AWS CLOUD SERVICES

Are you a pathfinder?

If YES, with AWS cloud solutions, your organization will push the boundaries of what is possible with the cloud infrastructure. 

 If we take a closer look at pathfinders and the way they approach the challenges, especially after COVID, patterns start to emerge, you can see common traits. Some do not let the rules define them, they do not accept the status quo, they instead find new and better paths forward. And in doing so, often change the game for everyone else, for the competitors, too. Others have the ability to see what others do not, and they use that knowledge to help people understand the challenges at hand so that they can understand and overcome them to make a difference. And still, others are intentional about giving those who come behind them the tools that they need to forge their own paths forward to succeed in their own efforts and own innovation. We need to create our own journeys, including CLOUD JOURNEY.

Amazon Web Services -AWS 

Amazon Web Services (AWS) is a Visionary in Cloud Provider. Its vision is for data science teams to use the entire breadth of the AWS portfolio and ML stack, with Amazon SageMaker at its core. Many of the supporting AWS components and services were considered in evaluating AWS’s offering. These included the SageMaker Studio IDE (which includes Autopilot, Notebooks, Model Monitor, Experiments and Debugger), Amazon EMR (including S3), AWS Glue, Amazon SageMaker Neo, Amazon SageMaker Ground Truth, Amazon SageMaker Clarify, Amazon SageMaker Data Wrangler, Amazon SageMaker Pipelines, AWS CloudWatch, AWS CloudTrail and others.

AWS is geographically diversified, and its client base spans many industries and business functions.

Amazon SageMaker continues to demonstrate formidable market traction, with a powerful ecosystem and considerable resources behind it.

Strengths

• Breadth and depth of cloud platform: Users can directly leverage AWS’s prepackaged AI services (such as Amazon Lex, Polly and Transcribe). SageMaker is also natively integrated with AWS’s many cloud data and analytics tools. Additionally, SageMaker provides extensive support for a broad range of popular and niche open-source software (OSS) libraries and frameworks.

• Performance, scalability and granularity of control: Amazon SageMaker and its supporting portfolio offer best-in-class performance and scalability. The platform supports a significant selection of hardware options optimized for various ML and deep learning frameworks, and features a pay-as-you-go pricing model with no minimum fees or upfront commitment, thus encouraging experimentation.

• Data labeling and human-in-the-loop capabilities: Amazon SageMaker Ground Truth supports labeling of training data, and Amazon’s Augmented AI (Amazon A2I) helps build optimal workflows for human review of deployed models. AWS connects customers with third-party marketplace vendors and the Amazon Mechanical Turk (MTurk) workforce for human labeling of data.

Cautions

• Evolving citizen data science appeal: AWS has made its platform more accessible, mainly through Autopilot, Data Wrangler, Pipelines and continued development of the SageMaker Studio IDE. Still, the platform is more popular among coders — it is not as intuitive for nontechnical users, compared with leading tools for citizen data scientists.

• Rapid pace of development needed to match competitors’ functionality: AWS’s flurry of new components and services is filling important gaps in its platform. However, these new capabilities are neither as proven nor as strong as other vendors’ capabilities for data preparation, user interfaces, collaboration and coherence.

• Maturing on-premises, hybrid and multicloud support: The majority of Amazon SageMaker customers operate in purely cloud environments. Some capabilities within the AWS portfolio change or become more complicated in hybrid, multicloud or on-premises environments. Multicloud support is evolving, however, and today most customers manage data, models and ML workloads within AWS.

Why you should Move to AWS if you are a Retailer? 

In AWS the main core principles that you are going to run into when you are choosing to scale this thing out are region and availability zone. That is where it all begins with AWS. 

 The Region is; what region of the World you want to be in. The availability zone in which data center in that region do you want to be in. Therefore, when AWS spins up a region, let's say they have got you in West Europe, they never put just a single data center, they always have at least two because they want resiliency, and redundancy between those two. Therefore, when you are starting to move into the AWS World, you will go in and set this up in whatever region of the World that you believe most of your traffic will come from and it is usually recommended to stand multiple data centers. The reason for that is; that even AWS has outages, and power failures and they may happen and so they always say that you need to get yourself outside of a single data set.    The second realm of AWS is gonna be different services. In AWS, the major service that you have when you get started is EC2.   EC2 is the Elastic Cloud Computing which is your virtual servers in the cloud, where you will come to spin up your virtual machines and tear them. I couple that almost with Auto Scaling which is exactly very cost-efficient when you compare to data warehouse unexpected. Then highlighted the fact as you scale up your site / E-Com platform, you can add servers or scale down depending on the load that is coming in. Auto Scaling can do that for you once it sees the number of visitors drill beyond a certain point, then it can add more servers or once your load decreases below a certain point, it can delete servers. And this directly impacts how much you pay for the service, it is very cost efficient.    EBS (Elastic Block Store) and S3 (Route) are the two major storage components of AWS. These are services of AWS and there are so many more up, to 200 fully-featured services. Glacier, for example, is another storage service of AWS.    S3 is designed as huge storage, you can store a massive amount of data there. It is replicated and made redundant across multiple data center locations and even published via the web. Therefore, it could be a web-accessible source.  EBS represents the source that your servers use when you come into a cloud. Therefore, when you move into a cloud, you will spin up an EC2 Server that will serve your web content or whatever else you have got going. Then you will map that to whatever stores you want, for example, to hold the hard drives of the virtual servers. 

Best Service for all  Industries

We build powerful capabilities, publish the edge of the cloud to new places with things like AI, 5G, and IoT. We are driving seamless integration of data, analytics, and machine learning (AI) that will be transformative for your business. We are tailoring services and applications to specific customer use cases and industries. The possibilities here are endless. The sophistication of the customers' use cases is amazing. Netflix boldly has gone all-in on AWS for 10 years and transformed their business and entertainment industry by pioneering streaming movies and creating original content on AWS. In 2012, NASA JPL engineers landed the SUV-size Curiosity rover on Mars by using AWS to stream the landing and support mission. Also, DECOMO NTT, Japan's largest telecom company, was the first to prove the power of the cloud for analytics by building a massive multi-petabyte data warehouse on AWS Redshift that ran queries 10 times faster than they'd run on-premises, yes 10 times faster.

You can dare to do anything fundamentally different than your competitors using cloud technology today. Time to reinvent your business and your industry. You see around the corners, and then even braved uncharted territories. 

AWS now offers over 200 fully featured services for computing, storage, database, machine learning analytics, AI, and more such as deep learning and robotics. At Keepgoing, we are going to innovate to offer the broadest and the deepest set of services and capabilities of AWS. You can expect to hear about AWS's latest innovations that will meet all your expectations today and tomorrow and by 2030. There are now millions of companies in every industry across every use case and around the world running their mission-critical applications on AWS. And AWS cloud now spans over 81 availability zones and 25 geographic regions around the world. Whether you need technical features, new customers, partners or investors, community or operational experience, keepgoing.ai continues to bring you the most capabilities you will find anywhere. AWS is the leading cloud service provider in the Gartner Magic Quadrant for the 11th consecutive year. It is still early days for you to keep going. Today, the cloud has become a tech revolution and an enabler of a fundamental shift in the way that businesses actually function. There is no industry that has not been touched, and no business that cannot be radically disrupted. Analysts estimate that perhaps five to 15% of IT spending has moved to the cloud, there are so many workloads that are going to move to the cloud in the coming years. 

SAGEMAKER 

 AWS launched SadeMaker in 2017 with 150 ML capabilities and features. Today a lot of companies are using SageMaker to train models with billions of parameters, to make hundreds of billions predecitions every month. Developers do not need to build train models whojust want to add intelligent capabilties like image and video recognition, speech, and natural language understanding, personalization, highly accurate enterprise search, range of AI services that make all this as easy as an API call. 

AMAZON QUICKSIGHT  

A scalable, embeddable ML-powered BI service built for the cloud 

*Create Easily 

*Publish 

*Embed interactive data visualizayions 

*Embed intelligent dashboards for your teams 

 Today companies are using QuickSight, ML-powered business intelligence solutions to easily create, publish, and embed interactive data visualizations and dashboards.  .  Enable everyone in your organization to act, which means they need to be able to find, analyze and understand that data.  

AWS ( QuickSight) Q,

Any business user can just ask a question using natural language, like 

"WHAT ARE THE TOP 10 PRODUCTS BY SALES IN 2021?"

 Your Q tool will return the answer the form of the visualization in seconds. NoSQL to wrtie, no dashboards to create, no need to wait on a busy data ecience team to help you, you just ask questions to your tool, receive a visualized answer and move on, keepgoing. Q is unique in that it returns the answers both with a high degree of accuracy, but also does not require extensive preparation of the data before Q can be used. It is the best of both worlds. QuickSight gives business unit users access to data and to analysis, but we can also create your own ML models to move from descriptive analytics, where they are simply summarizing insight of data to make predicitons about future outcomes. If you ask for more powerful capabilities, so you can quickly make more precise predicitons and apply machine learning to a range of problems like reducing churn, detecting fraud, forecasting sales or optimizing inventory. Most data analysts do not have the skills to do this today with BI tools. If you look back a few years, in fact, only a very small number of experts in machine learning had the skills to do this.

DATA INTEGRATION WITH FEDERATED QUERY FOR ATHENA AND REDSHIFT   

Databases, data lakes, AI analytcis and Machine LEarning  are acquired elements of any data strategy, which is why we built powerful capabilities in each. Even more powerful when you can combine them and do things like easily query and move data between your data stores, data lakes, anaytics and machine learning tools. To help with this, we can build capabilities with algorithms.   With query tools, you can write a single SQL query that combines data from multiple systems like your data lakes, and sends those results back to you on the fly. For example you could write a query in AWS Athena that combines gaming player profiles from Aurora MySQL, within gaming actions from DynamoDB to get a picture of what specific users and specific zip codes took what actions.   We can also build out direct integrations between the various services. For example today, you can access SageMaker from Redshift, Aurora or Neptune. This means that a Redshift user could write a SQL statement that called SageMaker behind the schenes to automatically build, train and optimize and bring a model back to Redshift as a function. So you can benefit from SageMakers best in class of ML without ever leacing Redshift. You can also see automatically Salesforce data inside the systmes. It is really powerful and just inredibly useful interaction for your sales people.    The key point is that we know your data is on a journey. All stops along the journey matter. You cannot skip any of them and at each step, you have to have the right capabilities, we are focused on continuing to build out all the tools you need for the entire journey. 

AWS SIMPLE STORAGE SERVICE - S3
S3 freed developers from having created expensive data storage systems of their own.
Today, the S3 service stores more than 100 trillion objects, and more than 16 million new instances are spun up on EC2 every day.

AMAZON ELASTIC COMPUTE CLOUD - EC2  
EC2, the concept of computing on-demand, all started with "the instance" service. Once you start using EC2, you will get more CPU, more memory, more storage. The first instance is the m1.small, m1.large, and m1.xlarge, for Windows. Features are Auto Scaling and Load Balancing. Also, there are more powerful instances to run HPC workloads, database and analytics, and enterprise applications. Some are more compute-optimized than storage-optimized and memory-optimized, GPU-based instances and Mac-based instances. Compute is so foundational, customers have an almost insatiable appetite for specialized instances. There are over 475 different instances within AWS.

EC2 was released as the first database service at AWS in 2006. Today, the foundational elements of almost any application, storage, compute and database are all available in the cloud. As one of the first cloud providers, AWS has proved the value for companies running their applications on this infrastructure platform. Cloud is not only for startups, enterprises started to use the cloud to compete in the market because it is certainly for mission-critical workloads.  

INSTANCES FOR EC2
You can expect to have fantastic price performance for compute-intensive workloads with the instances below for EC2.

ARM-BASED CHIPS - GRAVITON 1 - 2
Graviton is AWS's first processor with more cost savings for more workloads. It provides the best price-performance in EC2. 40% better price-performance than comparable x86-based AWS instances. Today thousands of companies are using Graviton-based instances and reaping the benefits and price performance for a very wide range of workloads, including big data analytics, game servers, and high-performance computing. Graviton - 3 is launched this year as the next generation of AWS-designed Arm Chips.

GRAVITON 3
Graviton3 chips are another big leap forward, 25% faster on average for general compute workloads than Graviton2, and they perform even better for certain specialized workloads, provide two times faster floating-point performance for scientific workloads, or 2 times faster for cryptographic workloads, and 3 times faster for machine learning workloads. And to help reduce the carbon footprint, Graviton3 processors use up to 60% less energy for the same performance as comparable instances.

C7g INSTANCE FOR EC2 (powered by Graviton3)
You can expect to have fantastic price performance for compute-intensive workloads.

INF1 INSTANCE FOR EC2
Training machine learning models and running inferences are highly computed intensive, and many of the companies want to find a way to lower the cost of those machine learning workloads. Inferentia is AWS's first machine learning chip and is optimized for high-performance inference.

Inf1 delivers up to 70% lower cost per inference and comparable GPU-based EC2 instances.

TRANIUM
Tranium, purpose-built for training deep learning models. You can use Trn1 to train machine learning models for apps like image recognition, natural language process NLP, fraud detection and forecasting. Trn1 is the first EC2 instance with up to 800 gigabits per second networking bandwidth. Therefore, it is absolutely great for large-scale, multi-node distributed training cases.

sometimes, with machine learning workloads, you need more processing than any other single instance can handle. and we can network these together in what call ultra-clusters consisting of tens of thousands of training accelerators interconnected with petabyte-scale networking. These training ultra clusters are powered by a powerful machine learning supercomputer for rapidly training the most complex, deep learning models with trillions of parameters.

With Tranium and inferentia-powered instances, customers can have the best price-performance from machine learning, from scaling training workloads, to accelerating deep learning workloads in production with high-performance inference, making the full power of machine learning available for all.

SAP
Thousands of companies have already been running SAP on AWS. Keepgoing.ai is working with SAP on a new initiative to power the SAP HANA Cloud with AWS Gravion processors. This delivers significant performance.

AWS MAINFRAME MODERNIZATION
Migrate, modernize, and run mainframe workloads on AWS.

It is a new service to make it faster to migrate, modernize and run mainframe applications on AWS. It cuts mainframe migration time by 2/3.

Now, a lot of companies, of course, have been running applications on mainframes for many decades. And companies in every industry, obviously, still reply to them. but mainframes are expensive, complicated, and there are fewer engineers who are learning to program COBOL these days. And maintaining a mainframe is another big problem. This is why many of these companies are trying to get off their mainframes as fast as they possibly can to gain agility and the elasticity of the cloud. Companies can reduce their cost by up to 70% or more after migrating.

Now, there are a couple of different paths that companies can take. some start with a lift-and-shift approach and bring the application pretty much as is. Others refactor and break the application down into microservices in the cloud. Neither road is as easy as you would like. And in fact, Whichever way you go, can take months even years.   
-You have to evaluate the complexity of the application source code,
-Understand the dependencies on other systems,
-Convert or recompile the code,
-You have to test it to make sure it all works before you move anything.
It can be a messy business and involves a lot of moving pieces.

With AWS Mainframe Modernization, it takes to move mainframe workloads to the cloud cut by as much as two-thirds using a complete of development, test, and deployment tools and a mainframe compatible runtime environment.

Mainframe Modernization helps you assess, and analyze your mainframe application for readiness. Choose the path you want, re-platform, or refactor. If you want to re-platform and move your app over AWS with minimal code changes, you can use mainframe modernizations recompilers to convert your code, and it is testing services and make sure you do not lose any functionality in the translation. and then you are ready to migrate to the services' new mainframe compatible EC2 runtime environment. If you want to refactor or decompose the application, so you can just run the components in EC2, in containers, in Lambda, then AWS Mainframe Modernization service can actually automatically convert the COBOL code over to Java for you.

The vast majority of applications and workloads will run in the cloud in the fullness of time. At the same time, many workloads today still run in your data centers, because you have deployed so many resources there for many years. And the term hybrid emerged, of course, to describe scenarios where customers are running them in both places. As AWS pioneered cloud infrastructure services, it has been building bridges back to your data centers for years. AWS has virtual private cloud or VPC, Direct Connects, storage gateway, and VMware, Red Hat, and NetApp to make it possible for you to use the same familiar software and tools that you know and love your data centers seamlessly on AWS. We build bridges between your applications and AWS for data centers, or elsewhere you require. Some customers also require about the pieces of IT and you might want to run in your data centers for a bit longer. For those applications, what you really need is to bring a bit of AWS on-premises, or some marketing claim like you see from some other cloud providers, but actual AWS, so you could use the same APIs, control plane hardware, and tools. Outposts were built for this. Outposts is not like AWS, it is AWS.

AWS OUTPOSTS
Run AWS Infrastructure and Services on Premises
*Fully managed and supported by AWS
*Same hardware that AWS runs in its data centers
*Same APIs, control plane hardware, and tools
*It enables to move more quickly to the cloud
*You can bridge back very well to your own data centers

Outposts is not like AWS, it is AWS. All the maintenance is delivered by AWS automatically. It comes with the same APIs, same control plane and same tools, and the same hardware. Today, it enables a lot of organizations to move more quickly to the cloud because they can bridge back very well to their own data centers. Companies across a number of industries are using Outposts, including DISH Wireless, Verizon, Morningstar, and Riot Games.

AWS IoT, AWS SNOW FAMILY, AWS WAVELENGTH SERVICES ARE PUSHING THE EDGE OF THE CLOUD
Now, as the world of applications continues to change, so is the definition of HYBRID, which means you need more new places than just your data centers. Many of the organizations are running apps in places beyond AWS regions and data centers. These apps rely heavily on the cloud for processing, analytics, storage, and machine learning, and in fact, many are possible because of the cloud. In some cases, companies need to compute and storage is done locally. The edge of the cloud is pushing outwards to facilities like factories and hospitals, remote locations like oil rigs and agricultural fields, and 5G networks. It is to unleash a wave of innovation in these sectors.

AWS SNOW DEVICES
They are one of the smaller Outposts form factors. They take up less space, you can bring AWS to offices, to factories, to hospitals. But also for companies who do work in remote places like oil rigs, and agricultural fields that may even not have connectivity. Some are calling this the rugged edge. For those, we can use AWS SNOW DEVICES to collect, store and analyze data on that rugged edge.

AWS WAVELENGTH
When you are building 5G mobile applications that need to sit at the 5G mobile edge, WAVELENGTH can be used as a service. Wavelength puts AWS compute and storage services within 5G networks, providing mobile edge computing infrastructure to enable ultra-low latency applications. Vodafone in Europe is using this service.

AWS CLOUD NETWORKING
It is AWS service to connect all devices. AWS is more dependent than ever on high-performance and reliable connectivity. We can literally connect everything today.

Robots in manufacturing lines, tablets in the hands of workers in factories, stores of retailers, connected air conditioners, escalators, forklifts, delivery vehicles that need a reliable data connection to manage logistics. And all these use cases require consistent, reliable connectivity.

Today, most enterprises use local wired Ethernet or Wifi networks for their connectivity. These systems were not designed to support connecting all of these things. Wired networks perform well, but they are expensive to deploy and upgrade. And they do not extend very well to mobile devices. Wifi is pretty easy and cheap to use, but it has a range of coverage issues. However, the promise of 5G is so exciting.

AWS PRIVATE 5G
Set up and scale a private mobile network in days!!! It is AWS Service to make it easy to deploy and manage your own private mobile network.
*AWS provided hardware, software, and SIMs,
*Automatic configuration,
*No per-device charges
*Operates shared spectrum

With 5G, you can easily connect tens of thousands of devices. The handoffs between access points are seamless, and you can maintain coverage over large areas, and you can get high bandwidth and low latency. But designing, building, and deploying a mobile network takes a lot of time and is a complicated process that required telecom expertise. Plus, you have to qualify and work with multiple vendors. And each has its own pricing models, most of which include a charge for each device and that adds up when we are talking about tens of thousands of devices or more, it is not easy for companies.

With AWS Private 5G, you can set up and scale a private mobile network in days. You get all the possibilities of mobile technology without the pain of long planning cycles, complex integrations, and high upfront costs. It is shockingly easy.  

If you want to build your own network and specify the network capacity, AWS ship you all the required hardware, software, and SIM cards. Once, they are powered on, the Private5G network just simply auto-configures and sets up a mobile network that can spend anything from your corporate office to a large campus, to a factory floor, or a warehouse. You just pop the SIM cards into your devices and everything is connected. Ordering additional capacity, provisioning additional devices, or managing access permissions can be done easily by just using the AWS console. Best of all, you can provision as many connected devices and users as you want without any per-device charges.

And with Private 5G, which operates in a shared spectrum, you do not even need a spectrum license, a one-stop shop, manage a private cellular network. You may start small and scale up as you need with pay-as-you-go pricing.

AWS RDS which supports five different relational engines.  It is relational database  service.

AWS AURORA is also the other relational database service of AWS, which is built from the ground up to give you the performance and availability of commercial-grade relational databases at 1/10 of the cost of other providers. Aurora contines to be the fastest service in AWS history.  

Also AWS Non-Relational Database Products   are

DYNAMODB for key-value 

DOCUMENTDB for documents 

ELASTICACHE for caching 

NEPTUNE for Graph 

TIMESTREAM for time-series 

QLDB for ledger 

KEYSPACES for wide column 

MEMORYDB for memory 

AMAZON DYNAMODB - If you application is global, and needs to scale to support tens of millions of reads and writes per second, with millisecond latency, we offer you DynamoDB.   

AWS NEPTUNE - If you need a graph database for applications connected datasets, we offer you Neptune.  

AWS MEMORYDB - If you need blazing fast Redis is compatible in-memory database that can process more than 13 trillion requests per day, we offer you MemoryDB for Redis.  

Companies need all of these powerful tools, not just one or even two, to deliver the experience of their end-users demand. 

AWS LAKE FORMATION (DATA LAKE) 

Build a Secure Data Lake in Days 

*Move, store, catalog (metadata) and clean your data faster with Machine Learning Tools *Manage access control from a single location 

*Enforce security policies across multiple services   

Data Lake Formation helps you collect real-time data and catalog metadata from databases and object storage, and move data into your AWS S3 data lake, clean and classify your data using machine learning algorithms and secure access to the sensitive data.

   GIVE CUSTOMIZED ACCESS TO YOUR DATA WITH AI  

Data Lake Formation gives you a single place to enforce access controls, and operate it the table and column level for that so all the users and services can access your data in the right way. This is incredibly important, especially when you want to give multiple teams applications and tools access to your data.   Take for example SALES DATA, not everyone needs access to all information nor should they have it. You might want account managers in France to see only French accounts or the marketing team to see only accounts with marketing activity. You probably want your finance team to have access to everything. To give customized access to slices of data, traditionally you have had to create manage multiple copies of the data, keep all the copies in sync and manage complex data pipelines. It is a lot of heavy lifting.   

ROW AND CELL-LEVEL SECURITY FOR LAKE FORMATION  

Our customers always ask for more targeted and direct way to govern access to their data lakes. and to eliminate this heavy lifting and provide you with that precision over access, we offer AWS Row and Cell-Level security for lake formation.  

Every company needs to get to real-time data. To make it easier we use machine learning algorithms with transactions for governed tables in data lake formations. Today's technology enable us to create new types of tables, govern tables that supports ACID (Atomicity, Consistency, Isolation, and Durability) transactions. So this data is added or changed in S3.

AWS ANALYTICS SERVICES  

AWS MUCK - To run analytics all with high performance if you can get quickly to insights. AMAZON EMR - For big data Processing; If you want to process vast amounts of unstructured data using popular open-source distrubited frameworks like Spark, Hive, Presto, WE OFFER YOU EMR which supports many of these frameworks. 

 AMAZON OPENSEACRH SERVICE - For Operational Analytics; If you want to quickly search and analyze large amounts of log data to monitor the health of your production systems and troubleshoot problems, WE OFFER YOU AWS OPEN SEARCH SERVICE which is successor to AWS Elastic Search Service. It also supports search 1.1., a community-driven open-source fork of Elastic Search and Kibana, and is available under the Apache Licence.  

AMAZON KINESIS AND MSK - For Real-time analytics; If you want to do real-time processing of streaming data, WE OFFER YOU KINESIS or manage streaming for Apache Kafka.  

AMAZON REDSHIFT - Data analytics for Data Warehousing; If you have structured data, where you need super-fast query results, WE OFFER YOU REDSHIFT which is the fastest cloud data warehouse. 

AMAZON ATHENA - For interactive query and 

AWS GLUE - For Data Integration

  There are some companies who want these benefits without having to touch any infrastructure at all. The do not want to tune and optimize clusters, and they do not need access to all the knobs and dials of the servers. Others do not want to have to deal with forecasting the infrastructure capacity that their applications need. We already eliminated the need for infrastructure and capacity management with cloud-based tools such as Athena and Glue.          

SERVESLESS AND ON-DEMAND ANALYTICS 

 REDSHIFT - EMR - MSK - KINESIS  

If you choose one of these options, you do not have to configure, scale,or manage clusters or servers, and you do not have to worry about provisioning capacity. Fire them up and the services automatically scale in seconds when busy, and scale back down when not. New Serverless and on-demand options are great for workloads where you do not want to have to plan capacity. Maybe it is a new application or one that is growing unpredictably, for example, a website selling tickets to a major event.  Cloud based Analytics Services not only deliver incredible performance and capabilities but also give you the option to take your analytics serverless.   Analytics helps us to understand what is going on in the market.  

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