One of the largest retailers in North America are considering Apache Geode for their new mobile loyalty application, to support their digital transformation effort. They would use Geode to provide operational data services for their mobile cloud service. This retailer needs to replace sluggish response times with sub-second response which will improved conversion rates. They also want to able to close the loop between data science findings and app experience. This way the right customer interaction is suggested when it is needed such as when customers are looking at their mobile app while walking in the store, or sending notifications at the individuals most likely shopping times. The final benefits of using Geode will include faster development cycles, increased customer loyalty, and higher revenue.
The Big Data Ecosystem for Financial ServicesDataStax
1) The document discusses the use of big data and modern data architectures in the financial services industry.
2) It notes challenges such as aging IT systems, high costs, and lack of resources that are barriers to using big data.
3) Reference architectures are presented from IBM, Oracle, and DataStax that incorporate technologies like Hadoop, Spark, and Cassandra to power applications like customer 360, fraud detection, and real-time analytics.
Big Data as Competitive Advantage in Financial ServicesCloudera, Inc.
Financial firms are under pressure to grow their business while containing risk and complying with many regulations world-wide. In addition, there is the growing demand from customers to improve their experience and offer new services over multiple channels.
Data is at the core of these capabilities but there are many challenges to overcome: fragmentation, security, quality, privacy, retention, to name a few.
We are going to hear about trends in the industry from IDC Financial Insights Research Director Bill Fearnley, followed by a discussion about how Cloudera has helped Transamerica turn their data into competitive advantage by creating an Enterprise Marketing and Analytics Engine.
VoltDB and HPE Vertica Present: Building an IoT Architecture for Fast + Big DataVoltDB
This webinar with Chris Selland of HPE Vertica and Dennis Duckworth of VoltDB addresses the growing challenges with managing a complex IoT solution and how to enable real-time operational interaction with comprehensive data analytics.
This document summarizes Manulife's global data strategy and data operations in Melbourne. It discusses establishing a balanced hub-and-spoke model to provide global consistency, talent, and dynamics. The data offices follow the business roadmap and have engineering, governance, and analytics functions. The enterprise data lake setup includes three physical instances across regions with identical technology stacks for operations, preview, validation, and DR. It ingests and stores various data sources and enables advanced analysis, digital connection of systems, and automated reporting use cases across regions.
Moving Beyond Batch: Transactional Databases for Real-time DataVoltDB
Join guest Forrester speaker, Principal Analyst Mike Gualtieri, and Dennis Duckworth Director of Product Marketing from VoltDB to learn how enterprises can create a real-time, “origin-zero” data architecture within transactional databases to become a real-time enterprise.
Hadoop is regarded as a key capability for implementing Big Data initiatives in the enterprise, but organizations have yet to realize its full business benefits. In this webinar, Pivotal and guest Forrester Research, Inc. Identify the use cases driving Hadoop adoption, and explore what is needed to transform initial investments into results.
Learn about:
Challenges Hadoop introduces, and how the right tools and platforms can help address them
Shifts in the industry with regards to SQL and NoSQL systems and their implications to Big Data analytics
Applying in-memory technologies for data management systems, data analytics, transactional processing and operational databases
Watch the on-demand webinar here:
http://www.pivotal.io/big-data/pivotal-forrester-operationalizing-data-analytics-webinar
Learn how to maximize business value from all of your data here: http://www.pivotal.io/big-data/pivotal-hd
Relying on Data for Strategic Decision-Making--Financial Services ExperienceCloudera, Inc.
This document discusses how financial services companies can leverage big data and machine learning to drive strategic decision making through improved customer insights and risk management. It outlines key data challenges around data silos, volume, and costs. Example use cases are provided for building customer 360 profiles and improving fraud detection. The presentation argues that an enterprise data hub on Cloudera can help integrate diverse data sources, power real-time analytics at scale, and enable new business opportunities in areas like customer experience and risk compliance.
Building a Modern FinTech Big Data InfrastructureDatabricks
The cloud is now the first choice for large-scale analytics, but organizations that have sunk investment into Hadoop on-premises are also challenged with maintaining operations. This can make a move to modern analytics platforms like Spark difficult or impossible. Learn about innovations for large-scale migration that can take full advantage of cloud-based analytics without disrupting operations.
Future-Proof Your Streaming Analytics Architecture- StreamAnalytix WebinarImpetus Technologies
Future-Proof Your Streaming Analytics Architecture- StreamAnalytix Webinar
View the webcast on http://bit.ly/1HFD8YR
The speakers from Forrester and Impetus talk about the options and optimal architecture to incorporate real-time insights into your apps that provisions benefitting from future innovation also.
Building a Modern Analytic Database with Cloudera 5.8Cloudera, Inc.
This document discusses building a modern analytic database with Cloudera. It outlines Marketing Associates' evaluation of solutions to address challenges around managing massive and diverse data volumes. They selected Cloudera Enterprise to enable self-service BI and real-time analytics at lower costs than traditional databases. The solution has provided scalability, cost savings of over 90%, and improved security and compliance. Future roadmaps for Cloudera's analytic database include faster SQL, improved multitenancy, and deeper BI tool integration.
How a Media Data Platform Drives Real-time Insights & Analytics using Apache ...Databricks
Roularta is a leading publishing company in Belgium. As digital news and channels move at a rapid pace and contain massive volumes of data, Roularta decided in 2019 to invest in a Spark-based data platform to drive true real-time website analytics and unlock insights on previously untouched (big) data sources. In this talk we’ll first explain why and how Roularta embarked from a classical data warehouse to a Spark-based Lakehouse using Delta. We’ll outline the series of publishing & marketing use-cases done in the last 12 months and highlight for each use-case the advantages of Spark and how the team further tuned performance to truly deliver insights with high velocity.
This document discusses Ford's data analytics strategy. It notes that the volume of data Ford collects is increasing significantly from connected vehicles and other sources. This includes up to 25 gigabytes per hour from individual vehicles. Ford is working to build applications and drive adoption of analytics across the company through education and training programs to democratize access to tools and infrastructure while ensuring privacy, security, and governance of customer data. The goal is to provide the right data, tools, and support to analysts and data scientists to improve products and services.
The document is a presentation about big data by Sanjay Sharma from Impetus Technologies. It discusses big data concepts and technologies like Hadoop, NoSQL, and MPP databases. It also covers big data tools and ecosystems, use cases, careers, and the impact of big data on IT and businesses. The presentation contains 41 slides covering these topics in detail with examples and diagrams.
Cloud expo june 2013: Building a Real Time Analytics Platform on Big Data in ...Sanjay Sharma
This document discusses building a real-time analytics platform on big data in the cloud. It outlines the need to move from batch processing to real-time analytics using approaches like streaming analytics and in-memory analytics. It provides examples of software that can be used for these approaches. The document also presents a reference architecture for a real-time analytics strategy, including components for data ingestion, processing, storage and business rules management. Impetus is introduced as a company that provides consulting and services for big data analytics.
The document summarizes resources available from DataStax for customers, including:
1) A SWAT team that provides help with projects, testing, and strategies for DataStax Enterprise and complementary technologies.
2) A customer advocacy team dedicated to customer engagement, satisfaction, and ensuring value from interactions with DataStax through programs like health checks and business reviews.
3) 24/7 global support centers and access to Cassandra experts to resolve issues.
4) Online training and certification programs.
5) A free startup program that provides unlimited use of DataStax Enterprise for eligible startups.
This document discusses measuring the business value of using Kafka to power event-driven applications. It begins by explaining why measuring value is important for ROI, stakeholder commitment, and benefits realization. It then outlines three real-world examples of using Kafka: resolving ATM disputes faster resulted in 50% less agent time and 75% fewer avoidable payments; a customer 360 application improved targeted offers for increased revenue and better inventory management; and a fraud prevention system enabled real-time detection and prevention, decreasing insurance premiums. The document concludes by recommending establishing credibility through sound assumptions, defining what is actually being measured, and accepting that value is subjective and changing over time.
Learn to Use Databricks for the Full ML LifecycleDatabricks
Machine learning development brings many new complexities beyond the traditional software development lifecycle. Unlike traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. In this talk, learn how to operationalize ML across the full lifecycle with Databricks Machine Learning.
Modern Data Platform Part 1: Data IngestionNilesh Shah
The document describes Azure Data Factory (ADF) as a fully-managed data integration service in the cloud that allows for hybrid data movement and orchestration of data pipelines wherever data lives. ADF enables connecting to various data sources, transforming and enriching data, and publishing data while meeting security and compliance needs. It provides integration runtimes including Azure, Azure-SSIS, and self-hosted to execute SQL Server Integration Services packages and provide data integration capabilities across cloud and on-premises environments.
Designing Fault-Tolerant Applications with DataStax Enterprise and Apache Cas...DataStax
Data resiliency and availability are mission-critical for enterprises today—yet we live in a world where outages are an everyday occurrence. Whether the problem is a single server failure or losing connectivity to an entire data center, if your applications aren’t designed to be fault tolerant, recovery from an outage can be painful and slow. Watch this on-demand webinar to look at best practices for developing fault-tolerant applications with DataStax Drivers for Apache Cassandra and DataStax Enterprise (DSE).
View recording: https://youtu.be/NT2-i3u5wo0
Explore all DataStax webinars: https://www.datastax.com/resources/webinars
Real-time Microservices and In-Memory Data GridsAli Hodroj
How in-memory data grids enable a real-time microservices architecture while diminishing the accidental complexity of persistence, orchestration, and fragmentation of scale.
#GeodeSummit - Wall St. Derivative Risk Solutions Using GeodePivotalOpenSourceHub
In this talk, Andre Langevin discusses how Geode forms the core of many Wall Street derivative risk solutions. By externalizing risk from trading systems, Geode-based solutions provide cross-product risk management at speeds suitable for automated hedging, while simultaneously eliminating the back office costs associated with traditional trading system based solutions.
#GeodeSummit: Easy Ways to Become a Contributor to Apache GeodePivotalOpenSourceHub
The document provides steps for becoming a contributor to the Apache Geode project, beginning with joining online conversations about the project, then test-driving it by building and running examples, and finally improving the project by reporting findings, fixing bugs, or adding new features through submitting code. The key steps are to join mailing lists or chat forums to participate in discussions, quickly get started with the project by building and testing examples in 5 minutes, and then test release candidates and report any issues found on the project's issue tracker or documentation pages. Contributions to the codebase are also welcomed by forking the GitHub repository and submitting pull requests with bug fixes or new features.
#GeodeSummit: Democratizing Fast Analytics with Ampool (Powered by Apache Geode)PivotalOpenSourceHub
Today, if events change the decision model, we wait until the next batch model build for new insights. By extending fast “time-to-decisions” into the world of Big Data Analytics to get fast “time-to-insights”, apps will get what used to be batch insights in near real time. The technology enabling this includes smart in-memory data storage, new storage class memory, and products designed to do one or more parts of an analysis pipeline very well. In this talk we describe how Ampool is building on Apache Geode to allow Big Data analysis solutions to work together with a scalable smart storage class memory layer to allow fast and complex end-to-end pipelines to be built -- closing the loop and providing dramatically lower time to critical insights.
#GeodeSummit: Combining Stream Processing and In-Memory Data Grids for Near-R...PivotalOpenSourceHub
This document discusses combining stream processing and in-memory data grids for near-real-time aggregation and notifications. It describes storing immutable event data and filtering and aggregating events in real-time based on requested perspectives. Perspectives can be requested at any time for historical or real-time event data. The solution aims to be scalable, resilient, and low latency using Apache Storm for stream processing, Apache Geode for the event log and storage, and deployment patterns to collocate them for better performance.
In this session we review the design of the current capabilities of the Spring Data GemFire API that supports Geode, and explore additional use cases and future direction that the Spring API and underlying Geode support might evolve.
In this session we review the design of the newly released off heap storage feature in Apache Geode, and discuss use cases and potential direction for additional capabilities of this feature.
This document discusses implementing a Redis adaptor using Apache Geode. It provides an overview of Redis data structures and commands, describes how Geode partitioned regions and indexes can be used to store and access Redis data, outlines advantages like scalability and high availability, and presents a roadmap for further development including supporting additional commands and performance optimization.
#GeodeSummit - Large Scale Fraud Detection using GemFire Integrated with Gree...PivotalOpenSourceHub
In this session we explore a case study of a large-scale government fraud detection program that prevents billions of dollars in fraudulent payments each year leveraging the beta release of the GemFire+Greenplum Connector, which is planned for release in GemFire 9. Topics will include an overview of the system architecture and a review of the new GemFire+Greenplum Connector features that simplify use cases requiring a blend of massively parallel database capabilities and accelerated in-memory data processing.
#GeodeSummit - Integration & Future Direction for Spring Cloud Data Flow & GeodePivotalOpenSourceHub
In this session we review the design of the current state of support for Apache Geode by Spring Cloud Data Flow, and explore additional use cases and future direction that Spring Cloud Data Flow and Apache Geode might evolve.
#GeodeSummit Keynote: Creating the Future of Big Data Through 'The Apache Way"PivotalOpenSourceHub
Keynote at Geode Summit 2016 by Dr. Justin Erenkrantz, Bloolmberg LP. Creating the Future of Big Data Through "The Apache Way" and why this matters to the community
#GeodeSummit - Where Does Geode Fit in Modern System ArchitecturesPivotalOpenSourceHub
The document discusses how Apache Geode fits into modern system architectures using the Command Query Responsibility Segregation (CQRS) pattern. CQRS separates reads and writes so that each can be optimized independently. Geode is well-suited as the read store in a CQRS system due to its ability to efficiently handle queries and cache data through regions. The document provides references on CQRS and related patterns to help understand how they can be applied with Geode.
Apache Apex and Apache Geode are two of the most promising incubating open source projects. Combined, they promise to fill gaps of existing big data analytics platforms. Apache Apex is an enterprise grade native YARN big data-in-motion platform that unifies stream and batch processing. Apex is highly scalable, performant, fault tolerant, and strong in operability. Apache Geode provides a database-like consistency model, reliable transaction processing and a shared-nothing architecture to maintain very low latency performance with high concurrency processing. We will also look at some use cases where how these two projects can be used together to form distributed, fault tolerant, reliable in memory data processing layer.
How Southwest Airlines Uses Geode
Distributed systems and fast data require new software patterns and implementation skills. Learn how Southwest Airlines uses Apache Geode, organizes team responsibilities, and approaches design tradeoffs. Drawing inspiration from real whiteboard conversations, we’ll explore: common development pitfalls, environment capacity planning, streaming data patterns like consumer checkpointing, support roles, and production lessons learned.
Every day, Apache Geode improves how Southwest Airlines schedules nearly 4,000 flights and serves over 500,000 passengers. It’s an essential component of Southwest’s ability to reduce flight delays and support future growth.
Apache Apex: Stream Processing Architecture and Applications Comsysto Reply GmbH
• Architecture highlights: high throughput, low-latency, operability with stateful fault tolerance, strong processing guarantees, auto-scaling etc
• Application development model, unified approach for real-time and batch use cases
• Tools for ease of use, ease of operability and ease of management
• How customers use Apache Apex in production
Pivoting Spring XD to Spring Cloud Data Flow with Sabby AnandanPivotalOpenSourceHub
Pivoting Spring XD to Spring Cloud Data Flow: A microservice based architecture for stream processing
Microservice based architectures are not just for distributed web applications! They are also a powerful approach for creating distributed stream processing applications. Spring Cloud Data Flow enables you to create and orchestrate standalone executable applications that communicate over messaging middleware such as Kafka and RabbitMQ that when run together, form a distributed stream processing application. This allows you to scale, version and operationalize stream processing applications following microservice based patterns and practices on a variety of runtime platforms such as Cloud Foundry, Apache YARN and others.
About Sabby Anandan
Sabby Anandan is a Product Manager at Pivotal. Sabby is focused on building products that eliminate the barriers between application development, cloud, and big data.
Apache Geode is an open source in-memory data grid that provides data distribution, replication and high availability. It can be used for caching, messaging and interactive queries. The presentation discusses Geode concepts like cache, region and member. It provides examples of how large companies use Geode for applications requiring real-time response, high concurrency and global data visibility. Geode's performance comes from minimizing data copying and contention through flexible consistency and partitioning. The project is now hosted by Apache and the community is encouraged to get involved through mailing lists, code contributions and example applications.
Build your first Internet of Things app today with Open SourceApache Geode
This document provides an overview of Apache Geode, an in-memory data management platform. It discusses using Geode for high-performance and scalable applications that require fast access to critical datasets. Key concepts explained include regions, caching of data, and the use of functions to enable distributed processing across a Geode cluster. The document also mentions integrations with Spark and Cloud Foundry that allow persisting RDDs in Geode and exposing regions as RDDs.
This slides provides description for how apex can be used by a developer. The slide also provides information about various components of Apex Operator lifecycle.
In this session, we will talk about two of the most promising incubating open source Projects, Apache Apex & Apache Geode and how together they attempt to solve shortcomings of existing big data analytics platforms.
Project Apex is an enterprise grade native YARN big data-in-motion platform that unifies stream processing as well as batch processing. Apex processes big data-in-motion in a highly scalable, highly performant, fault-tolerant, stateful, secure, distributed, and an easily operable way.
Apache Geode provides a database-like consistency model, reliable transaction processing and a shared-nothing architecture to maintain very low latency performance with high concurrency processing.
We will also look at some use cases where how these two projects can be used together to form distributed, fault tolerant, reliable In memory data processing layer.
Digital Shelf Analytics: Are you ready to rock the digital shelf?Joakim Gavelin
Digital Shelf Analytics can be an overwhelming concept to tackle. Join this easy-to-follow guide to learn how to take the guesswork out of digital merchandising and uncover their lost sales opportunities.
And how to continuously monitor market activities to optimize your product experiences and respond faster to market changes.
Kevin Benedict, Senior Analyst for Digital Transformation and Mobility at Cognizant, and Susan Miller, Chief Strategy Officer at AnyPresence, explore the ways companies can achieve an information advantage through digital and organizational transformation.
Sap makes-big-data-real-real-time-real-resultsasmae bouadil
SAP provides solutions to help organizations realize value from big data by accelerating data acquisition and analysis, applying insights across business operations, and achieving breakthrough business results. SAP's in-memory platform and applications make it possible to gain real-time insights from both structured and unstructured data sources. Customers in various industries have used SAP's solutions to personalize the customer experience, detect fraud, optimize networks and more.
This document discusses how big data is transforming the retail industry and how companies can leverage big data analytics. It provides an overview of how data has grown exponentially in recent decades due to the rise of technologies like mobile, social media, and cloud computing. The document then discusses how SAP provides big data analytics solutions tailored for retail businesses to help them gain insights from all this data to improve areas like customer experience, promotions, and sales effectiveness. It also provides examples of how specific retail customers have used SAP's big data solutions to address challenges and drive business value.
The First Kilometre: Building a Back-End That Sets You Up For Success Demac Media
The front end of an E-Commerce platform may get all the attention but more often than not, it's the back end that will determine whether you're a successful E-Commerce Case Study or a highlight of 5 Things You Don't Want to Do in 2014. This presentation will help to ensure it's not the latter.
Enabling digital business with governed data lakeKaran Sachdeva
Digital business is enabled by Artificial intelligence, Machine learning, and data science. Artificial intelligence and machine learning are dependent on right Information architecture and data foundation. Governed data lake infused with governance and data science platform gives you the power to take the organization in the digital transformation and AI journey.
Retail Analytics and BI with Looker, BigQuery, GCP & Leigha JarettDaniel Zivkovic
Leigha Jarett of GCP explains how to bring Cloud "superpowers" to your Data and modernize your Business Intelligence with Looker, BigQuery and Google Cloud services on an example of Cymbal Direct - one of Google Cloud's demo brands. The meetup recording with TOC for easy navigation is at https://youtu.be/BpzJU_S40ic.
P.S. For more interactive lectures like this, go to http://youtube.serverlesstoronto.org/ or sign up for our upcoming live events at https://www.meetup.com/Serverless-Toronto/events/
The document discusses an intelligent store platform called Altworx. It describes how Altworx integrates data from various store systems and sensors in real-time to provide operational intelligence. This allows Altworx to detect situations as they occur and recommend actions to the appropriate personnel. Examples are given of applications that could optimize checkout zones, track customer behavior, manage fitting rooms, and monitor energy usage and security. The platform aims to improve sales, costs, compliance and customer experience through proactive recommendations based on combining real-time store data.
Hyper Group’s Director and Co-Founder, Damon Bryan, will be presenting: ‘The Data Science behind Data Personalisation’.
BIBoss is an award-winning networking event for leaders in Business Intelligence and Data Science. Hyper Group’s Director and Co-Founder, Damon Bryan, presented: ‘The Data Science behind Data Personalisation’ and Infinity Works' Technical Director, Neil Dunlop, presented: ‘AI, where are we now?’, including a discussion around demystifying the preconceptions of AI.
'Six simple steps for retail innovation'
Alex is a mobile retail technology expert, with a multi-discipline skill set: user psychology and experience; mobile strategy; technical design; systems architecture; marketing; and consumer technology. he has designed and delivered award-winning mobile technology and strategy for some of the UK’s largest retailers, including Topshop, BHS, Mastercard and Burton, as well as several start-ups.
BIG Data & Hadoop Applications in RetailSkillspeed
The document discusses applications of big data and Hadoop in retail industries. It describes how retailers can use big data insights from consumer activities and brand sentiment analysis to personalize shopping experiences, optimize e-commerce, store layouts, and inventory levels. Hadoop is presented as a framework for processing and analyzing large datasets that retailers can use to gain these insights from consumer data and improve operations and sales.
Mohanbir Sawhney, Robert R. McCormick Tribune Foundation Clinical Professor of Technology Kellogg School of Management, Northwestern University presents at the 2012 Big Analytics Roadshow.
Companies are drinking from a fire hydrant of data that is too big, moving too fast and is too diverse to be analyzed by conventional database systems. Big Data is like a giant gold mine with large quantities of ore that is difficult to extract. To get value out of Big Data, enterprises need a new mindset and a new set of tools. They also need to know how to extract actionable insights from Big Data that can lead to competitive advantage. The Big Story of Big Data is not what Big Data is, but what it means for business value and competitive advantage.... read more: http://www.biganalytics2012.com/sessions.html#mohan_sawhney
AI in Retail - Where it Matters / What's NextDaniel Faggella
Daniel Faggella is the CEO and founder of TechEmergence, a market research and media firm focused on artificial intelligence. In this presentation, Faggella discusses current trends and applications of AI in retail, including the movement towards "instrumented retail environments" where large retailers are using cameras and sensors to optimize product placements, customer experiences, and marketing strategies based on data collection. He warns that most current "AI companies" are still in early pilot stages and that executives should focus on use cases that solve clear business problems rather than novelty applications.
Agile Mumbai 2022 - Kartik Dhokaai | AI Power SearchAgileNetwork
The document discusses AI powered search and its benefits over traditional search methods. It describes how AI powered search uses natural language processing and machine learning techniques like semantic annotations and text analysis to better understand user queries and return more relevant results. The document outlines key benefits like increased customer retention, sales, and conversion rates. It also discusses challenges in improving relevance for AI search, such as dealing with omnichannel contexts and unpredictable customer behavior.
Lazada is an ecommerce company in Southeast Asia that handles large amounts of customer data from transactions, purchases, and feedback. This presents big data challenges to make sense of it all and power real-time decisions. Lazada aims to provide personalized shopping experiences and actionable insights. Key challenges include migrating to new tools while maintaining data integrity. Lazada uses various BI tools like Qlikview, Tableau, and Alteryx to report, process, analyze data and power CRM systems. Lazada's data warehouse architecture ingests data from multiple sources to help the business better understand customers and optimize performance.
How can data solutions change the retail industry?
See the innovative solutions transforming Retail Industry.
Big Data, Artificial Intelligence, Augumented Reality.
See more at http://itmagination.com
AI, Content and Customer Experience: What’s the Future of Commerce?John Mullins
Darwinian principles in the hyper competitive world of online retail and what adaptations it takes to win. Presented at Retail Global 2018, The Gold Coast, Australia.
How Artificial Intelligence Is Transforming RetailBernard Marr
Retail is at a turning point where we are seeing businesses that are in-line with the pace of technological progress thriving, while those that are struggling to keep up are dropping by the wayside.
Similar to #GeodeSummit - Using Geode as Operational Data Services for Real Time Mobile Experience (20)
Here are the slides for Greenplum Chat #8. You can view the replay here: https://www.youtube.com/watch?v=FKFiyJDgdQk
The increased frequency and sophistication of high-profile data breaches and malicious hacking is putting organizations at continued risk of data theft and significant business disruption. Complicating this scenario is the unbounded growth of Big Data and petabyte-scale data storage, new open source database and distribution schemes, and the continued adoption of cloud services by enterprises.
Pivotal Greenplum customers often look for additional encryption of data-at-rest and data-in-motion. The massively parallel processing (MPP) architecture of Pivotal Greenplum provides an architecture that is unlike traditional OLAP on RDBMS for data warehousing, and encryption capabilities must address the scale-out architecture.
The Zettaset Big Data Encryption Suite has been designed for optimal performance and scalability in distributed Big Data systems like Greenplum Database and Apache HAWQ.
Here is a replay of our recent Greenplum Chat with Zettaset:
00:59 What is Greenplum’s approach for encryption and why Zettaset?
02:17 Results of field testing Zettaset with Greenplum
03:50 Introduction to Zettaset, the security company
05:36 Overview of Zettaset and their solutions
14:51 Different layers for encrypting data at rest
16:50 Encryption key management for big data
20:51 Zettaset BD Encrypt for data at rest and data in motion
22:19 How to mitigate encryption overhead with an MPP scale-out system
24:12 How to deploy BD Encrypt
25:50 Deep dive on data at rest encryption
30:44 Deep dive on data in motion encryption
36:72 Q: How does Zettaset deal with encrypting Greenplums multiple interfaces?
38:08 Q: Can I encrypt data for a particular column?
40:26 How Zettaset fits into a security strategy
41:21 Q: What is the performance impact on queries by encrypting the entire database?
43:28 How Zettaset helps Greenplum meet IT compliance requirements
45:12 Q: How authentication for keys is obtained
48:50 Q: How can Greenplum users try out Zettaset?
50:53 Q: What is a ‘Zettaset Security Coach’?
How to use the WAN Gateway feature of Apache Geode to implement multi-site and active-active failover, disaster recovery, and global scale applications.
Building Apps with Distributed In-Memory Computing Using Apache GeodePivotalOpenSourceHub
Slides from the Meetup Monday March 7, 2016 just before the beginning of #GeodeSummit, where we cover an introduction of the technology and community that is Apache Geode, the in-memory data grid.
GPORCA is newly open source advanced query optimizer that is a subproject of Greenplum Database open source project. GPORCA is the query optimizer used in commercial distributions of both Greenplum and HAWQ. In these distributions GPORCA has achieved 1000x performance improvement across TPC-DS queries by focusing on three distinct areas: Dynamic Partition Elimination, SubQuery Unnesting, and Common Table Expression.
Now that GPORCA is open source, we are looking for collaborators to help us realize the ultimate dream for GPORCA - to work with any database.
The new breed of data management systems in Big Data have to process so much data that optimization mistakes are magnified in traditional optimizers. Furthermore, coding and manual optimization of complex queries has proven to be hard.
In this session, Venkatesh will discuss:
- Overview of GPORCA
- How to add GPORCA to HAWQ with a build option
- How GPORCA could be made to work with any database
- Future vision for GPORCA and more immediate plans
- How to work with GPORCA, and how to contribute to GPORCA
Motivation and goals for off-heap storage
Off-heap features and usage
Implementation overview
Preliminary benchmarks: off-heap vs. heap
Tips and best practices
Zeppelin Interpreters
PSQL (to became JDBC in 0.6.x)
Geode
SpringXD
Apache Ambari
Zeppelin Service
Geode, HAWQ and Spring XD services
Webpage Embedder View
This document discusses Linux containers and PostgreSQL in Docker containers. It begins with an overview of containers, their advantages and disadvantages compared to virtual machines. It then discusses different implementations of containers like LXC and systemd-nspawn. A large portion of the document is dedicated to Docker containers - how to install, use images and volumes, and common commands. It concludes with best practices for running PostgreSQL in Docker containers, including mounting data volumes, linking containers, checking stats and processes.
This document summarizes transactions in Apache Geode, including:
- The semantics of repeatable read and optimistic concurrency control.
- The transaction API for basic, suspend/resume, and single entry operations.
- The implementation of using ThreadLocal to isolate transactions and conflict detection at commit.
- Handling transactions with replicated and partitioned regions, including failure scenarios.
- Support for client-initiated transactions, data colocation, and interaction with other Geode features.
- Types of exceptions and how to handle failures.
The document discusses Greenplum Database, an open source massively parallel processing (MPP) relational database system for big data. It provides an overview of Greenplum's architecture, including its master-segment structure and distributed transaction management. It also covers topics like defining data storage, distributions, partitioning, and analytics capabilities. Examples of Greenplum deployments are listed across various industries. Recent accomplishments and roadmap items are also summarized.
MADlib Architecture and Functional Demo on How to Use MADlib/PivotalRPivotalOpenSourceHub
This document discusses the MADlib architecture for performing scalable machine learning and analytics on large datasets using massively parallel processing. It describes how MADlib implements algorithms like linear regression across distributed database segments to solve challenges like multiplying data across nodes. It also discusses how MADlib uses a convex optimization framework to iteratively solve machine learning problems and the use of streaming algorithms to compute analytics in a single data scan. Finally, it outlines how the MADlib architecture provides scalable machine learning capabilities to data scientists through interfaces like PivotalR.
The document discusses how to build predictive models from noisy sensor data collected during oil and gas drilling operations. It notes that sensor data can be noisy, requiring data cleansing techniques to derive meaningful signals. It also discusses extracting relevant features from the cleansed sensor data and using those features to build predictive models, with the goal of predicting drilling failures and improving operations.
Retrieval Augmented Generation Evaluation with RagasZilliz
Retrieval Augmented Generation (RAG) enhances chatbots by incorporating custom data in the prompt. Using large language models (LLMs) as judge has gained prominence in modern RAG systems. This talk will demo Ragas, an open-source automation tool for RAG evaluations. Christy will talk about and demo evaluating a RAG pipeline using Milvus and RAG metrics like context F1-score and answer correctness.
DefCamp_2016_Chemerkin_Yury-publish.pdf - Presentation by Yury Chemerkin at DefCamp 2016 discussing mobile app vulnerabilities, data protection issues, and analysis of security levels across different types of mobile applications.
"Making .NET Application Even Faster", Sergey Teplyakov.pptxFwdays
In this talk we're going to explore performance improvement lifecycle, starting with setting the performance goals, using profilers to figure out the bottle necks, making a fix and validating that the fix works by benchmarking it. The talk will be useful for novice and seasoned .NET developers and architects interested in making their application fast and understanding how things work under the hood.
Garbage In, Garbage Out: Why poor data curation is killing your AI models (an...Zilliz
Enterprises have traditionally prioritized data quantity, assuming more is better for AI performance. However, a new reality is setting in: high-quality data, not just volume, is the key. This shift exposes a critical gap – many organizations struggle to understand their existing data and lack effective curation strategies and tools. This talk dives into these data challenges and explores the methods of automating data curation.
This PDF delves into the aspects of information security from a forensic perspective, focusing on privacy leaks. It provides insights into the methods and tools used in forensic investigations to uncover and mitigate privacy breaches in mobile and cloud environments.
Keynote : AI & Future Of Offensive SecurityPriyanka Aash
In the presentation, the focus is on the transformative impact of artificial intelligence (AI) in cybersecurity, particularly in the context of malware generation and adversarial attacks. AI promises to revolutionize the field by enabling scalable solutions to historically challenging problems such as continuous threat simulation, autonomous attack path generation, and the creation of sophisticated attack payloads. The discussions underscore how AI-powered tools like AI-based penetration testing can outpace traditional methods, enhancing security posture by efficiently identifying and mitigating vulnerabilities across complex attack surfaces. The use of AI in red teaming further amplifies these capabilities, allowing organizations to validate security controls effectively against diverse adversarial scenarios. These advancements not only streamline testing processes but also bolster defense strategies, ensuring readiness against evolving cyber threats.
Top 12 AI Technology Trends For 2024.pdfMarrie Morris
Technology has become an irreplaceable component of our daily lives. The role of AI in technology revolutionizes our lives for the betterment of the future. In this article, we will learn about the top 12 AI technology trends for 2024.
How UiPath Discovery Suite supports identification of Agentic Process Automat...DianaGray10
📚 Understand the basics of the newly persona-based LLM-powered Agentic Process Automation and discover how existing UiPath Discovery Suite products like Communication Mining, Process Mining, and Task Mining can be leveraged to identify APA candidates.
Topics Covered:
💡 Idea Behind APA: Explore the innovative concept of Agentic Process Automation and its significance in modern workflows.
🔄 How APA is Different from RPA: Learn the key differences between Agentic Process Automation and Robotic Process Automation.
🚀 Discover the Advantages of APA: Uncover the unique benefits of implementing APA in your organization.
🔍 Identifying APA Candidates with UiPath Discovery Products: See how UiPath's Communication Mining, Process Mining, and Task Mining tools can help pinpoint potential APA candidates.
🔮 Discussion on Expected Future Impacts: Engage in a discussion on the potential future impacts of APA on various industries and business processes.
Enhance your knowledge on the forefront of automation technology and stay ahead with Agentic Process Automation. 🧠💼✨
Speakers:
Arun Kumar Asokan, Delivery Director (US) @ qBotica and UiPath MVP
Naveen Chatlapalli, Solution Architect @ Ashling Partners and UiPath MVP
The Zaitechno Handheld Raman Spectrometer is a powerful and portable tool for rapid, non-destructive chemical analysis. It utilizes Raman spectroscopy, a technique that analyzes the vibrational fingerprint of molecules to identify their chemical composition. This handheld instrument allows for on-site analysis of materials, making it ideal for a variety of applications, including:
Material identification: Identify unknown materials, minerals, and contaminants.
Quality control: Ensure the quality and consistency of raw materials and finished products.
Pharmaceutical analysis: Verify the identity and purity of pharmaceutical compounds.
Food safety testing: Detect contaminants and adulterants in food products.
Field analysis: Analyze materials in the field, such as during environmental monitoring or forensic investigations.
The Zaitechno Handheld Raman Spectrometer is easy to use and features a user-friendly interface. It is compact and lightweight, making it ideal for field applications. With its rapid analysis capabilities, the Zaitechno Handheld Raman Spectrometer can help you improve efficiency and productivity in your research or quality control workflows.
"Building Future-Ready Apps with .NET 8 and Azure Serverless Ecosystem", Stan...Fwdays
.NET 8 brought a lot of improvements for developers and maturity to the Azure serverless container ecosystem. So, this talk will cover these changes and explain how you can apply them to your projects. Another reason for this talk is the re-invention of Serverless from a DevOps perspective as a Platform Engineering trend with Backstage and the recent Radius project from Microsoft. So now is the perfect time to look at developer productivity tooling and serverless apps from Microsoft's perspective.