Everything you need to Know about Serverless Microservices in AWS

Everything you need to Know about Serverless Microservices in AWS
It’s a well-known fact that handling multiple servers can be a painful experience, especially in the short run. Multiple servers mean multiple developers will need to work on the same code, making the code repository difficult to handle in the long run. One of the biggest disadvantages in the long run is the resiliency, which causes the whole back end to get bogged down, making the website crash and slow down eventually.

What are AWS Microservices?
The microservices architecture has been designed to solve all forms of front end and back end issues. The back end is wired to communicate with various small services through a network of HTTP or other messaging systems. Since the setup is rather elaborate, the whole procedure is time consuming and can take considerable time to setup. Post the setup formalities, a developer can benefit immensely by optimizing work through work parallelization and improved resiliency. Each developer can access and develop their own microservice, without worrying about code conflicts.

What does going Serverless mean?
The concept of going serverless is relatively new and has seen the day of light just recently. In an ideal situation, the traditional back end was deployed on a group of servers. Such an approach had its own set of advantages. It allowed the developers to control their own servers along with the infrastructure behind it. However, like everything else, it contributed a lot towards the cost, making it an inefficient solution for companies. Add a set of engineers to build, maintain and run the infrastructure, and your budget will increase manifold.

With the introduction of the serverless technology, all these problems can be solved considerably. You can make use of a service which will run your code, as well as take care of all your maintenance issues. What you do end up paying for is the time it usually takes to process each request thrown at the code. For this purpose, AWS offers the AWS Lambda service, which is somewhat similar to the functionality of Microsoft’s Azure Function and Google’s Cloud Functions.

What Services aid the Serverless Microservices?
Amazon API Gateway: API is a gateway service that offers the option to use a configurable REST API in the form of a service. You get to author your needs and create it in the form of a code. Say, for example, you decided what would happen if a particular HTTP Method is implemented and called on a certain HTTP Resource. In this case, say you want to execute and implement a Lambda function if the HTTP request comes through. API Gateway helps in mapping input and output data through a series of formats. Thankfully API Gateway is a fully fledged service, which is managed extensively, allowing you to pay for only what you use.

AWS Lambda Services: Being a pay as you go service, AWS Lambda is a well-managed service hub. It allows you to get rid of over provisioning costs, as well as avoid the need of any boot time, patching, as well as load balancing.

Amazon DynamoDB: Amazon DynamoDB is a document store wherein you can look up values through their key values multiple Availability Zones or data centers to bring about a subtle consistency. Like Lambda, it too is a 99% managed service, while the remaining 1% is free to perform reading and writing of code.

The Request Flow and how it Works with Microservices
In an ideal situation, it’s imperative to understand how the data flows through serverless microservices. The user’s HTTP hits the API Gateway; the API Gateway checks the HTTP request and figures if the request is valid or not. Through this approach, it makes multiple requests within the database and executes the business logic.

Another system which aids the processing of information within the serverless environment is the AWS CloudWatch. The AWS CloudWatch stores metrics in the form of numbers and text information in the form of logs. It also allows you to define your alarms over your metrics. At any given point of time, if your system begins to default, you can get an instant notification of the default using AWS SNS, making the process seamless and streamlined.

AWS Microservices are well balanced and fully managed, thereby allowing you to concentrate on performing multiple forms of other operational tasks. Through the concentration on other important tasks, the functionality of the code can be improved manifold, as it is performed through a series of automated tasks.

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Advantages of Cloud Analytics over On-Premise Analytics

Advantages of Cloud Analytics over On-Premise Analytics
Majority of the organizations now agree that data science is a great tool to scale-up, build and streamline their businesses. But, with this huge amount of data they are collecting, are the organizations really coping up to analyze and implement the decisions in time? Most of them, in-spite of having on-premise analytics teams are in disconnection with their operations part.

Having the in-house analytics teams linked to your Enterprise Resource Planning(ERP) systems can be sometimes be irresponsive due to data loads, might cause your sales teams to lose the real-time data, also can cause delay in response to the queries. Collection of data from various internal applications, devices, online media networks, consumer data and converting them into actionable insights can be a cost consuming (both time and capital costs) process for the organizations.

Is there any better way of utilizing your Company’s data towards reaping benefits?
Yes, most of your valuable data from modes of communication to collecting track-able data of consumer behavior lies in the cloud. Cloud computing allows you to easily consolidate information from all your communication channels and resources, and helps you to do it in a wider scale.

Cloud, basically helps the business’ data teams to re-establish the connection with their operations. And hence the business will be able to minimize the time and capital costs incurred, from the research and development of the product, marketing and sales to increasing the efficiency of your consumer support teams.

How does Cloud Analytics serve as a better and real-time mode of efficient data management?

Agile Computing Resources
Instead of handling speed and delivery time related hassles from your on-premise servers, cloud computing resources are high-powered and can deliver your queries and reports in no-time.

Ad hoc Deployment of Resources for Better Performance
If you are having an in-house analytics team, you should be concerned about an efficient warehouse, latency of your data over poor public internet, being up-to date with advanced tools and experience in handling the high demands for real-time BI or emergency queries. Employing Cloud services in data science and analytics can help your business scale-up by establishing a direct connection between them, reducing the latency and response issues to less than a millisecond.

Match, Consolidate and Clean Data Effortlessly
Real time Cloud analytics with real-time access to your online data keeps your data up-to date and organized, helping your Operations and Analytics teams function under the same roof. This makes sure of no mismatches and delays, helping you to also predict and implement finer decisions.

Cloud services are capable in sharing data and visualization and performing cross-organizational analysis, making the raw data more accessible and perceivable by a broader user base.

High Returns on Time Investments
Cloud services provide readily-available data models, uploads, application servers, advanced tools and analytics. You need not spend any time in building up a separate infrastructure, unlike employing on-premise analytics teams.

Your marketing teams can forecast and segment your campaign plans, the campaign reports and leads generated are readily available to your sales teams to follow-up, insights from sales and marketing and more real time consumer data can help your strategy teams in predicting crucial decisions or your support teams being immediately notified with consumer queries. Better the collaboration, higher are your returns, and an ideal cloud service can make this possible.

Flexible and Faster Adoption
Cloud-based applications are built with self-learning models and have a consumer friendly user experience unlike the on-premise applications. Cloud technologies learn to adopt as your business grows and can expand or adjust as your data storage and applications needs increase or decrease.

There are no upgrade costs or issues, and enabling new tools or applications require minimal IT maintenance. This keeps the business in a continuous flow without any interventions like the need for upgrading the on-premise infrastructure, and having to redo your integrations and other time consuming efforts.

Robustly built, Cloud analytics are reportedly more reliable than on-premise systems in times of a data breach. Detecting a breach or a security issue can be within hours or minutes with Cloud security whereas with an in-house team, it takes weeks or even months in detecting a breach. Your data is more trusted and secure with cloud computing.

Implementing cloud services in data science can be the best and most-effective infrastructure you can give to your business. They are agile, secure and flexible and help you to streamline each of your business process as Cloud services enable all your teams function under the same data foundation.

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5 Exciting New Database Services from AWS re:Invent 2017

New Database Services from AWS re:Invent 2017

AWS cloud division has geared up for revolutionizing the cloud infrastructure with unveiling of its much anticipated AWS event re:Invent 2017 cloud user conference which had a distinct focus on data and so-called serverless computing. It was the sixth annual re:Invent of the cloud market leader AWS which additionally laid emphasis on competitive prices along with modern suit. Five most exciting data services of the event are as follows:

1. Amazon Neptune
A new, faster, more reliable and fully-managed graph database service that will make it easy to build and run applications that work with highly connected datasets. Besides being a high-performance graph database engine optimized for storing billions of relationships and querying the graph with milliseconds latency, Amazon Neptune supports popular graph models Apache TinkerPop and W3C’s RDF, and their associated query languages TinkerPop Gremlin and RDF SPARQL for easy query navigation. It also powers graph use cases such as recommendation engines, fraud detection, knowledge graphs, drug discovery, and network security. It is secured with support for encryption at rest and in transit; can be fully managed, to ease out hardware provisioning, software patching, setup, configuration, or backups.

Currently available in preview with sign-up only in US East (N. Virginia) only on the R4 instance family and supports Apache TinkerPop Version 3.3 and the RDF/SPARQL 1.1 API

2. Amazon Aurora Multi-Master
Amazon Aurora Multi-Master allows the user to create multiple read/write master instances across multiple Availability Zones. This empowers applications to read and write data to multiple database instances in a cluster. Multi-Master clusters improve Aurora’s already high availability. If the user’s master instances fail, the other instances in the cluster will take over immediately for smart and flawless procession, maintaining read and write availability through instance failures or even complete AZ failures, with zero application downtime. It is a fully managed relational database that combines the performance and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases.

The preview of the product will be available for the Aurora MySQL-compatible edition, and people can participate by filling out the signup form on AWS’ official website.

3. Amazon DynamoDB On-Demand Backup
On-Demand Backup allows one to create full backups of DynamoDB tables data for data archival, helping them meet corporate and governmental regulatory requirements. People can also backup tables from a few megabytes to hundreds of terabytes of data, with no impact on performance and availability to your production applications. It processes back up requests in no time regardless of the size of tables, which makes the operators carefree of the backup schedules or long-running processes. All backups are automatically encrypted, cataloged, easily discoverable, and retained until manually deleted. It allows the facility of single-click backup and restore operations in the AWS Management Console or a single API call.

Initially it is being rolled out only to US East (N. Virginia), US East (Ohio), US West (Oregon), EU (Ireland) regions. In early 2018, users will be able to opt-in to DynamoDB Point-in-Time Restore (PITR) which will allow to restore your data up to the minute for the past 35 days, further protecting your data from loss due to application errors.

4. Amazon Aurora Serverless
An on-demand auto-scaling configuration for Amazon Aurora, Serverless will enable database’s automatic start up, shut down, and scale up or down capacity based on application’s needs. It enables the user to run relational database in the cloud without managing any database instances or clusters. It is built for applications with infrequent, intermittent or unpredictable workloads of likes as online games, low-volume blogs, new applications where demand is unknown, and dev/test environments that don’t need to run all the time. Current database solutions require a significant provisioning and management effort to adjust capacity, leading to worries about over- or under-provisioning of resources.We can also optionally specify the minimum and maximum capacity that an application needs, and only pay for the resources are consumed. The serverless computing is going to hugely benefit the world of relational databases.

5. Amazon DynamoDB Global Tables

The advanced Global Tables builds upon DynamoDB’s global footprint to provide a fully managed multi-region, multi-master global database that renders fast local read and write performance for massively scaled applications across the globe. It replicates data between regions and resolves update conflicts, enabling developers to focus on the application logic when building globally distributed applications. In addition, it enables various applications to stay highly available even in the unlikely event of isolation or degradation of an entire region.

Global Tables is available at the time only in five regions: US East (Ohio), US East (N. Virginia), US West (Oregon), EU (Ireland, and EU (Frankfurt).

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Why is Big Data Analytics Technology so Important

Big Data Analytics Technology

Yes! Big Data Analytics, as well as Artificial Intelligence, has truly shown its importance in today’s business activities. Corporations & Business sectors are coming up with their procedures to data analytics as the aggressive landscape modifications. Records analytics is slowly becoming entrenched in the enterprise. Today, it’s a well-known behavior and desired practice for companies to apply analytics to optimize something, whether or not it’s operational performance, false detection or purchaser reaction time.

To this point, usage has been pretty easy. Maximum agencies are still doing descriptive analytics (historic reporting) and their use of analytics is characteristic-unique. But in upcoming years more business areas will follow the leaders and boom their levels of class, the use of predictive and prescriptive analytics to optimize their operations. Moreover, extra groups will begin coupling feature-specific analytics to get new intuition & observation into client journeys, risk profiles, and marketplace opportunities.

The “leading” companies were also much more likely to have some sort of cross-purposeful analytics in vicinity enabled via a common framework that enables collaboration and statistics sharing. These pass-practical views allow agencies to recognize the effect of cross-useful dynamics consisting of supply chain effects.

Predictive and Prescriptive Analytics

Whilst descriptive analytics continues to be the maximum popular shape of analytics today, it is no longer the satisfactory manner to advantage a competitive side. Businesses that want to move beyond “doing business through the rear-view mirror” are the use of predictive and prescriptive analytics to decide what is going to possibly arise. Prescriptive analytics has the delivered advantage of recommending movement, which has been the number one gripe approximately descriptive and predictive analytics. The forward-searching abilities enabled through predictive and prescriptive analytics allow groups to plan for possible outcomes, excellent and bad.

Armed with the in all likelihood styles predictive and prescriptive analytics screen; agencies can identify fraud faster or intrude sooner when it seems that a consumer is set to churn. The mixed foresight and timelier action help corporations force extra sales, reduce risks, and improve consumer delight.

Artificial Intelligence (AI)

Artificial intelligence (AI) and gadget learning culture take analytics to new ranges, figuring out previously undiscovered patterns which can have profound outcomes on a commercial enterprise, consisting of identifying new product opportunities or hidden dangers.

Machine intelligence is already constructed into predictive and prescriptive analytics equipment, dashing insights and enabling the analysis of well-sized probabilities to determine the greatest route of movement or the first-rate set of alternatives. Over the years, extra state-of-the-art forms of AI will find their way into analytics systems, similarly enhancing the rate and accuracy of selection-making.

Governance and Security

Groups are supplementing their information with third-celebration records to optimize their operations, comprehensive of adapting useful resource degrees primarily based at the expected level of consumption. They are also sharing statistics with users and companions who necessitate robust governance and a focal point of safety to reduce information misuse and abuse. However, protection is turning into an increasing number of the complex as more ecosystems of records, analytics, and algorithms interact with every other.

Given latest excessive-profile breach instances, it has emerged as clean that governance and safety have to be applied to information at some point in its lifecycle to reduce facts-associated risks.

Developing Statistics

Facts volumes are developing exponentially as agencies connect to statistics outside their internal structures and weave IoT devices into their product lines and operations. Because the records volumes continue to grow, many groups are adopting a hybrid records warehouse/cloud strategy out of necessity. The businesses maximum in all likelihood to have all their records on-premises keep it there due to the fact they’re involved in security.

Groups incorporating not gadgets into their enterprise strategies are both adding an informational element to the bodily products they produce or including sensor-based total information to their existing corpus of statistics. Depending on what is being monitored and the use case, it could be that every piece of information does no longer have value and no longer each issue calls for human intervention. While one or each of those things are authentic, aspect analytics can help identify and remedy as a minimum some common issues routinely, routing the exceptions to human decision-makers.