Topics including: The transformative value of real-time data and analytics, and current barriers to adoption. The importance of an end-to-end solution for data-in-motion that includes ingestion, processing, and serving. Apache Kudu’s role in simplifying real-time architectures.
Part 1: Cloudera’s Analytic Database: BI & SQL Analytics in a Hybrid Cloud WorldCloudera, Inc.
3 Things to Learn About:
* On-premises versus the cloud: What’s the same and what’s different?
* Design and benefits of analytics in the cloud
* Best practices and architectural considerations
3 Things to Learn:
-How data is driving digital transformation to help businesses innovate rapidly
-How Choice Hotels (one of largest hoteliers) is using Cloudera Enterprise to gain meaningful insights that drive their business
-How Choice Hotels has transformed business through innovative use of Apache Hadoop, Cloudera Enterprise, and deployment in the cloud — from developing customer experiences to meeting IT compliance requirements
Consolidate your data marts for fast, flexible analytics 5.24.18Cloudera, Inc.
In this webinar, Cloudera and AtScale will showcase:
How a company can modernize their analytic architecture to deliver flexibility and agility to more end-users.
How using AtScale’s Universal Semantic layer can end the data chaos and allow business users to use the data in the modern platform.
Highlight the performance of AtScale and Cloudera’s analytic database with newly completed TPC-DS standard benchmarking.
Best practices for migrating from legacy appliances.
The document discusses how Sparklyr allows data scientists to access and work with data stored in Cloudera Enterprise using the popular RStudio IDE. It describes the challenges data scientists face in accessing secured Hadoop clusters and limitations of notebook environments. Sparklyr integration with RStudio provides a familiar environment for data scientists to access Hadoop data and compute using Spark, enabling distributed data science workflows directly in R. The presentation demonstrates how to analyze over a billion records using Spark and R through Sparklyr.
New Performance Benchmarks: Apache Impala (incubating) Leads Traditional Anal...Cloudera, Inc.
Recording Link: http://bit.ly/LSImpala
Author: Greg Rahn, Cloudera Director of Product Management
In this session, we'll review the recent set of benchmark tests the Apache Impala (incubating) performance team completed that compare Apache Impala to a traditional analytic database (Greenplum), as well as to other SQL-on-Hadoop engines (Hive LLAP, Spark SQL, and Presto). We'll go over the methodology and results, and we'll also discuss some of the performance features and best practices that make this performance possible in Impala. Lastly, we'll look at some recent advancements in in Impala over the past few releases.
Big data journey to the cloud rohit pujari 5.30.18Cloudera, Inc.
We hope this session was valuable in teaching you more about Cloudera Enterprise on AWS, and how fast and easy it is to deploy a modern data management platform—in your cloud and on your terms.
How to Build Multi-disciplinary Analytics Applications on a Shared Data PlatformCloudera, Inc.
The document discusses building multi-disciplinary analytics applications on a shared data platform. It describes challenges with traditional fragmented approaches using multiple data silos and tools. A shared data platform with Cloudera SDX provides a common data experience across workloads through shared metadata, security, and governance services. This approach optimizes key design goals and provides business benefits like increased insights, agility, and decreased costs compared to siloed environments. An example application of predictive maintenance is given to improve fleet performance.
Part 2: Apache Kudu: Extending the Capabilities of Operational and Analytic D...Cloudera, Inc.
3 Things to Learn About:
*How Apache Kudu enables users to do more than ever before with their Analytic and Operational Databases
*How Cloudera has built two versatile databases to help our customers tackle their hardest problems.
*How the addition of Apache Kudu to this mix will enable new use cases around real-time analytics, internet of things, time series data, and more.
3 Things to Learn About:
•Get an overview of Cloudera’s cybersecurity solution
*See a live demo of the solution in action
*Interact with Cloudera’s cybersecurity expert through live Q&A
Transforming Insurance Analytics with Big Data and Automated Machine Learning Cloudera, Inc.
This document discusses how machine learning and big data analytics can transform the insurance industry. It provides an overview of how automated machine learning works and its benefits for insurers, including higher returns on investment. Specific use cases discussed include underwriting triage, pricing, claims management, and fraud prevention. The document also addresses key data challenges for insurers and how a unified data platform can help bring different data sources together for machine learning. It promotes the idea that automated machine learning solutions can make machine learning more accessible, affordable and inclusive for organizations.
Gartner Data and Analytics Summit: Bringing Self-Service BI & SQL Analytics ...Cloudera, Inc.
For self-service BI and exploratory analytic workloads, the cloud can provide a number of key benefits, but the move to the cloud isn’t all-or-nothing. Gartner predicts nearly 80 percent of businesses will adopt a hybrid strategy. Learn how a modern analytic database can power your business-critical workloads across multi-cloud and hybrid environments, while maintaining data portability. We'll also discuss how to best leverage the increased agility cloud provides, while maintaining peak performance.
Cloudera can help optimize Splunk deployments by providing more cost-effective scalability, increased data flexibility, and enhanced analytics capabilities. Cloudera can ingest data from Splunk indexes and apply enrichment using open-source machine learning before storing the data in its data hub. This provides a single platform for advanced analytics like SQL and Python/R scripts across both historical and new data. Initial use cases include offloading event data from Splunk to reduce costs and loading additional context sources to gain better insights.
Get started with Cloudera's cyber solutionCloudera, Inc.
Cloudera empowers cybersecurity innovators to proactively secure the enterprise by accelerating threat detection, investigation, and response through machine learning and complete enterprise visibility. Cloudera’s cybersecurity solution, based on Apache Spot, enables anomaly detection, behavior analytics, and comprehensive access across all enterprise data using an open, scalable platform. But what’s the easiest way to get started?
Part 2: Cloudera’s Operational Database: Unlocking New Benefits in the CloudCloudera, Inc.
3 Things to Learn About:
*On-premises versus the cloud
*Design & benefits of real-time operational data in the cloud
*Best practices and architectural considerations
Big data journey to the cloud maz chaudhri 5.30.18Cloudera, Inc.
We hope this session was valuable in teaching you more about Cloudera Enterprise on AWS, and how fast and easy it is to deploy a modern data management platform—in your cloud and on your terms.
Making Self-Service BI a Reality in the EnterpriseCloudera, Inc.
For most analysts, the pace of analytics and data science can be frustrating. The common waterfall approach works well for the fixed reports, but it can be a lengthy process to request additional data sets, create new reports, or serve new use cases. So it’s no surprise that organizations are looking to shift towards a self-service model, empowering business users to discover and iterate quickly.
However, it’s not just about opening up this access, but also ensuring the results are accurate and trusted. When there are petabytes of data, how does a user know which tables to use and which are most relevant? How do you strike the balance between discovery and agility, while still meeting enterprise governance standards to truly get more value from your data?
During this webinar, you’ll learn how to empower end-users to make self-service BI a reality within your organization while fostering governance collaboration between all data stakeholders. We’ll discuss and demo:
Strategies of consolidating data across silos for fast, flexible access
Enabling easy discovery and exploration, including understanding which data to trust and where to start
New capabilities for intelligent query assistance as well as immediate performance optimizations and recommendations as-you-go
Collaboration and access outside of just SQL for data science and beyond
In addition, we will walk through best practices and considerations when developing your organizational strategy around self-service analytics, and highlight several real-world success stories from a wide range of industries.
3 things to learn:
Strategies of consolidating data across silos for fast, flexible access
Enabling easy discovery and exploration, including understanding which data to trust and where to start
New capabilities for intelligent query assistance as well as immediate performance optimizations and recommendations as-you-go
Part 1: Lambda Architectures: Simplified by Apache KuduCloudera, Inc.
3 Things to Learn About:
* The concept of lambda architectures
* The Hadoop ecosystem components involved in lambda architectures
* The advantages and disadvantages of lambda architectures
Advanced Analytics for Investment Firms and Machine LearningCloudera, Inc.
Learn how Cloudera Data Science Workbench helps you to:
Accelerate analytics projects from data exploration to production
Create a self-service data science platform
Deploy your models faster and share them with other data scientists
How to Build Continuous Ingestion for the Internet of ThingsCloudera, Inc.
The Internet of Things is moving into the mainstream and this new world of data-driven products is transforming a vast number of industry sectors and technologies.
However, IoT creates a new challenge: how to build and operationalize continual data ingestion from such a wide and ever-changing array of endpoints so that the data arrives consumption-ready and can drive analysis and action within the business.
In this webinar, Sean Anderson from Cloudera and Kirit Busu, Director of Product Management at StreamSets, will discuss Hadoop's ecosystem and IoT capabilities and provide advice about common patterns and best practices. Using specific examples, they will demonstrate how to build and run end-to-end IOT data flows using StreamSets and Cloudera infrastructure.
This document discusses using Cloudera Enterprise to analyze data from connected cars. It begins with an overview of the connected car market and use cases such as predictive maintenance, usage-based insurance, and mobility management. Examples are given of how major automakers and insurance companies are using connected car data and analytics. The rest of the document focuses on Cloudera Enterprise's capabilities for ingesting, storing, processing, and analyzing large volumes of diverse connected car data in real-time and batch modes. A demo is outlined to showcase predictive maintenance, usage-based insurance, and public services use cases.
Using Big Data to Transform Your Customer’s Experience - Part 1 Cloudera, Inc.
3 Things to Learn About:
-How the Customer Insights Solution helped
- How customer insights can improve customer loyalty, reduce customer churn, and increase upsell opportunities
- Which real-world use cases are ideal for using big data analytics on customer data
Building a Data Hub that Empowers Customer Insight (Technical Workshop)Cloudera, Inc.
We have seen the evolution with the Bi and Data Science fields from the structured data warehouse to data lake and finally, to the data hub. This session will cover the key steps required to building a data hub, examining how best to align and engage stakeholders and develop architectural sanction to enable your organisations to realise new customer insights and better enable you to achieve business objectives.
3 Things to Learn About:
*The IoT ecosystem and data management considerations for IoT
*Top IoT use cases and data architecture strategies for managing the sheer volume and variety of IoT data
*Real-life case studies on how our customers are using Cloudera Enterprise to drive insights and analytics from all of their IoT data
Securing the Data Hub--Protecting your Customer IP (Technical Workshop)Cloudera, Inc.
Your data is your IP and its security is paramount. The last thing you want is for your data to become a target for threats. This workshop will focus on the realities of protecting your customer’s IP from external and internal threats with battle hardened technologies and methodologies. Another key concept that will be examined is the connection of people, processes and technology. In addition, the session will take a look at authentication and authorisation, auditing and data lineage as well as the different groups required to play a part in the modern data hub. We will also look at how to produce high impact operation reports from Cloudera’s RecordService a new core security layer that centrally enforces fine-grained access control policy, which helps close the feedback loop to ensure awareness of security as a living entity within your organisation.
El documento habla sobre el derecho a la propiedad como uno de los derechos humanos fundamentales. Reconoce que la propiedad puede tomar muchas formas y debe ejercerse de manera responsable con la sociedad y el medio ambiente. También describe las características del derecho de propiedad como real, exclusivo, perpetuo y relativo a la ley, así como las condiciones bajo las cuales el Estado puede expropiar una propiedad por necesidad pública o seguridad nacional.
The document provides an overview of Kafka & Couchbase integration patterns. It introduces Couchbase and Kafka, describes how Kafka Connect enables real-time data pipelines between data systems, and how the Couchbase Kafka connector integrates Couchbase with Kafka pipelines. Use cases for the connector include using Couchbase as a data source or sink within Kafka streams. The document concludes with demos of Couchbase as a source and sink using the connector.
Este documento presenta una introducción a los vectores en tres dimensiones. Explica que los vectores son entes matemáticos que requieren una magnitud, dirección y sentido para expresarse. Luego define elementos clave como vectores unitarios, operaciones algebraicas con vectores como suma y multiplicación por escalares, y descomposición de vectores en componentes rectangulares. Finalmente, introduce conceptos como producto escalar y vectorial entre vectores.
An international collaboration in the design experience of a MOOC series. MOOCs for Teachers, the partnership and the design choices made by the team, involving international experts
How Big Data Can Enable Analytics from the Cloud (Technical Workshop)Cloudera, Inc.
In this workshop, we will look outside the box and help expand the problem space to include issues you may not have thought were possible before Big Data. From Near Real Time (NRT) recommendation engines, loan applications to churn detection, Big Data is answering new questions and providing organisations with a competitive edge through revenue increase, cost savings and risk mitigation. We will take a special look at the role the Cloud can play in elevating your analytics environment. We will discuss real world examples of how Big Data answers these questions and does it at a lower cost outlay.
The independent film Whiplash had its premiere at the Sundance Film Festival in 2013 and was distributed in the United States on October 10, 2014 in a limited release. Whiplash was a financial success, grossing $49 million worldwide against a $3.3 million budget. The film won several awards including an Academy Award for Best Film Editing.
Building Streaming And Fast Data Applications With Spark, Mesos, Akka, Cassan...Lightbend
This webinar discusses building streaming and fast data applications with technologies like Spark, Mesos, Akka, Cassandra and Kafka. It covers how microservices and fast data architectures are converging due to similar design problems and data becoming the dominant problem. The webinar also introduces Lightbend's Fast Data Platform for building streaming data systems and microservices with best practices, sample applications and machine learning-based monitoring and management.
Este documento describe tres funciones singulares comúnmente usadas en análisis de circuitos eléctricos: la función escalón, la función impulso y la función rampa. La función escalón cambia abruptamente de 0 a 1 en un momento dado, la función impulso es la derivada de la función escalón y tiene un valor infinito en un punto, y la función rampa es la integral de la función escalón y tiene una pendiente constante. Estas funciones singulares son herramientas matemáticas útiles para modelar cambios abruptos en tensiones y corrientes en circuitos.
Identificacion de peligros y evaluacion de riesgos en oficinas taller de nve...Alex Cumbicus Saavedra
Este documento describe la identificación de peligros y evaluación de riesgos en oficinas y talleres de investigación de accidentes. Define conceptos clave como peligro, riesgo, incidente y accidente. Explica la matriz de análisis de riesgos y presenta un ejemplo de investigación de incidente utilizando el método de los 5 por qué. El objetivo es identificar las causas para implementar acciones correctivas y prevenir futuros incidentes.
Christopher Greene is a healthcare IT consultant with over 12 years of experience advising Fortune 500 healthcare companies on projects valued at $200 million. He has expertise in EMR/EHR systems, clinical applications, and healthcare business systems. Greene has worked with over 50 healthcare organizations, including hospitals, insurance companies, and government agencies. He specializes in system integrations, data standards, regulatory compliance, and user training.
The Vortex of Change - Digital Transformation (Presented by Intel)Cloudera, Inc.
The vortex of change continues all around us – inside the company, with our customers and partners. A new norm is upon us. Business models are being turned upside down – the hunters now the hunted, global equalization – size is no longer a guarantee of success. The innovative survive and thrive…the nervous and slow go under...what does all this change means for you? Find out how does Intel’s strengths help our customers in this world of change.
This document summarizes some experiments conducted by researchers from the Universidad Autónoma de Madrid on model-driven engineering. It introduces the researchers and their work analyzing meta-models, model transformations, and evaluating modeling tools and notations with users. Specific experiments discussed include checking meta-models for refactoring opportunities, statically analyzing transformations to find errors, and evaluating domain-specific modeling tools and construction notations. The researchers find multi-level modeling patterns commonly occur in meta-models and show examples of refactoring modeling scenarios to a multi-level structure.
Real-time data processing serverless architecture can eliminate the need to provision and manage servers required to process files or streaming data in real time. In this session, we will cover the fundamentals of using AWS Lambda to process data in real-time from push sources such as AWS Iot and pull sources such as Amazon DynamoDB Streams or Amazon Kinesis. We'll also discuss best practices and do a deep dive into AWS Lambda real-time stream processing.
See the full talk here: https://www.infoq.com/presentations/lsm-append-data-structures
This talk is about the beauty of sequential access and append only data structures. We'll do this in the context of a little known paper entitled “Log Structured Merge Trees”. LSM describes a surprisingly counterintuitive approach to storing and accessing data in a sequential fashion. It came to prominence in Google's Big Table paper and today, the use of Logs, LSM and append only data structures drive many of the world's most influential storage systems: Cassandra, HBase, RocksDB, Kafka and more. Finally we'll look at how the beauty of sequential access goes beyond database internals, right through to how applications communicate, share data and scale.
Modern Data Challenges require Modern Graph TechnologyNeo4j
This session focuses on key data trends and challenges impacting enterprises. And, how graph technology is evolving to future-proof data strategy and architectures.
Streaming analytics webinar | 9.13.16 | Guest: Mike Gualtieri from ForresterCubic Corporation
Business success relies heavily on taking the right action, at the right time, all the time. And actions are dictated by data. But the batch-oriented, collect-store-contemplate model employed by Big Data Analytics technologies is incomplete because it does not make use of live data in real time. Without live, real-time data insights gathered are not up-to-date, and cannot accurately inform applications and services that would benefit from continuous, real-time context for time-sensitive decisions.
To thrive, businesses need to be able to use both live and historical data in their applications and services, continuously, concurrently, and correctly and the only technology currently capable of handling it is streaming analytics. Streaming analytics computes data right now, when it can be analyzed and put to good use to make applications of all kinds contextual and smarter.
This webinar held in collaboration with Forrester, Inc., showcased how streaming analytics applications can be built in minutes, to:
- Aggregate, enrich, and analyze a high throughput of data from multiple, disparate live data sources and in any format to identify patterns, detect opportunities, automate actions, and dynamically adapt
- Easily ingest streaming data from multiple disparate sources to multiple sources, within and between cloud and on-premises environments
- Analyze and act on data as it arrives, without needing to store, eliminating unnecessary security risks and storage costs
- Enable real-time analytics with existing business intelligence and data assets.
Capgemini Leap Data Transformation Framework with ClouderaCapgemini
https://www.capgemini.com/insights-data/data/leap-data-transformation-framework
The complexity of moving existing analytical services onto modern platforms like Cloudera can seem overwhelming. Capgemini’s Leap Data Transformation Framework helps clients by industrializing the entire process of bringing existing BI assets and capabilities to next-generation big data management platforms.
During this webinar, you will learn:
• The key drivers for industrializing your transformation to big data at all stages of the lifecycle – estimation, design, implementation, and testing
• How one of our largest clients reduced the transition to modern data architecture by over 30%
• How an end-to-end, fact-based transformation framework can deliver IT rationalization on top of big data architectures
Confluent Partner Tech Talk with BearingPointconfluent
This document discusses best practices for debugging client applications in Kafka streams. It begins by asking a question about debugging practices for producers, consumers, and Kafka streams applications. It then describes a Partner Technical Sales Enablement offering that includes live sessions and on-demand learning paths on topics like Confluent fundamentals and use cases. It outlines additional support for partners through technical workshops, coaching, and solution discovery sessions. The document concludes by stating the goal of Partner Tech Talks is to provide insights and inspiration through use case discussions.
Apache spark empowering the real time data driven enterprise - StreamAnalytix...Impetus Technologies
Apache Spark is one of the most popular Big Data frameworks today. It is fast becoming the de facto technology choice for stream processing, real-time analytics, data science and machine learning applications at scale. It has moved well beyond the early-adopter phase, is supported by a vibrant open source community and is enjoying accelerated adoption in enterprises.
Join our guest speaker from Forrester Research, VP & Principal Analyst, Mike Gualtieri and StreamAnalytix, Product Head, Anand Venugopal for a discussion on the trends and directions defining the growing importance of Apache Spark for stream processing, machine learning and other advanced data analytics applications.
Webinar: Transforming Customer Experience Through an Always-On Data PlatformDataStax
According to Forrester Research, leaders in customer experience drive 5.1X revenue growth over laggards. And although 84% of companies aspire to be a leader in this space, only 1 in 5 successfully delivers good or great customer experience. Join us for our next webinar where Mike Gualtieri, VP and Principal Analyst at Forrester Research and Rajay Rai, Head of Digital Engineering at Macquarie Bank will share how Customer Experience can drive business results such as faster revenue growth, longer customer retention, greater employee engagement and improved profit margins.
View webinar recording: https://youtu.be/eEc5tx-nHvI
Explore past DataStax webinars: http://www.datastax.com/resources/webinars
The document discusses a webinar on enabling 360-degree business insights with SAP data. It provides biographies of the two featured speakers, John Myers from EMA and Kevin Petrie from Attunity. It outlines the agenda which includes topics on the rise of data-driven strategies, strategic data integration, integrating enterprise application data and modern data integration technologies. It also provides information on how to watch the on-demand webinar or join the conversation on social media.
These slides—based on the webinar featuring John L Myers, managing research director for data and analytics at leading IT analyst firm Enterprise Management Associates (EMA), and Neil Barton, chief technology officer at WhereScape—highlight how the world of streaming data pipelines and automation practices for analytical environments intersect to provide value to both business stakeholders and corporate technologists.
View these slides to learn about:
- Drivers behind the growth of streaming usage scenarios
- Challenges that streaming data presents
- Value of automation techniques and technologies
- Benefits of applying automation to streaming data pipelines
- How WhereScape® automation with Streaming can fast-track streaming data use in your data landscape
This document discusses big data business opportunities and solutions. It notes that big data solutions are tailored to specific data types and workloads. Common business domains for big data include web analytics, clickstream analysis using the ELK stack, and big data in the cloud to provide auto-scaling, low costs, and use of cloud services. Effective big data solutions require data governance, cluster modeling, and analytics and visualization.
IBM Relay 2015: Cloud is All About the Customer IBM
Debuting new research data, Forrester's John Rymer discusses the rapid growth of "customer-centric" workloads in the cloud and the challenges many organizations have faced with private cloud.
Learn more by visiting our Bluemix Hybrid page: http://ibm.co/1PKN23h
Speaker: John Rymer (Analyst, Forrester)
Delivering Analytics at The Speed of Transactions with Data FabricDenodo
Watch full webinar here: https://bit.ly/3aAMTDD
It is no more an argument that data is the most critical asset for any business to succeed. While 85% of organizations want to improve their use of data insights in their decision making, according to a Forrester Survey, 91% of the respondents report that improving the use of data insights in decision making is challenging. To make data driven decision, organizations often turn to the data lakes, data lakehouses, cloud data warehouse etc. as their single source data repository. But the hard reality is that data is and will be spread across various repositories across cloud and regional boundaries.
Learn from renowned Forrester analyst and VP at Forrester, Noel Yuhanna:
- Why Data Fabric Is the best way to unify distributed data
- How Data Fabric be leveraged for data discovery, predictive analytics, data science and more
- Why data virtualization technology is key in building an Enterprise Data Fabric
Accelerating Time to Success for Your Big Data Initiatives☁Jake Weaver ☁
1. The document discusses the challenges of implementing big data initiatives, including sizing infrastructure, finding skilled professionals, and managing changing priorities over time.
2. It recommends partnering with a managed services provider to simplify big data implementation and gain expertise, flexibility, and time-to-market benefits.
3. The CenturyLink big data solutions suite includes managed Hadoop and analytics platforms to optimize data storage, integration, and analysis for customers.
Modernize your Infrastructure and Mobilize Your DataPrecisely
Modernizing your infrastructure can get complicated really fast. The keys to success involve breaking down data silos and moving data to the cloud in real time. But building data pipelines to mobilize your data in the cloud can be time consuming. You need solutions that decrease bandwidth, ensure data consistency, and enable data migration and replication in real-time; solutions that help you build data pipelines in hours, not days.
Watch this on-demand webinar to learn about the trends and pitfalls related to modernizing your infrastructure to cloud, how the pace of on-prem data growth demands accelerating data streaming to analytics platforms, and why mobilizing your data for the cloud improves business outcomes.
Strategy session 5 - unlocking the data dividend - andy steerAndy Steer
"A recent study completed by IDC examined the economic benefits accrued to organisations that made basic levels of investment in distinct areas of analytics and data management compared with the benefits accrued by organisations that opted for a broader and more diverse set of investments. The conclusion was that the leading organisations expect to capture in excess of $1.5 trillion more in value from their data and analytics initiatives over the next 4 years. This represents a 60% higher data dividend for the leading organisations.
To achieve these benefits organisations need to embrace the changing reality of the new data driven society and make a break from the beliefs and best practices inherent in traditional Business Intelligence programmes.
During the presentation Andy will expand on the data dividend concept, outline the 4 key investment areas that should be getting your attention and perhaps most importantly, explain how your existing SAP BusinessObjects technology can help you take your share of the estimated £53 billion UK data dividend."
Big Data, Big Thinking: Untapped OpportunitiesSAP Technology
The document discusses a webinar by SAP and Ernst & Young on big data. It explores big data adoption trends, how organizations can leverage big data to improve business performance and manage risks, and common use cases across industries like retail, transportation, and government. The webinar provides guidance on how organizations can get started with big data initiatives by identifying executive sponsors, use cases, architectural gaps, and building a business case to justify investment.
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.
The document discusses IBM's Big Data Platform for turning large and complex data into business insights. It provides an overview of key big data challenges faced by organizations and how the IBM platform addresses these challenges through solutions that handle the volume, velocity, variety and veracity of big data. These solutions include analytics, data warehousing, streaming analytics and Hadoop technologies. Use cases are presented for big data exploration, enhancing customer views, security intelligence, operations analysis and augmenting data warehouses.
Navigating the Workday Analytics and Reporting EcosystemWorkday, Inc.
Learn how to maximize the reporting and analytics capability in Workday for finance and HR teams. This slide deck dives into the functionality of Workday Prism Analytics and Workday People Analytics.
Modern Business Intelligence - Design and ImplementationsDavid J Rosenthal
During the first two “waves” of business intelligence, IT professionals and business analysts were the keepers of BI. They made BI accessible and consumable for end users.
While this approach still applies to complex business intelligence needs, today there is a new “wave.” This third wave of BI makes BI available to every kind of user.
As customer strive to take advantage of the digital transformation that is occurring in virtually every industry, they need to re-evaluate how they engage with their customers/prospects, how they transform their products and operations, and how they empower and understand their employees.
In today’s world, doing each of these things is more and more reliant upon data…traditionally, everything you knew about your customers and prospects was available in your business application systems and in the heads of employees. You learned almost everything about your products BEFORE they left your warehouse. Employees used technology more to enter data than to learn from it.
In the data-driven world we live in today, leveraging intelligent insights from data across customers, products, and employees is critical to be able to stay competitive and keep up with or lead digital transformation in any industry. And this isn’t just a customer’s typical business application data – it’s also about augmenting the customer’s data with additional data (e.g. search, employee behavioral data, sentiment data, benchmark data, etc..) – and applying the right intelligence to drive meaningful insights.
Albiorix Technology brings the top 10 digital transformation trends to watch out for in 2023 that you can adopt to improve your business performance.
For More Information: https://www.albiorixtech.com/blog/digital-transformation-trends/
The document discusses using Cloudera DataFlow to address challenges with collecting, processing, and analyzing log data across many systems and devices. It provides an example use case of logging modernization to reduce costs and enable security solutions by filtering noise from logs. The presentation shows how DataFlow can extract relevant events from large volumes of raw log data and normalize the data to make security threats and anomalies easier to detect across many machines.
Cloudera Data Impact Awards 2021 - Finalists Cloudera, Inc.
The document outlines the 2021 finalists for the annual Data Impact Awards program, which recognizes organizations using Cloudera's platform and the impactful applications they have developed. It provides details on the challenges, solutions, and outcomes for each finalist project in the categories of Data Lifecycle Connection, Cloud Innovation, Data for Enterprise AI, Security & Governance Leadership, Industry Transformation, People First, and Data for Good. There are multiple finalists highlighted in each category demonstrating innovative uses of data and analytics.
2020 Cloudera Data Impact Awards FinalistsCloudera, Inc.
Cloudera is proud to present the 2020 Data Impact Awards Finalists. This annual program recognizes organizations running the Cloudera platform for the applications they've built and the impact their data projects have on their organizations, their industries, and the world. Nominations were evaluated by a panel of independent thought-leaders and expert industry analysts, who then selected the finalists and winners. Winners exemplify the most-cutting edge data projects and represent innovation and leadership in their respective industries.
The document outlines the agenda for Cloudera's Enterprise Data Cloud event in Vienna. It includes welcome remarks, keynotes on Cloudera's vision and customer success stories. There will be presentations on the new Cloudera Data Platform and customer case studies, followed by closing remarks. The schedule includes sessions on Cloudera's approach to data warehousing, machine learning, streaming and multi-cloud capabilities.
Machine Learning with Limited Labeled Data 4/3/19Cloudera, Inc.
Cloudera Fast Forward Labs’ latest research report and prototype explore learning with limited labeled data. This capability relaxes the stringent labeled data requirement in supervised machine learning and opens up new product possibilities. It is industry invariant, addresses the labeling pain point and enables applications to be built faster and more efficiently.
Data Driven With the Cloudera Modern Data Warehouse 3.19.19Cloudera, Inc.
In this session, we will cover how to move beyond structured, curated reports based on known questions on known data, to an ad-hoc exploration of all data to optimize business processes and into the unknown questions on unknown data, where machine learning and statistically motivated predictive analytics are shaping business strategy.
Introducing Cloudera DataFlow (CDF) 2.13.19Cloudera, Inc.
Watch this webinar to understand how Hortonworks DataFlow (HDF) has evolved into the new Cloudera DataFlow (CDF). Learn about key capabilities that CDF delivers such as -
-Powerful data ingestion powered by Apache NiFi
-Edge data collection by Apache MiNiFi
-IoT-scale streaming data processing with Apache Kafka
-Enterprise services to offer unified security and governance from edge-to-enterprise
Introducing Cloudera Data Science Workbench for HDP 2.12.19Cloudera, Inc.
Cloudera’s Data Science Workbench (CDSW) is available for Hortonworks Data Platform (HDP) clusters for secure, collaborative data science at scale. During this webinar, we provide an introductory tour of CDSW and a demonstration of a machine learning workflow using CDSW on HDP.
Shortening the Sales Cycle with a Modern Data Warehouse 1.30.19Cloudera, Inc.
Join Cloudera as we outline how we use Cloudera technology to strengthen sales engagement, minimize marketing waste, and empower line of business leaders to drive successful outcomes.
Leveraging the cloud for analytics and machine learning 1.29.19Cloudera, Inc.
Learn how organizations are deriving unique customer insights, improving product and services efficiency, and reducing business risk with a modern big data architecture powered by Cloudera on Azure. In this webinar, you see how fast and easy it is to deploy a modern data management platform—in your cloud, on your terms.
Modernizing the Legacy Data Warehouse – What, Why, and How 1.23.19Cloudera, Inc.
Join us to learn about the challenges of legacy data warehousing, the goals of modern data warehousing, and the design patterns and frameworks that help to accelerate modernization efforts.
Leveraging the Cloud for Big Data Analytics 12.11.18Cloudera, Inc.
Learn how organizations are deriving unique customer insights, improving product and services efficiency, and reducing business risk with a modern big data architecture powered by Cloudera on AWS. In this webinar, you see how fast and easy it is to deploy a modern data management platform—in your cloud, on your terms.
Explore new trends and use cases in data warehousing including exploration and discovery, self-service ad-hoc analysis, predictive analytics and more ways to get deeper business insight. Modern Data Warehousing Fundamentals will show how to modernize your data warehouse architecture and infrastructure for benefits to both traditional analytics practitioners and data scientists and engineers.
Explore new trends and use cases in data warehousing including exploration and discovery, self-service ad-hoc analysis, predictive analytics and more ways to get deeper business insight. Modern Data Warehousing Fundamentals will show how to modernize your data warehouse architecture and infrastructure for benefits to both traditional analytics practitioners and data scientists and engineers.
The document discusses the benefits and trends of modernizing a data warehouse. It outlines how a modern data warehouse can provide deeper business insights at extreme speed and scale while controlling resources and costs. Examples are provided of companies that have improved fraud detection, customer retention, and machine performance by implementing a modern data warehouse that can handle large volumes and varieties of data from many sources.
Extending Cloudera SDX beyond the PlatformCloudera, Inc.
Cloudera SDX is by no means no restricted to just the platform; it extends well beyond. In this webinar, we show you how Bardess Group’s Zero2Hero solution leverages the shared data experience to coordinate Cloudera, Trifacta, and Qlik to deliver complete customer insight.
Federated Learning: ML with Privacy on the Edge 11.15.18Cloudera, Inc.
Join Cloudera Fast Forward Labs Research Engineer, Mike Lee Williams, to hear about their latest research report and prototype on Federated Learning. Learn more about what it is, when it’s applicable, how it works, and the current landscape of tools and libraries.
Analyst Webinar: Doing a 180 on Customer 360Cloudera, Inc.
451 Research Analyst Sheryl Kingstone, and Cloudera’s Steve Totman recently discussed how a growing number of organizations are replacing legacy Customer 360 systems with Customer Insights Platforms.
Build a modern platform for anti-money laundering 9.19.18Cloudera, Inc.
In this webinar, you will learn how Cloudera and BAH riskCanvas can help you build a modern AML platform that reduces false positive rates, investigation costs, technology sprawl, and regulatory risk.
Introducing the data science sandbox as a service 8.30.18Cloudera, Inc.
How can companies integrate data science into their businesses more effectively? Watch this recorded webinar and demonstration to hear more about operationalizing data science with Cloudera Data Science Workbench on Cazena’s fully-managed cloud platform.
Discovery Series - Zero to Hero - Task Mining Session 1DianaGray10
This session is focused on providing you with an introduction to task mining. We will go over different types of task mining and provide you with a real-world demo on each type of task mining in detail.
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.
The History of Embeddings & Multimodal EmbeddingsZilliz
Frank Liu will walk through the history of embeddings and how we got to the cool embedding models used today. He'll end with a demo on how multimodal RAG is used.
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
Finetuning GenAI For Hacking and DefendingPriyanka Aash
Generative AI, particularly through the lens of large language models (LLMs), represents a transformative leap in artificial intelligence. With advancements that have fundamentally altered our approach to AI, understanding and leveraging these technologies is crucial for innovators and practitioners alike. This comprehensive exploration delves into the intricacies of GenAI, from its foundational principles and historical evolution to its practical applications in security and beyond.
Increase Quality with User Access Policies - July 2024Peter Caitens
⭐️ Increase Quality with User Access Policies ⭐️, presented by Peter Caitens and Adam Best of Salesforce. View the slides from this session to hear all about “User Access Policies” and how they can help you onboard users faster with greater quality.
"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.
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.
Demystifying Neural Networks And Building Cybersecurity ApplicationsPriyanka Aash
In today's rapidly evolving technological landscape, Artificial Neural Networks (ANNs) have emerged as a cornerstone of artificial intelligence, revolutionizing various fields including cybersecurity. Inspired by the intricacies of the human brain, ANNs have a rich history and a complex structure that enables them to learn and make decisions. This blog aims to unravel the mysteries of neural networks, explore their mathematical foundations, and demonstrate their practical applications, particularly in building robust malware detection systems using Convolutional Neural Networks (CNNs).
Self-Healing Test Automation Framework - HealeniumKnoldus Inc.
Revolutionize your test automation with Healenium's self-healing framework. Automate test maintenance, reduce flakes, and increase efficiency. Learn how to build a robust test automation foundation. Discover the power of self-healing tests. Transform your testing experience.
Ingest: Collecting the Data Today’s data-in-motion conversation, like the data journey itself, starts with ingestion. The increase in sensor-generated data associated with IoT, combined with the demands for social media data collection, has created a deluge of unstructured data that is difficult for organizations to contend with. As a common initial bottleneck in the data-in-motion journey, organizations often reach for a robust ingestion solution. However, it’s important to understand ingestion as part of a broader real-time data context; it’s a critical component, but only the first of three.
Cloudera takes an open-source approach to ingestion, as it does with all three stages of the data-in-motion journey. Identifying the need for a streaming data capture system, Cloudera led the development of Apache Flume, the open standard for collecting and moving a vast amount of log data. The subsequent integration of Flume with Apache Kafka created an ingest architecture that has been replicated across Cloudera’s customer base in a variety of use cases. With Flume and Kafka, Cloudera deploys the leading streaming ingest platform. Flume can provide light weight agents deployed on edge nodes that number in the hundreds or thousands, each of which can be tiered to enable efficient ingest topologies. The integration between Kafka and Flume is bidirectional, meaning either component can be a producer or consumer of data depending on the specifics of your use case. A rising trend in data ingestion is the use of a rich visual interface that enables a user to interact with their ingestion architecture in an easy-to-use manner. While Cloudera delivers all the functionality underneath, we partner with best-in-class partners such as Streamsets, Cask, and others to deliver rich visualization. This enables Cloudera to focus on our core competency of data management, while enabling vendors with large engineering teams dedicated to visualization to focus on theirs. Portability, neutrality, and history of success for companies like Informatica,Talend, and others in similar spaces creates the best experience for our customers.
Cloudera relies on Spark Streaming to process data once it is ingested. As the leading open-source processing framework for real-time use cases, Spark Streaming is an open standard and one of the most easily-recognizable components of the broader Apache Hadoop™ ecosystem. Cloudera has a the broadest base of Hadoop-adjacent experience with Apache Spark™ and Spark Streaming; this is a product of early adoption and integration of these projects into Cloudera Enterprise. CLOUDERA ENTERPRISE: THE INDUSTRY STANDARD FOR A COMPLETE DATA-IN-MOTION SOLUTION 5 WHITE PAPER Spark Streaming provides the strongest processing solution for data-in-motion use cases as a result of: • Best-in-class performance: - High throughput ensures that jobs will not bottleneck at the processing stage - Sub-second latency enables real-time capabilities • Best-in-class API and Features: - Easy-to-use SQL based API’s for authoring streaming jobs help expand the number of use cases and value of data in motion - “Exactly once” stream processing semantics help ensure accuracy - Sliding window computations enable fast insights into time period data slices - Built-in API’s for maintaining and updating in-memory information • Best-in-class ecosystem: - Largest set of vendors working with and around Spark among available processing engines, enabling access to latest innovations - Broadest and deepest machine learning library (MLib) is seamlessly integrated Spark Streaming from Cloudera, in particular, benefits users through the most robust integration into the ingestion and serving phases that bookend the data-in-motion story. This integration ensures a fast, easy, and secure delivery of processed data to the serving stage of data in motion.
Whereas ingestion and processing have a relatively consistent flow irrespective of use case, the serving phase of a data-in-motion solution requires a variety of options in order to deliver the right data, to the right place, at the right time. Without this ability to quickly serve data to decision points, a solution loses its real-time capability and ceases to become a data-in-motion solution. Cloudera has a variety of options that help serve the diverse needs of individual use cases: • Apache Kudu™: A new, Cloudera-initiated Apache project, Kudu offers the unique ability to do fast scans on fast data. With an overwhelming number of data-in-motion use cases requiring analysis or visualization of streaming data, Kudu can enable the required batch analysis and real-time serving within the same storage layer. • Apache HBase™: HBase offers the best random read/write performance of any component within the Hadoop ecosystem. This capability, combined with high levels of concurrent access, enables online applications and operational needs that require the ability to query the latest data. • Cloudera Search: Powered by Apache Solr™, Cloudera Search democratizes data by enabling non-technical users to perform SQL-like, faceted search in natural language. Solr’s native integration into Cloudera Enterprise generates faster and more secure results. • Apache Kafka: Kafka’s fast, scalable, and durable design enables hundreds of megabytes of reads and writes per second, from thousands of clients.In addition to playing a role in ingestion, Kafka can be used to serve data to applications and users. This “last mile” step in the data-in-motion story is arguably the most critical step, which is why this breadth of options is necessary. Each use case, including the tendencies and workflows of the expected users, requires a different set of data access capabilities. Cloudera can meet any requirement through these tools, and can do so as the final step in an end-to-end data-in-motion story.