Google Cloud Professional Data Engineer Certification Preparation Guide

Cloud computing is revolutionizing how businesses manage their data. Businesses are turning to cloud engineers to help them make sense of this new paradigm.

A cloud professional engineer can help organizations make better use of their data, by analyzing key metrics, identifying trends, and developing strategies for improving business operations.

In this study guide you will find information on: 

1. Who is a data engineer
2. Why become a data engineer
3. GCP Professional Data Engineer certification exam details
4. Free and paid resources
5. Important exam topics
6. Pro exam tips
7. What's next
8. Conclusion

So, let begin our journey for GCP Professional Cloud Data certification

Who Is a Data Engineer?

Data engineers are professionals who design and build databases that store information for applications, as well as ensuring those databases are protected from hackers. The data engineer is taking on the central role in the world of IT. For some knowledge professionals, data engineering is a career choice that has never been more important or in-demand. 

Why Become a Data Engineer?

A data engineer is a professional who designs and builds the databases that store data for applications, as well as ensuring that those databases are adequately protected from hackers. 

As per Talent.com the average salary for a Google Professional Data Engineer in the USA is around $147,000 per year. The lowest entry-level salary of a Google Data Engineer is $141,375 per year, whereas the highest pay-out for experienced data engineers is recorded to be $175,000 per year.

With experience over time, the salary will also hike gradually.

The job responsibilities of a cloud data engineer typically include:

• Analyzing and organizing complex raw data, and perform exploratory analyses to answer business problems. A Data Engineer can perform complex data analysis to find trends and patterns and reporting on the results in the form of dashboards, reports and data visualizations.
• Building required infrastructure for optimal extraction, transformation and loading of data from various data sources using public cloud platforms like Google Cloud, Amazon Web Services or Azure Cloud.
• Build data pipelines that enable the organization to collect data points from millions of users and process the results in near real-time. Use these tools to provide actionable insight into key business performance metrics including operational efficiency and customer acquisition.
• Working with stakeholders including data, design, product and executive teams and assisting them with Predictive and Prescriptive modeling to enable them make good business decisions. 
• Working with stakeholders including the Executive, Product, Data and Design teams to support their data infrastructure needs while assisting with data-related technical issues.

GCP Professional Data Engineer Certification Exam Details

There are no prerequisites for Google Cloud Professional Data Engineer certification. 

Duration of the Exam	2 Hours
Exam Fee	$200 USD
Exam format	Multiple Choice (Single Answer and Multiple Answers)
Number of Questions	50

GCP Professional Data Engineer Certification Exam Details

Resources To Pass GCP Professional Data Engineer Certification

First and foremost, make sure you acclimate yourself with the official exam guide. The topics included changes frequently, and often the training material do not change. So it is important to stay current on the  exam topics. Below are some more resources to help you pass the actual exam. 

1. Google, every now and then provides free training for its courses. So, before you go anywhere else, check out this learning path - official training link. 

2. Free Sample questions from GCP. Click here.

3. Paid Practice exam. These practice tests will will help you to prepare better. 

Related

Practice exams for GCP Associate Cloud Engineer

Practice exams for GCP Professional Cloud Architect

4. Here is a free handy course book from Linux Academy. This book is slightly dated, but the contents are still relevant. 

5. Google has excellent documentation on architecture. Here is an important link for all topics related to BigData and Analytics, databases. 

6. Free Google Machine Learning CrashCourse.

7. Sign up for a Google Cloud Webinar.

8. Here is a good cheat sheet on Github.

Important Exam Topics For GCP Professional Data Engineer Exam

As per Google this certification is suited for the role definition: “A Data Engineer should also be able to leverage, deploy, and continuously train pre-existing machine learning models.” You don't need to have industry experience, but having the basic knowledge of machine learning, and various GCP services is crucial to pass this certification. 

You should understand all the topics covered in the exam-guide. Below are some of the exam topics that you'd see some questions on. 

Designing data processing systems

Important topics for the exam include the following: 

1. Datastore, BigTable - including Development and Production instances, Disk Types (HDD vs. SSD, quotas, performance, BigQuery, Cloud Dataproc, Cloud Spanner, CloudSQL
2. High Availability, Replication including Read Replicas for failover
3. Migration from sql to no-sql
4. Cloud SQL SLA
5. Cloud Pub-Sub, Kafka, Cloud Dataflow, and windowing
6. Encryption

Building and operationalizing data processing systems

Important topics for the exam include the following:

1. HDFS vs. Google Cloud Storage for Dataproc workloads, Migrating Hadoop jobs.
2. Building and designing pipelines
3. Google Cloud Logging
4. Overview of Google Databases Products

Operationalizing machine learning models

Important topics for the exam include the following:

1. Regularization
2. Overfitting/Underfitting
   • Google Course
3. Machine Learning workflow
4. Google Cloud Vision
   • Google Cloud AutoML
   • Google Cloud Vision API
   • AutoML Tutorial from Google
   • Auto ML Batch-Predict
5. Natural Language
   • Preparing and Training data
   • Analyzing Sentiment
6. Google Cloud Artificial Intelligence (AI) Platform
7. Google Cloud TPUs
8. Finally read through Google Cloud ML key term glossary

Ensuring solution quality

Important topics for the exam include the following:

1. BigTable Performance and Quotas and Limits
2. Best Practices recommended by Google Cloud. 
   • BigQuery
   • Stackdriver and Logging
   • IAM and security
   • Google Cloud Storage
   • Speech to text best practices

Pro Exam Tips For GCP Professional Data Engineer Exam

1. Nothing beats hands-on practice in Google Console. So, if you don't have practical knowledge, create your Google Developer account and do some hands-on labs as you progress through various topics described above. 
2. For exam preparation, you need to practice through exam questions. Google has free questions listed in the resources section of this blog. It is highly recommended that you use these practice exam questions for getting that last minute practice. 
3. If you are not sure of the answer choice, flag questions for review later. Do not spend more time on one question.
4. Use the process of elimination for removing answer choices that make no sense at all. Your probability of guessing the right answer increases by eliminating wrong choices. 

What's Next

Once you have cleared your Google Cloud Professional Data Engineer Exam, it's time to move on to next certifications. But first, you should celebrate your success.

Don't forget to share your experiences with others if you found this blog helpful. Sharing your experience can help many others save their time and energy while preparing for the exam, and we all understand how valuable time is for everyone.

Here is an invitation from Reviewnprep’s community to share your journey and encourage others. And you get $5 Amazon Gift card for doing so!!

As a cloud engineer it is important to keep up with various offerings from Google. Check out other Google Cloud Platform Certifications offered.

Alternatively, you can check out Career Journey tool, a one-stop-shop on how to prepare for a certification.

Career Journey is a free tool from ReviewNPrep that allows you to navigate certifications based on your current industry or role. 

The tool will help you determine which Google certification would be suitable for you and provides excellent baseline content such as duration, cost, and even expected salary boost. 

Conclusion

With over 1 billion people using Gmail, YouTube or Android every month, it’s hard to deny that anything made by Google has the potential to be revolutionary. When it comes to data engineering, it’s no different. This field is constantly evolving and offering new opportunities for professionals who are hungry for more knowledge and challenge in their cloud career.

What is Amazon RDS?

The evolution of the technological landscape is clearly evident in the introduction of new offerings every day. Cloud computing is undoubtedly one of the prominent milestones in the progress of technology as we know it today. Now, enterprises could make the most of cloud computing for cost-effective access to computing resources. As a result, the adoption of public cloud services became a trend that still continues to rage on!

One of the notable names that emerged in the area of public cloud services is AWS (Amazon Web Services). With a massive and interesting assortment of services, products, and features, AWS is presently at the top of the public cloud service market. Amazon RDS is one of the notable services of AWS that addresses one of the significant computing resource requirements. 

Preparing to become a certified AWS professional? Check our AWS Certifications Training Courses now!

The following discussion would aim at an illustration of informative details regarding AWS RDS. Readers could find the definition of RDS on AWS, the components in its architecture, and the different types of instances. In addition, the discussion would also focus extensively on the features, benefits, and pricing for Amazon’s RDS. Most important of all, the discussion would also include a step-wise illustration of the process to create a database (DB) instance on RDS. 

Definition of Amazon RDS

First things first, what is Amazon RDS? RDS stands for Relational Database Service. Is RDS the database of AWS? Now, this is huge confusion for the new audience as the term “RDS” implies directly towards the database. However, Amazon Relational Database Service (RDS) is a web service. It helps in setting up, operating, and scaling relational databases on the AWS cloud easily.

The functionalities of Amazon’s RDS are evident in its cost-effectiveness and resizable capacities for an industry-standard relational database. Amazon’s RDS also serves effectively in the management of general database administration tasks. In order to obtain a better understanding of “what is RDS,” let us reflect on the different scenarios in which it is applicable.

Here’re Top 10 AWS Services You Should Know About

Importance of Amazon RDS

As of now, we know that AWS RDS is the managed web service of AWS for the relational database. In order to know about the significance of Amazon’s RDS, it is essential to find out the scenarios that demand its application. Primarily, RDS addresses various complicated and resource-intensive management tasks for a relational database. In addition, it also serves the following purposes for becoming one of the most popular AWS offerings. 

• Upon purchasing a server, users get CPU, IOPS, memory, and storage bundled together. In the case of AWS Database service with RDS, all of these components are available separately for better ease of independent scaling. In the case of requirements of more CPU, storage, or lower IOPS, then users can allocate them easily. 
• Amazon’s RDS also helps in the management of backups, recovery, automatic failure detection, and software patching.
• AWS RDS offers a managed service experience by excluding shell access to DB instances. In addition, it also imposes restrictions on access to specific system procedures and tables that need advanced privileges. 
• RDS also provides the facility of performing automated backups according to requirements or the creation of own backup snapshots manually. These backups are ideal for restoring a particular database in the reliable and efficient restore process of Amazon’s RDS. 
• Another prominent functionality of AWS Database management service with RDS is evident in the higher availability. The facility of a synchronous secondary instance with a primary instance helps in failover when problems emerge. In addition, users could leverage MySQL, PostgreSQL, or MariaDB Read Replicas for increasing read scaling. 
• Amazon RDS also offers the flexibility for using database products that users are familiar with. The distinct options include MySQL, Microsoft SQL, PostgreSQL, Oracle, MariaDB Server database (DB) engines. As a result, code, applications, and tools working on existing databases would also function seamlessly with Amazon’s RDS. 
• The response to ‘what is RDS’ on AWS also emphasizes on the security aspects in the RDS databases. The facility of AWS Identity and Access Management (IAM) helps in exerting control over access privileges. The functionalities of AWS IAM with RDS involve the definition of users and permissions. Furthermore, users can also safeguard their databases by placing the databases in a virtual private cloud (VPC).

The architecture of Amazon RDS

Now, let us focus on the next most crucial entry in this discussion for understanding Amazon RDS! How can you use Amazon’s RDS without knowing about its components? Awareness regarding the basic components in AWS RDS architecture is an essential step in understanding Amazon’s RDS service. The primary components of Amazon’s RDS include DB instances, regions and availability zones, security groups, DB parameter groups, and DB option groups.

Here are some brief explanations about each significant element in the architecture of Amazon’s RDS –

DB Instances

DB instances are the basic entities that form the RDS service of Amazon. As an isolated database environment in the cloud, DB instances store multiple databases created by users. Most important of all, you can access DB instances with tools and applications that you use for a standalone database instance.

The AWS Command Line Interface, AWS Management Console, and Amazon RDS API are the ideal tools for creating DB instances. Another important aspect to note in the case of DB instances on Amazon RDS is the facility of different DB instance classes. The different DB instance classes have unique computation and memory capacity for a DB instance. The discussion would focus on the different instance types in Amazon’s RDS in an upcoming section. 

Regions and Availability Zones

The next important element in the AWS RDS architecture refers to regions and availability zones. Highly available datacenters work for the storage of AWS resources. The location of a particular data center is known as a ‘region.’ You can find multiple Availability Zones (AZ) in each region.

The design of AZs involves isolation from the failure of other AZs. Users can deploy a DB instance in multiple AZs, thereby ensuring a failover. Therefore, if one AZ is unavailable, then the user can turn to a second AZ. In such cases, the failover instance is the standby, and the original instance is the primary instance. 

Security Groups

Security groups are an essential addition to the architecture of Amazon RDS. The security group provides control over access to a DB instance. The security group specifies a range of IP addresses and the EC2 instances with access privileges for controlling access to DB instances. There are three distinct types of security groups on Amazon’s RDS.

The security groups are the VPC security group, DB security group, and EC2 security group. The VPC security group helps in controlling access to DB instance inside a VPC. The DB security group helps in controlling access to DB instances that are not in a VPC. The EC2 security group is ideal for controlling access to an EC2 instance. In addition, you can also use the EC2 security group with a DB instance. 

DB Parameter Groups and DB Option Groups

The other important elements in AWS RDS architecture refer to DB parameter groups and DB option groups. The DB parameter groups contain engine configuration values applicable to one or multiple DB instances of the same instance class. Users receive a default Parameter group with default values if they don’t specify a DB parameter group, for instance. The DB option groups are effective tools for simpler management of databases on Amazon RDS. 

Types of Instances on Amazon’s RDS

Now, let us focus on the different types of instances that you can find on Amazon’s RDS. AWS provides an assortment of instance types, ideal for various relational database use cases. The different AWS RDS instance types have unique combinations of CPU, networking capacity, storage, and memory. Users get ample flexibility with the different options of RDS instance types of AWS for selecting the right mix of resources for their database.

Furthermore, each instance type has the facility of different instance sizes. As a result, users could scale their database according to target workload requirements. Furthermore, it is essential to note that all instance types are not supported in every database engine, region, version, or edition. Here is an outline of the different RDS instance types on Amazon RDS. 

The first category of AWS RDS instance types involves the general-purpose instance types. The general-purpose instance types include T3, T2, M5, and M4. T3 instances serve as next-generation burstable general-purpose instance type. They provide a baseline level of CPU performance alongside the ability to burst CPU usage at any time according to requirements. T2 instances are also burstable general-purpose instances. M5 instances are the latest in the line of general-purpose instances and are the successors of the M4 instances. Both M5 and M4 instances provide an appropriate mix of computing, network, and memory resources. 

The next class of instance types on Amazon RDS refers to memory-optimized instances. The different types of instances in this category are R5, R4, X1e, X1, and Z1d. R5 instances provide 5% additional memory per vCPU as compared to R4. R4 is also ideal for memory-intensive database workloads. X1e instances are specifically meant for high-performance databases. X1 instances are suitable for large-scale, enterprise-class, and in-memory applications. Z1d is suitable for relational database workloads on AWS with higher per-core licensing costs. 

The burstable performance DB instance classes are the last entry among AWS RDS instance types. T3 and T2 are prominent mentions in this category of DB instance classes. The primary function of these instance types is to provide burst in CPU power according to requirements for a specific duration of time. 

Creating an RDS Database Instance on Amazon’s RDS

The most important piece of information in an Amazon RDS tutorial is the process of creating an RDS DB instance. Users can find distinct instruction sheets for the creation of Amazon RDS DB instance on different database engines. However, it is essential to note that the process remains almost the same, irrespective of the database engine chosen. For this discussion, we choose the Microsoft SQL Server database engine and attempt the creation of an RDS DB instance. 

Access the AWS Management Console and choose the “New Console” instructions. Users can create a DB instance for a Microsoft SQL Server database engine with the “Easy Create” option enabled or disabled. “Easy Create” option allows us to specify DB engine type, DB instance identifier, and DB instance size. In addition, “Easy Create” also leverages the default settings of other configuration options.

If you don’t enable “Easy Create” on the console, then you can specify an additional configuration when creating an Amazon RDS database. The configuration options can include the aspects of availability, maintenance, security, and backups. The following process complies with the condition in which “Standard Create” is evident in place of “Easy Create.” Users could create Microsoft SQL Server DB instance with the “Easy Create” option by following a different set of instructions. 

1. The first step for the creation of DB instance in any Amazon RDS tutorial involves signing in to the AWS Management Console. Now, access the Amazon RDS console through the link https://console.aws.amazon.com/rds/. 
2. Navigate to the top-right corner of the RDS console and select the AWS Region where you want to create the DB instance.
3. Explore the navigation pane to find out and select the “Databases” option.
4. Click on the “Create Database” option.
5. Now, you will find a “Create database” window. First of all, navigate to the “Choose a database creation method” tab. Click on the “Standard Create” option. 
6. You can also find an “Engine options” tab in the “Create database” window. Choose the “Microsoft SQL Server” option.
7. Users should also select the “Edition” for the SQL Server DB engine. The availability of SQL Server editions varies according to the AWS Region.
8. Now, you have to access the “Templates” tab and then select the template that aligns with your use case. For example, choosing a “Production” template would provide pre-selected aspects in a subsequent phase. The options include a Multi-AZ failover option, the “Enable deletion protection” option, and the “Provisioned IOPS” storage option.
9. Select the “Settings” and then “Credential Settings” for entering your master password. Untick the checkbox “Auto-generate a password.” The new DB instance uses an automatically generated password for the master user, by default.
10. Users should then specify the DB instance settings for the remaining sections.
11. After selecting an automatically generated password for Amazon RDS DB instance, you can find the “View credential details” button on the “Databases” page. Connect to the new DB instance as the master user by leveraging the user name and password that are available.
12. In the “Databases” option, select a name for the new SQL Server DB instance. You can find the details about the new DB instance on the RDS console. In addition, the Amazon RDS instance will also show a status indicating “Creating” until the creation of the DB instance and its readiness for use. Users can connect to the DB instance when the state of the instance changes to “available.”

The specification of a database name is a crucial aspect of every Amazon RDS tutorial. The name value of the database depends on the database engine. For example, the name of the database for MariaDB and MySQL database engines is the name of the database hosted in DB instance. Databases hosted with the same DB instance should have different names within a particular instance.

In the case of Oracle Database engine, Amazon RDS database name should have the value of ORACLE_SID, that users should provide when connecting to the instance. The database name is not a supported parameter in the case of the Microsoft SQL Server database engine. In the case of the PostgreSQL database engine, the name of the database hosted in DB instance is the name of the database. 

Looking for AWS RDS Labs to practice in real-time? Check our courses which have hands-on labs –

AWS Certified Solutions Architect Associate

AWS Certified Developer Associate

AWS Certified SysOps Administrator Associate

AWS Certified DevOps Engineer Professional

Pricing for Amazon’s RDS

One of the most important concerns in this discussion relates to AWS RDS pricing. The most notable highlight of pricing in the case of Amazon’s RDS is the pay-as-you-go policy of AWS. You have to pay only for the services you use without any minimum fees or charges for setup. Amazon’s RDS is a part of the AWS free tier, thereby encouraging users to adopt RDS for database management. Let us find out additional details regarding AWS RDS pricing, such as factors that determine the charges for using RDS. In addition, we shall also take a look at the time when billing for RDS DB instance on AWS starts and ends. 

The billing for Amazon RDS DB instances depends on the following factors:

• DB instance hours differ according to the DB instance in use. The billing for partial DB instance hours is the same as that for full hours. The billable DB instance hours start as soon as the DB instance becomes available. The DB instance hours terminate upon the deletion or instance failure of instance.   
• Storage capacity provisioned to a DB instance also influences billing. The bill becomes pro-rated in the event of scaling the provisioned storage capacity in the concerned billing month. 
• The total number of I/O requests per month also determines to bill for Amazon’s RDS DB instances.
• The provisioned IOPS rate i.e., the number of IOPS provisioned every month, is also an influential factor in the billing for RDS. 
• Internet data transfer rate to and from a DB instance 
• Backup storage involving the backup retention period and the additional database snapshots also determine the pricing of RDS DB instances. 

AWS RDS pricing starts right from the moment of availability of a DB instance. The pricing continues until the termination of the concerned DB instance. The pricing for Amazon RDS instances with multi-AZ deployment depends on the same factors as the general DB instances. 

Features of Amazon Relational Database Service

The objective of this discussion would remain unfulfilled without reflecting on the features of Amazon RDS. Here is an outline of the notable features of the database management service on AWS. 

• Users could leverage the Amazon Command Line Interface for RDS, AWS Management Console, or general API calls for accessing capabilities of a production-ready relational database in few minutes. Pre-configuration of RDS DB instances with parameters and settings according to the database engine and instance class provides adequate flexibility. 
• Automatic software patching for relational database software alongside the facility of optional control over DB instance patching is a commendable feature. 
• The analysis of configuration and usage metrics from DB instances helps Amazon’s RDS provide recommendations on best practice guidance. The recommendations address different areas, such as for instance types, database engine versions, networking, and storage. 
• The general-purpose SSD storage and provisioned IOPS storage helps in addressing a wider range of database workloads in Amazon RDS. 
• The features of push-button compute scaling, Read Replicas, and easy storage scaling offers the benefits of scalability with Amazon’s RDS.
• Automatic host replacement, automated backups, multi-AZ deployments, and database snapshots are the features in Amazon’s RDS for ensuring higher availability and durability. 
• The security features on Amazon’s RDS service are network isolation, resource-level permissions with AWS IAM, and encryption at rest and in transit.
• The ease of manageability with Amazon’s RDS becomes evident with the features of configuration governance, event notifications, and monitoring and metrics. 

Benefits of Amazon’s RDS

Based on these features, we can find various benefits from Amazon’s RDS for the management of databases on the cloud. Here are some of the credible advantages that you can get from Amazon Relational Database Service (RDS). 

1. Lower administrative stress

The foremost benefit of RDS service on AWS is the reduction of administrative burden. The easy of use of RDS is evident in the limited need for infrastructure provisioning or installation and maintenance of database software. 

2. Matching up to the demands of business

AWS provides efficient scaling of computing and storage resources of a database without any downtime. The facility of reading Replicas helps in offloading read traffic from a primary database instance.

3. Availability and durability

The benefits of reliability, availability, and durability are specifically evident in the AWS infrastructure itself. The facility of multiple Availability Zones (AZ) is the main driving factor for the higher availability of RDS instances.

4. Speed, performance, security, and cost-effectiveness

The speed and performance of Amazon’s RDS service depend on the powerful storage options. In addition, the concerns of security are minimal in the case of Amazon’s RDS due to the integration of AWS IAM. Finally, the cost-effectiveness of Amazon’s RDS with the model of pay-as-you-go is the most appealing benefit for enterprises. 

Here we’ve covered AWS Database interview questions & Answers that will help you develop your knowledge to find the right job in AWS domain!

Conclusion

On a concluding note, the above-mentioned discussion shows promising insights about the functionalities and features of Amazon RDS. The comprehensiveness of features in Amazon’s RDS service alongside the facility of AWS documentation for the same are commendable highlights. New AWS audience could use the AWS documentation, such as getting started guides to understand Amazon’s RDS better.

Furthermore, the facility of step-wise instructions for the creation of RDS DB instances on different database engines offers simplicity. Readers should try going through the official AWS website to find these instructions in properly documented formats. If you want to learn more about Amazon’s RDS, then this discussion is just the right starting point for you!  

Amazon Relational Database Service (RDS) is an important topic for the AWS Certifications. Not only this, you need to prepare for all the exam objectives to pass an AWS certification exam. If you are preparing for any AWS certification, don’t miss to check out our AWS Certification Training Courses. So, choose your course, prepare well and get ready to pass the exam!

Reviews for Certification

Maybe you’re struggling with the same question that everyone once encounters – Should I do a certification in my field if I know how to effectively do my job?

This article will help you decide whether getting certified is the way to go or not. For the purpose of this discussion, I will discuss examples from my field of Information Technology (IT). However, the idea applies to all lines of jobs, be it Healthcare, Nursing, Insurance or whatever field you are in.

So, below is my reasoning:

Stand apart from the crowd: You may not be looking for another job and getting certified wouldn’t necessarily get you a job if you were out in the market. However, the job markets tend to change often. Companies go through ups and downs and then there is always the outsourcing of jobs that happen more often than not. So, if I were a hiring manager, and if it was a decision between two candidates who had same experience and skillset (all other things being equal), I would go for a certified candidate. Certifications act as an unbiased barometer for a hiring manager to select the right candidate. 

Keep up with the latest and greatest: Technology is changing at a rapid rate. Take Amazon Web Services (AWS) as an example. Every other month AWS adds new features to their already expansive suite of cloud portal. Certifications are an inexpensive way to showcase that you’re keeping your expertise current and up to date. AWS has very demanding and difficult certification exams. By passing these exams you’re creating a gold standard in your area of expertise.

Increase your earning potential: While it is not always the case, but some highly qualified jobs ask for certifications and with that comes the big bucks. As per Glassdoor, the average base pay for Cloud Architect is $142K per year. Having a stamp of approval from AWS as a certified cloud architect definitely demands that pay.

Increase your promotion chances: If you have a good boss, he/she will definitely notice your extra efforts you are putting in to get certified. Nothing helps more than getting business aligning certifications and letting your boss know that you are ready to take the next step in your career. It opens up the opportunity door for you to do something new.

Personal Satisfaction: If you’re like me, and want to keep up with the next new thing, certifications are a great way to go about it. The certifications guide you to learn what’s important and once you have cleared the exam, it gives you a sense of satisfaction and pride.

Oh yes! The bragging rights come with each certification you clear.

I hope this helps you decide whether you should go for a new certification or not. There is no wrong answer here. And if you want to get certified, choose the one that you are most passionate about. May the force be with you!

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Reviews for Certification

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Review for AWS Certifications

Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform, offering over 175 fully featured services from data centers globally. With the advent of AWS cloud in 2004, business found a new opportunity to have low variable costs and replace high infrastructure expenses upfront. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—are using AWS to lower costs, become more agile, and innovate faster.

Check out our AWS Certification Reviews on how to prepare for AWS certifications.

What is AWS?

Amazon Web Services (AWS) is the market leader in IaaS (Infrastructure-as-a-Service) and PaaS (Platform-as-a-Service) for cloud ecosystems, which can be combined to create a scalable cloud application without worrying about delays related to infrastructure provisioning (compute, storage, and network) and management.

Quick Glance at AWS Critical Services

While the list of AWS increases with every given day, below are some of the featured services:

Amazon VPC – Isolated cloud resources

Amazon EC2 – Elastic virtual servers in the cloud

Amazon Simple Storage Service (S3) – Scalable storage in the cloud

AWS Lambda – Run code without thinking about servers

Amazon Route 53 – AWS DNS service

Amazon Cloud Watch – Monitor AWS environment

Amazon Aurora – High-performance managed relational database

Amazon DynamoDB – Managed NoSQL database

Amazon RDS – Managed relational database service for MySQL, PostgreSQL, Oracle, SQL Server, and MariaDB

Amazon Lightsail – Launch and manage virtual private servers

Amazon SageMaker – Build, train, and deploy machine learning models at scale

Let’s dive into a little bit more detail on some of these.

Amazon VPC - Amazon Virtual Private Cloud (VPC) provisions an isolated section of the AWS Cloud to launch AWS resources within a self-defined virtual network. This gives enterprises complete control over their virtual networking environment. Companies can use Amazon VPC with combined with their own internal network and harness the true power of cloud.

Amazon EC2: Server configuration and hosting - Amazon Elastic Compute Cloud (EC2) delivers secure, reliable compute capacity and simplifies elastic web-scale computing for developers, allowing them to build failure-resistant applications. Amazon EC2 changes the economics of computing by allowing the pay as go model for capacity that you actually use. This allows the companies to quickly scale capacity, both up and down, as the computing requirements change. What’s more customers have complete control and the ability to interact with all instances.

Amazon S3: Data storage and movement – S3 offers scalability, data availability, security and performance. It gives customers of all sizes and industries the ability to store and protect any amount of data for a range of use cases such as websites, mobile applications, backup and restore, archive, enterprise applications, IoT devices and big data analytics. According to AWS, Amazon S3 is designed for 99.999999999% (11 nines) of durability, and stores data for millions of applications for companies all around the world.

AWS Lambda: Serverless Compute - AWS Lambda gives companies serverless compute capabilities that allow them to run code for virtually any type of application or backend service without provisioning and running servers. This service is fully managed by AWS, so no administration is needed – upload code and let Lambda handle everything needed to run and scale your code with high availability. Companies only pay for the compute time consumed.

Amazon Route 53: The AWS DNS service – Route 53 is the network of DNS Servers hosted in various AWS regions all around the world. It handles DNS routing with the high-speed and low cost. Translate machine hosts and named application to IP addresses and back within your VPC and connect resources like web servers, S3 buckets, and elastic load balancers. Using API, developers can easily automate the configuration changes to Route 53.

Amazon Cloud Watch: Environment Monitoring – Be it DevOps engineers, site reliability engineers (SREs), developers or IT managers, everyone can get actionable data and insights about applications through Amazon Cloud Watch. This service allows user to react to system-wide performance changes, optimize resource utilization and get a unified view of the operational health of AWS resources, applications and services that run on AWS and on-premise servers. Data is presented in forms, metrics, logs and events. User can even take actions based on the health of resources, example scale-up or scale-down.

Let’s get you started on your AWS journey - Check out our AWS Certification Reviews on how to prepare for AWS certifications.

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