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There are a lot of AWS career paths from which you can choose. AWS certification validates your expertise and credibility, putting you in a position to get the job that you seek in an organization where you want to be. AWS certifications tell employers that you have undergone rigorous AWS training and will be capable of implementing what you have learned on the job.
Amazon Web Services, popularly known as AWS Certification in the market, is a secure cloud service platform. It offers various functions for a business to scale up and grow such as computing power, content delivery, database storage and many other functionalities.
AWS Certification is a good career move for those who want to explore and grow in the field of cloud computing. With the help of AWS Certification , an individual, company, enterprises can get a cloud computing platform. For starting a career in AWS Certification as a fresher, first of all, you need to undergo training for AWS Certification .
Yes, you can get a job even when you are an AWS Certification fresher. But make sure you know all about AWS Certification Cloud. However, only learning about the AWS Certification cloud is not enough to get your hands on a good job.
We are happy and proud to say that we have strong relationship with over 700+ small, mid-sized and MNCs. Many of these companies have openings for AWS Certification . Moreover, we have a very active placement cell that provides 100% placement assistance to our students. The cell also contributes by training students in mock interviews and discussions even after the course completion.
AWS Certification is primarily an Infrastructure as a Service (IaaS) Cloud Platform. You need not know any programming to be successful in AWS Certification “mostly”. However, all of that depends on exact role you get into. If you are writing scripts/programs for automation/control of AWS Certification services, programming knowledge is a must.
- Having basic knowledge of operating systems like Windows OS, Linux etc
- As Visualization play a major role in AWS Certification you need to have the understanding of it
- Networking is an essential skill as all operations on cloud platform involves it.
- Understanding the difference between the Public and Private cloud
- Last but not the least, you must have basic command over coding
- AWS Certification , Python or C# Most architects have a software development background.
- Networking.
- Data storage fundamentals.
- Security foundations.
- AWS Certification service selection.
- Cloud-specific patterns and technologies.
- Communication.
Our courseware is designed to give a hands-on approach to the students in AWS Certification . The course is made up of theoretical classes that teach the basics of each module followed by high-intensity practical sessions reflecting the current challenges and needs of the industry that will demand the students time and commitment.
The future of AWS Certification is bright. Infact, it's the future of modern day computing. Cloud computing, machine learning, IOT, etc are some of the domains which have a lot to offer in the near future.
Depending on how much experience you already have, it’s possible to learn AWS Certification in 3 days with our instructor-led courses comprising of 18 hours of training. If you’re looking to gain AWS Certification certification this will take a little longer, as you’ll also need to prepare for the exam.
- Customization.
- Flexibility & Scalability.
- PaaS Offerings.
- Security.
- Scheduling.
- Recovery.
- Consistency.
- Global Architecture.
AWS takes user satisfaction crown in big data study
AWS has come out on top in a new poll of big data software users.
SoftwareReviews’ 2020 Big Data Quadrant Awards, published last week, surveyed users on how satisfied they were with their big data platform.
- AWS placed first, beating out IBM InfoSphere and Microsoft Azure Big Data to be named the top vendor for user satisfaction.
- Participants were asked to rate their vendor on capabilities, product features, and how likely they were to recommend their software provider.
- Vendors were also measured on their users’ perception of their products, using metrics such as service, negotiation, product impact, conflict resolution, strategy, and innovation; ratings that SoftwareReviews collectively refers to as The Net Emotional Footprint Score.
- All three of the top-ranking vendors earned an impressive Net Emotional Footprint score, sitting between 82% and 85%. The average rating across all vendors surveyed was 77%.
- The overall composite score for AWS Big Data across all metrics was 8.1/10, with users praising its analytics, data science tools, and data security in particular.
- Some 82% of users stated that they were likely to recommend the product portfolio, which includes products such as Amazon S3, Amazon Glacier, AWS Glue, Amazon Athena, Amazon Redshift and Amazon QuickSight.
- For all its benefits, the programming model of the public cloud started out as a clone of the private data center model.
- This was intentional; the goal of a cloud vendor like AWS was first and foremost to drive cloud adoption, and only secondarily to reimagine how developers should architect their applications.
- Early managed services, like Amazon S3, were seen as adjuncts to “normal” disk-based storage.
- In fact, many of these early services were opportunistic, such as taking an in-house service already in use by Amazon’s retail developers and spinning it out as a cloud service.
- But these newfangled services remained a modest part of the offering in the early days of the public cloud—the revenue came largely from virtualized infrastructure in the form of servers and disk drives.
- Over time, however, application developers noticed something interesting about the parts of their applications that ran on these “managed” services; they tended to be easier to construct (and thus faster to deliver), often cost less than their DIY counterparts (even when those were built in the public cloud), and—oddly enough for something that offered those benefits—were often less expensive to operate.
- Amazon’s early managed services—S3’s object storage, SQS’s queues, and SNS’s messages—had what was then a revolutionary property from a conventional IT perspective; an application-level “pay only for what you use” billing model.
- Thanks to its high score, AWS Big Data was placed in the “Leader” section of SoftwareReviews’ February 2020 Data Quadrant.
AWS Big Data scores:
- Composite score: 8.1/10
- Net Emotional Footprint: +84
- Vendor Capabilities: 79%
- Product Features: 80%
- Likeliness to recommend: 82%
Other vendors evaluated in the study were Hortonworks, Cloudera, SAS, and Vertica.
Igor Ikonnikov, research advisor at Info-Tech Research Group, said: “Managing big data is quite different from building a traditional data warehouse: the volume and complexity of data, its variable velocity – as well as unpredictability of analytical use cases – requires multi-phased and modularized architecture that is flexible enough to adapt without rebuilding everything from the ground up.
“A vendor’s ability to provide a complete toolkit for multi-phased and multi-faceted data management and advanced analytics solution implementation – either with own technology or via seamless integration with other technologies – has become the main differentiator in the big data space.”
Few Factors that boost up are,
Focus on adaptability
Given the speed that big data is advancing, a sustainable data team needs to cultivate a culture of open-mindedness and continuous learning. To truly innovate, a data professional must be able to look beyond what was there before, and be prepared to acclimatize for the future.
Hire a capable communicator
Stereotypes would have us believe that it’s normal for scientists and tech guys to be recluses with a lack of communication skills, but that won’t fly for those who are part of a big data team.
Big data pros, especially analysts and scientists, need to be able to communicate effectively with people who won’t always “speak the same language,” be excellent storytellers, and be able to use visual communications to maximize impact.
Don’t get hung up on titles or background
One company’s “data analyst” could be another’s “data scientist” or “data visualizer”. In the big data world, there is no standard definition for job titles, so when you’re looking for talent, don’t limit your search by job title.
Also, don’t rule out a candidate because they don’t have the right bits of paper. Just because a candidate doesn’t hold a relevant degree doesn’t necessarily make them less capable. It’s important to delve deeper into their experience, look at what projects they’ve worked on, and what kind of potential a candidate might offer given a little direction.