Hortonworks DataFlow delivers data to streaming analytics platforms, inclusive of Storm, Spark and Flink
These are slides from an Apache Flink Meetup: Integration of Apache Flink and Apache Nifi, Feb 4 2016
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.
Beyond Messaging Enterprise Dataflow powered by Apache NiFiIsheeta Sanghi
This document discusses Apache NiFi, an open source software project that provides a dataflow solution for gathering, processing, and delivering data between systems. NiFi addresses challenges with traditional messaging systems by allowing for data routing, transformation, prioritization, and provenance tracking. It uses a flow-based programming model where data moves through a directed graph of processes connected by queues. The project started at the National Security Agency in 2006 and became a top-level Apache project in 2015.
State of the Apache NiFi Ecosystem & CommunityAccumulo Summit
This talk will discuss the state of the Apache NiFi Ecosystem & Community.
Apache NiFi is an integrated data logistics platform for automating the movement of data between disparate systems. It provides real-time control that makes it easy to manage the movement of data between any source and any destination. It is data source agnostic, supporting disparate and distributed sources of differing formats, schemas, protocols, speeds and sizes such as machines, geo location devices, click streams, files, social feeds, log files and videos and more. It is configurable plumbing for moving data around, similar to how Fedex, UPS or other courier delivery services move parcels around. And just like those services, Apache NiFi allows you to trace your data in real time, just like you could trace a delivery.
NiFi processors allow data to be processed as it flows through the system. This document discusses how to create a custom NiFi processor by using the nifi-processor-bundle-archetype Maven archetype to generate the project structure. It also covers deploying the custom processor by building a NAR file with Maven and placing it in the NiFi installation directory so that the new processor will be available. Key methods for customizing processor behavior like init, onSchedule, and onTrigger are also outlined.
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 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.
Beyond Messaging Enterprise Dataflow powered by Apache NiFiIsheeta Sanghi
This document discusses Apache NiFi, an open source software project that provides a dataflow solution for gathering, processing, and delivering data between systems. NiFi addresses challenges with traditional messaging systems by allowing for data routing, transformation, prioritization, and provenance tracking. It uses a flow-based programming model where data moves through a directed graph of processes connected by queues. The project started at the National Security Agency in 2006 and became a top-level Apache project in 2015.
State of the Apache NiFi Ecosystem & CommunityAccumulo Summit
This talk will discuss the state of the Apache NiFi Ecosystem & Community.
Apache NiFi is an integrated data logistics platform for automating the movement of data between disparate systems. It provides real-time control that makes it easy to manage the movement of data between any source and any destination. It is data source agnostic, supporting disparate and distributed sources of differing formats, schemas, protocols, speeds and sizes such as machines, geo location devices, click streams, files, social feeds, log files and videos and more. It is configurable plumbing for moving data around, similar to how Fedex, UPS or other courier delivery services move parcels around. And just like those services, Apache NiFi allows you to trace your data in real time, just like you could trace a delivery.
NiFi processors allow data to be processed as it flows through the system. This document discusses how to create a custom NiFi processor by using the nifi-processor-bundle-archetype Maven archetype to generate the project structure. It also covers deploying the custom processor by building a NAR file with Maven and placing it in the NiFi installation directory so that the new processor will be available. Key methods for customizing processor behavior like init, onSchedule, and onTrigger are also outlined.
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
Taking DataFlow Management to the Edge with Apache NiFi/MiNiFiBryan Bende
This document provides an overview of a presentation about taking dataflow management to the edge with Apache NiFi and MiniFi. The presentation discusses the problem of moving data between systems with different formats, protocols, and security requirements. It introduces Apache NiFi as a solution for dataflow management and introduces Apache MiniFi for managing dataflows at the edge. The presentation includes a demo and time for Q&A.
Yifeng Jiang gives a presentation introducing Apache Nifi. He begins with an overview of himself and the agenda. He then provides an introduction to Nifi including terminology like FlowFile and Processor. Key aspects of Nifi are demonstrated including the user interface, provenance tracking, queue prioritization, cluster architecture, and a demo of real-time data processing. Example use cases are discussed like indexing JSON tweets and indexing data from a relational database. The presentation concludes that Nifi is an easy to use and powerful system for processing and distributing data with 90 built-in processors.
NiFi Best Practices for the EnterpriseGregory Keys
The document discusses best practices for implementing Apache NiFi in an enterprise. It recommends establishing a Center of Excellence (COE) to align stakeholders, provide guidance, and develop standards and processes for NiFi deployment. The COE should work with business leaders to understand data flow needs and ensure NiFi is delivering business value. When scaling NiFi across a large enterprise, it may make sense to have multiple semi-autonomous NiFi clusters for different business groups rather than one large cluster. Reusable templates, components, and patterns can help with development efficiencies.
Apache NiFi: latest developments for flow management at scaleAbdelkrim Hadjidj
The document discusses Apache NiFi, an open source dataflow management platform. It provides an overview of NiFi's capabilities including over 225 processors for common data access, transformation, and management tasks. The presentation demonstrates NiFi and its web-based user interface, zero-master clustering architecture, and extensibility via custom processors and controllers. New features discussed include component versioning, change data capture from MySQL, and a record-based processing mechanism for improved data handling.
Introduction: 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.
Speakers: Andy LoPresto, Timothy Spann
As Apache Solr becomes more powerful and easier to use, the accessibility of high quality data becomes key to unlocking the full potential of Solr’s search and analytic capabilities. Traditional approaches to acquiring data frequently involve a combination of homegrown tools and scripts, often requiring significant development efforts and becoming hard to change, hard to monitor, and hard to maintain. This talk will discuss how Apache NiFi addresses the above challenges and can be used to build production-grade data pipelines for Solr. We will start by giving an introduction to the core features of NiFi, such as visual command & control, dynamic prioritization, back-pressure, and provenance. We will then look at NiFi’s processors for integrating with Solr, covering topics such as ingesting and extracting data, interacting with secure Solr instances, and performance tuning. We will conclude by building a live dataflow from scratch, demonstrating how to prepare data and ingest to Solr.
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.
The First Mile - Edge and IoT Data Collection With Apache Nifi and MiniFiDataWorks Summit
Apache NiFi MiNiFi enables data collection in a brand new environment - small sensor footprint, intermittent or limited bandwidth distributed system, and disposable or short-lived hardware. You can prioritize this data or perform initial analysis on the edge, as well as immediately encrypt and protect it.
Concept: Apache NiFi offers a revolutionary data flow management system and extensive integration of existing data production, consumption and analysis ecosystems, all of which are robust data delivery and a (data) logging infrastructure It is protected by. Learn about the additional project Apache MiNiFi, which extends the scope of NiFi's power to the maximum. MiNiFi is a lightweight application that can be placed on hardware that is one order of magnitude smaller than the existing standard data collection platform and is less powerful. As a JVM-enabled native agent MiNiFi enables data gathering in a brand new environment - small sensor footprint, intermittent or limited bandwidth distributed system, and disposable or short-lived hardware. You can prioritize this data or perform initial analysis on the edge, as well as immediately encrypt and protect it. Regional governance and regulatory policies are applied to geopolitical boundaries and comply with legal requirements. And all of this configuration can be done from the existing NiFi and central control using the stable data UI that the data flow administrator has already liked and trusted.
Required prior knowledge / targeted participants: Developers and data flow administrators need some knowledge of Apache NiFi as a platform for routing, conversion, and data delivery through the system (a brief overview is provided ). In this talk we will focus on extending data collection, routing, data history, and NiFi control functions, through IoT / edge integration via MiNiFi.
Key Points: Participants will learn about the opportunity to collect and capture data flows close to the source of data, "edge", such as IoT devices, vehicles, machines, etc. Participants prioritize, filter, protect, and manipulate this data in the initial data lifecycle and understand the potential for data visibility and performance improvement.
Introduction: 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.
The First Mile -- Edge and IoT Data Collection with Apache NiFi and MiNiFiDataWorks Summit
Apache NiFi provided a revolutionary data flow management system with a broad range of integrations with existing data production, consumption, and analysis ecosystems, all covered with robust data delivery and provenance infrastructure. Now learn about the follow-on project which expands the reach of NiFi to the edge, Apache MiNiFi. MiNiFi is a lightweight application which can be deployed on hardware orders of magnitude smaller and less powerful than the existing standard data collection platforms. With both a JVM compatible and native agent, MiNiFi allows data collection in brand new environments — sensors with tiny footprints, distributed systems with intermittent or restricted bandwidth, and even disposable or ephemeral hardware. Not only can this data be prioritized and have some initial analysis performed at the edge, it can be encrypted and secured immediately. Local governance and regulatory policies can be applied across geopolitical boundaries to conform with legal requirements. And all of this configuration can be done from central command & control using an existing NiFi with the trusted and stable UI data flow managers already love.
This document provides an overview of Apache NiFi and the new MiNiFi project. It begins with introductions to Apache NiFi, its key features, and what is new in version 1.0.0. It then introduces MiNiFi, describing it as a way to deploy NiFi flows to edge systems with limited resources. The rest of the document demonstrates the NiFi and MiNiFi architectures and how they work together, and provides an example deployment to a courier service. It concludes with a demo of NiFi and MiNiFi.
Dataflow Management From Edge to Core with Apache NiFiDataWorks Summit
What is “dataflow?” — the process and tooling around gathering necessary information and getting it into a useful form to make insights available. Dataflow needs change rapidly — what was noise yesterday may be crucial data today, an API endpoint changes, or a service switches from producing CSV to JSON or Avro. In addition, developers may need to design a flow in a sandbox and deploy to QA or production — and those database passwords aren’t the same (hopefully). Learn about Apache NiFi — a robust and secure framework for dataflow development and monitoring.
Abstract: Identifying, collecting, securing, filtering, prioritizing, transforming, and transporting abstract data is a challenge faced by every organization. Apache NiFi and MiNiFi allow developers to create and refine dataflows with ease and ensure that their critical content is routed, transformed, validated, and delivered across global networks. Learn how the framework enables rapid development of flows, live monitoring and auditing, data protection and sharing. From IoT and machine interaction to log collection, NiFi can scale to meet the needs of your organization. Able to handle both small event messages and “big data” on the scale of terabytes per day, NiFi will provide a platform which lets both engineers and non-technical domain experts collaborate to solve the ingest and storage problems that have plagued enterprises.
Expected prior knowledge / intended audience: developers and data flow managers should be interested in learning about and improving their dataflow problems. The intended audience does not need experience in designing and modifying data flows.
Takeaways: Attendees will gain an understanding of dataflow concepts, data management processes, and flow management (including versioning, rollbacks, promotion between deployment environments, and various backing implementations).
Current uses: I am a committer and PMC member for the Apache NiFi, MiNiFi, and NiFi Registry projects and help numerous users deploy these tools to collect data from an incredibly diverse array of endpoints, aggregate, prioritize, filter, transform, and secure this data, and generate actionable insight from it. Current users of these platforms include many Fortune 100 companies, governments, startups, and individual users across fields like telecommunications, finance, healthcare, automotive, aerospace, and oil & gas, with use cases like fraud detection, logistics management, supply chain management, machine learning, IoT gateway, connected vehicles, smart grids, etc.
Running Apache NiFi with Apache Spark : Integration OptionsTimothy Spann
A walk-through of various options in integration Apache Spark and Apache NiFi in one smooth dataflow. There are now several options in interfacing between Apache NiFi and Apache Spark with Apache Kafka and Apache Livy.
Data at Scales and the Values of Starting Small with Apache NiFi & MiNiFiAldrin Piri
This document discusses Apache NiFi and Apache MiNiFi. It begins with an overview of NiFi, describing its key features like guaranteed delivery, data buffering, and data provenance. It then introduces MiNiFi as a smaller version of NiFi that can operate on edge devices with limited resources. A use case is presented of a courier service gathering data from disparate sources using both NiFi and MiNiFi. The document concludes by discussing the NiFi ecosystem and encouraging participation in the community.
Apache NiFi Crash Course - San Jose Hadoop SummitAldrin Piri
This document provides an overview of Apache NiFi and dataflow. It begins with defining what dataflow is and the challenges of moving data effectively. It then introduces Apache NiFi, describing its key features like guaranteed delivery, data buffering, prioritized queuing, and data provenance. The document discusses NiFi's architecture including its use of FlowFiles to move data agnostically through processors. It also covers NiFi's extension points and integration with other systems. Finally, it describes a live demo use case of using NiFi to integrate real-time traffic data for urban planning.
The document provides steps for debugging a local NiFi processor, including getting the NiFi source code from GitHub, setting up NiFi and an IDE, and launching the IDE in debug mode to trigger breakpoints when a processor starts. It recommends using a feature branch workflow and links to Apache NiFi contribution guides.
Apache Spark 2.0 set the architectural foundations of structure in Spark, unified high-level APIs, structured streaming, and the underlying performant components like Catalyst Optimizer and Tungsten Engine. Since then the Spark community has continued to build new features and fix numerous issues in releases Spark 2.1 and 2.2.
Apache Spark 2.3 has made similar strides too, introducing new features and resolving over 1300 JIRA issues. Likewise, Apache Spark 2.4 will have many JIRA issues resolved over 1100. In this talk, I want to skim and go through those notable features and changes.
Best practices and lessons learnt from Running Apache NiFi at RenaultDataWorks Summit
No real-time insight without real-time data ingestion. No real-time data ingestion without NiFi ! Apache NiFi is an integrated platform for data flow management at entreprise level, enabling companies to securely acquire, process and analyze disparate sources of information (sensors, logs, files, etc) in real-time. NiFi helps data engineers accelerate the development of data flows thanks to its UI and a large number of powerful off-the-shelf processors. However, with great power comes great responsibilities. Behind the simplicity of NiFi, best practices must absolutely be respected in order to scale data flows in production & prevent sneaky situations. In this joint presentation, Hortonworks and Renault, a French car manufacturer, will present lessons learnt from real world projects using Apache NiFi. We will present NiFi design patterns to achieve high level performance and reliability at scale as well as the process to put in place around the technology for data flow governance. We will also show how these best practices can be implemented in practical use cases and scenarios.
Speakers
Kamelia Benchekroun, Data Lake Squad Lead, Renault Group
Abdelkrim Hadjidj, Solution Engineer, Hortonworks
Flink and NiFi, Two Stars in the Apache Big Data ConstellationMatthew Ring
Presented to the Chicago Apache Flink Meetup, Jan. 19, 2016
Goal: To provide a non-exhaustive but interesting demonstration of Apache NiFi and Apache Flink working together. Included a demo of NiFi and Flink together to simulate a simplified trading ecosystem of Brokers and Day Traders, with streaming market data, orders, executions and P/L results.
Robert Metzger - Connecting Apache Flink to the World - Reviewing the streami...Flink Forward
http://flink-forward.org/kb_sessions/connecting-apache-flink-with-the-world-reviewing-the-streaming-connectors/
Getting data in and out of Flink in a reliable fashion is one of the most important tasks of a stream processor. This talk will review the most important and frequently used connectors in Flink. Apache Kafka and Amazon Kinesis Streams both fall into the same category of distributed, high-throughput and durable publish-subscribe messaging systems. The talk will explain how the connectors in Flink for these systems are implemented. In particular we’ll focus on how we ensure exactly-once semantics while consuming data and how offsets/sequence numbers are handled. We will also review two generic tools in Flink for connectors: A message acknowledging source for classical message queues (like those implementing AMQP) and a generic write ahead log sink, using Flink’s state backend abstraction. The objective of the talk is to explain the internals of the streaming connectors, so that people can understand their behavior, configure them properly and implement their own connectors.
Taking DataFlow Management to the Edge with Apache NiFi/MiNiFiBryan Bende
This document provides an overview of a presentation about taking dataflow management to the edge with Apache NiFi and MiniFi. The presentation discusses the problem of moving data between systems with different formats, protocols, and security requirements. It introduces Apache NiFi as a solution for dataflow management and introduces Apache MiniFi for managing dataflows at the edge. The presentation includes a demo and time for Q&A.
Yifeng Jiang gives a presentation introducing Apache Nifi. He begins with an overview of himself and the agenda. He then provides an introduction to Nifi including terminology like FlowFile and Processor. Key aspects of Nifi are demonstrated including the user interface, provenance tracking, queue prioritization, cluster architecture, and a demo of real-time data processing. Example use cases are discussed like indexing JSON tweets and indexing data from a relational database. The presentation concludes that Nifi is an easy to use and powerful system for processing and distributing data with 90 built-in processors.
NiFi Best Practices for the EnterpriseGregory Keys
The document discusses best practices for implementing Apache NiFi in an enterprise. It recommends establishing a Center of Excellence (COE) to align stakeholders, provide guidance, and develop standards and processes for NiFi deployment. The COE should work with business leaders to understand data flow needs and ensure NiFi is delivering business value. When scaling NiFi across a large enterprise, it may make sense to have multiple semi-autonomous NiFi clusters for different business groups rather than one large cluster. Reusable templates, components, and patterns can help with development efficiencies.
Apache NiFi: latest developments for flow management at scaleAbdelkrim Hadjidj
The document discusses Apache NiFi, an open source dataflow management platform. It provides an overview of NiFi's capabilities including over 225 processors for common data access, transformation, and management tasks. The presentation demonstrates NiFi and its web-based user interface, zero-master clustering architecture, and extensibility via custom processors and controllers. New features discussed include component versioning, change data capture from MySQL, and a record-based processing mechanism for improved data handling.
Introduction: 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.
Speakers: Andy LoPresto, Timothy Spann
As Apache Solr becomes more powerful and easier to use, the accessibility of high quality data becomes key to unlocking the full potential of Solr’s search and analytic capabilities. Traditional approaches to acquiring data frequently involve a combination of homegrown tools and scripts, often requiring significant development efforts and becoming hard to change, hard to monitor, and hard to maintain. This talk will discuss how Apache NiFi addresses the above challenges and can be used to build production-grade data pipelines for Solr. We will start by giving an introduction to the core features of NiFi, such as visual command & control, dynamic prioritization, back-pressure, and provenance. We will then look at NiFi’s processors for integrating with Solr, covering topics such as ingesting and extracting data, interacting with secure Solr instances, and performance tuning. We will conclude by building a live dataflow from scratch, demonstrating how to prepare data and ingest to Solr.
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.
The First Mile - Edge and IoT Data Collection With Apache Nifi and MiniFiDataWorks Summit
Apache NiFi MiNiFi enables data collection in a brand new environment - small sensor footprint, intermittent or limited bandwidth distributed system, and disposable or short-lived hardware. You can prioritize this data or perform initial analysis on the edge, as well as immediately encrypt and protect it.
Concept: Apache NiFi offers a revolutionary data flow management system and extensive integration of existing data production, consumption and analysis ecosystems, all of which are robust data delivery and a (data) logging infrastructure It is protected by. Learn about the additional project Apache MiNiFi, which extends the scope of NiFi's power to the maximum. MiNiFi is a lightweight application that can be placed on hardware that is one order of magnitude smaller than the existing standard data collection platform and is less powerful. As a JVM-enabled native agent MiNiFi enables data gathering in a brand new environment - small sensor footprint, intermittent or limited bandwidth distributed system, and disposable or short-lived hardware. You can prioritize this data or perform initial analysis on the edge, as well as immediately encrypt and protect it. Regional governance and regulatory policies are applied to geopolitical boundaries and comply with legal requirements. And all of this configuration can be done from the existing NiFi and central control using the stable data UI that the data flow administrator has already liked and trusted.
Required prior knowledge / targeted participants: Developers and data flow administrators need some knowledge of Apache NiFi as a platform for routing, conversion, and data delivery through the system (a brief overview is provided ). In this talk we will focus on extending data collection, routing, data history, and NiFi control functions, through IoT / edge integration via MiNiFi.
Key Points: Participants will learn about the opportunity to collect and capture data flows close to the source of data, "edge", such as IoT devices, vehicles, machines, etc. Participants prioritize, filter, protect, and manipulate this data in the initial data lifecycle and understand the potential for data visibility and performance improvement.
Introduction: 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.
The First Mile -- Edge and IoT Data Collection with Apache NiFi and MiNiFiDataWorks Summit
Apache NiFi provided a revolutionary data flow management system with a broad range of integrations with existing data production, consumption, and analysis ecosystems, all covered with robust data delivery and provenance infrastructure. Now learn about the follow-on project which expands the reach of NiFi to the edge, Apache MiNiFi. MiNiFi is a lightweight application which can be deployed on hardware orders of magnitude smaller and less powerful than the existing standard data collection platforms. With both a JVM compatible and native agent, MiNiFi allows data collection in brand new environments — sensors with tiny footprints, distributed systems with intermittent or restricted bandwidth, and even disposable or ephemeral hardware. Not only can this data be prioritized and have some initial analysis performed at the edge, it can be encrypted and secured immediately. Local governance and regulatory policies can be applied across geopolitical boundaries to conform with legal requirements. And all of this configuration can be done from central command & control using an existing NiFi with the trusted and stable UI data flow managers already love.
This document provides an overview of Apache NiFi and the new MiNiFi project. It begins with introductions to Apache NiFi, its key features, and what is new in version 1.0.0. It then introduces MiNiFi, describing it as a way to deploy NiFi flows to edge systems with limited resources. The rest of the document demonstrates the NiFi and MiNiFi architectures and how they work together, and provides an example deployment to a courier service. It concludes with a demo of NiFi and MiNiFi.
Dataflow Management From Edge to Core with Apache NiFiDataWorks Summit
What is “dataflow?” — the process and tooling around gathering necessary information and getting it into a useful form to make insights available. Dataflow needs change rapidly — what was noise yesterday may be crucial data today, an API endpoint changes, or a service switches from producing CSV to JSON or Avro. In addition, developers may need to design a flow in a sandbox and deploy to QA or production — and those database passwords aren’t the same (hopefully). Learn about Apache NiFi — a robust and secure framework for dataflow development and monitoring.
Abstract: Identifying, collecting, securing, filtering, prioritizing, transforming, and transporting abstract data is a challenge faced by every organization. Apache NiFi and MiNiFi allow developers to create and refine dataflows with ease and ensure that their critical content is routed, transformed, validated, and delivered across global networks. Learn how the framework enables rapid development of flows, live monitoring and auditing, data protection and sharing. From IoT and machine interaction to log collection, NiFi can scale to meet the needs of your organization. Able to handle both small event messages and “big data” on the scale of terabytes per day, NiFi will provide a platform which lets both engineers and non-technical domain experts collaborate to solve the ingest and storage problems that have plagued enterprises.
Expected prior knowledge / intended audience: developers and data flow managers should be interested in learning about and improving their dataflow problems. The intended audience does not need experience in designing and modifying data flows.
Takeaways: Attendees will gain an understanding of dataflow concepts, data management processes, and flow management (including versioning, rollbacks, promotion between deployment environments, and various backing implementations).
Current uses: I am a committer and PMC member for the Apache NiFi, MiNiFi, and NiFi Registry projects and help numerous users deploy these tools to collect data from an incredibly diverse array of endpoints, aggregate, prioritize, filter, transform, and secure this data, and generate actionable insight from it. Current users of these platforms include many Fortune 100 companies, governments, startups, and individual users across fields like telecommunications, finance, healthcare, automotive, aerospace, and oil & gas, with use cases like fraud detection, logistics management, supply chain management, machine learning, IoT gateway, connected vehicles, smart grids, etc.
Running Apache NiFi with Apache Spark : Integration OptionsTimothy Spann
A walk-through of various options in integration Apache Spark and Apache NiFi in one smooth dataflow. There are now several options in interfacing between Apache NiFi and Apache Spark with Apache Kafka and Apache Livy.
Data at Scales and the Values of Starting Small with Apache NiFi & MiNiFiAldrin Piri
This document discusses Apache NiFi and Apache MiNiFi. It begins with an overview of NiFi, describing its key features like guaranteed delivery, data buffering, and data provenance. It then introduces MiNiFi as a smaller version of NiFi that can operate on edge devices with limited resources. A use case is presented of a courier service gathering data from disparate sources using both NiFi and MiNiFi. The document concludes by discussing the NiFi ecosystem and encouraging participation in the community.
Apache NiFi Crash Course - San Jose Hadoop SummitAldrin Piri
This document provides an overview of Apache NiFi and dataflow. It begins with defining what dataflow is and the challenges of moving data effectively. It then introduces Apache NiFi, describing its key features like guaranteed delivery, data buffering, prioritized queuing, and data provenance. The document discusses NiFi's architecture including its use of FlowFiles to move data agnostically through processors. It also covers NiFi's extension points and integration with other systems. Finally, it describes a live demo use case of using NiFi to integrate real-time traffic data for urban planning.
The document provides steps for debugging a local NiFi processor, including getting the NiFi source code from GitHub, setting up NiFi and an IDE, and launching the IDE in debug mode to trigger breakpoints when a processor starts. It recommends using a feature branch workflow and links to Apache NiFi contribution guides.
Apache Spark 2.0 set the architectural foundations of structure in Spark, unified high-level APIs, structured streaming, and the underlying performant components like Catalyst Optimizer and Tungsten Engine. Since then the Spark community has continued to build new features and fix numerous issues in releases Spark 2.1 and 2.2.
Apache Spark 2.3 has made similar strides too, introducing new features and resolving over 1300 JIRA issues. Likewise, Apache Spark 2.4 will have many JIRA issues resolved over 1100. In this talk, I want to skim and go through those notable features and changes.
Best practices and lessons learnt from Running Apache NiFi at RenaultDataWorks Summit
No real-time insight without real-time data ingestion. No real-time data ingestion without NiFi ! Apache NiFi is an integrated platform for data flow management at entreprise level, enabling companies to securely acquire, process and analyze disparate sources of information (sensors, logs, files, etc) in real-time. NiFi helps data engineers accelerate the development of data flows thanks to its UI and a large number of powerful off-the-shelf processors. However, with great power comes great responsibilities. Behind the simplicity of NiFi, best practices must absolutely be respected in order to scale data flows in production & prevent sneaky situations. In this joint presentation, Hortonworks and Renault, a French car manufacturer, will present lessons learnt from real world projects using Apache NiFi. We will present NiFi design patterns to achieve high level performance and reliability at scale as well as the process to put in place around the technology for data flow governance. We will also show how these best practices can be implemented in practical use cases and scenarios.
Speakers
Kamelia Benchekroun, Data Lake Squad Lead, Renault Group
Abdelkrim Hadjidj, Solution Engineer, Hortonworks
Flink and NiFi, Two Stars in the Apache Big Data ConstellationMatthew Ring
Presented to the Chicago Apache Flink Meetup, Jan. 19, 2016
Goal: To provide a non-exhaustive but interesting demonstration of Apache NiFi and Apache Flink working together. Included a demo of NiFi and Flink together to simulate a simplified trading ecosystem of Brokers and Day Traders, with streaming market data, orders, executions and P/L results.
Robert Metzger - Connecting Apache Flink to the World - Reviewing the streami...Flink Forward
http://flink-forward.org/kb_sessions/connecting-apache-flink-with-the-world-reviewing-the-streaming-connectors/
Getting data in and out of Flink in a reliable fashion is one of the most important tasks of a stream processor. This talk will review the most important and frequently used connectors in Flink. Apache Kafka and Amazon Kinesis Streams both fall into the same category of distributed, high-throughput and durable publish-subscribe messaging systems. The talk will explain how the connectors in Flink for these systems are implemented. In particular we’ll focus on how we ensure exactly-once semantics while consuming data and how offsets/sequence numbers are handled. We will also review two generic tools in Flink for connectors: A message acknowledging source for classical message queues (like those implementing AMQP) and a generic write ahead log sink, using Flink’s state backend abstraction. The objective of the talk is to explain the internals of the streaming connectors, so that people can understand their behavior, configure them properly and implement their own connectors.
Integrating Apache NiFi and Apache FlinkHortonworks
Hortonworks DataFlow delivers data to streaming analytics platforms, inclusive of Storm, Spark and Flink
These are slides from an Apache Flink Meetup: Integration of Apache Flink and Apache Nifi, Feb 4 2016
This document discusses using Apache Spark and Apache NiFi together for data lakes. It outlines the goals of a data lake including having a central data repository, reducing costs, enabling easier discovery and prototyping. It also discusses what is needed for a Hadoop data lake, including automation of pipelines, governance, and interactive data discovery. The document then provides an example ingestion project and describes using Apache Spark for functions like cleansing, validating, and profiling data. It outlines using Apache NiFi for the pipeline design with drag and drop functionality. Finally, it demonstrates ingesting and preparing data, data self-service and transformation, data discovery, and operational monitoring capabilities.
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
The document introduces Akka, an open-source toolkit for building distributed, concurrent applications on the JVM. It provides a programming model called the actor model that makes it easier to build scalable and fault-tolerant systems. Actors process messages asynchronously and avoid shared state, providing a simpler approach to concurrency than traditional threads and locks. Akka allows actors to be distributed across a network, enabling applications to scale out elastically.
This introductory level talk is about Apache Flink: a multi-purpose Big Data analytics framework leading a movement towards the unification of batch and stream processing in the open source.
With the many technical innovations it brings along with its unique vision and philosophy, it is considered the 4 G (4th Generation) of Big Data Analytics frameworks providing the only hybrid (Real-Time Streaming + Batch) open source distributed data processing engine supporting many use cases: batch, streaming, relational queries, machine learning and graph processing.
In this talk, you will learn about:
1. What is Apache Flink stack and how it fits into the Big Data ecosystem?
2. How Apache Flink integrates with Hadoop and other open source tools for data input and output as well as deployment?
3. Why Apache Flink is an alternative to Apache Hadoop MapReduce, Apache Storm and Apache Spark.
4. Who is using Apache Flink?
5. Where to learn more about Apache Flink?
NJ Hadoop Meetup - Apache NiFi Deep DiveBryan Bende
Apache NiFi is a software platform created by Apache to automate the flow of data between systems. It addresses challenges of global enterprise data flow with features like visual command and control, data lineage tracking, data prioritization, and secure data transfer. NiFi is commonly used for reliable transfer of data between systems, delivery of data to analytic platforms, and data enrichment/preparation tasks like format conversion and extraction. It is not intended for distributed computation, complex event processing, or joins.
Originally created for Hadoop Summit 2016: Melbourne.
http://www.hadoopsummit.org/melbourne/
Apache NiFi is becoming a defacto tool for handling orchestration, routing and mediation of data in the highly complex and heterogeneous world of Big Data, connecting many components (in-motion and at-rest) of its ecosystem into one homogenous and secure data flow. And while features such as security, provenance, dynamic prioritization and extensibility have long captured the attention of the enterprises, the innovation in NiFi land continues. This hands-on talk consisting of live demos and code will concentrate on what’s new an exciting in the world of NiFi. It will cover the newest and most advanced features of NiFi as well as demonstrate some of the "work in progress" essentially giving you a preview into the future.
Big Data Day LA 2016/ Big Data Track - Building scalable enterprise data flow...Data Con LA
This document discusses Apache NiFi and stream processing. It provides an overview of NiFi's key concepts of managing data flow, data provenance, and securing data. NiFi allows users to visually build data flows with drag and drop processors. It offers features such as guaranteed delivery, data buffering, prioritized queuing, and data provenance. NiFi is based on Flow-Based Programming and is used to reliably transfer data between systems, enrich and prepare data, and deliver data to analytic platforms.
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
Apache NiFi - Flow Based Programming MeetupJoseph Witt
These are the slides from the July 11th Meetup in Toronto for the Flow Based Programming meetup group at Lighthouse covering Enterprise Dataflow with Apache NiFi.
The document provides an introduction and overview of Apache NiFi and its architecture. It discusses how NiFi can be used to effectively manage and move data between different producers and consumers. It also summarizes key NiFi features like guaranteed delivery, data buffering, prioritization, and data provenance. Finally, it briefly outlines the NiFi architecture and components as well as opportunities for the future of the MiniFi project.
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
The document discusses Apache NiFi and its role in the Hadoop ecosystem. It provides an overview of NiFi, describes how it can be used to integrate with Hadoop components like HDFS, HBase, and Kafka. It also discusses how NiFi supports stream processing integrations and outlines some use cases. The document concludes by discussing future work, including improving NiFi's high availability, multi-tenancy, and expanding its ecosystem integrations.
Building Data Pipelines for Solr with Apache NiFiBryan Bende
This document provides an overview of using Apache NiFi to build data pipelines that index data into Apache Solr. It introduces NiFi and its capabilities for data routing, transformation and monitoring. It describes how Solr accepts data through different update handlers like XML, JSON and CSV. It demonstrates how NiFi processors can be used to stream data to Solr via these update handlers. Example use cases are presented for indexing tweets, commands, logs and databases into Solr collections. Future enhancements are discussed like parsing documents and distributing commands across a Solr cluster.
Dataflow Management From Edge to Core with Apache NiFiDataWorks Summit
What is “dataflow?” — the process and tooling around gathering necessary information and getting it into a useful form to make insights available. Dataflow needs change rapidly — what was noise yesterday may be crucial data today, an API endpoint changes, or a service switches from producing CSV to JSON or Avro. In addition, developers may need to design a flow in a sandbox and deploy to QA or production — and those database passwords aren’t the same (hopefully). Learn about Apache NiFi — a robust and secure framework for dataflow development and monitoring.
Abstract: Identifying, collecting, securing, filtering, prioritizing, transforming, and transporting abstract data is a challenge faced by every organization. Apache NiFi and MiNiFi allow developers to create and refine dataflows with ease and ensure that their critical content is routed, transformed, validated, and delivered across global networks. Learn how the framework enables rapid development of flows, live monitoring and auditing, data protection and sharing. From IoT and machine interaction to log collection, NiFi can scale to meet the needs of your organization. Able to handle both small event messages and “big data” on the scale of terabytes per day, NiFi will provide a platform which lets both engineers and non-technical domain experts collaborate to solve the ingest and storage problems that have plagued enterprises.
Expected prior knowledge / intended audience: developers and data flow managers should be interested in learning about and improving their dataflow problems. The intended audience does not need experience in designing and modifying data flows.
Takeaways: Attendees will gain an understanding of dataflow concepts, data management processes, and flow management (including versioning, rollbacks, promotion between deployment environments, and various backing implementations).
Current uses: I am a committer and PMC member for the Apache NiFi, MiNiFi, and NiFi Registry projects and help numerous users deploy these tools to collect data from an incredibly diverse array of endpoints, aggregate, prioritize, filter, transform, and secure this data, and generate actionable insight from it. Current users of these platforms include many Fortune 100 companies, governments, startups, and individual users across fields like telecommunications, finance, healthcare, automotive, aerospace, and oil & gas, with use cases like fraud detection, logistics management, supply chain management, machine learning, IoT gateway, connected vehicles, smart grids, etc.
Speaker: Andy LoPresto, Sr. Member of Technical Staff, Hortonworks
Data Con LA 2018 - Streaming and IoT by Pat AlwellData Con LA
Hortonworks DataFlow (HDF) is built with the vision of creating a platform that enables enterprises to build dataflow management and streaming analytics solutions that collect, curate, analyze and act on data in motion across the datacenter and cloud. Do you want to be able to provide a complete end-to-end streaming solution, from an IoT device all the way to a dashboard for your business users with no code? Come to this session to learn how this is now possible with HDF 3.1.
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
This document discusses extending the functionality of Apache NiFi through custom processors and controller services. It provides an overview of the NiFi architecture and repositories, describes how to create extensions with minimal dependencies using Maven archetypes, and notes that most extensions can be developed within hours. Quick prototyping of data flows is possible using existing binaries, applications, and scripting languages. Resources for the NiFi developer guide and example Maven projects are also listed.
The First Mile – Edge and IoT Data Collection with Apache NiFi and MiNiFiDataWorks Summit
Apache NiFi MiNiFi allows data collection in brand new environments — sensors with tiny footprints, distributed systems with intermittent or restricted bandwidth, and even disposable or ephemeral hardware. Not only can this data be prioritized and have some initial analysis performed at the edge, it can be encrypted and secured immediately.
Abstract: Apache NiFi provided a revolutionary data flow management system with a broad range of integrations with existing data production, consumption, and analysis ecosystems, all covered with robust data delivery and provenance infrastructure. Now learn about the follow-on project which expands the reach of NiFi to the edge, Apache MiNiFi. MiNiFi is a lightweight application which can be deployed on hardware orders of magnitude smaller and less powerful than the existing standard data collection platforms. With both a JVM compatible and native agent, MiNiFi allows data collection in brand new environments — sensors with tiny footprints, distributed systems with intermittent or restricted bandwidth, and even disposable or ephemeral hardware. Not only can this data be prioritized and have some initial analysis performed at the edge, it can be encrypted and secured immediately. Local governance and regulatory policies can be applied across geopolitical boundaries to conform with legal requirements. And all of this configuration can be done from central command & control using an existing NiFi with the trusted and stable UI data flow managers already love.
Expected prior knowledge / intended audience: developers and data flow managers should have a passing knowledge of Apache NiFi as a platform for routing, transforming, and delivering data through systems (a brief overview will be provided). The talk will focus on extending the data collection, routing, provenance, and governance capabilities of NiFi to IoT/edge integration via MiNiFi.
Takeaways: Attendees will learn about opportunities to bring their data flow and capture closer to the "edge" -- sources of data like IoT devices, vehicles, machinery, etc. They will understand the possibilities to prioritize, filter, secure, and manipulate this data earlier in the data lifecycle to enhance their data visibility and performance.
Speaker: Andy LoPresto, Sr. Member of Technical Staff, Hortonworks
This document provides an overview of Apache NiFi 1.0 and discusses its new enhancements, including a modernized UI with a complete interface redesign, multitenant authorization capabilities, zero master clustering, and foundational work for software development lifecycles. It also outlines NiFi's use for data flow management and integration with downstream systems.
Connecting the Drops with Apache NiFi & Apache MiNiFiDataWorks Summit
Demand for increased capture of information to drive analytic insights into an organizations' assets and infrastructure is growing at unprecedented rates. However, as data volume growth soars, the ability to provide seamless ingestion pipelines becomes operationally complex as the magnitude of data sources and types expands.
This talk will focus on the efforts of the Apache NiFi community including subproject, MiNiFi; an agent based architecture and its relation to the core Apache NiFi project. MiNiFi is focused on providing a platform that meets and adapts to where data is born while providing the core tenets of NiFi in provenance, security, and command and control. These capabilities provide versatile avenues for the bi-directional exchange of information across data and control planes while dealing with the constraints of operation at opposite ends of the scale spectrum tackling the first and last miles of dataflow management.
We will highlight ongoing and new efforts in the community to provide greater flexibility with deployment and configuration management of flows. Versioned flows provide greater operational flexibility and serve as a powerful foundation to orchestrate the collection and transmission from the point of data's inception through to its transmission to consumers and processing systems.
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
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.
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Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
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zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
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Paper Link: https://eprint.iacr.org/2024/257
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
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Northern Engraving | Nameplate Manufacturing Process - 2024Northern Engraving
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Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyScyllaDB
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Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
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- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
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4. Deployment Using ArgoCD for Edge Devices
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5. Introduction to Apache Kafka and S3
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Digital Banking in the Cloud: How Citizens Bank Unlocked Their MainframePrecisely
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5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
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