THE CURRENT STATE OF DATA & ANALYTICS (Chapter 2.1 of “All AWS Data Analytics Services”)

“The Age of Algorithms”


The term “big data” was first described in 1944 as “information explosion.” In 2001 an article published by the Meta Group, “3D Data Management: Controlling Data Volume, Velocity, and Variety“, first described what has been generally accepted as the 3 defining definitions of big data (the “3 V’s”). For you history buffs, you can read an interesting story published in Forbes entitled “A Very Short History of Big Data” here.

The sheer volume of data generated by applications and infrastructure is increasing beyond comprehension: however for the first time, teams will be embracing an algorithmic approach – known as “Algorithmic IT Operations” (AIOps) – to see what’s happening in the network in real time, diagnose the issue and then automate a fix.

For decades, companies have been making business decisions based on traditional relational enterprise data, such as transactions. Then, “big data” came into the picture. Along with “big data” came massive volumes of both structured and unstructured data that’s so large it’s difficult to process using traditional database and software techniques. In fact, there’s more unstructured data in the world today than structured. The volume is too big, it comes from many different sources in many different formats, it moves too fast, and it normally exceeds processing capabilities available on-premises. But this data, when captured, formatted, manipulated and stored pulls powerful insights – some never before imagined – through analytics.

The focus has now shifted from “advanced analytics” to “advancing analytics”, which will be brought into self-service tools. With more users advancing their analytics, AI will play a bigger role in organizations.

In 2017, “big data” will be subsumed into the topic of Artificial Intelligence (AI). Big data is an enabler of AI and not an end in itself.

The shift is an increased valuation of critical thinking in the workplace as people realize there’s not a deficit of data in the enterprise, but a deficit of insight. The question for big data is “what can I learn from it?, or “where can I make meaningful insights?” AI and machine learning (ML) will be the big players, and companies will need to ask questions that their data can answer through these 2 transformative technologies.

You can read the previous post here.

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Posted in Algorithmic IT Operations, Amazon Web Services, Amazon Web Services Analytic Services, AWS Analytic Services, AWS Analytics, AWS BI, Old Buzzword: Big Data | Tagged , , | Leave a comment

DATA MIXOLOGY AND THE END OF DATA SILOS (Chapter 1.4 of “All AWS Data Analytic Services”)

Data Mixology & the End of Data Silos

Data Mixology & the End of Data Silos


The role of the CIO has changed dramatically over the past decade. With rise to new roles like the Chief Digital Officer and the Chief Customer Officer, we are seeing a rise in the importance of digital transformation happening NOW, and the importance of it happening not just in the technology of a company, but across the entire organization. Traditional solutions are more multidimensional and technology CANNOT be used as a crutch. A focus on breaking down silos, will give innovation more room to flourish and collaboration becomes easier.

AWS removes limits to the types of database and storage technologies you can use by providing managed database services that offer enterprise performance at open source cost. This results in applications running on many different data technologies, using the right technology for each workload.

Sample 1 of the Different Data Involved in One Solution

Sample 1 of the Different Data Involved in One Solution

Sample 2 of the Different Data Involved in One Solution

Sample 2 of the Different Data Involved in One Solution

Sample 3 of the Different Data Involved in One Solution

Sample 3 of the Different Data Involved in One Solution

Sample 4 of the Different Data Involved in One Solution

Sample 4 of the Different Data Involved in One Solution

Sample 5 of the Different Data Involved in One Solution

Sample 5 of the Different Data Involved in One Solution

These new trends and technologies will be at the core of digital transformation efforts in 2017 and many will continue far beyond the next year. There is no question that digital transformation is no longer an option as the need to build an organization that can change both its technology and its culture rapidly will be core to not only surviving in the time of business disruption, but building a business model that is agile, adaptable and designed to thrive long into the future where change is the only constant.

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#gottaluvAWS! #gottaluvAWSMarketplace!


Posted in Amazon Web Services, Amazon Web Services Analytic Services, AWS Analytics, AWS BI, AWS Marketplace, Data Mixology, End of Data Silos | Leave a comment

FASTER TIME TO ACTIONABLE INSIGHT (Chapter 1.3 in “All-AWS-Data-Analytic-Services”)

Faster Time to Actionable Insights & ROI

Faster Time to Actionable Insights & ROI


AWS resources can be instantiated in seconds, you can treat these as “disposable” resources – not hardware or software you’ve spent months deciding which to choose and spending a significant up-front expenditure without knowing if it will solve your problems. The “Services not Servers” mantra of AWS provides many ways to increase developer productivity, operational efficiency and the ability to “try on” various solutions available on AWS Marketplace – the largest ecosystem of popular software vendors and integrators of any provider —  to find the perfect fit for your business needs without commitment to long-term contracts. Spin up a pre-configured analytical software solution in minutes, not months.

AWS provides an extensive set of managed services that help you build, secure, and scale big data analytics applications quickly and easily. Whether your applications require real-time streaming, a data warehouse solution, or batch data processing, AWS provides the infrastructure and tools to perform virtually any type of big data project.

When you combine the managed AWS services with software solutions available from popular software vendors on AWS Marketplace, you can get the precise business intelligence and big data analytical solutions you want that augment and enhances your project beyond what the services themselves provide. You get to data-driven results faster by decreasing the time it takes to plan, forecast, and make software provisioning decisions. This greatly improves the way you build business analytics solutions and run your business, giving your organization the agility to experiment and innovate with the click of a button.

Ongoing developments in AWS cloud computing are rapidly moving the promise of deriving business value from big data in real-time into a reality. With billions of devices globally already streaming data, forward-thinking companies have begun to leverage AWS to reap huge benefits from this data storm.

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#gottaluvAWS! #gottaluvAWSMarketplace!




Posted in Amazon Web Services, Amazon Web Services Analytic Services, AWS Analytic Services, AWS Analytics, AWS BI, AWS Marketplace, Faster Time to Data-Driven Results, Faster Time to ROI, Rapid Analytical Insights, Rapid Innovation | Leave a comment

KEY ENABLERS TO BIG DATA ANALYTICS FOR EVERYONE (Chapter 1.2 in “All-AWS-Data-Analytics-Services”)

Big Data Analytics for Everyone

Big Data Analytics for Everyone


Digital transformation reshapes every aspect of a business. As digital technology continues to evolve, successful digital transformation will require careful collaboration, thoughtful planning, and the inclusion of every department.

Digital transformation has morphed from a trend to a central component of modern business strategy. IT has become the data hero. It’s finally IT’s time to break the cycle and evolve from producer to enabler. IT is at the helm of this digital transformation of self-service analytics at scale. IT is providing the flexibility and agility a business needs to innovate while balancing governance, data security, and compliance. Organizations are empowered to make data-driven decisions at the speed of business, shaping the future of how successful companies are run.

A Partial List of Key Enablers

  • Cloud Computing: The adoption of cloud computing has made expensive analytics affordable. Analyzing extensive data sets require noteworthy compute capacity that can fluctuate in size based on the data inputs and type of analytics. This characteristic of scaling workloads is perfectly suited AWS’s pay-as-you-go cloud model, where applications can scale up and down based on demand. Being able to analyze data quickly to derive valuable insights can be done within minutes rather than months, and you only pay for what you use.
  • Adaptability: Change happens fast in business. Modern enterprises succeed when they adapt to industry and marketplace shifts and incorporate new technology into company culture and regular operations. However, digital transformation isn’t only about technology, it’s about bringing together the power of technology with a culture that embraces the change that it can lead for the organization.
  • The Importance of User Experience: The customer experience is the ultimate goal of any digital transformation. Customers are more cautious than ever; they’ll turn away from brands that don’t align with their values and needs. A top-notch user experience is paramount to keeping customers involved and engaged with your brand. This is a comprehensive process. Anywhere and everywhere customers can interact with your business, and the experience must be consistent and positive. For example, entrepreneurs can use analytics to discover where customers are coming from (whether it’s a social media platform, a blog, or somewhere else) and streamline the interaction in those high-traffic areas. Every touch point matters, and those leading the transformation should constantly be asking how are we removing friction and enhancing the experience for every customer regardless of where they are in the journey.
  • Rapid Innovation: Proactive innovation is one of the best ways to stay competitive in an evolving marketplace. New technology needs to be assessed, tested, analyzed, and judged more quickly than ever before. Businesses can no longer afford to waste time and resources implementing new tools that offer no real value. This means a “Fail fast, to succeed faster,” mentality. Some projects will work straight away, others will have significant learning curves. The faster your organization can go from idea to implementation the more it can embrace opportunities to transform and even disrupt markets and internal business models. Using the AWS Marketplace, explained throughout this blog series, will help you understand an inexpensive and convenient way to do this.
  • Leverage Remote Workers: Young professionals prefer flexibility over compensation. Mobile technology and bandwidth proliferation allow businesses to connect with and retain top talent anywhere in the world. Digital transformations encourage telecommute capabilities, giving the ability to hire the most talented employees.
  • Application Programming Interfaces (APIs): New tools and technologies need more than one avenue for viability; otherwise, their value quickly drops. APIs are a powerful way to embrace true digital transformation. eBay and PayPal are two companies that relied significantly on these technologies, enabling them to manage an incredible high volume of transactions. Companies need to tie together more best of breed technologies via APIs. Rather than marrying a platform, the API will open doors for multiple platforms together in a fast, flexible ecosystem. Sourcing from multiple vendors creates the best user experience. This will be further confirmed many times in this blog series when I talk about leveraging AWS Marketplace with AWS Services.

Previous post can be found here.

Next post can be found here.

#gottaluvAWS! #gottaluvAWSMarketplace!

Posted in Amazon Web Services, Analytical User Experience, Analytics-Driven Organization, AWS Analytic Services, AWS Analytics, AWS Marketplace, Business Adaptability, Cloud Computing, Digital Transformation, Key Enablers Analytics for Everyone, Leverage APIs, Rapid Innovation | Leave a comment


We've Surpassed This Already...

We’ve Surpassed This Already…


We live in a data-driven world. For Fortune 500 companies, the value of data is clear and compelling. They invest millions of dollars annually in information systems that improve their performance and outcomes. Independent businesses have the same need to be data-driven; however there’s a persistent entrepreneurial resistance to becoming truly metrics-driven. Founders are often tempted to postpone building necessary metrics in favor of spending time and resources on building products.

While that might work in the short-term, it will very quickly come back to haunt them. Very few companies have successfully achieved exponential growth, raised capital, or negotiated strong exits without first having a solid analytics model that has been iterated upon for many months or years.

“The Analytics-Driven Organization”

As companies become analytics-driven, they aren’t just enjoying incremental improvements. The benefits enabled by analytical data processing becomes the heart of the business – enabling new applications and business processes, using a variety of data sources and analytical solutions – giving insight into their data never dreamed of and giving them a great competitive advantage.

Below you’ll find a description of some of the types of data analytic insight types, and common use cases for each.

Descriptive: Descriptive Analytics uses business intelligence and data mining to ask “What has happened?” Descriptive Analytics mines data to provide trending information on past or current events that can give businesses the context they need for future actions. Descriptive Analytics are characterized by the use of KPIs. It drills down into data to uncover details such as the frequency of events, the cost of operations and the root cause of failures. Most traditional business intelligence reporting falls into this realm, but complex and sophisticated analytic techniques also fall into this realm when their purpose is to describe or characterize past events and states. Summary statistics, clustering techniques, and association rules used in market basket analysis are all examples of Descriptive Analytics.

Diagnostic: Diagnostic Analytics examines data or content to answer the question “Why did it happen?” It’s characterized by techniques such as drill-down, data discovery, data mining and correlations. You can think of it as the casual inference and the comparative effect of different variables on a particular outcome. While Descriptive Analytics might be concerned with describing how large or significant a particular outcome is, it’s more focused on determining what factors and events contributed to the outcomes. As more and more cases are included in a particular analysis and more factors or dimensions are included, it may be impossible to determine precise, limited statements regarding sequences and outcomes. Contradictory cases, data sparseness, missing factors (“unknown unknowns”), and data sampling and preparation techniques all contribute to uncertainty and the need to qualify conclusions in Diagnostic Analytics as occurring in a “probability space”. Training algorithms for classification and regression techniques can be seen as falling into this space since they combine the analysis of past events and states with probability distributions. Other examples of Diagnostic Analytics include attribute importance, principle component analysis, sensitivity analysis and conjoint analysis.

Discovery: Discovery Analytics doesn’t begin with a pre-definition but rather with a goal. It approaches the data in an iterative process of “explore, discover, verify and operationalize.” This method uncovers new insights and then builds and operationalizes new analytic models that provide value back to the business. The key to delivering the most value through Discovery Analytics is to enable as many users as possible across the organization to participate in it to harness the collective intelligence. Discovery Analytics searches for patterns or specific items in a data set. It uses applications such as geographical maps, pivot tables and heat maps to make the process of finding patterns or specific items rapid and intuitive. Examples of Discovery Analytics include using advanced analytical geospatial mapping to find location intelligence or frequency analysis to find concentrations of insurance claims to detect fraud.

Predictive: Predictive Analytics asks “What could happen?” It’s used to make predictions about unknown future events. It uses many techniques from data mining, machine learning and artificial intelligence. This type of analytics is all about understanding predictions based on quantitative analysis on data sets. It’s in the realm of “predictive modeling” and statistical evaluation of those models. Examples of Predictive Analytics includes classification models, regression models, Monte Carlo analysis, random forest models and Bayesian analysis. It helps businesses anticipate likely scenarios so they can plan ahead, rather than reacting to what already happened.

Prescriptive: Prescriptive Analytics uses optimization and simulation to ask “What should we do?” It explores a set of possible actions and suggests actions based on Descriptive and Predictive Analyses of complex data. It’s all about automating future actions or decisions which are defined programmatically through an analytical process. The emphasis is on defined future responses or actions and rules that specify what actions to take. While simple threshold based “if then” statements are included in Prescriptive Analytics, highly sophisticated algorithms such as neural nets are also typically in the realm of Prescriptive Analytics because they’re focused on making a specific prediction. Examples include recommendation engines, next best offer analysis, cueing analysis with automated assignment systems and most operations research optimization analyses.

Previous post can be found here.

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Posted in Analytics-Driven Organization, Benefits of Analytics, Discovery & Predictive Analytics, Metrics-Driven World | Leave a comment

All of Amazon Web Services (AWS) Data Analytics Services – Table of Contents

Extracting Insights and Actionable Information from Data

Extracting Insights and Actionable Information from Data

Amazon Web Services (AWS) Data Analytics Processing

Learn the comprehensive set of data and analytical services to handle every step of the analytics process chain, ideal usage patterns and anti-patterns on AWS.

Data is not only getting bigger (in “Volume”) and in ever-increasing different formats (the “Variety”) faster (the “Velocity”), but the need to derive “Value” quickly through analytics to provide actionable insights for businesses is indeed a differentiating factor between successful businesses that can mitigate risk and respond to customer actions in near real-time vs. other businesses that will fall behind in the day and age of data deluge.

In addition to the metaphorical “V’s” mentioned above to describe big data, there is one more: “Veracity” – being sure your data is clean prior to any analytics performed whatsoever. Garbage in, garbage out. There’s no time to waste making improper, mal-informed decisions based on dirty data. This is paramount. Using solutions in the AWS Marketplace make this crucial and difficult step easy.

Analyzing extensive data sets require noteworthy compute capacity that normally exceeds processing capabilities available on-premises. In addition, that compute capacity can fluctuate in size based on the data inputs and the type of analytics being performed. This characteristic of scaling workloads is perfectly suited to AWS and the AWS Marketplace’s pay-as-you-go cloud model, where applications can scale up and down (horizontally and vertically) based on demand. Extracting insights and actionable information from data can be done within minutes rather than months, and you only pay for what you use.

The vast majority of big data use cases deployed in the cloud today run on AWS, with unique customer references for big data analytics, of which 67 are enterprise, household names. You can build virtually any big data analytics application and support any workload regardless of the volume, velocity, and variety of data. With 50+ services and hundreds of features added every year, AWS provides everything you need to collect, store, process, analyze, and visualize big data on the cloud.

Let’s dive into these services and learn the ideal usage patterns of each so you can improve the way you build business analytics solutions and run your business.

Without Data Analytics, You're Guessing at Business Decisions!

Without Data Analytics, You’re Guessing at Business Decisions!

Each item in the Table of Contents below will become a link when live.


    1. We’re Living in a Metrics-Driven World
    2. Key Enablers  to Big Data Analytics for Everyone
    3. Faster Time to Actionable Insights & ROI
    4. Data Mixology and the End of Data Silos
    1. The “old” buzz word: “Big Data”
    2. The “new” buzz word: “Digital Transformation”
    3. 2017: The Year of Intelligence
    1. Capacity Guessing
    2. Scaling Workloads
    3. Processing Power
    4. Cloud & Data Security
    5. Traditional Data Warehouses
    6. Traditional Relational Database Management Systems
    7. Discover, Migrate & Deploy Pre-Configured Big Data BI & Advanced Analytic Solutions in Minutes – and Pay Only for What You Use by the Hour!!!
    1. AWS Big Data Analytics Advantages
    2. Some Specific Advantages of AWS Big Data Analytics
    3. AWS Marketplace for Big Data Analytics Advantages
    4. Some Specific Advantages of AWS Marketplace in Big Data Analytics
    5. Self-Managed Big Data Analytics Solutions on AWS Marketplace Examples
    6. AWS Marketplace “Hidden” Helpful Sites to Find Solutions
    1. AWS Direct Connect
    2. AWS Snowball
    3. AWS Storage Gateway
    4. Amazon Kinesis Firehose
    5. Amazon S3 Transfer Acceleration
    6. AWS Database Migration Service
    7. AWS Marketplace Solutions for Data Collection
    1. Amazon SWF
    2. AWS Data Pipeline
    3. AWS Lambda
    1. Amazon S3
    2. Amazon DynamoDB
    3. Amazon Aurora
    1. Understanding the Need for Cloud Tools
    2. Where to Discover Pricing for Individual and Bundled AWS Services
    3. Where to Price Out a Production Cloud Solution vs On-Prem
    4. Monitoring Analytical Processes with Amazon CloudWatch
    5. Ensuring Clean Data Prior to Analyzing
    6. Batch Processing vs. Real-Time Processing
    7. Where to Find AWS Sample Data for Practice
    1. Amazon Redshift Overview
    2. Amazon Redshift vs. Traditional Data Warehouses
    3. Amazon Redshift Benefits
    4. Amazon Redshift Interfaces
    5. Amazon Redshift Integration with Other AWS Services
    6. Amazon Redshift Data Integration, Loading & Cluster Configuration
    7. Amazon Redshift Ideal Usage Patterns and Anti-Patterns
    8. AWS Marketplace Solution Matillion ETL for Redshift
    1. Amazon Kinesis Streams
    2. Amazon Kinesis Applications Using Amazon Kinesis Client Library
    3. Real-Time Streaming Data Producers
    4. Amazon Kinesis Data Stores
    5. Amazon Kinesis Integration with Other AWS Services
    6. Amazon Kinesis Stream Pre-Requisites: Identity & Access Management (IAM) Policies and Users
    7. Creating & Implementing Amazon Kinesis Streams
    8. The Role of Amazon Kinesis Firehose & Amazon Kinesis Analytics
    9. Amazon Kinesis Streams Ideal Usage Patterns & Anti-Patterns
    1. Amazon EMR Overview & Benefits vs. Traditional Hadoop MapReduce
    2. Open Source Frameworks: EMR Interfaces
    3. Amazon EC2 Instance Configurations for Use with Amazon EMR
    4. Amazon EMR Data Stores
    5. Amazon EMR Cluster Pre-Requisites
    6. Launching an Amazon EMR Cluster
    7. Processing Data with Amazon EMR
    8. Amazon EMR Integration with Other AWS Services
    9. A Custom Framework of AWS Services for Data Lake Ingestion
    10. Amazon EMR Ideal Usage Patterns & Anti-Patterns
    1. Amazon Athena Overview
    2. Amazon Athena Data Formats, Data Types & Compression Formats
    3. Defining Queries, Schemas & SerDe
    4. Amazon Athena Interfaces
    5. Amazon Athena Pre-Requisites
    6. Creating a Database & Table in Amazon Athena & Querying Data
    7. Amazon Athena Integration with Other AWS Services
    8. Amazon Athena Use Case Samples
    9. Analysis & Visualization Integrations with Amazon Athena
    1. Amazon Elasticsearch Service Overview vs. Open Source Elasticsearch
    2. Amazon ESS Benefits
    3. Amazon ESS Integration with Logstash & Kibana
    4. ESS ELK Stack on AWS Marketplace
    5. ESS Integration with Other AWS Services
    6. ESS Interfaces
    7. ESS Ideal Usage Patterns & Anti-Patterns
    1. Amazon ML Overview
    2. Amazon ML Benefits
    3. Amazon ML Key Features
    4. Generating Predictions with Amazon ML
    5. Amazon ML Integration with Other AWS Services
    6. Amazon ML Interfaces and SDKs
    7. Amazon ML Ideal Usage Patterns & Anti-Patterns
    1. Amazon AI Overview
    2. Amazon Lex
    3. Amazon Rekognition
    4. Amazon Polly
    5. AWS Deep Learning AMI’s
    6. Amazon Deep Learning CloudFormation Template
    1. Amazon QuickSight Overview
    2. Amazon QuickSight Benefits
    3. What is SPICE?
    4. Amazon QuickSight Data Sources
    5. Amazon QuickSight Data Cleansing
    6. Amazon QuickSight Data Interfaces
    7. Amazon QuickSight Sample Data
    8. Amazon QuickSight Supported Visualization Types
    9. Building a Data Visualization with Amazon QuickSight
    1. AWS Glue Overview
    2. Integration with Other AWS Services
    3. Register Data Sources with AWS Glue
    4. Select a Data Source & Target: Watch the Magic!
    5. Schedule & Run AWS Glue Jobs
    6. Running AWS Glue: A Fully Managed ETL Service to Prepare Data for Analytics
    1. Amazon Mobile Analytics Overview
    2. Amazon Mobile Analytics Benefits
    3. Amazon Mobile Analytics Supported Platforms
    4. Amazon Mobile Analytics: More Details
    5. Amazon Mobile Analytics Integration with Other AWS Services
    6. Amazon Mobile Analytics Pre-Requisites
    7. Using Amazon Mobile Analytics to Understand User Engagement
    1. Amazon Pinpoint Overview
    2. Amazon Pinpoint Benefits
    3. Amazon Pinpoint Supported Platforms
    4. Amazon Pinpoint Interfaces
    5. Amazon Pinpoint Analytics
    6. Creating an Amazon Pinpoint Campaign
    1. AWS Data Pipeline Overview
    2. AWS Data Pipeline Supported Databases
    3. AWS Data Pipeline Data Stores
    4. AWS Data Pipeline Compute Services to Transform Data
    5. AWS Data Pipeline Data Nodes, Activities, Preconditions & Schedules
    6. AWS Data Pipeline Interfaces & Task Runner
    7. Steps to Complete Before You Create an AWS Data Pipeline
    8. Copy CSV Data Between Amazon S3 Buckets Using AWS Data Pipeline
    1. Amazon S3 Overview
    2. Amazon S3 “Flavors”
    3. Amazon S3 Lifecycle
    4. Examples of Amazon S3’s Benefits in Large-Scale Analytics
    1. Amazon RDS Aurora Overview vs. Traditional RDBMS
    2. Amazon Aurora Benefits
    3. Amazon Aurora’s Architecture
    4. Amazon Aurora Interfaces
    5. Amazon Aurora’s Integration with Other AWS Services
    6. Create an Amazon Aurora Cluster
    7. Amazon Aurora’s PostgreSQL Support
    1. Amazon DynamoDB Overview
    2. Amazon DynamoDB Benefits in Analytical Processing
    3. Amazon DynamoDB Integration with Other AWS Services
    4. Amazon DynamoDB Interfaces
    5. Creating & Querying a DynamoDB Database

First Chapter can be found here.

#gottaluvAWS! #gottaluvAWSMarketplace!

Posted in Amazon Artificial Intelligence, Amazon Athena, Amazon Aurora, Amazon DynamoDB, Amazon EC2 On-Demand Instances, Amazon Elastic MapReduce, Amazon Elasticsearch Service, Amazon IAM, Amazon Kinesis Family, Amazon Machine Learning, Amazon Mobile Analytics, Amazon Pinpoint, Amazon QuickSight, Amazon Redshift Data Warehouse, Amazon Web Services, Amazon Web Services Analytic Services, AWS Analytic Services, AWS Analytics, AWS BI, AWS Data Collection, AWS Data Orchestration, AWS Data Pipeline, AWS Glue, AWS Marketplace, AWS S2 | 6 Comments

I Want to Create an eLearning Class on “All AWS Analytics Offerings”, So…

A photo from AWS re:Invent 2016 annotated by my great friend, Jen Underwood of ImpactAnalytix

A photo from AWS re:Invent 2016 annotated by my great friend, Jen Underwood of ImpactAnalytix

I Want to Create an eLearning Class on “All AWS Analytics Offerings”, but…

eLearning companies seem to want more targeted Business Intelligence (BI) & Analytical topics. It makes sense to appeal to certain demographics who want to know a specific type of BI/Analytic Solution – but, if they don’t know their options, how do they really “know” they “know” what type of BI/analytical solution is the right fit?

As a geek, I’d run to any place I could get a holistic view of  everything available insofar as Big Data BI & Analytics in the AWS cloud, & I don’t think I’m alone!

SHORT HISTORY FOR THOSE WHO NEED IT (everyone else skip a few paragraphs to “MY AWS BIG DATA BI & ANALYTIC AGENDA”):

Amazon Web Services has been serious about analytics for years, which is why they have the broadest platform for big data in the market today, with deep and rapidly expanding functionality across big data stores, data warehousing, distributed analytics, real-time streaming, machine learning, and business intelligence.

Gartner1 confirms AWS has the most diverse customer base and the broadest range of use cases, including enterprise mission-critical applications. For the sixth consecutive year, Gartner1 also confirms AWS is the overwhelming market share leader, with over 10 times more cloud compute capacity in use than the aggregate total of the other 14 providers in their Magic Quadrant!


There’s a plethora of options on AWS Marketplace to run big data analytics software solutions available from popular vendors that are already pre-configured on an Amazon Machine Image (AMI) that solve a variety of very specific needs. You can visit the AWS Marketplace Big Data Analytics-specific site by clicking the bottom left icon on the AWS Marketplace site or by clicking here to view the premier AWS Marketplace solution providers for transforming and moving your data, processing and analyzing your data, and reporting and visualizing your data.


I’ve created an entire proposal and Table of Contents for every AWS BI & Analytic Solution, the data stores, etc. involved needed for all end-to-end AWS BI & Analytical Solutions.

My intention here is to blog about it, rather than make videos. At least people will be able to read their options without me spending as much time needed to create time-intensive video how-to’s on everything.

To me it’s a compromise to the delivery methodology for  the ultimate data-driven, metrics-driven solutions, providing companies with the kind of information they need to survive in today’s competitive business world, but DataLeader is here to help!

I will start this all-inclusive AWS BI/Analytics series of blog posts with a Table of Contents (TOC) that as I finish each part, each section of the TOC will become a link to that section.

Until next time! TOC coming in my next work break 😉



Posted in Amazon Web Services, AWS Analytic Services, AWS BI, AWS Marketplace | Leave a comment