Emerging Trends in Big Data, Analytics, Machine Learning, and Internet-of-Things.

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This presentation was delivered on March 7, 2018 at Gartner's Data and Analytics Summit in Grapevine, TX. Rahul Pathak, GM at AWS discusses Next Gen Architecture on AWS.

Technology

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Rahul Pathak
General Manager, Amazon Web Services
Emerging Trends in Big Data,
Analytics, Machine Learning,
and Internet-of-Things
Next Gen Architectures on AWS

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Customer requirements are evolving
Data variety and data volumes are increasing rapidly
Customers want to democratize
access to data in a governed way
Security and cost remain key decision factors
Analytic needs are evolving beyond
batch reports to real-time and predictive
Customers are looking to incorporate voice,
image recognition, and IoT use cases into applications

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Traditionally, analytics used to look like this

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Purpose-built

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
AWS big data and analytic services
Insights
Analytics
Data Lake
Data Movement

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Data Lakes on AWS

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Data Lakes, Analytics, and IoT Portfolio from AWS
Broadest, deepest set of analytic services

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Security & Compliance

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Certifications
United States
Asia Pacific
Europe

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Customer Momentum

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Exchange Data
• 12 equities
markets
• 4 options markets
SIP Data
• SIP trades
• SIP NBBO
• OPRA
Broker Dealer data
• 4000 plus firms
Third Party Data
• Bloomberg
• Thompson Reuters
• DTCC
• OCC
Management
Amazon S3 Amazon
Glacier
Intake
Linkage
Normalization
Validation
Analytics
Amazon Redshift
Amazon EMR
Machine Learning
API API
RDS
IAM
KMS
Usage Stats
• 33k+
• 20Pb+
Structured and
unstructured data
Millions of documents
25K data checks daily
Normalization
33,000 servers daily
Centralized data
Normalized data
Integrated data
Discoverable
Direct data query
ML/AI platforms
Applications/
Visualizations

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Serverless Analytics
Deliver cost-effective analytic solutions faster
$

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Amazon confidential

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Video
Amazon Rekognition Video
1. Video is uploaded
and stored to S3
2. Rekognition Video
creates metadata for
people and objects with
time segments for search
4. Lambda also pushes
the metadata and confidence
scores into Elasticsearch
3. The output is persisted
as metadata into
DynamoDB to
ensure durability
Amazon
Rekognition
Video
AWS LambdaAmazon S3
Amazon
Elasticsearch
Amazon
DynamoDB

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Autonomous Driving

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Enabling all types of data-driven analytics

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Q&A

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This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).

Driving Business Innovation: Latest Generative AI Advancements & Success Story

Safe Software

Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency. During the hour, we’ll take you through: Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board. Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes. Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI. We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI. This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!

Generating privacy-protected synthetic data using Secludy and Milvus

Zilliz

During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.

[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...

Jason Yip

The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.

JavaLand 2024: Application Development Green Masterplan

Miro Wengner

Skybuffer SAM4U tool for SAP license adoption

Tatiana Kojar

Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool. SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.

zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...

Alex Pruden

Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security. Paper Link: https://eprint.iacr.org/2024/257

Your One-Stop Shop for Python Success: Top 10 US Python Development Providers

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Essentials of Automations: Exploring Attributes & Automation Parameters

Safe Software

Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they? Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality. You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.

How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf

Chart Kalyan

Harnessing the Power of NLP and Knowledge Graphs for Opioid Research

Neo4j

Mutation Testing for Task-Oriented Chatbots

Pablo Gómez Abajo

Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots. To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.

Artificial Intelligence and Electronic Warfare

Papadakis K.-Cyber-Information Warfare Analyst & Cyber Defense/Security Consultant-Hellenic MoD

Monitoring and Managing Anomaly Detection on OpenShift.pdf

Tosin Akinosho

Monitoring and Managing Anomaly Detection on OpenShift Overview Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices. Key Topics Covered 1. Introduction to Anomaly Detection - Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems. 2. Understanding Edge (IoT) - Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source. 3. What is ArgoCD? - Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices. 4. Deployment Using ArgoCD for Edge Devices - Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD. 5. Introduction to Apache Kafka and S3 - Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions. 6. Viewing Kafka Messages in the Data Lake - Learn how to view and analyze Kafka messages stored in a data lake for better insights. 7. What is Prometheus? - Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices. 8. Monitoring Application Metrics with Prometheus - Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system. 9. What is Camel K? - Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes. 10. Configuring Camel K Integrations for Data Pipelines - Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow. 11. What is a Jupyter Notebook? - Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text. 12. Jupyter Notebooks with Code Examples - Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.

AppSec PNW: Android and iOS Application Security with MobSF

Ajin Abraham

Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application. This talk covers: Using MobSF for static analysis of mobile applications. Interactive dynamic security assessment of Android and iOS applications. Solving Mobile app CTF challenges. Reverse engineering and runtime analysis of Mobile malware. How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.

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Fueling AI with Great Data with Airbyte Webinar

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Taking AI to the Next Level in Manufacturing.pdf

ssuserfac0301

Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as: 1. How quickly AI is being implemented in manufacturing. 2. Which barriers stand in the way of AI adoption. 3. How data quality and governance form the backbone of AI. 4. Organizational processes and structures that may inhibit effective AI adoption. 6. Ideas and approaches to help build your organization's AI strategy.

"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor Ivaniuk

Fwdays

At this talk we will discuss DDoS protection tools and best practices, discuss network architectures and what AWS has to offer. Also, we will look into one of the largest DDoS attacks on Ukrainian infrastructure that happened in February 2022. We'll see, what techniques helped to keep the web resources available for Ukrainians and how AWS improved DDoS protection for all customers based on Ukraine experience

Main news related to the CCS TSI 2023 (2023/1695)

Jakub Marek

An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers. The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 . The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .

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Emerging Trends in Big Data, Analytics, Machine Learning, and Internet-of-Things.

1. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Rahul Pathak General Manager, Amazon Web Services Emerging Trends in Big Data, Analytics, Machine Learning, and Internet-of-Things Next Gen Architectures on AWS

2. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Customer requirements are evolving Data variety and data volumes are increasing rapidly Customers want to democratize access to data in a governed way Security and cost remain key decision factors Analytic needs are evolving beyond batch reports to real-time and predictive Customers are looking to incorporate voice, image recognition, and IoT use cases into applications

18. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Exchange Data • 12 equities markets • 4 options markets SIP Data • SIP trades • SIP NBBO • OPRA Broker Dealer data • 4000 plus firms Third Party Data • Bloomberg • Thompson Reuters • DTCC • OCC Management Amazon S3 Amazon Glacier Intake Linkage Normalization Validation Analytics Amazon Redshift Amazon EMR Machine Learning API API RDS IAM KMS Usage Stats • 33k+ • 20Pb+ Structured and unstructured data Millions of documents 25K data checks daily Normalization 33,000 servers daily Centralized data Normalized data Integrated data Discoverable Direct data query ML/AI platforms Applications/ Visualizations

25. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Video Amazon Rekognition Video 1. Video is uploaded and stored to S3 2. Rekognition Video creates metadata for people and objects with time segments for search 4. Lambda also pushes the metadata and confidence scores into Elasticsearch 3. The output is persisted as metadata into DynamoDB to ensure durability Amazon Rekognition Video AWS LambdaAmazon S3 Amazon Elasticsearch Amazon DynamoDB

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