It’s easy to feel lost in the sea of buzzwords and acronyms that permeate modern digital culture. You will hear acronyms and abbreviations like IoT, Azure, AWS, AI, Hadoop, Big Data, ITIL, NodeJS, and PowerBI even if you only do light web browsing.
To clear up some of the murk, let’s examine one of the most talked-about ideas: AWS big data. If you are new to big data and cloud computing, or if you just need a refresher, this article will help you get up to speed on the fundamentals of AWS Big Data.
The next step in understanding AWS Big Data will involve first understanding what Big Data is.
What is Big Data?
Big data refers to a large quantity of data that can be in any of three forms: structured, semi-structured, or unstructured. Because of the sheer magnitude of the data, standard analysis methods and storage mechanisms are overwhelmed.
The “Five Vs” are a convenient framework that many data analysts use to summarize the characteristics of big data.
- Volume- There is a lot of data!
- Velocity- Rapid data accumulation is occurring. Social media, mobile devices, networks, and machines all contribute to the constant and enormous wave of data that is being generated today.
- Variety – The information is gathered from a wide variety of internal and external resources.
- Veracity- Due to the data being collected from so many different places, there are bound to be some inconsistencies, uncertainties, and repetitions.
- Value – If the information is not analyzed, processed, and converted into actionable insights, it is useless to the business.
A common analogy is that attempting to make sense of and act upon raw big data is like trying to drink from a firehose.
Furthermore, there are other types of analysis besides the Five V model. Instead of “Veracity,” “Variability” is used by some analysts when referring to the “Four V.”
Check out this AWS guide if you want to learn more about big data. There won’t be any unanswered questions left after reading it.
Let’s define AWS first, then move on to AWS Big Data.
What is AWS?
Amazon Web Services, or AWS for short, is an Amazon subsidiary that offers a wide range of cloud computing services and products on demand. Developer tools, email, IoT, mobile development, networking, remote computing, security, servers, and storage are just some of the many services offered by AWS on a pay-as-you-go basis. There are two primary offerings within AWS. Amazon provides a virtual machine service called EC2 (Amazon Elastic Compute Cloud), as well as a scalable data object storage system called S3.
When it comes to cloud computing, AWS is the clear frontrunner. This guide will help you get the most out of this robust platform and all it has to offer.
So, let’s move on to the next step of our AWS Big Data education and start exploring the AWS Big Data services.
Amazon Web Services’ Big-Data-Specific Offerings
The vast AWS platform includes many helpful tools that can be used by programmers, data scientists, and marketers. AWS offers services in the following four areas of big data:
- Data Ingestion
You need not consume the information! The term “data ingestion” refers to the process of gathering unprocessed data from various locations and mediums, such as logs, mobile devices, transaction records, and more. To manage the sheer volume and variety of big data, you need a powerful platform like Amazon Web Services.
- Data Storage
Again, AWS has the storage space to accommodate all of that information. AWS provides a highly available, dependable, and scalable data storage solution, making it simple to retrieve data from anywhere in the world, even when it’s transferred over a network.
- Data Processing
The next step, following the data’s collection and storage, is processing, or the transformation of the data from its raw form into something that can be used and interacted with. Among the many tasks that fall under the umbrella of “data processing,” aggregating, sorting, and joining are among the most common. Once the data has been transformed into actionable information, it can be archived for later use or presented to the appropriate parties through the use of data visualization tools and business intelligence.
This last part involves end-users digging into datasets for more useful insights and improved business value. There are a plethora of data visualization tools that can take your processed data and turn it into visual elements like maps, charts, and graphs so you can better understand it.
Tools for Big Data Analysis on AWS
In order to offer solutions in the realm of big data, you must have access to the appropriate tools. The transformation of big data from its raw state into something useful and actionable is a challenging objective, but one that is achievable with the right tools.
Fortunately, AWS provides a comprehensive set of tools and services to tackle the unique problems presented by each subfield of big data. Let’s take a look at a subset of tools that has been sorted using those criteria.
- Data Ingestion
In an earlier section of the article, we used the analogy of trying to drink from a firehose to describe the challenge of dealing with massive amounts of data. That’s why it’s not surprising (and totally fitting) that a data ingestion tool is called Firehose. Compressing, batching, encrypting, and running Lambda functions on data are all possible with Amazon Kinesis Firehose. When it comes to loading data lakes, data stores, or analytics tools with real-time streaming data, Firehose reliably delivers it to Amazon’s S3. Firehose requires no ongoing administration and automatically scales to meet the productive capacity of any organization’s data.
- AWS Snowball
When moving large amounts of data from on-premises storage systems or Hadoop clusters to Amazon S3 buckets, you can rely on the AWS Snowball data transport resource to get the job done quickly, easily, and safely. You can order a Snowball device through the AWS management console and have it delivered after you’ve created a job. The files and their directories can be transferred to the device after connecting it to your LAN and installing the Snowball client. Simply return the Snowball to Amazon Web Services once the transfer is complete, and the information will be loaded into your S3 storage bucket.
- Data Storage
When we talk about S3, we’re referring to Amazon S3, which is an extremely scalable, secure, and long-lasting object storage resource that can be used to store any kind of data collected from anywhere. Information gathered by enterprise software, web services, mobile apps, and IoT sensors is kept in S3. Additionally, it can store unlimited data while maintaining an unprecedented level of accessibility. As a ringing endorsement, Amazon S3 is built on the same scalable storage infrastructure that Amazon uses to power its own global eCommerce network.
- AWS Glue
As a data service, AWS Glue consolidates metadata in one place and makes ETL (extract, transform, and load) more manageable. Through the AWS Management Console, a data scientist needs only a few mouse clicks to set up and launch an ETL job. AWS Glue includes a data catalog that acts as a persistent metadata store for all data assets, making it possible for analysts to search and query all data from a unified perspective.
- Data Processing
Having an AWS tool that plays well with Apache Spark and Hadoop is a good idea. As a managed service that can swiftly and easily process massive amounts of data, Amazon Elastic MapReduce (EMR) is a perfect fit. In addition to Spark and Hadoop, EMR supports 18 additional open-source initiatives. In addition, it has managed EMR Notebooks that can be used for data science, data engineering, and collaboration.
Unlike conventional processing solutions, Amazon Redshift does not require a sizable financial outlay from analysts to run complex analytics queries against massive volumes of structured data. Redshift Spectrum, which is included with Redshift, allows data analysts to execute SQL queries directly against exabytes of either structured or unstructured data stored in S3, eliminating the need for unnecessary data movement.
Amazon Quicksight is a service provided by Amazon Web Services that allows users to design engaging visuals and high-quality interactive dashboards that can be accessed from any device with a web browser. This BI tool makes use of Amazon Web Services’ (AWS) Super-fast, Parallel, In-memory Calculation Engine (SPICE) to quickly calculate data and create graphs.
Though there are many other options for processing large amounts of data, the ones mentioned above are the top choices if you’re using Amazon Web Services.
Considering a Data Analyst Profession?
In order to meet their IT requirements, businesses and other organizations are increasingly turning to cloud-based solutions. The revenue generated by the public cloud is expected to rise steadily between 2018 and 2022, according to projections. As a result, there is a significant need for qualified cloud computing specialists. Cloud computing and its deployment models are introduced in Simplilearn’s AWS Big Data Certification training course. This course dives deep into Amazon Web Services (AWS) and all of the services it has to offer, such as Kinesis Analytics, AWS big data storage, processing, analysis, visualization, and security, and a plethora of machine learning algorithms.
You’ll get 40 hours of training, projects relevant to the field, round-the-clock support, and personal mentoring as part of Simplilearn’s innovative Blended Learning method. If you finish the course with a good grade, you can brag to prospective employers that you have the knowledge and abilities necessary to do the job. Obtaining the necessary certification could be the deciding factor in whether you are offered the job or someone else.
Payscale reports that the median annual salary for an AWS data engineer is $97,786, with a maximum salary of $134,000.
Visit Simplilearn today to turbocharge your cloud computing skills if you’re in the market for a new career that pays well and has a solid future outlook, or if you’re already in the field of big data and want to advance.