Last Updated on January 19, 2022 by
Big data refers to extremely large datasets defined by the challenges they pose in terms of very high volumes of a wide variety of data generated from different sources with such high velocity, yet they contain such high value that traditional data processing tools and software cannot extract. Big data gets complex in nature with each passing day. For this reason, businesses are always evaluating their requirements for better tools and software to enable them to collect, store, process, and analyze the data efficiently to gain valuable insights for decision-making.
While there are many technologies that can manage the entire data cycle efficiently, Amazon Web Services (AWS) has stood out as the leading cloud service solution for more than a decade. AWS offers more than 175 service offerings across 25 domains including cloud services, storage, database, robotics, machine learning, networking, and analytics and many other solutions for securely running big data applications and workloads. By far the greatest benefit of adopting AWS services for big data is its high elastic capacity and the cost advantage since businesses do not need to incur hefty initial costs towards hardware and software purchase nor go into the expenses of maintaining infrastructure.
With an AWS data analytics certification or related qualification, some experience, and the right tools, big data professionals can undertake all the big data processes from the collection, pre-processing, storage, analysis, and visualization efficiently to draw valuable insights that will be of benefit to the organization.
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What are the benefits of AWS for Big Data?
Organizations leveraging big data for insights prefer public cloud platforms as they offer the capacity, resources, services, and flexibility to run big data operations cost-effectively. This makes it economical and viable for both large and small businesses to access, deploy, and run their big data tools, technologies, and processes in cloud platforms.
AWS presents several invaluable benefits for enterprises that intend to access and process their big data on cloud platforms.
- AWS fully supports big data technologies like Hadoop and Spark that businesses use for big data batch and stream workloads no matter their volume, velocity, or variety.
- As big data is large and complex, it requires advanced tools and hardware to manage it, a solution that a data center is too limited in capacity to provide. AWS public cloud hosts thousands of servers in data centers across the globe hence a ready infrastructure for enterprises running their workloads on AWS cloud.
- AWS provides a range of secured and fully managed solutions and infrastructure for building, deploying, managing, and scaling applications with ease.
- The pay-as-you-go billing model is convenient and cost-effective as big data applications running on AWS can scale up or down depending on business needs without hefty capital investments, long procedures, and complicated provisioning processes.
- Finally, a big advantage that cannot be overlooked is the fact that resources and services deployed on the cloud can be accessed from anywhere across the globe. AWS has the largest global footprint that is still expanding enabling its customers to expand their operations fast to literally any location across the globe.
AWS Big Data
AWS big data refers to the collection of AWS solutions designed to support big data implementation including collection, storage, analytics, and visualization services.
Some common AWS tools and technologies for big data include:
1. Big data ingestion – Amazon Kinesis
Kinesis is an AWS service that captures and analyzes real-time data streams per second from various sources. It gathers data from IoT devices, clickstream data, and application logs. Kinesis is also integrable with other AWS services and big data tools such as Redshift, EMR (elastic MapReduce), S3 storage, and Lambda to enable big data to be exported to other services for other processes. Kinesis platform is easily scalable and supports the deployment of custom streaming data applications that cater to specific big data requirements.
2. Server Provisioning – AWS Lambda
Lambda is an AWS compute service that allows users to run code for applications and backend services without having to provision or manage servers. Users organize code into various real-time file and stream processing functions, data filtering and transformation, querying, real-time analytics, and other functions. This service will run a function only when it is required while also enabling automatic scaling of the functions.
3. Storage – AWS S3
Amazon Simple Storage Service is a scalable secure high-performance data staging and storage service offered by AWS. AWS S3 acts as a secure low-latency and high-availability platform for receiving, storing, and accessing data. It comprises two components, objects, and buckets. Objects are data files identifiable by unique keys. Objects can be documents, videos, audio, or images from various sources, which contain data. Buckets, on the other hand, are storage containers for objects. AWS S3 buckets are also given unique names that cannot be shared by other buckets across the globe.
4. Big data processing – AWS EMR
EMR is a fully-managed AWS platform based on the Hadoop distributed file system for running big data processes and analytics. It supports Hadoop frameworks like Hive, Spark, Hbase, and Pig. EMR presents a fast and cost-effective framework for large-scale data storage and processing to facilitate analytics and business intelligence processes. EMR also features managed Notebooks for data science, engineering, and development. EMR is used for such functions as predictive analytics, log processing and analysis, threat analytics, bioinformatics, genomics, and more.
5. Big Data warehouse – Amazon Redshift
Redshift is an AWS data warehousing solution built with the capacity to process and migrate exabytes of data fast and securely. Featuring an encrypted easy-to-use interface on AWS, Redshift can be deployed and a new cluster set up with a few clicks. Redshift is a viable solution analyzing large volumes of data, log analyses, and others imported from multiple sources as it features the Massively Parallel Processing (MPP) function for faster processing speeds and Spectrum for running SQL queries on data in S3 without taking it through ETL processes.
It uses SQL to query structured and semi-structured data whose results are useful for real-time business intelligence analytics. As with other AWS, users do not have to worry about infrastructure and service management. Redshift integrates with other AWS services including S3 and EMR as well as on-premise databases and data warehouses.
6. Predictive Analytics – Amazon Machine Learning
AWS offers a wide range of services covering emerging technologies like IoT and machine learning. Amazon ML service supports the development and training of ML models and predictive applications. It is built with wizards and multiple pre-built models allowing users of all skill levels to use it to discover patterns and actionable insights in large datasets without necessarily possessing the expertise to do so. Models can be created from data available in AWS S3, Redshift, or Amazon Relational Database Service (RDS).
7. Visualization – Amazon Quicksight
Building visualizations are an important part of the big data cycle as they make it easy for users of data to understand the information contained in data. AWS QuickSight is an ML-powered business intelligence service that enables users to build dashboards for performing ad hoc analyses and creating visualizations from multiple data sources.
Amazon QuickSight uses SPICE (Super-Fast, Parallel, in-memory calculation engine) that retrieves and persists data until it is manually deleted for faster generation of interactive queries and quick access to insights using different devices.
AWS is clearly ahead of its competition in the cloud services market. With robust frameworks and platforms, trusted security, and a global footprint, AWS can be relied upon to deliver services for the efficient management of big data. With rich documentation for all its services and great user support, users have an easier time deploying AWS services on demand.
Read more: AWS training- The Right Way to Become a Master of AWS
