Professionals...

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