BIG DATA & DATA ANALYSIC
Big data and cloud computing can be used to benefit your small business and drastically increase efficiency. Big data and cloud computing are two innovations that have changed mainstream technology and the methods in which data are handled. These technologies have changed the outlook of data in such a way that they have served as foundations for many crucial innovations. Let’s have a look at how these two technological advancements complement each other and provide opportunities for organizations to innovate and succeed.
What do big data and cloud bring to the table?
The world runs online. Whatever we do online, we leave data behind us. Whether we’re just surfing the web, using social media, shopping, researching or something else, we leave a trail of date in our wake. A recent survey found that 42 percent of Americans use the internet several times a day, while 21 percent reported being online almost constantly. All this data, when collected and analyzed, gives great insights to various companies, which are leveraged to provide better services and products to consumers. This can lead to revenue increases. But when such huge quantities of data are collected, it isn’t feasible for companies to store them offline in their own servers. In the U.S. alone, most companies have at least 100 terabytes of stored data. All these companies cannot have individual servers with huge computing power, while also taking care of security and maintenance. That’s where cloud computing comes in. Cloud computing creates affordable and easy storage of data in cloud servers, and the data can be retrieved when requested. Companies that provide such cloud facilities, like Amazon and Google, take care of all work associated with the process and companies can just store data in the cloud. When the combination of big data and cloud computing was first initiated, it opened the road to endless possibilities. Various fields have seen many drastic changes that were made possible by this combination. It changed the decision-making process for companies and gave a huge advantage to analysts, who could base their results on concrete data.
Offer scalable and cost-effective infrastructure
The introduction of cloud computing platforms has cut down the costs that companies spend on managing and maintaining data. Depending on the budget and need for security, companies can now opt for private cloud options, where internal resources can be stored on the cloud and big data analysis can be implemented from it. Many companies prefer hybrid cloud options in which companies can use on-demand storage spaces while using analytics through public servers. It allows the companies to scale their storage capacity up or down as per their requirements. Either way, companies do not need to spend huge amounts on the computing power the servers consume as they just need to manage and maintain the data.
Increase productivity
Companies can concentrate on more important things once they gain insights from big data. Leaving all the storage-related activities to cloud-based servers also allows for more time to work on major projects. This drastically increases employee productivity. Ability for real-time data analysis Long gone are the days when data were available in a batch. Now, real-time data makes it possible to work efficiently by making use of the current data. Predictive analysis makes sure that companies can know what lies ahead. The faster the analysts can interpret the data and come to actionable conclusions, the better the results.
Quicker data processing
In traditional systems, managing the data took a big chunk of time and companies had to spend extra time on data processing. But now, data processing takes just a few minutes. Any big data analytics platform like Apache Hadoop can be used to combine the unstructured data from social media and combine it with structured data like the original consumer details available. Data transferring is as simple as clicking a button. Some major cloud servers offer physical transfers of data from their located data center to the cloud center, which is useful when companies are migrating to the cloud for the first time and have a large amount of data stored on physical servers.
Major advantage for small businesses
In the past, only large-scale companies had the capacity and resources to make use of big data. Various cloud computing platforms have allowed small-scale companies to store their data at affordable costs and use the data as efficiently as larger organizations. Small-scale companies can purchase a cloud platform of their choice and start storing and analyzing data without any additional computing charges or responsibilities.
AWS Products
The following categories represent the core products of AWS.
Compute and Networking Services [Details]
- Amazon EC2 (Provides virtual servers in the AWS cloud)
- Amazon VPC (Provides an isolated virtual network for your virtual servers)
- Elastic Load Balancing (Distributes network traffic across your set of virtual servers)
- Auto Scaling (Automatically scales your set of virtual servers based on changes in demand)
- Amazon Route 53 (Routes traffic to your domain name to a resource, such as a virtual server or a load balancer)
- AWS Lambda (Runs your code on virtual servers from Amazon EC2 in response to events)
- Amazon ECS (Provides Docker containers on virtual servers from Amazon EC2)
Storage and Content Delivery Services [Details]
- Amazon S3 (Scalable storage in the AWS cloud)
- CloudFront (A global content delivery network (CDN))
- Amazon EBS (Network attached storage volumes for your virtual servers)
- Amazon Glacier (Low-cost archival storage)
Security and Identity Services [Details]
- Amazon RDS (Provides managed relational databases)
- Amazon Redshift (A fast, fully-managed, petabyte-scale data warehouse)
- Amazon DynamoDB (Provides managed NoSQL databases)
- Amazon ElastiCache (An in-memory caching service)
Analytics Services [Details]
Amazon EMR (Amazon EMR) uses Hadoop, an open source framework, to manage and process data. Hadoop uses the MapReduce engine to distribute processing using a cluster.
- Amazon EMR (You identify the data source, specify the number and type of EC2 instances for the cluster and what software should be on them, and provide a MapReduce program or run interactive queries)
- AWS Data Pipeline (to regularly move and process data)
- Amazon Kinesis (real-time processing of streaming data at a massive scale)
- Amazon ML (use machine learning technology to obtain predictions for their applications using simple APIs. Amazon ML finds patterns in your existing data, creates machine learning models, and then uses those models to process new data and generate predictions)
Application Services [Details]
- Amazon AppStream (Host your streaming application in the AWS cloud and stream the input and output to your users’ devices)
- Amazon CloudSearch (Add search to your website)
- Amazon Elastic Transcoder (Convert digital media into the formats required by your users’ devices)
- Amazon SES (Send email from the cloud)
- Amazon SNS (Send or receive notifications from the cloud)
- Amazon SQS (Enable components in your application to store data in a queue to be retrieved other components)
- Amazon SWF (Coordinate tasks across the components of your application)
Management Tools [Detail]
- Amazon CloudWatch (Monitor resources and applications)
- AWS CloudFormation (Provision your AWS resources using templates)
- AWS CloudTrail (Track the usage history for your AWS resources by logging AWS API calls)
- AWS Config (View the current and previous configuration of your AWS resources, and monitor changes to your AWS resources)
- AWS OpsWorks (Configure and manage the environment for your application, whether in the AWS cloud or your own data center)
- AWS Service Catalog (Distribute servers, databases, websites, and applications to users using AWS resources)
How do I use it?
AWS can be accessed through:
- AWS Management Console
- AWS Command Line Interface (AWS CLI)
- Command Line Tools
- AWS Software Development Kits (SDK)
- Query APIs
There is a detailed guide on how to install and use each of these options in the documentation. As you can see it takes a while to get familiar with each tool to get into some sort of workflow.
Keypoints on AWS and what is good?
- Elastic pay-per-use infrastructure
- On demand resources
- Scalability
- Global infrastructure
- Reduced time to market
- Increased opportunities for innovation
- Enhanced security
Example Steps for deploying an app
- Load balancer tier (eg Elastic Load Balancing)
- Web/app tier (eg EC2 AutoScaling Group)
- Caching tier (eg ElastiCache for Memcached)
- Database tier (eg Multi-AZ RDS)
- Static content (eg Amazon S3)
- Content Delivery (with Amazon CloudFront)