AWS Solutions for Big Data Management
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The volume of data generated by organizations is massive, continuous, and diverse. But it holds a lot of very useful intelligence. Collecting and mining this data, or Big Data, to use its accepted term, needs to be done in a planned phase-wise manner. AWS offers a range of services that take you from solutions to collect your data from multiple sources, to moving and storing this data in the cloud, where it can be easily accessed and processed, to analyzing the data and finally sharing it in a way that allows collaborative working.
As we have seen data is continuously generated in a variety of formats from diverse sources. Organizations need to have resources to collect, store, analyze and share this data and the resultant intelligence. The hardware and software used in traditional ecosystems are just not able to handle such high volumes or diversity of data generation.
To do this you need a software application that is platform agnostic and designed specifically for distribution. In short… software like Hadoop.
Alongside this, you also need the hardware infrastructure to process Big Data. And the hardware has to be easily scalable and available. Cloud Computing with its characteristics of elasticity, scalability, pay-per-use, and absence of capital expense, makes it ideal for handling Big Data, as it makes scalable infrastructure available at much lower costs compared to traditional hardware.
AWS Cloud provides a range of services to help with Data Collection, Storage, Processing, and Sharing. Each of these services is linked to the various phases of Big Data management.
Data Collection & Storage
AWS Import/Export: When you need to move large volumes of data into and out of your AWS cloud, Import/Export speeds up the process using portable storage devices for transport. And since it uses Amazon’s high-speed internal network, thereby bypassing the Internet, it is often faster and more cost effective than using the internet to transfer your data.
Import/Export is one of the AWS services that allows you to collect vast amounts of data and put it onto AWS infrastructure for further processing. AWS also offers a host of storage solutions natively designed for the cloud.
Amazon Simple Storage Service (S3): Amazon S3 is a simple and reliable storage solution that uses a user-friendly interface to store and retrieve data from anywhere on the internet.
Amazon S3 is eminently scalable, which means you can put as much data as you need into it, and it will scale up automatically, making it the ideal storage solution for large volumes of data for analysis.
S3 works with Apache Hadoop file systems, as the requirements of a file system are met by S3. This means Hadoop can be used to run MapReduce algorithms on EC2 servers, reading data and writing results back to S3.
Amazon S3 offers storage classes that you can opt for according to how available you need your data to be and how often you intend to access it. For instance, Amazon Glacier is an S3 storage service that is perfect for providing secure, low-cost, long-term storage for data archiving and backup.
You can also connect your on-prem storage center with AWS S3 for a secure connection that allows you to securely store data in the AWS cloud for scalable and cost-effective storage.
AWS Storage Gateway makes it possible to easily move data generated on-premises to AWS Cloud for storage and processing in an automated and reliable manner.
Amazon also offers a managed relational database service, Amazon RDS to set up, operate, and scale a relational database on AWS infrastructure. AWS RDS supports commonly used database systems like MySQL, Oracle, and MS SQL Server.
Amazon also offers a fully managed non-relational database service known as Amazon DynamoDB. This fully managed AWS NoSQL database service is fast, highly reliable, and cost-effective, making it ideal for internet-scale applications. AWS DynamoDB provides fast and reliable performance at any scale.
To handle the velocity of real-time read/write functionality, most databases, especially RDS, function well at certain capacities. The problem is the data collection never stops. Given modern telemetry and the explosion of IoT, the volume of data even the most robust traditional relational database will be swamped. Worse, the variety of data also poses a problem. You most likely want to run business intelligence queries on data coming from a variety of data sources like your logistics, accounts, and sales systems, but traditional databases don’t handle queries against multiple databases easily. When data becomes too complex for traditional relational databases you need a solution engineered specifically for Big Data. You need Amazon Gateway.
Amazon Gateway is AWS’s data warehousing service. It is hugely scalable and can accommodate petabyte-sized data sets, and it can provide information on historical data from any point in the past, even from the previous hour. You can run also run SQL queries against petabytes of unstructured data in data lakes. Beyond its capacity to handle large data sets, Redshift allows you to achieve up to 10x performance compared to traditional databases.
Data Analytics & Computation
When it comes to storing and processing extremely large datasets, it is much easier and more efficient to use multiple computers working in parallel rather than one large computer. Apache Hadoop allows you to use a network of many computers to analyze large data sets quickly.
AWS EMR (Elastic MapReduce) combines Hadoop’s data storage and analytics with Amazon’s many other services Amazon EMR is integrated with other AWS services, like EC2, S3, and CloudWatch), Amazon RDS, DynamoDB, etc. This way AWS users can access Hadoop storage and analytics without needing to leave the platform or maintain a discrete Hadoop cluster.
AWS EMR benefits include :
⦿ Ease of use
⦿ Cluster Auto-scaling
⦿ On-demand compute power
Amazon EMR uses a customized Apache Hadoop framework to deliver distributed processing of data at scale. The name EMR comes from the use of the distributed data processing architecture known as MapReduce.
Amazon EC2: EC2 is Amazon’s basic instance, a virtual machine (VM) that allows scalable deployment of applications. You can deploy an EC2 by booting an Amazon Machine Image (AMI) through Web service to create a VM which can hold any software you require it to.
You can launch as many EC2 instances as you need and scale them up and down in line with your traffic demands. You can configure your EC2 instances in an Auto-scaling Group, which automates the scaling of your instances, reducing the need to forecast traffic.
Data Collaboration & Sharing
Once your data has been analyzed and processed, you probably need to share it with various teams and stakeholders. This collaboration and sharing can take place in several ways: using BI software to generate reports, sharing it using another application, or storing it in flat files to be used by some other processes.
AWS offers several services to aid collaboration and storage, such as S3, EC2, RDS, Redshift, and DynamoDB. You can use these to make your data/analytics accessible to other users/consumers of data in their preferred formats.
AWS Data Pipeline: The ginormous volumes of data generated by multiple sources need to be moved and processed. Managing large-scale data migration and processing is a tedious activity, requiring a high level of automation with continuous monitoring. AWS Data Pipeline is an easy-to-use, automated solution to move data from multiple sources both within AWS and outside AWS and transform data.
AWS Data pipeline is fast highly scalable and a fully managed service. Easy to provision pipelines to move and transform data, AWS Data Pipeline saves development efforts and reduces maintenance costs.
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