Publish date July 11, 2017
Analyzing large data sets requires significant computing capacity that can vary in size based on the amount of input data and the type of analysis. This characteristic of big data workloads is ideally suited to the pay-as-you-go cloud computing model, where applications can easily scale up and down based on demand. As requirements change, you can easily resize your environment (horizontally or vertically) on AWS to meet your needs, without having to wait for additional hardware or being required to over invest to provision enough capacity.
For Important enterprise applications that run on a traditional infrastructure, system architects have to make additional provisioning to take care of spike in additional data due to increase in business needs. Provisioning additional capacity and computing power in AWS is very quick. Your system can run as close to optimal efficiency as the big data applications demand dictates.
Amazon offers a broad and fully integrated portfolio of cloud computing services to help you build, secure, and deploy your big data applications. With AWS, there’s no hardware to procure, and no infrastructure to maintain and scale, so you can focus your resources on uncovering new insights. With new capabilities and features added regularly, you’ll always be able to leverage the latest technologies without making long-term investment commitments.
Data Pipeline is a data orchestration product that helps in creating complex data processing workloads. It helps in moving, copying, transforming and enriching data. Data Pipeline manages the scheduling, orchestration, and monitoring of the pipeline activities as well as any logic required to handle failure scenarios. Data Pipeline can read and write data from AWS storage services as well as your on-premise storage systems. It supports a range of data processing services including EMR, Spark, Hive, Pig, and can execute Unix/Linux shell commands.
For high-frequency, real-time streaming data collection, processing and analytics AWS provides managed Kinesis service. Data publishers can push data in real-time to a Kinesis stream where it is processed by consuming applications using the Kinesis Client Library and Connector Library. Kinesis Analytics can be used for streaming data analysis, and Kinesis Firehose can be used for large-scale data ingestion. Information is pushed to a Kinesis Firehose delivery stream where it automatically routes to an S3, Redshift service. Amazon RedShift is a tool designed to work with data from up to dozens of petabytes. Powered by PostgreSQL, it mostly applies to any SQL applications with minimum changes. The service supports client side compression and server side encryption.
Predictive analytics is possible through machine learning. AWS makes it very easy to create predictive models without the need to learn complex algorithms. User creation is guided through the process of selecting data, preparing data, training and evaluating models through a simple wizard-based UI. It is also possible to create models via the AWS SDK. Once trained the model can be used to create predictions via online API (request/response) or a batch API for processing multiple input records.
AWS offers the ability to use other scalable services that augment to build sophisticated big data applications. AWS offers set of IoT tools which let connected devices interact with cloud applications and other connected devices. Also, AWS has many options to help get data into the cloud, including secure devices like AWS Import/Export Snowball to accelerate petabyte-scale data transfers.
One more service that has gained importance in big data development on AWS is AWS Lambda. Lambda is a service that can run application code on top of Amazon cloud infrastructure, releasing developers from worrying about infrastructure management. It also includes operational and administrative tasks such as resource provisioning and scaling, monitoring system health, applying security patches to the underlying resources, and code deployment.
We often talk to customers running Elasticsearch clusters on AWS. Elasticsearch is a distributed search server offering powerful search functionality over schema-free documents in (near) real time. Due to its distributed nature, Elasticsearch is ideal for performing complex queries over a large dataset, and EC2 provides a convenient platform to scale as required.Elasticsearch takes advantage of EC2’s on-demand machine architecture enabling the addition and removal of EC2 instances and corresponding Elasticsearch nodes as capacity and performance requirements change.
As mobile continues to grow in usage rapidly, you can use the suite of services within the AWS Mobile Amazon Web Services – Big Data Analytics Options on AWS to collect and measure app usage and data or export that data to another service for further custom analysis. These capabilities of the AWS platform make it an ideal fit for solving big data problems, and many customers have implemented successful big data analytics workloads on AWS.
You can turn your organization’s data into valuable information with Amazon big data solution. You can transform archived, current, or future application data into an asset to help your business. AWS provides Big Data tools that let your teams become more productive, easier to try new things and roll out projects sooner.
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Ankur Jain ,Senior Solution Architect, Big data , Cloud @ YASH Technologies
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