Do you know how much data is generated in a day?
– 500 million tweets
– 294 billion emails
– 4 petabytes of data on Facebook
– 4 terabytes of data from each connected device
– 65 billion messages on WhatsApp
– 5 billion searches are made
Today, we live in the Information age where a massive amount of data is being generated from a growing number of sources every moment. Our actions, decisions, and even existence in the digital world generate data, which offers us enormous opportunities for amending current business methods and practices. Thus there’s a critical need to adopt big data ecosystems.
As companies churn out a massive volume of data capturing zillion bytes of information every second, big data analytics is challenging the existing modes of business with technological innovations. These companies realize the power of data they own, and the way they use them can provide them with a competitive edge. But how?
In this blog, we are going to see how AWS can help you to tackle the challenges of big data, and we will cover the following topics –
- Why big data on AWS?
- How can AWS solve big data challenges?
- What solutions and tools does AWS provides for handling different stages involved in big data?
If you are a Solutions Architect/SysOps Administrator interested in learning more about designing and implementing big data solutions or a Data Scientist/Analyst interested in learning about big data solutions on AWS, this blog is for you. This blog will provide you with insights into how AWS can help you handle your organization’s growing amount of data with an ecosystem of analytical solutions.
Why Big Data on AWS?
The big data ecosystem is growing tremendously, giving rise to a plethora of tools and applications. The volume, velocity, and variety of data that is being generated have overwhelmed the capabilities of analytics and infrastructure we have today.
However, companies encounter many challenges when they try to deploy and run big data applications in their environments or use private or public cloud platforms. Failure to address these challenges can result in reduced productivity and escalating costs.
Big Data Pain Points
- Breaking down silos – Having pockets of data at different places controlled by different people inherently obscures data. This is undoubtedly not scalable, elastic, or flexible.
- Analyzing diverse datasets – Data structures and information may vary while using different systems and approaches to data management.
- Managing data access – With data stored in different locations, it isn’t easy to access all at once and integrate it with external tools for analysis.
- Incorporating machine learning (ML)
As data gets bigger, Amazon Web Services (AWS) is positioning itself to help organizations and enterprises leverage a flood of information to create more business value at a lower cost. AWS offers a wide range of fully integrated cloud tools and services that allow you to manage big data applications by reducing costs, scaling to meet demand, and increasing the speed of innovation.
Across the industries, many organizations are taking advantage of the AWS cloud to perform big data analytics and meet the challenges of the increasing volume, variety, and velocity of digital information.
Benefits of AWS on Big Data
- Immediate Availability – AWS does not imply the need for any hardware procurement or maintenance and scaling of infrastructure.
- Broad & Deep Capabilities – AWS continuously updates its big data offerings with new features to support virtually any application & workload.
- Trusted & Secure – AWS provides capabilities across facilities, networks, software, and business processes to meet the strictest requirements.
- Hundreds of Partners & Solutions – An extensive partner ecosystem can help you bridge the skills gap and get started with big data even faster.
AWS can transform your organization’s data into valuable information with big data solutions from Amazon. Organizations looking to start or expand their Big Data practice should consider AWS for its broad and deep capabilities to support any workload virtually.
How can AWS solve Big Data challenges?
As the amount of data grows, AWS has many options to get that data to the cloud. AWS has numerous solutions for the development and deployment of big data workloads.
AWS Solution for Big Data
- Data Ingestion – Data ingestion is the process of obtaining and importing data for immediate use or storage. The data sources may be almost anything – in-house apps, databases, transaction records, spreadsheets, or information scraped from the internet.
- Data Storage – Storing big data requires highly scalable solutions to handle data before and after processing. AWS offers a scalable, secure, and durable storage area, granting you easy access even for data sent over the network.
- Data Processing and Analysis – Processing and analyzing solutions enable transforming raw data into data consumable for analytics. Data processing converts raw data into a more understandable format necessary to be interpreted and utilized throughout an organization.
- Data Visualization – Data visualization tools help you convert processed data into graphical representations for better understanding, like turning information into visual elements like charts and graphs.
AWS Tools for Big Data
AWS platform makes it an ideal fit for solving big data problems, and many organizations have implemented successful big data analytics workloads on AWS. So, the next agenda of our discussion is the different services or tools for Big Data on AWS for collection, processing, storage, and analysis.
Amazon Kinesis is an ideal platform for streaming data on AWS. It provides the option for building custom streaming data applications and enables you to process and analyze data in real-time. With Kinesis, you can ingest real-time data such as application logs, website clickstreams, IoT telemetry data, and more into your databases.
AWS Snowball securely and efficiently migrates bulk data from on-premises storage platforms and Hadoop clusters to S3 buckets. AWS Snowball uses secure, rugged devices to bring AWS computing and storage capabilities to your edge environments and transfer data into and out of AWS.
Amazon Simple Storage Service (Amazon S3) offers industry-leading scalability, data availability, security, and performance. Amazon S3 is designed for 99.999999999% (11 9’s) of durability and stores data for millions of applications for companies all around the world. This means customers of all sizes and industries can use it to store and protect any amount of data for a range of use cases, such as websites, mobile applications, backup and restore, archive, enterprise applications, IoT devices, and big data analytics.
AWS Glue is a fully managed extract, transform, and load (ETL) service that you can use to catalog your data, clean it, enrich it, and move it reliably between data stores. AWS Glue is serverless, so there is no infrastructure to set up or manage.
Amazon Elastic MapReduce (EMR)
Amazon EMR provides a highly distributed computing framework to process and store data quickly and cost-effectively. For big data processing, Apache Spark and Hadoop are popular frameworks. Amazon EMR uses Apache Hadoop to distribute your data and processing across a resizable cluster of Amazon EC2 instances and allows you to use the most common Hadoop tools such as Hive, Pig, Spark, and so on.
Amazon Redshift is a fast, fully-managed, petabyte-scale data warehouse service that makes it simple and cost-effective to analyze all your data efficiently using your existing business intelligence tools. It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and is designed to cost less than a tenth of the cost of most traditional data warehousing solutions.
Amazon QuickSight is a high-speed, easy-to-use, cloud-powered business analytics service that makes it easy to build visualizations, perform ad-hoc analysis, and get business insights, anytime, on any device. It can connect to a wide variety of data sources. Amazon QuickSight enables organizations to scale their business analytics capabilities to hundreds of thousands of users and deliver fast and responsive query performance using a Super-fast, Parallel, In-memory Calculation Engine (SPICE).
The other notable AWS big data tools that contribute to the effective use of Big data on AWS are –
- Amazon Lambda
- Amazon DynamoDB
- Amazon Elasticsearch Service
- Amazon Athena
- Amazon Machine Learning (Amazon ML)
With the deluge of business data today, it is necessary to have a partner that helps organize, optimize, and transform the data that enables you to achieve business goals by making data readily available for analysis and action.
From creating data pipelines to processing, storing, and enabling access to processed data, Scalex’s data engineering services help companies replace their costly, burdensome in-house data infrastructure into robust systems prepared for business analytics. We help you unlock actionable insights with a modern data strategy on AWS and ensure your data is in the right place at the right time and in the proper structure.
Big Data has endless possibilities. Its enormous capabilities are magnified through AWS services, making it easier for companies to manage, store and analyze their data. Whether you’re looking to build more innovative applications, build a reliable ad serving platform, improve your customer’s digital experience, or improve operational efficiency – AWS has broad capabilities to fit all your needs.
Simple explanation and well written. I am a data analyst, and I found this post while surfing the internet. I liked the graphical representation of tools shown in the post.