Introduction to AWS Big Data

What is Big Data?

The term “big data” describes a massive amount of structured, semi-structured, and unstructured data pulled from a diverse variety of sources. The data volume is so large that traditional techniques and databases can’t handle it.

Many data analysts break down big data’s characteristics into the easily remembered “Five V’s.”

• Volume- There is a lot of data!
• Velocity- The data rapidly accumulates. The data flow comes from social media, mobile devices, networks, and machines, in both a continuous and massive wave
• Variety - The data comes from many sources, both inside and outside of the organization
• Veracity- The data often contains inconsistencies, uncertainties, and duplication, a natural result of pulling the information from numerous and diverse sources
• Value - The data has no worth unless it can be analyzed, processed, and turned into something useful to the organization

Using an often-repeated metaphor, making sense of and using big data in its unprocessed form is comparable to drinking from a firehose at full blast.

Incidentally, the Five V method isn’t the only breakdown. Some analysts prefer to call it “Four V”, and use “Variability” instead of “Veracity.”

If you want to explore the concept of big data further, check out this AWS tutorial. It will answer any further questions you may have about it.

Now before we get further into understanding AWS Big Data, let us first understand what AWS is.

What is AWS?

AWS stands for Amazon Web Services, a subsidiary of Amazon, that provides a wide selection of cloud computing services and products on demand. Using a pay-as-you-go model, AWS includes developer tools, email, Internet of Things (IoT), mobile development, networking, remote computing, security, servers, and storage, to name a handful. AWS consists of two main products. There’s EC2 (Amazon Elastic Compute Cloud), Amazon’s virtual machine service, and S3; a scalable data object storage system.

AWS is considered the world’s most comprehensive and widely used cloud platform. You can learn even more about this powerful platform with this tutorial to gain a better understanding of its services.

Next, let us get learning on AWS Big Data by learning AWS solutions for Big Data.

AWS Solutions for Big Data

AWS’s sprawling platform brings with it a series of useful solutions available for developers, analysts, and marketers alike. The following are the four fields of big data that AWS provides solutions for:

• Data Ingestion 

  No, this doesn’t mean you have to eat the data! Data ingestion encompasses collecting raw data from many sources like logs, mobile devices, transaction records, and more. You need a massive platform like AWS to handle the quantity and diversity of big data.

• Data Storage 

  All that data needs to reside somewhere, and once again, AWS has the capacity for it. AWS offers a scalable, secure, and durable storage area, granting you easy access even for data sent over the network.

• Data Processing 

  Once the data has been collected and given a place to reside in storage, the next stage is processing—turning it from its raw form into something that can be used and interacted with. Data processing entails performing functions such as aggregating, sorting, and joining, plus advanced features and algorithms. After the data gets processed into a useful resource, it can be stored for future processing or presented for use by employing data visualization tools and business intelligence.

• Visualization 

  This final aspect encompasses the exploration of datasets by end-users to extract actionable insights and better value for the organization. There are many data visualization tools available that convert processed data into graphical representations for better understanding—turning information into visual elements such as maps, charts, and graphs.

Available AWS Tools for Big Data

You need the right tools to provide solutions for the big data fields. Turning the raw volume of big data into something actionable and valuable is a formidable task, but it’s a manageable goal if you have the right resources.

Fortunately, AWS offers an impressive collection of resources and solutions designed to meet the challenges introduced by each big data field. Let’s look at a tool sampling, broken down using the fields above.

• Data Ingestion
  

  Earlier in the article, we compared tackling big data with drinking from a firehose. With that in mind, it’s unsurprising (and very appropriate!) that someone would name a data ingestion tool “Firehose.” Amazon Kinesis Firehose performs data compression, batching, encryption, and Lambda functions. Firehose delivers real-time streaming data to Amazon’s S3, reliably loading it into data lakes, data stores, or analytics tools. Firehose automatically scales to match the productive capacity of any organization’s data and it requires no ongoing administration.

• AWS Snowball
  

  AWS Snowball is a data transport resource that efficiently and securely migrates bulk data from your in-house, on-premises storage platforms, and Hadoop clusters, into S3 buckets. After you create a job using the AWS management console, you automatically have a Snowball device shipped to you. Just connect it to your LAN, install the Snowball client, then transfer the files and their directories to the device. When you’re finished with the transfer, just ship the Snowball back to Amazon Web Services, and they will move the data into your S3 bucket.

• Data Storage 

  Speaking of S3, Amazon S3 is a highly scalable, secure, durable object storage resource that can store any type of data taken from anywhere. S3 stores data collected from corporate applications, websites, mobile devices, and IoT devices and sensors. It can also store any amount of data with an unsurpassed degree of availability. Amazon S3 employs the same scalable storage infrastructure that Amazon itself uses to run its worldwide eCommerce network, which is a ringing endorsement for the tool!

• AWS Glue

  AWS Glue is a data service that stores metadata in a central repository and simplifies the ETL (extract, transform, and load) process. A data analyst can create and run an ETL job with just a few clicks from the AWS Management Console. AWS Glue comes with a built-in data catalog that functions as a persistent metadata store for every data asset, allowing analysts to search and query all data in a single view.

• Data Processing
  

  Apache Spark and Hadoop are popular data processing frameworks, so it’s smart to have an AWS tool that works well with them. Amazon’s EMR fits the bill nicely, as it provides a managed service that quickly and effortlessly processes enormous amounts of data. EMR supports 19 different open-source projects, including the previously mentioned Spark and Hadoop. Furthermore, it comes with managed EMR Notebooks, suitable for collaboration, data engineering, and data science development. 

• Redshift
  

  Amazon Redshift lets analysts run complex analytics queries against massive volumes of structured data without needing a significant financial outlay—almost 90 percent less than traditional processing solutions. Redshift comes with Redshift Spectrum, which lets data analysts run SQL queries directly against exabytes of either structured or unstructured data stored in S3 without requiring unnecessary data movement.

• Visualization 

  Amazon Quicksight is an Amazon Web Services utility that creates eye-catching visualizations and excellent interactive dashboards that are accessible from any mobile device or web browser. This business intelligence resource uses AWS' Super-fast, Parallel, In-memory Calculation Engine (SPICE) to perform data calculations and generate graphs rapidly.

There are plenty of other big data processing tools and resources available, but the above list represents the best tools if you use Amazon Web Services.

Do You Want a Career in Data Analytics?

Businesses and other organizations are increasingly turning to cloud-based solutions for their IT needs. Forecasts show a steady increase in public cloud revenue between 2018 and 2022. Consequently, there’s a great demand for cloud computing professionals. Simplilearn’s Caltech Post Graduate Program in Data Science have  real-life industry-based projects, 24/7 support, and dedicated mentoring.

According to Payscale, an AWS data engineer can earn an average of USD 97,786 per year, with an upper-range salary of USD 134,000.

So, if you’re on the lookout for a new career that offers great compensation and security, or even if you’re already working with big data and want to upskill, visit Simplilearn today, and supercharge your cloud computing skills!