The document describes how to create a live dataflow in Hortonworks DataFlow in 7 minutes. It involves dragging and dropping two processors onto a canvas - one for data intake and one for data output - configuring each processor, connecting them, and starting the flow. The dataflow can then be dynamically adjusted and tuned in real-time. Hortonworks DataFlow also allows viewing data provenance to trace the lineage and changes of data as it flows through the system.
This workshop will provide a hands on introduction to simple event data processing and data flow processing using a Sandbox on students’ personal machines.
Format: A short introductory lecture to Apache NiFi and computing used in the lab followed by a demo, lab exercises and a Q&A session. The lecture will be followed by lab time to work through the lab exercises and ask questions.
Objective: To provide a quick and short hands-on introduction to Apache NiFi. In the lab, you will install and use Apache NiFi to collect, conduct and curate data-in-motion and data-at-rest with NiFi. You will learn how to connect and consume streaming sensor data, filter and transform the data and persist to multiple data sources.
Pre-requisites: Registrants must bring a laptop that has the latest VirtualBox installed and an image for Hortonworks DataFlow (HDF) Sandbox will be provided.
Speaker: Andy LoPresto
This workshop will provide a hands on introduction to simple event data processing and data flow processing using a Sandbox on students’ personal machines.
Format: A short introductory lecture to Apache NiFi and computing used in the lab followed by a demo, lab exercises and a Q&A session. The lecture will be followed by lab time to work through the lab exercises and ask questions.
Objective: To provide a quick and short hands-on introduction to Apache NiFi. In the lab, you will install and use Apache NiFi to collect, conduct and curate data-in-motion and data-at-rest with NiFi. You will learn how to connect and consume streaming sensor data, filter and transform the data and persist to multiple data sources.
Pre-requisites: Registrants must bring a laptop that has the latest VirtualBox installed and an image for Hortonworks DataFlow (HDF) Sandbox will be provided.
Speaker: Andy LoPresto
Using Spark Streaming and NiFi for the next generation of ETL in the enterpriseDataWorks Summit
In recent years, big data has moved from batch processing to stream-based processing since no one wants to wait hours or days to gain insights. Dozens of stream processing frameworks exist today and the same trend that occurred in the batch-based big data processing realm has taken place in the streaming world so that nearly every streaming framework now supports higher level relational operations.
On paper, combining Apache NiFi, Kafka, and Spark Streaming provides a compelling architecture option for building your next generation ETL data pipeline in near real time. What does this look like in an enterprise production environment to deploy and operationalized?
The newer Spark Structured Streaming provides fast, scalable, fault-tolerant, end-to-end exactly-once stream processing with elegant code samples, but is that the whole story?
We discuss the drivers and expected benefits of changing the existing event processing systems. In presenting the integrated solution, we will explore the key components of using NiFi, Kafka, and Spark, then share the good, the bad, and the ugly when trying to adopt these technologies into the enterprise. This session is targeted toward architects and other senior IT staff looking to continue their adoption of open source technology and modernize ingest/ETL processing. Attendees will take away lessons learned and experience in deploying these technologies to make their journey easier.
This presentation was created as an introduction to the Apache NiFi project; to be followed by “Lab 0” of the “Realtime Event Processing in Hadoop with NiFi, Kafka and Storm” tutorial hosted here: http://hortonworks.com/hadoop-tutorial/realtime-event-processing-nifi-kafka-storm/#section_1
How to Build HR Lakes on AWS to Unlock New Business Insights (DAT367) - AWS r...Amazon Web Services
In this session, learn how to create an HR data lake on AWS to develop a more wholistic view of people data to enable secure, self-service reporting for HR business partners and managers, and to use more advanced data science tools to unlock new insights to reduce retention, enhance hiring practices, and improve employee productivity. We provide examples of HR insights and the business value they can drive, walk through a reference architecture example for an HR data lake, and outline key steps and best practices as you design and launch your HR data lake project. AWS services addressed in this session include AWS Lambda, Amazon S3, AWS Glue, and Amazon Athena.
This is the first time I introduced the concept of Schema-on-Read vs Schema-on-Write to the public. It was at Berkeley EECS RAD Lab retreat Open Mic Session on May 28th, 2009 at Santa Cruz, California.
[This is work presented at SIGMOD'13.]
The use of large-scale data mining and machine learning has proliferated through the adoption of technologies such as Hadoop, with its simple programming semantics and rich and active ecosystem. This paper presents LinkedIn's Hadoop-based analytics stack, which allows data scientists and machine learning researchers to extract insights and build product features from massive amounts of data. In particular, we present our solutions to the "last mile" issues in providing a rich developer ecosystem. This includes easy ingress from and egress to online systems, and managing workflows as production processes. A key characteristic of our solution is that these distributed system concerns are completely abstracted away from researchers. For example, deploying data back into the online system is simply a 1-line Pig command that a data scientist can add to the end of their script. We also present case studies on how this ecosystem is used to solve problems ranging from recommendations to news feed updates to email digesting to descriptive analytical dashboards for our members.
Welcome to Online Payroll & HRMS, our customizable solution to automate HR & Payroll processes for small and medium companies.
Payroll module can be customized as per India policies and also as per international country policies.
Please visit us at www.payrollsoftware.co.in
For Inquiries:
ankur@orangewebtech.com
hector@orangewebtech.com
ERPNext is an end-to-end business solution that helps you to manage all your business information in one application and use it to not only manage operations but also enables you to take informed decisions well in time to remain ahead of your competition. It forms a backbone of your business to add strength, transparency and control to your enterprise.
CON6619 - OpenWorld Presentation. Oracle data integration, big data, data governance, and cloud integration. Replication, ETL, Data Quality, Streaming Big Data, and Data Preparation
MiNiFi is a recently started sub-project of Apache NiFi that is a complementary data collection approach which supplements the core tenets of NiFi in dataflow management, focusing on the collection of data at the source of its creation. Simply, MiNiFi agents take the guiding principles of NiFi and pushes them to the edge in a purpose built design and deploy manner. This talk will focus on MiNiFi's features, go over recent developments and prospective plans, and give a live demo of MiNiFi.
The config.yml is available here: https://gist.github.com/JPercivall/f337b8abdc9019cab5ff06cb7f6ff09a
Learn how Hortonworks Data Flow (HDF), powered by Apache Nifi, enables organizations to harness IoAT data streams to drive business and operational insights. We will use the session to provide an overview of HDF, including detailed hands-on lab to build HDF pipelines for capture and analysis of streaming data.
Recording and labs available at:
http://hortonworks.com/partners/learn/#hdf
Using Spark Streaming and NiFi for the next generation of ETL in the enterpriseDataWorks Summit
In recent years, big data has moved from batch processing to stream-based processing since no one wants to wait hours or days to gain insights. Dozens of stream processing frameworks exist today and the same trend that occurred in the batch-based big data processing realm has taken place in the streaming world so that nearly every streaming framework now supports higher level relational operations.
On paper, combining Apache NiFi, Kafka, and Spark Streaming provides a compelling architecture option for building your next generation ETL data pipeline in near real time. What does this look like in an enterprise production environment to deploy and operationalized?
The newer Spark Structured Streaming provides fast, scalable, fault-tolerant, end-to-end exactly-once stream processing with elegant code samples, but is that the whole story?
We discuss the drivers and expected benefits of changing the existing event processing systems. In presenting the integrated solution, we will explore the key components of using NiFi, Kafka, and Spark, then share the good, the bad, and the ugly when trying to adopt these technologies into the enterprise. This session is targeted toward architects and other senior IT staff looking to continue their adoption of open source technology and modernize ingest/ETL processing. Attendees will take away lessons learned and experience in deploying these technologies to make their journey easier.
This presentation was created as an introduction to the Apache NiFi project; to be followed by “Lab 0” of the “Realtime Event Processing in Hadoop with NiFi, Kafka and Storm” tutorial hosted here: http://hortonworks.com/hadoop-tutorial/realtime-event-processing-nifi-kafka-storm/#section_1
How to Build HR Lakes on AWS to Unlock New Business Insights (DAT367) - AWS r...Amazon Web Services
In this session, learn how to create an HR data lake on AWS to develop a more wholistic view of people data to enable secure, self-service reporting for HR business partners and managers, and to use more advanced data science tools to unlock new insights to reduce retention, enhance hiring practices, and improve employee productivity. We provide examples of HR insights and the business value they can drive, walk through a reference architecture example for an HR data lake, and outline key steps and best practices as you design and launch your HR data lake project. AWS services addressed in this session include AWS Lambda, Amazon S3, AWS Glue, and Amazon Athena.
This is the first time I introduced the concept of Schema-on-Read vs Schema-on-Write to the public. It was at Berkeley EECS RAD Lab retreat Open Mic Session on May 28th, 2009 at Santa Cruz, California.
[This is work presented at SIGMOD'13.]
The use of large-scale data mining and machine learning has proliferated through the adoption of technologies such as Hadoop, with its simple programming semantics and rich and active ecosystem. This paper presents LinkedIn's Hadoop-based analytics stack, which allows data scientists and machine learning researchers to extract insights and build product features from massive amounts of data. In particular, we present our solutions to the "last mile" issues in providing a rich developer ecosystem. This includes easy ingress from and egress to online systems, and managing workflows as production processes. A key characteristic of our solution is that these distributed system concerns are completely abstracted away from researchers. For example, deploying data back into the online system is simply a 1-line Pig command that a data scientist can add to the end of their script. We also present case studies on how this ecosystem is used to solve problems ranging from recommendations to news feed updates to email digesting to descriptive analytical dashboards for our members.
Welcome to Online Payroll & HRMS, our customizable solution to automate HR & Payroll processes for small and medium companies.
Payroll module can be customized as per India policies and also as per international country policies.
Please visit us at www.payrollsoftware.co.in
For Inquiries:
ankur@orangewebtech.com
hector@orangewebtech.com
ERPNext is an end-to-end business solution that helps you to manage all your business information in one application and use it to not only manage operations but also enables you to take informed decisions well in time to remain ahead of your competition. It forms a backbone of your business to add strength, transparency and control to your enterprise.
CON6619 - OpenWorld Presentation. Oracle data integration, big data, data governance, and cloud integration. Replication, ETL, Data Quality, Streaming Big Data, and Data Preparation
MiNiFi is a recently started sub-project of Apache NiFi that is a complementary data collection approach which supplements the core tenets of NiFi in dataflow management, focusing on the collection of data at the source of its creation. Simply, MiNiFi agents take the guiding principles of NiFi and pushes them to the edge in a purpose built design and deploy manner. This talk will focus on MiNiFi's features, go over recent developments and prospective plans, and give a live demo of MiNiFi.
The config.yml is available here: https://gist.github.com/JPercivall/f337b8abdc9019cab5ff06cb7f6ff09a
Learn how Hortonworks Data Flow (HDF), powered by Apache Nifi, enables organizations to harness IoAT data streams to drive business and operational insights. We will use the session to provide an overview of HDF, including detailed hands-on lab to build HDF pipelines for capture and analysis of streaming data.
Recording and labs available at:
http://hortonworks.com/partners/learn/#hdf
Hortonworks Data in Motion Webinar Series Part 7 Apache Kafka Nifi Better Tog...Hortonworks
Apache NiFi, Storm and Kafka augment each other in modern enterprise architectures. NiFi provides a coding free solution to get many different formats and protocols in and out of Kafka and compliments Kafka with full audit trails and interactive command and control. Storm compliments NiFi with the capability to handle complex event processing.
Join us to learn how Apache NiFi, Storm and Kafka can augment each other for creating a new dataplane connecting multiple systems within your enterprise with ease, speed and increased productivity.
https://www.brighttalk.com/webcast/9573/224063
Agenda:
1.Data Flow Challenges in an Enterprise
2.Introduction to Apache NiFi
3.Core Features
4.Architecture
5.Demo –Simple Lambda Architecture
6.Use Cases
7.Q & A
Webinar Series Part 5 New Features of HDF 5Hortonworks
Overview of the newest features of Hortonworks DataFlow highlighting the new processors, new user interface, edge intelligence powered by Apache MiNiFi and new support for multi-tenancy and new zero master clustering architecture
Hortonworks Data In Motion Webinar Series Pt. 2Hortonworks
How Hortonworks DataFlow (HDF), powered by Apache NIFi, MiNiFi, Kafka and Storm, and it’s associated HDF Certification Program make it easier and faster to integrate different systems together. Highlights on the latest partner integrations from HPE, SAS, Attunity, Impetus Technologies, Kepware and Midfin Systems. “
Watch the webinar on-demand: http://hortonworks.com/webinar/make-big-data-ecosystem-work-better/
HDF Partner certification program: http://hortonworks.com/partners/product-integration-certification/#hdf-integration
Hortonworks Data in Motion Webinar Series - Part 1Hortonworks
VIEW THE ON-DEMAND WEBINAR: http://hortonworks.com/webinar/introduction-hortonworks-dataflow/
Learn about Hortonworks DataFlow (HDFTM) and how you can easily augment your existing data systems – Hadoop and otherwise. Learn what Dataflow is all about and how Apache NiFi, MiNiFi, Kafka and Storm work together for streaming analytics.
Hortonworks Data In Motion Series Part 4Hortonworks
How real-world enterprises leverage Hortonworks DataFlow/Apache NiFi to to create real-time data flows in record time to enable new business opportunities, improve customer retention, accelerate big data projects from months to minutes through increased efficiency and reduced costs.
On-Demand webinar: http://hortonworks.com/webinar/paradigm-shift-business-usual-real-time-dataflows-record-time/
Dynamic Column Masking and Row-Level Filtering in HDPHortonworks
As enterprises around the world bring more of their sensitive data into Hadoop data lakes, balancing the need for democratization of access to data without sacrificing strong security principles becomes paramount. In this webinar, Srikanth Venkat, director of product management for security & governance will demonstrate two new data protection capabilities in Apache Ranger – dynamic column masking and row level filtering of data stored in Apache Hive. These features have been introduced as part of HDP 2.5 platform release.
Enabling the Real Time Analytical EnterpriseHortonworks
Combining IOT, Customer Experience and Real-Time Enterprise Data within Hadoop. What if you could derive real-time insights using ALL of your data? Join us for this webinar and learn how companies are combining “new” real-time data sources (i.e. IOT, Social, Web Logs) with continuously updated enterprise data from SAP and other enterprise transactional systems, providing deep and up-to-the-second analytical insights. This presentation will include a demonstration of how this can be achieved quickly, easily and affordably by utilizing a joint solution from Attunity and Hortonworks.
Double Your Hadoop Hardware Performance with SmartSenseHortonworks
Hortonworks SmartSense provides proactive recommendations that improve cluster performance, security and operations. And since 30% of issues are configuration related, Hortonworks SmartSense makes an immediate impact on Hadoop system performance and availability, in some cases boosting hardware performance by two times. Learn how SmartSense can help you increase the efficiency of your Hadoop hardware, through customized cluster recommendations.
View the on-demand webinar: https://hortonworks.com/webinar/boosts-hadoop-hardware-performance-2x-smartsense/
Extendible data model for real-time business process analysisMarcello Leida
This slides presents a promising data representation model for real time monitoring of business processes. The main benefit of this representation is that is transparent to the data creation and analysis processes and it is extensible at real-time.
The model is based on a shared vocabulary defined using RDF standard representation allowing independence between applications.
This model is a novel approach to real-time process data representation and paves the road to a complete new breed of applications for business process analysis
Harnessing Data-in-Motion with HDF 2.0, introduction to Apache NIFI/MINIFIHaimo Liu
Introducing the new Hortonworks DataFlow (HDF) release, HDF 2.0. Also provides introduction to the flow management part of the platform, powered by Apache NIFI and MINIFI.
Learn about HDF and how you can easily augment your existing data systems - Hadoop and otherwise. Learn what Dataflow is all about and how Apache NiFi, MiNiFi, Kafka and Storm work together for streaming analytics.
Presentation from Future of Data Boston Meetup on Oct 24, 2017.
Streaming data is rich with insights but these insights can be difficult to find due to the difficulty of developing and deploying streaming applications. During this presentation we will show how to build and deploy a complex streaming application in a few minutes using open source tools. First we will build an application using Streaming Analytics Manager and Schema Registry that ingests data into Apache Druid. Then we will use Apache Superset to build beautiful, informative dashboards.
Streamline Apache Hadoop Operations with Apache Ambari and SmartSenseHortonworks
Apache Ambari 2.5 helps customers simplify the experience for provisioning, managing, monitoring, securing and troubleshooting Hadoop deployments. Find out how the combination of Ambari and SmartSense delivers a path to success to help IT get Hadoop up and running effectively. The end result – you get the full business impact management and benefits of Big Data for your organization.
https://hortonworks.com/webinar/streamline-apache-hadoop-operations-apache-ambari-smartsense/
Using Apache® NiFi to Empower Self-Organising TeamsSebastian Carroll
Even though many organisations are moving to Agile methods, data transport architectures continue to be change-resistant. Given that data is now key to many teams and organisation can we really practice agility if we can't control the data we rely on? Apache NiFi can help alleviate this by giving the control to the teams and placing the decisions into the hands of those most capable of making them.
[Hortonworks] Future Of Data: Madrid - HDF & Data in motionRaúl Marín
First meetup event Future Of Data event where we introduced Hortonworks DataFlow (HDF).
The slides describe what HDF is, and we presented a very simple demo about sentiment analysis of tweets using Apache OpenNLP as the NLP framework to do so.
Apache Ambari is used by thousands of Hadoop Operators to manage the deployment, lifecycle, and automation of DevOps for Hadoop ecosystem projects. The Ambari engineering team will talk about improvements being made to the automation, metrics, logging, upgrade, and other core frameworks within Ambari as the project is being re-imagined.
Starting out, Apache Ambari installed a handful of Apache Hadoop ecosystem projects, on a few operating systems, and helped with the most basic Hadoop operational tasks. Today, the product manages over 20 different services, runs on multiple major operating systems and versions, and automates many of the most challenging Hadoop operational tasks in the most secure customer environments.
As part of this talk, the engineering team will walk you through what we've learned, the challenges we've overcome, and how the Apache Ambari community has changed the product to handle them. The future is fast approaching, and with it comes new on-premise and cloud deployment architectures. See how Apache Ambari is being re-imagined to handle these new challenges.
Speaker
Paul Codding, Product Management Director, Hortonworks
Oliver Szabo, Senior Software Engineer, Hortonworks
How is it that one system can query terabytes of data, yet still provide interactive query support? This talk will discuss two of the underlying technologies that allow Apache Hive to support fast query response, both on-premise in HDFS and in cloud object stores such as S3 and WASB.
LLAP was introduced in Hive 2.6. It provides standing processes that securely cache Hive’s columnar data and can do query processing without ever needing to start tasks in Hadoop. We will cover LLAP’s architecture, intended uses cases, and performance numbers for both on-premise and in the cloud.
The second technology is the integration of Hive with Apache Druid. Druid excels at low-latency, interactive queries over streaming data. Its method of storing data makes it very well suited for OLAP style queries. We will cover how Hive can be integrated with Druid to support real-time streaming of data from Kafka and OLAP queries.
Speaker: Alan Gates, Co-Founder, Hortonworks
Stream processing has become the defacto standard for building real-time ETL and Stream Analytics applications. We see batch workloads move into Stream processing to to act on the data and derive insights faster. With the explosion of data with "Perishable Insights" such IoT and machine-generated data, Stream Processing + Predictive Analytics is driving tremendous business value. This is evidenced by the explosion of Stream Processing frameworks like proven and evolving Apache Storm and newer frameworks such as Apache Flink, Apache Apex, and Spark Streaming.
Today, users have to choose and try to understand the benefits of each of these frameworks and not only that they have to learn the new APIs and also operationalize their applications. To create value faster, we are introducing new open source tool - Streamline. It is a self-service tool that will ease building streaming application and deploy the streaming application across multiple frameworks/engines that users prefer in a snap. It simplifies integration with Machine Learning models for scoring and classification of data for Predictive Analytics. It provides an elegant way to build Analytics dashboards to derive business insights out of the streaming data and to allow the business users to consume it easily.
In this talk, we will outline the fundamentals of real-time stream processing and demonstrate Streamline capabilities to show how it simplifies building real-time streaming analytics applications.
Hadoop & DevOps : better together by Maxime Lanciaux.
From deployment automation with tools (like jenkins, git, maven, ambari, ansible) to full automation with monitoring on HDP2.5+.
Big Data Day LA 2016/ Big Data Track - Building scalable enterprise data flow...Data Con LA
Connecting enterprise systems has always been a tough task. Modern IoT applications have exacerbated the issue by the need to integrate legacy systems with novel high velocity data streams. Various patterns like messaging, REST, etc. have been proposed, but they necessitate rearchitecting the integration layer which is extremely arduous. In this talk we will show you how to use Apache NiFi to solve your data integration, movement and ingestion problems. Next, we will examine how Apache NiFi can be used to construct durable, scalable and responsive IoT apps in conjunction with other stream processing and messaging frameworks.
Using Spark Streaming and NiFi for the Next Generation of ETL in the EnterpriseDataWorks Summit
In recent years, big data has moved from batch processing to stream-based processing since no one wants to wait hours or days to gain insights. Dozens of stream processing frameworks exist today and the same trend that occurred in the batch-based big data processing realm has taken place in the streaming world so that nearly every streaming framework now supports higher level relational operations.
On paper, combining Apache NiFi, Kafka, and Spark Streaming provides a compelling architecture option for building your next generation ETL data pipeline in near real time. What does this look like in an enterprise production environment to deploy and operationalized?
The newer Spark Structured Streaming provides fast, scalable, fault-tolerant, end-to-end exactly-once stream processing with elegant code samples, but is that the whole story?
We discuss the drivers and expected benefits of changing the existing event processing systems. In presenting the integrated solution, we will explore the key components of using NiFi, Kafka, and Spark, then share the good, the bad, and the ugly when trying to adopt these technologies into the enterprise. This session is targeted toward architects and other senior IT staff looking to continue their adoption of open source technology and modernize ingest/ETL processing. Attendees will take away lessons learned and experience in deploying these technologies to make their journey easier.
Speaker: Andrew Psaltis, Principal Solution Engineer, Hortonworks
Druid: Sub-Second OLAP queries over Petabytes of Streaming DataDataWorks Summit
When interacting with analytics dashboards in order to achieve a smooth user experience, two major key requirements are sub-second response time and data freshness. Cluster computing frameworks such as Hadoop or Hive/Hbase work well for storing large volumes of data, although they are not optimized for ingesting streaming data and making it available for queries in realtime. Also, long query latencies make these systems sub-optimal choices for powering interactive dashboards and BI use-cases.
In this talk we will present Druid as a complementary solution to existing hadoop based technologies. Druid is an open-source analytics data store, designed from scratch, for OLAP and business intelligence queries over massive data streams. It provides low latency realtime data ingestion and fast sub-second adhoc flexible data exploration queries.
Many large companies are switching to Druid for analytics, and we will cover how druid is able to handle massive data streams and why it is a good fit for BI use cases.
Agenda -
1) Introduction and Ideal Use cases for Druid
2) Data Architecture
3) Streaming Ingestion with Kafka
4) Demo using Druid, Kafka and Superset.
5) Recent Improvements in Druid moving from lambda architecture to Exactly once Ingestion
6) Future Work
Learn more: http://hortonworks.com/hdf/
Log data can be complex to capture, typically collected in limited amounts and difficult to operationalize at scale. HDF expands the capabilities of log analytics integration options for easy and secure edge analytics of log files in the following ways:
More efficient collection and movement of log data by prioritizing, enriching and/or transforming data at the edge to dynamically separate critical data. The relevant data is then delivered into log analytics systems in a real-time, prioritized and secure manner.
Cost-effective expansion of existing log analytics infrastructure by improving error detection and troubleshooting through more comprehensive data sets.
Intelligent edge analytics to support real-time content-based routing, prioritization, and simultaneous delivery of data into Connected Data Platforms, log analytics and reporting systems for comprehensive coverage and retention of Internet of Anything data.
Learn more: http://hortonworks.com/hdf/
Log data can be complex to capture, typically collected in limited amounts and difficult to operationalize at scale. HDF expands the capabilities of log analytics integration options for easy and secure edge analytics of log files in the following ways:
More efficient collection and movement of log data by prioritizing, enriching and/or transforming data at the edge to dynamically separate critical data. The relevant data is then delivered into log analytics systems in a real-time, prioritized and secure manner.
Cost-effective expansion of existing log analytics infrastructure by improving error detection and troubleshooting through more comprehensive data sets.
Intelligent edge analytics to support real-time content-based routing, prioritization, and simultaneous delivery of data into Connected Data Platforms, log analytics and reporting systems for comprehensive coverage and retention of Internet of Anything data.
Achieving a 360-degree view of manufacturing via open source industrial data ...DataWorks Summit
Continuously improving factory operations is of critical importance to manufacturers. Consider the facts: the total cost of poor quality amounts to a staggering 20% of sales (American Society of Quality), and unplanned downtime costs plants approximately $50 billion per year (Deloitte).
The most pressing questions are: which process variables effect quality and yield and which process variables predict equipment failure? Getting to those answers is providing forward thinking manufacturers a leg up over competitors.
The speakers address the data management challenges facing today's manufacturers, including proprietary systems and siloed data sources, as well as an inability to make sensor-based data usable.
Integrating enterprise data from ERP, MES, maintenance systems, and other sources with real-time operations data from sensors, PLCs, SCADA systems, and historians represents a major first step. But how to get started? What is the value of a data lake? How are AI/ML being applied to enable real time action?
Join us for this educational session, which includes a view into a roadmap for an open source industrial IoT data management platform.
Key Takeaways:
• Understand key use cases commonly undertaken by manufacturing enterprises
• Understand the value of using multivariate manufacturing data sources, as opposed to a single sensor on a piece of equipment
• Understand advances in big data management and streaming analytics that are paving the way to next-generation factory performance
Speakers
Michael Ger, General Manager Manufacturing and Automotive, Hortonworks
Wade Salazar, Solutions Engineer, Hortonworks
Similar to Design a Dataflow in 7 minutes with Apache NiFi/HDF (20)
Hortonworks DataFlow (HDF) 3.3 - Taking Stream Processing to the Next LevelHortonworks
The HDF 3.3 release delivers several exciting enhancements and new features. But, the most noteworthy of them is the addition of support for Kafka 2.0 and Kafka Streams.
https://hortonworks.com/webinar/hortonworks-dataflow-hdf-3-3-taking-stream-processing-next-level/
IoT Predictions for 2019 and Beyond: Data at the Heart of Your IoT StrategyHortonworks
Forrester forecasts* that direct spending on the Internet of Things (IoT) will exceed $400 Billion by 2023. From manufacturing and utilities, to oil & gas and transportation, IoT improves visibility, reduces downtime, and creates opportunities for entirely new business models.
But successful IoT implementations require far more than simply connecting sensors to a network. The data generated by these devices must be collected, aggregated, cleaned, processed, interpreted, understood, and used. Data-driven decisions and actions must be taken, without which an IoT implementation is bound to fail.
https://hortonworks.com/webinar/iot-predictions-2019-beyond-data-heart-iot-strategy/
Getting the Most Out of Your Data in the Cloud with CloudbreakHortonworks
Cloudbreak, a part of Hortonworks Data Platform (HDP), simplifies the provisioning and cluster management within any cloud environment to help your business toward its path to a hybrid cloud architecture.
https://hortonworks.com/webinar/getting-data-cloud-cloudbreak-live-demo/
Johns Hopkins - Using Hadoop to Secure Access Log EventsHortonworks
In this webinar, we talk with experts from Johns Hopkins as they share techniques and lessons learned in real-world Apache Hadoop implementation.
https://hortonworks.com/webinar/johns-hopkins-using-hadoop-securely-access-log-events/
Catch a Hacker in Real-Time: Live Visuals of Bots and Bad GuysHortonworks
Cybersecurity today is a big data problem. There’s a ton of data landing on you faster than you can load, let alone search it. In order to make sense of it, we need to act on data-in-motion, use both machine learning, and the most advanced pattern recognition system on the planet: your SOC analysts. Advanced visualization makes your analysts more efficient, helps them find the hidden gems, or bombs in masses of logs and packets.
https://hortonworks.com/webinar/catch-hacker-real-time-live-visuals-bots-bad-guys/
We have introduced several new features as well as delivered some significant updates to keep the platform tightly integrated and compatible with HDP 3.0.
https://hortonworks.com/webinar/hortonworks-dataflow-hdf-3-2-release-raises-bar-operational-efficiency/
Curing Kafka Blindness with Hortonworks Streams Messaging ManagerHortonworks
With the growth of Apache Kafka adoption in all major streaming initiatives across large organizations, the operational and visibility challenges associated with Kafka are on the rise as well. Kafka users want better visibility in understanding what is going on in the clusters as well as within the stream flows across producers, topics, brokers, and consumers.
With no tools in the market that readily address the challenges of the Kafka Ops teams, the development teams, and the security/governance teams, Hortonworks Streams Messaging Manager is a game-changer.
https://hortonworks.com/webinar/curing-kafka-blindness-hortonworks-streams-messaging-manager/
Interpretation Tool for Genomic Sequencing Data in Clinical EnvironmentsHortonworks
The healthcare industry—with its huge volumes of big data—is ripe for the application of analytics and machine learning. In this webinar, Hortonworks and Quanam present a tool that uses machine learning and natural language processing in the clinical classification of genomic variants to help identify mutations and determine clinical significance.
Watch the webinar: https://hortonworks.com/webinar/interpretation-tool-genomic-sequencing-data-clinical-environments/
IBM+Hortonworks = Transformation of the Big Data LandscapeHortonworks
Last year IBM and Hortonworks jointly announced a strategic and deep partnership. Join us as we take a close look at the partnership accomplishments and the conjoined road ahead with industry-leading analytics offers.
View the webinar here: https://hortonworks.com/webinar/ibmhortonworks-transformation-big-data-landscape/
In this exclusive Premier Inside Out, you will hear from Druid committer Slim Bouguerra, Staff Software Engineer and Product Manager Will Xu. These Hortonworkers will explain the vision of these components, review new features, share some best practices and answer your questions.
View the webinar here: https://hortonworks.com/webinar/hortonworks-premier-apache-druid/
Accelerating Data Science and Real Time Analytics at ScaleHortonworks
Gaining business advantages from big data is moving beyond just the efficient storage and deep analytics on diverse data sources to using AI methods and analytics on streaming data to catch insights and take action at the edge of the network.
https://hortonworks.com/webinar/accelerating-data-science-real-time-analytics-scale/
TIME SERIES: APPLYING ADVANCED ANALYTICS TO INDUSTRIAL PROCESS DATAHortonworks
Thanks to sensors and the Internet of Things, industrial processes now generate a sea of data. But are you plumbing its depths to find the insight it contains, or are you just drowning in it? Now, Hortonworks and Seeq team to bring advanced analytics and machine learning to time-series data from manufacturing and industrial processes.
Blockchain with Machine Learning Powered by Big Data: Trimble Transportation ...Hortonworks
Trimble Transportation Enterprise is a leading provider of enterprise software to over 2,000 transportation and logistics companies. They have designed an architecture that leverages Hortonworks Big Data solutions and Machine Learning models to power up multiple Blockchains, which improves operational efficiency, cuts down costs and enables building strategic partnerships.
https://hortonworks.com/webinar/blockchain-with-machine-learning-powered-by-big-data-trimble-transportation-enterprise/
Delivering Real-Time Streaming Data for Healthcare Customers: ClearsenseHortonworks
For years, the healthcare industry has had problems of data scarcity and latency. Clearsense solved the problem by building an open-source Hortonworks Data Platform (HDP) solution while providing decades worth of clinical expertise. Clearsense is delivering smart, real-time streaming data, to its healthcare customers enabling mission-critical data to feed clinical decisions.
https://hortonworks.com/webinar/delivering-smart-real-time-streaming-data-healthcare-customers-clearsense/
Making Enterprise Big Data Small with EaseHortonworks
Every division in an organization builds its own database to keep track of its business. When the organization becomes big, those individual databases grow as well. The data from each database may become silo-ed and have no idea about the data in the other database.
https://hortonworks.com/webinar/making-enterprise-big-data-small-ease/
Driving Digital Transformation Through Global Data ManagementHortonworks
Using your data smarter and faster than your peers could be the difference between dominating your market and merely surviving. Organizations are investing in IoT, big data, and data science to drive better customer experience and create new products, yet these projects often stall in ideation phase to a lack of global data management processes and technologies. Your new data architecture may be taking shape around you, but your goal of globally managing, governing, and securing your data across a hybrid, multi-cloud landscape can remain elusive. Learn how industry leaders are developing their global data management strategy to drive innovation and ROI.
Presented at Gartner Data and Analytics Summit
Speaker:
Dinesh Chandrasekhar
Director of Product Marketing, Hortonworks
HDF 3.1 pt. 2: A Technical Deep-Dive on New Streaming FeaturesHortonworks
Hortonworks DataFlow (HDF) is the complete solution that addresses the most complex streaming architectures of today’s enterprises. More than 20 billion IoT devices are active on the planet today and thousands of use cases across IIOT, Healthcare and Manufacturing warrant capturing data-in-motion and delivering actionable intelligence right NOW. “Data decay” happens in a matter of seconds in today’s digital enterprises.
To meet all the needs of such fast-moving businesses, we have made significant enhancements and new streaming features in HDF 3.1.
https://hortonworks.com/webinar/series-hdf-3-1-technical-deep-dive-new-streaming-features/
Hortonworks DataFlow (HDF) 3.1 - Redefining Data-In-Motion with Modern Data A...Hortonworks
Join the Hortonworks product team as they introduce HDF 3.1 and the core components for a modern data architecture to support stream processing and analytics.
You will learn about the three main themes that HDF addresses:
Developer productivity
Operational efficiency
Platform interoperability
https://hortonworks.com/webinar/series-hdf-3-1-redefining-data-motion-modern-data-architectures/
Unlock Value from Big Data with Apache NiFi and Streaming CDCHortonworks
Apache NiFi is an easy to use, powerful, and reliable system to process and distribute data. It provides an end-to-end platform that can collect, curate, analyze, and act on data in real-time, on-premises, or in the cloud with a drag-and-drop visual interface. It’s being used across industries on large amounts of data that had stored in isolation which made collaboration and analysis difficult.
Join industry experts from Hortonworks and Attunity as they explain how Apache NiFi and streaming CDC technology provides a distributed, resilient platform for unlocking the value of data in new ways.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
HDF supports over 90 difference processors to accelerate the process of ingesting and processing data. There are ready-made “off the shelf” processors for data collection, data processing. For example – in alphabetical order, not necessarily popularity: EncryptContent, ExecuteFlumeSink, ExecuteFlumeSource, ExecuteSQL, ExtractHL7, GetFTP, GetHTTP, PutKafka, MergeContent, MonitorActivity, PutEmail, PutHDFS, SpltJSON, TransformXML.
There are many different processors, some of which are designed to simplify collection of big data from popular data sources. Twitter is one of them. Others include:
This is a very unique capability of dataflow – the ability to see processors update in real time This gives data developers and data scientists the ability to quickly verify hypothesis and as well enable on-time decision making – within the relevant time-window.
Once the data flow is established, it can be dynamically manipulated, replicated and transformed. This removes the need to develop code in a test environment, and then porting to a production environment. Being able to immediately test within the production environment, accelerates the time to insight.
And all of this is tracked so when you get to the point of “what did I try before again”, or “what happened last time”, it is readily accessible via the GUI interface.
HDF provides very fine-grained, high fidelity reporting about the origins of data, how it was used, who used it etc.