Optimizing Insights with Big Data on AWS

Companies of all sizes generate valuable data to use for increasing customer satisfaction, raising profitability, and reducing costs. Data insights refine your vision and are an essential tool that leads to confident, proactive, evidence-based decisions.

When your data surpasses the capacity of traditional databases, performance is constrained, thus limiting your business’s efficacy. Therefore, the urgency for maintaining the quality and integrity of this digital goldmine.

What is Big Data?

Big data describes a mass of structured and unstructured complex information that businesses gather and use to address diverse issues. Volume, velocity, and variety define the fundamental components for capturing and processing exceptional data aggregates.

Three Vs of Big Data

Volume
Social media companies like Facebook produce an exorbitant amount of data derived from photos, videos, posts, etc. Such a load exceeds the threshold of traditional databases, requiring an efficient, sustainable repository.

Velocity
Velocity refers to the speed at which data is generated. Big data enables the rapid processing of incoming data flow so as to avoid congestion.

Variety
This element concerns incoming structured and unstructured data like images or videos and human-generated data like texts, emails, or voice messages. The data can be distributed into detailed categories, enabling comprehensive storage and streamlined management.

The three types of data

Descriptive analytics help users identify what happened and why. An example of this type of analytics is traditional query and reporting environments with scorecards and dashboards.

Predictive analytics determine the likelihood of an event happening in a specific feature, such as premature alert systems, fraud discovery, preventive maintenance apps, and forecasting.

Prescriptive analytics offer specific recommendations based on data. They help address variables; what approach should I take if X, Y, or Z happens?

Why does Big Data matter?

Capturing data about how people interact with your product or service yields valuable insight for assessing your business’s plans and progress. If customers engage with X but not with Y, there’s an identifiable reason which analytics can parse instantly for a rational course adjustment.

Big data gathers this key information in order for you to make deft decisions from undigested offerings, leading to improved quality, customer loyalty, constant referrals, increased ROI, etc. Without this information, you’re essentially taking a shot in the dark at how to make your business prosper.

Big Data Challenges

Big data challenges include storing and analyzing voluminous, rapidly growing, diverse data and deciding how to properly manage it. Failure to appropriately address these complications results in escalating costs and diminished productivity.

Managing large data
As our way of life moves online, data’s role is increasingly vital. Unstructured data — data not being housed through a traditional database — is difficult to manage and requires complex tools and skill sets for proper curation.

Real-time processing
When data processing lags, opportunities are lost, which highlights the need for lightning-fast access to meaningful information. Real-time processing delivers accurate results in fractions of a second, helping you maintain data-driven decisions.

Lack of talent
In order to effectively organize and manage analytics, organizations need experts versatile in current big data skills. These professionals are in high demand, earning appreciable salaries ranging from $135,000 to $196,000 dollars, depending on location and experience.

Integration of disparate data sources
Data emerges from a variety of sources, including social media, email, documents, etc. Combining through all of this data and rendering it comprehensible for reports and non-technical staff calls for sensitivity and nuance.

Securing data
In the digital age, data has become a valuable commodity that attracts cyberthieves — knowledge is power! Thus, the need for big data repositories or analyses to depend on a bulwark of additional security measures to protect this sensitive and invaluable data.

Ways to collect data online

Online Tracking
Track customer interaction through your website or application. Customers can generate up to 40 data points, such as time spent on your site, where they clicked, and much more. Use this information to hone your presence and messaging.

Transactional Data Tracking
If you sell goods or services online, you can store transactional records. This information determines consumption patterns, helping you target ads and offer deals at the right time and place to the ideal audience.

Marketing Analytics
Track how people are interacting with your ad campaigns or social media content. If your public engages with a specific piece of content, you’ll know where and how to target more of that material for maximum response.

Subscription or Registration
Ask for basic information about your customer, e.g., email, first and last name, and phone number during registration to acquire essential information for targeted marketing campaigns and improving the user experience with personalized messaging.

AWS Big Data Analytics Services

When existing databases and applications struggle to scale and support sudden influx in data, big data AWS services fill the breach. These services are designed to efficiently process insights to accelerate reliable decision making.

Analytics

Interactive Analytics
Easily analyze data in Amazon S3 using standard SQL with Amazon Athena.

Data warehousing
Amazon Redshift enables you to run SQL and analytic queries against structured and unstructured data without moving data.

Big data processing
Amazon EMR, lets you efficiently process vast quantities of data for data engineering, data science development, and collaboration.

Real-time analytics
Leverage Amazon Kineses to collect, process, and analyze streams of data as it reaches your data lake, giving you on-the-spot responsiveness.

Operational analytics
Elasticsearch is your top pick to evaluate, refine, aggregate, and visualize data in near real-time for application monitoring, log analytics, and clickstream analytics.

Dashboards and visualizations
Effortlessly deliver insights company-wide using Amazon Quicksight.

Data Movement

Real-time data movement
By leveraging Amazon Kinesis and capabilities, manage data streams and receive instant analytics.

Data Lakes

Object storage
Store and receive any amount of data from anywhere by harnessing Amazon S3.

Backup and archive
With Amazon S3 Glacier, securely, reliably, and cost-consciously manage storage classes for data archiving and extended backup.

Data catalog
AWS Glue enables you to extract, transform, and load (ETL) service that makes it simple for customers to process data analytics.

Third-party data
Using AWS Data Exchange, find and subscribe to third-party data.

Predictive analytics and machine learning

Frameworks and interfaces
Accelerate deep learning at any scale in the cloud with AWS Deep Learning.

Platform services
Amazon Sagemaker: Develop, train, and deploy machine learning models at scale.

Monitoring Data

All-in-one monitoring
Using DataDog, monitor all of your data services on one platform at any scale.