Kognitio Spark! Modern Data Platform

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Hadoop Meets Mature BI: Where the rubber meets the road for the Modern Data Platform. This presenattion supports the Spark! Event in Atlanta, where Kognitio is a key sponsor. The event discusses the shift in how information is collected, stored and analyzed in a Big Data World. More on the Radiant Advisors' Spark! Events at http://radiantadvisors.com/spark/

Technology Business

@Kognitio #SparkEvent
Hadoop meets Mature BI:
Where the rubber meets the road for
the Modern Data Platform
Michael Hiskey
Futurist, Product Evangelist
(and VP, Marketing and Business Development
www.kognitio.com

@Kognitio #SparkEvent
Today, and the Future
Big DataAdvanced Analytics
In-memory
Modern Data Platform
Hybrid Data Ecosystem ‘Logical Data Warehouse’
Predictive Analytics
Data Scientists
Data

@Kognitio #SparkEvent
64% Have invested/plan to
invest in Big Data Tech
Have started using it8%
Via TechCrunch, 23 Sept 2013
Average TBs of
stored data200
Walmart DW in 19992x
Insights & Publications May, 2011

@Kognitio #SparkEvent
The Data Scientist
Sexiest job of the 21st Century?

@Kognitio #SparkEvent
Data
Scientist
The Analytical Enterprise
Business
Analyst
Systems
Admin

@Kognitio #SparkEvent
Remember: Decision Support Systems?
…accessed with ease
and simplicity
Historical information, latency
BI tools have plateaued
0 1 2 3 4 5 6 7 8 9
Advanced analytics &
data science
More math…a lot more math

@Kognitio #SparkEvent
create externalscript LM_PRODUCT_FORECAST environment rsint
receives ( SALEDATE DATE, DOW INTEGER, ROW_ID INTEGER, PRODNO INTEGER, DAILYSALES
partition by PRODNO order by PRODNO, ROW_ID
sends ( R_OUTPUT varchar )
isolate partitions
script S'endofr( # Simple R script to run a linear fit on daily sales
prod1<-read.csv(file=file("stdin"), header=FALSE,row.names
colnames(prod1)<-c("DOW","ID","PRODNO","DAILYSALES")
dim1<-dim(prod1)
daily1<-aggregate(prod1$DAILYSALES, list(DOW = prod1$DOW),
daily1[,2]<-daily1[,2]/sum(daily1[,2])
basesales<-array(0,c(dim1[1],2))
basesales[,1]<-prod1$ID
basesales[,2]<-(prod1$DAILYSALES/daily1[prod1$DOW+1,2])
colnames(basesales)<-c("ID","BASESALES")
fit1=lm(BASESALES ~ ID,as.data.frame(basesales))
Behind the
numbers

@Kognitio #SparkEvent
What has changed?
More
connected-users?
More-connected
users?

@Kognitio #SparkEvent
Don’t be a Railroad Stoker!
Highly skilled engineering required …
but the world innovated around them.

@Kognitio #SparkEvent
Machine learning
algorithms Dynamic
Simulation
Statistical
Analysis
Clustering
Behavior
modelling
The drive for deeper understanding
Reporting & BPM
Fraud detection
Dynamic
Interaction
Technology/Automation
Analytical Complexity
Campaign
Management

@Kognitio #SparkEvent
Key: “Graduation”
Projects will need
to Graduate
from the
Data Science Lab
and become part
of
Business as Usual

@Kognitio #SparkEvent
Your goal:
PRESS HERE
…and really cool Big Data stuff happens!

@Kognitio #SparkEvent
© 20th Century Fox

@Kognitio #SparkEvent
 No need to pre‐process
 No need to align to schema
 No need to triage
Null storage concerns

@Kognitio #SparkEvent
Hadoop just too
slow for interactive
BI!
…loss of train‐
of‐thought
“while Hadoop shines as a processing
platform, it is painfully slow as a query tool”

@Kognitio #SparkEvent
Lots of these
Not so many of these
inherently disk oriented
typically low ratio of CPU to Disk
Hadoop is…

@Kognitio #SparkEvent
Analytics needs
low latency, no I/O wait
High speed in‐memory processing

A* Modern Data Platform
Reference Architecture
Analytical
Platform Near‐line
Storage
(optional)
Access
Application &
Client Layer
All BI Tools All OLAP Clients Excel
Persistence
Layer Hadoop
Clusters
Enterprise Data
Warehouses
Legacy
Systems
…
Reporting
Cloud
Storage
*(not THE)

© Hortonworks Inc. 2013
(another) Next-Generation Data Architecture
Page 21
APPLICATIONSDATA SYSTEMS
Microsoft Applications
DATA SOURCES
Traditional Sources
(RDBMS, OLTP, OLAP)
In‐memory MPP Accelerator
BI Tools & OLAP Clients
TRADITIONAL REPOS
RDBMS EDW MPP
OPERATIONAL
TOOLS
MANAGE &
MONITOR
DEV & DATA
TOOLS
BUILD &
TEST
New Sources
(web logs, email, sensors, social media)
HORTONWORKS
DATA PLATFORM

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In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation. We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios. Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!

Artificial Intelligence for XMLDevelopment

Octavian Nadolu

In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject. We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup. Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved. The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring. The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise. By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.

Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf

Malak Abu Hammad

Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers: * What is Vector Search? * Importance and benefits of vector search * Practical use cases across various industries * Step-by-step implementation guide * Live demos with code snippets * Enhancing LLM capabilities with vector search * Best practices and optimization strategies Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications. #MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology

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Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf

Paige Cruz

Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack. While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack. I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:

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GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...

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Dr. Sean Tan, Head of Data Science, Changi Airport Group Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.

みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...

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Securing your Kubernetes cluster_ a step-by-step guide to success !

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Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster. However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks. In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.

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Epistemic Interaction - tuning interfaces to provide information for AI support

Alan Dix

Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024 https://alandix.com/academic/papers/synergy2024-epistemic/ As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.

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Leonard Jayamohan, Partner & Generative AI Lead, Deloitte This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.

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1. @Kognitio #SparkEvent Hadoop meets Mature BI: Where the rubber meets the road for the Modern Data Platform Michael Hiskey Futurist, Product Evangelist (and VP, Marketing and Business Development www.kognitio.com

3. @Kognitio #SparkEvent Today, and the Future Big DataAdvanced Analytics In-memory Modern Data Platform Hybrid Data Ecosystem ‘Logical Data Warehouse’ Predictive Analytics Data Scientists Data

4. @Kognitio #SparkEvent 64% Have invested/plan to invest in Big Data Tech Have started using it8% Via TechCrunch, 23 Sept 2013 Average TBs of stored data200 Walmart DW in 19992x Insights & Publications May, 2011

5. @Kognitio #SparkEvent The Data Scientist Sexiest job of the 21st Century?

6. @Kognitio #SparkEvent Data Scientist The Analytical Enterprise Business Analyst Systems Admin

7. @Kognitio #SparkEvent Remember: Decision Support Systems? …accessed with ease and simplicity Historical information, latency BI tools have plateaued 0 1 2 3 4 5 6 7 8 9 Advanced analytics & data science More math…a lot more math

8. @Kognitio #SparkEvent create externalscript LM_PRODUCT_FORECAST environment rsint receives ( SALEDATE DATE, DOW INTEGER, ROW_ID INTEGER, PRODNO INTEGER, DAILYSALES partition by PRODNO order by PRODNO, ROW_ID sends ( R_OUTPUT varchar ) isolate partitions script S'endofr( # Simple R script to run a linear fit on daily sales prod1<-read.csv(file=file("stdin"), header=FALSE,row.names colnames(prod1)<-c("DOW","ID","PRODNO","DAILYSALES") dim1<-dim(prod1) daily1<-aggregate(prod1$DAILYSALES, list(DOW = prod1$DOW), daily1[,2]<-daily1[,2]/sum(daily1[,2]) basesales<-array(0,c(dim1[1],2)) basesales[,1]<-prod1$ID basesales[,2]<-(prod1$DAILYSALES/daily1[prod1$DOW+1,2]) colnames(basesales)<-c("ID","BASESALES") fit1=lm(BASESALES ~ ID,as.data.frame(basesales)) Behind the numbers

9. @Kognitio #SparkEvent What has changed? More connected-users? More-connected users?

10. @Kognitio #SparkEvent Don’t be a Railroad Stoker! Highly skilled engineering required … but the world innovated around them.

11. @Kognitio #SparkEvent Machine learning algorithms Dynamic Simulation Statistical Analysis Clustering Behavior modelling The drive for deeper understanding Reporting & BPM Fraud detection Dynamic Interaction Technology/Automation Analytical Complexity Campaign Management

12. @Kognitio #SparkEvent Key: “Graduation” Projects will need to Graduate from the Data Science Lab and become part of Business as Usual

13. @Kognitio #SparkEvent Your goal: PRESS HERE …and really cool Big Data stuff happens!

14. @Kognitio #SparkEvent Data flow

16. @Kognitio #SparkEvent  No need to pre‐process  No need to align to schema  No need to triage Null storage concerns

17. @Kognitio #SparkEvent Hadoop just too slow for interactive BI! …loss of train‐ of‐thought “while Hadoop shines as a processing platform, it is painfully slow as a query tool”

18. @Kognitio #SparkEvent Lots of these Not so many of these inherently disk oriented typically low ratio of CPU to Disk Hadoop is…

19. @Kognitio #SparkEvent Analytics needs low latency, no I/O wait High speed in‐memory processing

20. A* Modern Data Platform Reference Architecture Analytical Platform Near‐line Storage (optional) Access Application & Client Layer All BI Tools All OLAP Clients Excel Persistence Layer Hadoop Clusters Enterprise Data Warehouses Legacy Systems … Reporting Cloud Storage *(not THE)

21. © Hortonworks Inc. 2013 (another) Next-Generation Data Architecture Page 21 APPLICATIONSDATA SYSTEMS Microsoft Applications DATA SOURCES Traditional Sources (RDBMS, OLTP, OLAP) In‐memory MPP Accelerator BI Tools & OLAP Clients TRADITIONAL REPOS RDBMS EDW MPP OPERATIONAL TOOLS MANAGE & MONITOR DEV & DATA TOOLS BUILD & TEST New Sources (web logs, email, sensors, social media) HORTONWORKS DATA PLATFORM

22. Analytical Platform

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