Now unless you’ve been a hermit or a monk living in total isolation, you will have heard of Amazon Web Services and AWS Big Data. It’s a sign of an emerging global market and the entire world becoming smaller and smaller every day. Why? The current estimate for the cloud computing market in 2020, according to Forbes (a new prediction, highly reliable), is a staggering 411 Billion USD$! Visit the following link to read more and see the statistics for yourself:
To know more, refer to Wikipedia for the following terms by clicking on them, which mark, in order the evolution of cloud computing (I will also provide the basic information to keep this article as self-contained as possible):
This was the beginning of the revolution called cloud computing. Companies and industries across verticals understood that they could let experts manage their software development, deployment, and management for them, leaving them free to focus on their key principle – adding value to their business sector. This was mostly confined to the application level. Follow the heading link for more information, if required.
PaaS began when companies started to understand that they could outsource both software management and operating systems and maintenance of these platforms to other companies that specialized in taking care of them. Basically, this was SaaS taken to the next level of virtualization, on the Internet. Amazon was the pioneer, offering SaaS and PaaS services worldwide from the year 2006. Again the heading link gives information in depth.
After a few years in 2011, the big giants like Microsoft, Google, and a variety of other big names began to realize that this was an industry starting to boom beyond all expectations, as more and more industries spread to the Internet for worldwide visibility. However, Amazon was the market leader by a big margin, since it had a five-year head start on the other tech giants. This led to unprecedented disruption across verticals, as more and more companies transferred their IT requirements to IaaS providers like Amazon, leading to (in some cases) savings of well over 25% and per-employee cost coming down by 30%.
After all, why should companies set up their own servers, data warehouse centres, development centres, maintenance divisions, security divisions, and software and hardware monitoring systems if there are companies that have the world’s best experts in every one of these sectors and fields that will do the job for you at less than 1% of the cost the company would incur if they had to hire staff, train them, monitor them, buy their own hardware, hire staff for that as well – the list goes on-and-on. If you are already a tech giant like, say Oracle, you have everything set up for you already. But suppose you are a startup trying to save every penny – and there and tens of thousands of such startups right now – why do that when you have professionals to do it for you?
There is a story behind how AWS got started in 2006 – I’m giving you a link, so as to not make this article too long:
OK. So now you may be thinking, so this is cloud computing and AWS – but what does it have to do with Big Data Speciality, especially for startups? Let’s answer that question right now.
A startup today has a herculean task ahead of them.
Not only do they have to get noticed in the big booming startup industry, they also have to scale well if their product goes viral and receives a million hits in a day and provide security for their data in case a competitor hires hackers from the Dark Web to take down their site, and also follow up everything they do on social media with a division in their company managing only social media, and maintain all their hardware and software in case of outages. If you are a startup counting every penny you make, how much easier is it for you to outsource all your computing needs (except social media) to an IaaS firm like AWS.
You will be ready for anything that can happen, and nothing will take down your website or service other than your own self. Oh, not to mention saving around 1 million USD$ in cost over the year!If you count nothing but your own social media statistics, every company that goes viral has to manage Big Data! And if your startup disrupts an industry, again, you will be flooded with GET requests, site accesses, purchases, CRM, scaling problems, avoiding downtime, and practically everything a major tech company has to deal with!
Bro, save your grey hairs, and outsource all your IT needs (except social media – that you personally need to do) to Amazon with AWS!
And the Big Data Speciality?
Having laid the groundwork, let’s get to the meat of our article. The AWS certified Big Data Speciality website mentions the following details:
The AWS Certified Big Data – Specialty exam validates technical skills and experience in designing and implementing AWS services to derive value from data. The examination is for individuals who perform complex Big Data analyses and validates an individual’s ability to:
Implement core AWS Big Data services according to basic architecture best practices
Design and maintain Big Data
Leverage tools to automate data analysis
So, what is an AWS Big Data Speciality certified expert? Nothing more than an internationally recognized certification that says that you, as a data scientist can work professionally in AWS and Big Data’s requirements in Data Science.
Please note: the eligibility criteria for an AWS Big Data Speciality Certification is:
Minimum five years hands-on experience in a data analytics field
Background in defining and architecting AWS Big Data services with the ability to explain how they fit in the data life cycle of collection, ingestion, storage, processing, and visualization
Experience in designing a scalable and cost-effective architecture to process data
To put it in layman’s terms, if you, the data scientist, were Priyanka Chopra, getting the AWS Big Data Speciality certification passed successfully is the equivalent of going to Hollywood and working in the USA starring in Quantico!
Suddenly, a whole new world is open at your feet!
But don’t get too excited: unless you already have five years experience with Big Data, there’s a long way to go. But work hard, take one step at a time, don’t look at the goal far ahead but focus on every single day, one day, one task at a time, and in the end you will reach your destination. Persistence, discipline and determination matters. As simple as that.
From whizlabs.org
Five Advantages of an AWS Big Data Speciality
1. Massive Increase in Income as a Certified AWS Big Data Speciality Professional (a long term 5 years plus goal)
Everyone who’s anyone in data science knows that a data scientist in the US earns an average of 100,000 USD$ every year. But what is the average salary of an AWS Big Data Speciality Certified professional? Hold on to your hat’s folks; it’s 160,000 $USD starting salary. And with just two years of additional experience, that salary can cross 250,000 USD$ every year if you are a superstar at your work. Depending upon your performance on the job! Do you still need a push to get into AWS? The following table shows the average starting salaries for specialists in the following Amazon products: (from www.dezyre.com)
Top Paying AWS Skills According to Indeed.com
AWS Skill
Salary
DynamoDB
$141,813
Elastic MapReduce (EMR)
$136,250
CloudFormation
$132,308
Elastic Cache
$125,625
CloudWatch
$121,980
Lambda
$121,481
Kinesis
$121,429
Key Management Service
$117,297
Elastic Beanstalk
$114,219
Redshift
$113,950
2. Wide Ecosystem of Tools, Libraries, and Amazon Products
From slideshare.net
Amazon Web Services, compared to other Cloud IaaS services, has by far the widest ecosystem of products and tools. As a Big Data specialist, you are free to choose your career path. Do you want to get into AI? Do you have an interest in ES3 (storage system) or HIgh-Performance Serverless computing (AWS Lambda). You get to choose, along with the company you work for. I don’t know about you, but I’m just writing this article and I’mseriouslyexcited!
3. Maximum Demand Among All Cloud Computing jobs
If you manage to clear the certification in AWS, then guess what – AWS certified professionals have by far the maximum market demand! Simply because more than half of all Cloud Computing IaaS companies use AWS. The demand for AWS certifications is the maximum right now. To mention some figures: in 2019, 350,000 professionals will be required for AWS jobs. 60% of cloud computing jobs ask for AWS skills (naturally, considering that it has half the market share).
4. Worldwide Demand In Every Country that Has IT
It’s not just in the US that demand is peaking. There are jobs available in England, France, Australia, Canada, India, China, EU – practically every nation that wants to get into IT will welcome you with open arms if you are an AWS certified professional. And look no further than this site. AWS training will be offered soon, here: on Dimensionless.in. Within the next six months at the latest!
5. Affordable Pricing and Free One Year Tier to Learn AWS
Amazon has always been able to command the lowest prices because of its dominance in the market share. AWS offers you a free 1 year of paid services on its cloud IaaS platform. Completely free for one year. AWS training materials are also less expensive compared to other offerings. The following features are offered free for one single year under Amazon’s AWS free tier system:
The following is a web-scrape of their free-tier offering:
AWS Free Tier One Year Resources Available
There were initially seven pages in the Word document that I scraped from www.aws.com/free. To really have a look, go to the website on the previous link and see for yourself on the following link (much more details in much higher resolution). Please visit this last mentioned link. That alone will show you why AWS is sitting pretty on top of the cloud – literally.
Final Words
Right now, AWS rules the roost in cloud computing. But there is competition from Microsoft, Google, and IBM. Microsoft Azure has a lot of glitches which costs a lot to fix. Google Cloud Platform is cheaper but has very high technical support charges. A dark horse here: IBM Cloud. Their product has a lot of offerings and a lot of potential. Third only to Google and AWS. If you are working and want to go abroad or have a thirst for achievement, go for AWS. Totally. Finally, good news, all Dimensionless current students and alumni, the languages that AWS is built on has 100% support for Python! (It also supports, Go, Ruby, Java, Node.js, and many more – but Python has 100% support).
Keep coming to this website – expect to see AWS courses here in the near future!
International Data Corp. (IDC) expects worldwide revenue for big data and business analytics (BDA) solutions to reach $260 billion in 2022, with a compound annual growth rate (CAGR) of 11.9%. It values the current market at $166 billion, up 11.7% over 2017.
The industries making the largest investments in big data and business analytics solutions are banking, manufacturing, professional services, and government. At a high level, organizations are turning to Big Data and analytics solutions to navigate the convergence of their physical and digital worlds
In this blog, we will be looking into various Big Data solutions provided by AWS(Amazon Web Services). This will give an idea about different services available on AWS for obtaining Big Data capabilities for their Businesses/Organisations.
Also, if you are looking to learn Big Data, then you will really like this amazing course
What is Big Data?
Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.
Big Data comprises of 4 important V’s which defines the characteristics of Big Data. Let us discuss these ones before moving to AWS
Volume — The name ‘Big Data’ itself is related to a size which is enormous. Size of data plays a very crucial role in determining value out of data. Also, whether a particular data is Big Data or not, is dependent upon the volume of data. Hence, ‘Volume’ is one of the important characteristic while dealing with ‘Big Data’.
Variety — The next aspect of ‘Big Data’ is its variety. Variety refers to heterogeneous sources and the nature of data, both structured and unstructured. During earlier days, spreadsheets and databases were the only sources of data. Nowadays, analysis applications use data in the form of emails, photos, videos, monitoring devices, PDFs, audio, etc. This variety of unstructured-data poses certain issues for storage, mining and analyzing data.
Velocity — The term ‘velocity’ refers to the speed of generation of data. How fast the data is generated and processed to meet the demands, determines real potential in the data. Big Data Velocity deals with the speed at which data flows in from sources like business processes, application logs, networks, and social media sites, sensors, Mobile devices, etc. Also, the flow of data is massive and continuous.
Variability — This refers to the inconsistency which can be shown by the data at times, thus hampering the process of being able to handle and manage the data effectively.
If you are looking to learn Big Data online then follow the link here
What is AWS?
AWS comprises of many different cloud computing products and services. The highly profitable Amazon division provides servers, storage, networking, remote computing, email, mobile development and security. Furthermore. AWS can be split into two main products: EC2, Amazon’s virtual machine service and S3, a storage system by Amazon. It is so large and present in the computing world that it’s now at least 10 times the size of its nearest competitor and hosts popular websites like Netflix and Instagram
AWS is split into 12 global regions, each of which has multiple availability zones in which its servers are located. These serviced regions are split in order to allow users to set geographical limits on their services (if they so choose), but also to provide security by diversifying the physical locations in which data is held.
AWS solutions for Big Data
AWS has numerous solutions for all the development and deployment purposes. Also, in the field of Data Science and Big Data, AWS has come up with recent developments in different aspects of Big Data handling. Before jumping to tools, let us understand different aspects in Big Data for which AWS can provide solutions
Data Ingestion Collecting the raw data — transactions, logs, mobile devices and more — is the first challenge many organizations face when dealing with big data. A good big data platform makes this step easier, allowing developers to ingest a wide variety of data — from structured to unstructured — at any speed — from real-time to batch.
Storage of Data Any big data platform needs a secure, scalable, and durable repository to store data prior to or even after processing tasks. Depending on your specific requirements, you may also need temporary stores for data-in-transit
Data Processing This is the step where data transformation happens from its raw state into a consumable format — usually by means of sorting, aggregating, joining and even performing more advanced functions and algorithms. The resulting data sets undergo storage for further processing or made available for consumption via business intelligence and data visualization tools.
Visualisation Big data is all about getting high value, actionable insights from your data assets. Ideally, data is available to stakeholders through self-service business intelligence and agile data visualization tools that allow for fast and easy exploration of datasets. Depending on the type of analytics, end-users may also consume the resulting data in the form of statistical “predictions” — in the case of predictive analytics — or recommended actions — in the case of prescriptive analytics.
AWS tools for Big Data
In the previous sections, we looked at the fields in Big Data where AWS can provide solutions. Additionally, AWS has multiple tools and services in its arsenal to enable customers with the capabilities of Big Data
Let us look at the various solutions provided by AWS for handling different stages involved in handling Big Data
Ingestion
Kinesis Amazon Kinesis Firehose is a fully managed service for delivering real-time streaming data directly to Amazon S3. Kinesis Firehose automatically scales to match the volume and throughput of streaming data and requires no ongoing administration. Kinesis Firehose is configurable to transform streaming data before it’s stored in Amazon S3. Its transformation capabilities include compression, encryption, data batching, and Lambda functions. Kinesis Firehose can compress data before it’s storage in Amazon S3. It currently supports GZIP, ZIP, and SNAPPY compression formats. GZIP is a better choice because it can be used by Amazon Athena, Amazon EMR, and Amazon Redshift. Kinesis Firehose encryption supports Amazon S3 server-side encryption with AWS Key Management Service (AWS KMS) for encrypting delivered data in Amazon S3
Snowball You can use AWS Snowball to securely and efficiently migrate bulk data from on-premises storage platforms and Hadoop clusters to S3 buckets. After you create a job in the AWS Management Console, a Snowball appliance will be automatically shipped to you. After a Snowball arrives, connect it to your local network, install the Snowball client on your on-premises data source, and then use the Snowball client to select and transfer the file directories to the Snowball device. The Snowball client uses AES-256-bit encryption. No encryption keys with the Snowball device the makes data transfer process is highly secure. After the data transfer is complete, the Snowball’s E Ink shipping label will automatically update. Ship the device back to AWS. Upon receipt at AWS, data transfer takes place from the Snowball device to your S3 bucket and stored as S3 objects in their original/native format. Snowball also has an HDFS client, so data migration may happen directly from Hadoop clusters into an S3 bucket in its native format.
Storage
Amazon S3 Amazon S3 is secure, highly scalable, durable object storage with millisecond latency for data access. S3 can store any type of data from anywhere — websites and mobile apps, corporate applications, and data from IoT sensors or devices. It can also store and retrieve any amount of data, with unmatched availability, and built from the ground up to deliver 99.999999999% (11 nines) of durability. S3 Select focuses on data read and retrieval, reducing response times up to 400%. S3 provides comprehensive security and compliance capabilities that meet even the most stringent regulatory requirements.
AWS Glue AWS Glue is a fully manageable service that provides a data catalogue to make data in the data lake discoverable. Additionally, it has the ability to do extract, transform, and load (ETL) to prepare data for analysis. Also, the inbuilt data catalogue is like a persistent metadata store for all data assets, making all of the data searchable, and queryable in a single view.
Processing
EMR For big data processing using the Spark and Hadoop, Amazon EMR provides a managed service that makes it easy, fast, and cost-effective to process vast amounts data. Furthermore, EMR supports 19 different open-source projects including Hadoop, Spark, and HBase. Also it comes with managed EMR Notebooks for data engineering, data science development, and collaboration. Each project updates in EMR within 30 days of a version release. It ensures you have the latest and greatest from the community, effortlessly.
Redshift For data warehousing, Amazon Redshift provides the ability to run complex, analytic queries against petabytes of structured data. Also, it includes Redshift Spectrum that runs SQL queries directly against Exabytes of structured or unstructured data in S3 without the need for unnecessary data movement. Amazon Redshift is less than a tenth of the cost of traditional solutions. Start small for just $0.25 per hour, and scale out to petabytes of data for $1,000 per terabyte per year.
Visualisations
Amazon QuickSight For dashboards and visualizations, Amazon Quicksight provides you fast, cloud-powered business analytics service. It makes it easy to build stunning visualizations and rich dashboards. Additionally, they can be accessed from any browser or mobile device.
Conclusion
Amazon Web Services provides a fully integrated portfolio of cloud computing services. Furthermore, tt helps you build, secure, and deploy your big data applications. Also, with AWS, there’s no hardware to procure and infrastructure to maintain and scale. Due to this, you can focus your resources on uncovering new insights. With new features added constantly, you’ll always be able to leverage the latest technologies without making long-term investment commitments.
Additionally, if you are interested in learning Big Data and NLP, click here to get started
Furthermore, if you want to read more about data science, you can read our blogs here
Also, the following are some suggested blogs you may like to read
The advent of Cloud computing has made it possible for many organizations to rapidly scale their current analytics operations. It involves very little maintenance overhead. This has, in turn, created a need to build strategies for migration to the Cloud. In this blog, we will discuss the various factors to consider while evaluating different Cloud technologies.
What is cloud computing?
Cloud Computing is an Information Technology (IT) paradigm that enables ubiquitous access to shared pools of configurable system resources. It also provides higher-level services that can be rapidly provisioned with minimal management effort, often over the Internet. Cloud Computing relies on the sharing of resources to achieve coherence and economy of scale, similar to a utility. By using a Cloud-based solution for computing, organizations can significantly reduce their IT infrastructure. It costs while focusing on their core business.
Advantages of cloud computing
Scalability
With the advent of Cloud infrastructure, it has become virtually effortless to scale an organization’s infrastructure up or down. This is due to the infrastructure essentially being the responsibility of the Cloud service provider. The customer only needs to specify the required configuration of the application or service without worrying about procuring the necessary infrastructure.
Reliability
Since cloud providers handle the infrastructure and its maintenance, any periodic or immediate maintenance activities adhere to the predefined SLA, essentially creating a highly reliable system.
High availability
Providers generally have servers located in physical locations across the world and ensure highly available data and services through multiple replication strategies.
Reduced operational costs
When opting for a Cloud vendor, the infrastructure becomes their responsibility hence eliminating the most cost associated with operations/maintenance for the customer. This pulls the cost down to virtually zero.
Increased IT effectiveness
The IT team is now able to focus solely on software development without worrying about hardware limitations or maintenance. The utopia of building a platform with almost no hardware constraints allows for more robust platform development. It also increases overall effectiveness
Cloud services providers
Amazon Web Services Amazon Web Services, commonly referred to as AWS, was the starting point for the Cloud Computing paradigm with its launch of EC2 compute instances in 2006. AWS has documented all the services very well and seamlessly integrate with other provided services at almost zero cost for transfer of data between services. AWS is cost-effective, highly scalable with high availability. It provides spawning and allows for usage of services both programmatically and through the UI console. AWS comprises of more than 90 different services, spanning a wide range of use cases including computing, storage, networking, database, analytics, application services, deployment, management, mobile, developer tools, and tools for machine learning and the Internet of Things.
Google Cloud Platform
Google Cloud Platform, also known as GCP, is built with power and simplicity in mind. GCP offers services which can seamlessly integrate with other Google products, providing access to a wide range of services in the domain of computing, data storage, data analytics, and machine learning. It also has a wide set of management tools which work on top of these services.
Where is the difference?
As cloud computing continues to find its way into MNC big and small, the choice of the right cloud computing solution has become a talking point for specialists and business owners alike. Among public cloud providers, Amazon Web Services (AWS) seems to have the lead in the competition, with Google Cloud and Microsoft Azure close behind.
Let us focus on some key differences between Google cloud services and AWS. We can differentiate between both of them based upon
Pricing
Features
Implementation
Security
Support
Pricing
When comparing Google Cloud vs AWS, both handle billing differently. And to be honest, neither of them provide a very straightforward way of easily calculating this unless you are very familiar with the platforms. More generally a difference in pricing is not much but google cloud services can turn out to be a tad cheaper in long run!
Google’s Cloud is a winner when it comes to computing and storage costs. For example, a 2 CPUs/8GB RAM instance will cost $69/month with AWS, compared to only $52/month with GCP (25% cheaper). As for cloud storage costs, GCP’s regional storage costs are only 2 cents/GB/month vs 2.3 cents/GB/month for AWS. Additionally, GCP offers a “multi-regional” cloud storage option, where the data is automatically replicated across several regions for the very little added cost (total of 2.6 cents/GB/month).
Here are their monthly calculators if you’re just starting:
Estimating monthly spend with both of these cloud providers can be a challenge. There are even entire tools out there such as reOptimize or Cloudability which were built to help you understand your bills better. Essentially AWS offers you a dashboard which provides insights into your bill. Google Cloud Platform provides estimated exports via their BigQuery tool. However, both providers are doing things to decrease costs and make billing easier.
Features
In this parameter, we will divide features into 3 major parts which are most essentially used. On those features, we will try to list out differences between Google cloud and AWS.
Let us also have a look at the 3 most common services provided by both of them
Compute: The first category is how Google Compute Engine and AWS EC2 handle their virtual machines (instances). The technology behind Google Cloud’s VMs is KVM, whereas the technology behind AWS EC2 VMs is Xen. Both offer a variety of predefined instance configurations with specific amounts of virtual CPU, RAM, and network. However, they have a different naming convention, which can at first be confusing. Google Compute Engine refers to them as machine types, whereas Amazon EC2 refers to them as instance types.
Storage: One of the most common use cases for public IaaS cloud computing is storage and that’s for good reason: Instead of buying hardware and managing it, users simply upload data to the cloud and pay for how much they put there.
Networking: Google Cloud and AWS both utilize different networks and partners to interconnect their data centres across the globe and deliver content via ISPs to end users. They offer a variety of different products to accomplish this.
Implementation
AWS provides a nice and easy page to start using their services.
You can see that they break it down by the platform you wish to work on, so whether you are making an iOS app, or writing in PHP, they provide some sample code to begin the integration.
Lastly, we have the process of starting with Google — named ‘Cloud Launcher’.
They equally provide some starting documentation and list some useful benefits
Support
Both Google Cloud and AWS have extensive documentation and community forums which you can take advantage of for free.
However, if you need assistance or support right away, you’ll have to pay. Both Google Cloud and AWS have support plans, but you’ll definitely want to read the fees involved as they can add up quite fast. Both providers include an unlimited number of account and billing support cases, with no long-term contracts.
Cheapest support plan, Silver, starts at $150/month minimum
The next level support plan, Gold, starts at a $400/month minimum, but at this level, GCP will bill you a minimum of 9% of product usage fees (decreases as spend increases)
Cheapest paid support plan, Developer, starts at $29/month or 3% of monthly AWS usage
The next level support plan, Business, starts at a $100/month minimum, but at this level, AWS will bill you a minimum of 10% of product usage fees (decreases as spend increases)
Security
In their Second Annual Cloud Computing Survey (2017), Clutch surveyed 283 IT professionals at businesses across the United States that currently use a cloud computing service. In regards to security, they found that almost 70% of professionals were more comfortable storing data in the cloud than their previous legacy systems.
All the data stored on EC2 instances is encrypted under 256-bit AES. Each encryption key is also encrypted with a set of regularly changed master keys.
Network firewalls built into Amazon VPC, and web application firewall capabilities in AWS WAF let you create private networks. They control access to your instances and applications.
AWS Identity and Access Management (IAM), AWS Multi-Factor Authentication, and AWS Directory Services allow for defining, enforcing, and managing user access policies.
AWS has audit-friendly service features for PCI, ISO, HIPAA, SOC and other compliance standards.
All the data stored on persistent disks and is encrypted under 256-bit AES and each encryption key is also encrypted with a set of regularly changed master keys. By default.
Commitment to enterprise security certifications (SSAE16, ISO 27017, ISO 27018, PCI, and HIPAA compliance).
Only authenticated and authorized requests from other components that coming to Google storage stack are required.
Google Cloud Identity and Access Management (Cloud IAM) was launched in September 2017 to provide predefined roles that give granular access to specific Google Cloud Platform resources and prevent unwanted access to other resources.
Conclusion
After going through different aspects and components of cloud services, we can form a conclusion that
