This document provides an overview of Apache NiFi and data flow fundamentals. It begins with an introduction to Apache NiFi and outlines the agenda. It then discusses data flow and streaming fundamentals, including challenges in moving data effectively. The document introduces Apache NiFi's architecture and capabilities for addressing these challenges. It also previews a live demo of NiFi and discusses the NiFi community.
Unlock Value from Big Data with Apache NiFi and Streaming CDCHortonworks
The document discusses Apache NiFi and streaming change data capture (CDC) with Attunity Replicate. It provides an overview of NiFi's capabilities for dataflow management and visualization. It then demonstrates how Attunity Replicate can be used for real-time CDC to capture changes from source databases and deliver them to NiFi for further processing, enabling use cases across multiple industries. Examples of source systems include SAP, Oracle, SQL Server, and file data, with targets including Hadoop, data warehouses, and cloud data stores.
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.
Kafka for Real-Time Replication between Edge and Hybrid CloudKai Wähner
Not all workloads allow cloud computing. Low latency, cybersecurity, and cost-efficiency require a suitable combination of edge computing and cloud integration.
This session explores architectures and design patterns for software and hardware considerations to deploy hybrid data streaming with Apache Kafka anywhere. A live demo shows data synchronization from the edge to the public cloud across continents with Kafka on Hivecell and Confluent Cloud.
Apache Kafka is the de facto standard for data streaming to process data in motion. With its significant adoption growth across all industries, I get a very valid question every week: When NOT to use Apache Kafka? What limitations does the event streaming platform have? When does Kafka simply not provide the needed capabilities? How to qualify Kafka out as it is not the right tool for the job?
This session explores the DOs and DONTs. Separate sections explain when to use Kafka, when NOT to use Kafka, and when to MAYBE use Kafka.
No matter if you think about open source Apache Kafka, a cloud service like Confluent Cloud, or another technology using the Kafka protocol like Redpanda or Pulsar, check out this slide deck.
A detailed article about this topic:
https://www.kai-waehner.de/blog/2022/01/04/when-not-to-use-apache-kafka/
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
Real-time Twitter Sentiment Analysis and Image Recognition with Apache NiFiTimothy Spann
A walk through of creating a dataflow for ingest of twitter data and analyzing the stream with NLTK Vader Python Sentiment Analysis and Inception v3 TensorFlow via Python in Apache NiFi. Storage in Hadoop HDFS.
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.
Unlock Value from Big Data with Apache NiFi and Streaming CDCHortonworks
The document discusses Apache NiFi and streaming change data capture (CDC) with Attunity Replicate. It provides an overview of NiFi's capabilities for dataflow management and visualization. It then demonstrates how Attunity Replicate can be used for real-time CDC to capture changes from source databases and deliver them to NiFi for further processing, enabling use cases across multiple industries. Examples of source systems include SAP, Oracle, SQL Server, and file data, with targets including Hadoop, data warehouses, and cloud data stores.
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.
Kafka for Real-Time Replication between Edge and Hybrid CloudKai Wähner
Not all workloads allow cloud computing. Low latency, cybersecurity, and cost-efficiency require a suitable combination of edge computing and cloud integration.
This session explores architectures and design patterns for software and hardware considerations to deploy hybrid data streaming with Apache Kafka anywhere. A live demo shows data synchronization from the edge to the public cloud across continents with Kafka on Hivecell and Confluent Cloud.
Apache Kafka is the de facto standard for data streaming to process data in motion. With its significant adoption growth across all industries, I get a very valid question every week: When NOT to use Apache Kafka? What limitations does the event streaming platform have? When does Kafka simply not provide the needed capabilities? How to qualify Kafka out as it is not the right tool for the job?
This session explores the DOs and DONTs. Separate sections explain when to use Kafka, when NOT to use Kafka, and when to MAYBE use Kafka.
No matter if you think about open source Apache Kafka, a cloud service like Confluent Cloud, or another technology using the Kafka protocol like Redpanda or Pulsar, check out this slide deck.
A detailed article about this topic:
https://www.kai-waehner.de/blog/2022/01/04/when-not-to-use-apache-kafka/
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
Real-time Twitter Sentiment Analysis and Image Recognition with Apache NiFiTimothy Spann
A walk through of creating a dataflow for ingest of twitter data and analyzing the stream with NLTK Vader Python Sentiment Analysis and Inception v3 TensorFlow via Python in Apache NiFi. Storage in Hadoop HDFS.
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.
Real-Life Use Cases & Architectures for Event Streaming with Apache KafkaKai Wähner
Streaming all over the World: Real-Life Use Cases & Architectures for Event Streaming with Apache Kafka.
Learn about various case studies for event streaming with Apache Kafka across industries. The talk explores architectures for real-world deployments from Audi, BMW, Disney, Generali, Paypal, Tesla, Unity, Walmart, William Hill, and more. Use cases include fraud detection, mainframe offloading, predictive maintenance, cybersecurity, edge computing, track&trace, live betting, and much more.
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.
Hadoop 3.0 has been years in the making, and now it's finally arriving. Andrew Wang and Daniel Templeton offer an overview of new features, including HDFS erasure coding, YARN Timeline Service v2, YARN federation, and much more, and discuss current release management status and community testing efforts dedicated to making Hadoop 3.0 the best Hadoop major release yet.
This document summarizes Netflix's use of Kafka in their data pipeline. It discusses how Netflix evolved from using S3 and EMR to introducing Kafka and Kafka producers and consumers to handle 400 billion events per day. It covers challenges of scaling Kafka clusters and tuning Kafka clients and brokers. Finally, it outlines Netflix's roadmap which includes contributing to open source projects like Kafka and testing failure resilience.
Designing Event-Driven Applications with Apache NiFi, Apache Flink, Apache Spark
DevNexus 2022 Atlanta
https://devnexus.com/presentations/7150/
This talk is a quick overview of the How, What and WHY of Apache Pulsar, Apache Flink and Apache NiFi. I will show you how to design event-driven applications that scale the cloud native way.
This talk was done live in person at DevNexus across from the booth in room 311
Tim Spann
Tim Spann is a Developer Advocate for StreamNative. He works with StreamNative Cloud, Apache Pulsar, Apache Flink, Flink SQL, Apache NiFi, MiniFi, Apache MXNet, TensorFlow, Apache Spark, big data, the IoT, machine learning, and deep learning. Tim has over a decade of experience with the IoT, big data, distributed computing, streaming technologies, and Java programming. Previously, he was a Principal DataFlow Field Engineer at Cloudera, a Senior Solutions Architect at AirisData, a Senior Field Engineer at Pivotal and a Team Leader at HPE. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton on big data, the IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as IoT Fusion, Strata, ApacheCon, Data Works Summit Berlin, DataWorks Summit Sydney, and Oracle Code NYC. He holds a BS and MS in computer science.
Grafana Loki: like Prometheus, but for LogsMarco Pracucci
Loki is a horizontally-scalable, highly-available log aggregation system inspired by Prometheus. It is designed to be very cost-effective and easy to operate, as it does not index the contents of the logs, but rather labels for each log stream.
In this talk, we will introduce Loki, its architecture and the design trade-offs in an approachable way. We’ll both cover Loki and Promtail, the agent used to scrape local logs to push to Loki, including the Prometheus-style service discovery used to dynamically discover logs and attach metadata from applications running in a Kubernetes cluster.
Finally, we’ll show how to query logs with Grafana using LogQL - the Loki query language - and the latest Grafana features to easily build dashboards mixing metrics and logs.
Apache Pinot Case Study: Building Distributed Analytics Systems Using Apache ...HostedbyConfluent
The document describes Apache Pinot, an open source distributed real-time analytics platform used at LinkedIn. It discusses the challenges of building user-facing real-time analytics systems at scale. It initially describes LinkedIn's use of Apache Kafka for ingestion and Apache Pinot for queries, but notes challenges with Pinot's initial Kafka consumer group-based approach for real-time ingestion, such as incorrect results, limited scalability, and high storage overhead. It then presents Pinot's new partition-level consumption approach which addresses these issues by taking control of partition assignment and checkpointing, allowing for independent and flexible scaling of individual partitions across servers.
Dynamically Scaling Data Streams across Multiple Kafka Clusters with Zero Fli...Flink Forward
Flink Forward San Francisco 2022.
Flink consumers read from Kafka as a scalable, high throughput, and low latency data source. However, there are challenges in scaling out data streams where migration and multiple Kafka clusters are required. Thus, we introduced a new Kafka source to read sharded data across multiple Kafka clusters in a way that conforms well with elastic, dynamic, and reliable infrastructure. In this presentation, we will present the source design and how the solution increases application availability while reducing maintenance toil. Furthermore, we will describe how we extended the existing KafkaSource to provide mechanisms to read logical streams located on multiple clusters, to dynamically adapt to infrastructure changes, and to perform transparent cluster migrations and failover.
by
Mason Chen
The Top 5 Apache Kafka Use Cases and Architectures in 2022Kai Wähner
This document discusses the top 5 use cases and architectures for data in motion in 2022. It describes:
1) The Kappa architecture as an alternative to the Lambda architecture that uses a single stream to handle both real-time and batch data.
2) Hyper-personalized omnichannel experiences that integrate customer data from multiple sources in real-time to provide personalized experiences across channels.
3) Multi-cloud deployments using Apache Kafka and data mesh architectures to share data across different cloud platforms.
4) Edge analytics that deploy stream processing and Kafka brokers at the edge to enable low-latency use cases and offline functionality.
5) Real-time cybersecurity applications that use streaming data
This document provides an overview of Apache NiFi, an open source system for automated data flows. It discusses how NiFi originated from NSA technology and Flow-Based Programming. It describes NiFi's core concepts like FlowFiles, Processors, and Connections. It also covers how NiFi can be used for messaging and data distribution tasks like acquiring, processing, and storing massive amounts of data from various sources in a secure and scalable way. The document concludes with use cases for NiFi and a demonstration of moving log data between systems using NiFi's visual interface and tracking capabilities.
Data ingestion and distribution with apache NiFiLev Brailovskiy
In this session, we will cover our experience working with Apache NiFi, an easy to use, powerful, and reliable system to process and distribute a large volume of data. The first part of the session will be an introduction to Apache NiFi. We will go over NiFi main components and building blocks and functionality.
In the second part of the session, we will show our use case for Apache NiFi and how it's being used inside our Data Processing infrastructure.
As organizations pursue Big Data initiatives to capture new opportunities for data-driven insights, data governance has become table stakes both from the perspective of external regulatory compliance as well as business value extraction internally within an enterprise. This session will introduce Apache Atlas, a project that was incubated by Hortonworks along with a group of industry leaders across several verticals including financial services, healthcare, pharma, oil and gas, retail and insurance to help address data governance and metadata needs with an open extensible platform governed under the aegis of Apache Software Foundation. Apache Atlas empowers organizations to harvest metadata across the data ecosystem, govern and curate data lakes by applying consistent data classification with a centralized metadata catalog.
In this talk, we will present the underpinnings of the architecture of Apache Atlas and conclude with a tour of governance capabilities within Apache Atlas as we showcase various features for open metadata modeling, data classification, visualizing cross-component lineage and impact. We will also demo how Apache Atlas delivers a complete view of data movement across several analytic engines such as Apache Hive, Apache Storm, Apache Kafka and capabilities to effectively classify, discover datasets.
ksqlDB: A Stream-Relational Database Systemconfluent
Speaker: Matthias J. Sax, Software Engineer, Confluent
ksqlDB is a distributed event streaming database system that allows users to express SQL queries over relational tables and event streams. The project was released by Confluent in 2017 and is hosted on Github and developed with an open-source spirit. ksqlDB is built on top of Apache Kafka®, a distributed event streaming platform. In this talk, we discuss ksqlDB’s architecture that is influenced by Apache Kafka and its stream processing library, Kafka Streams. We explain how ksqlDB executes continuous queries while achieving fault tolerance and high vailability. Furthermore, we explore ksqlDB’s streaming SQL dialect and the different types of supported queries.
Matthias J. Sax is a software engineer at Confluent working on ksqlDB. He mainly contributes to Kafka Streams, Apache Kafka's stream processing library, which serves as ksqlDB's execution engine. Furthermore, he helps evolve ksqlDB's "streaming SQL" language. In the past, Matthias also contributed to Apache Flink and Apache Storm and he is an Apache committer and PMC member. Matthias holds a Ph.D. from Humboldt University of Berlin, where he studied distributed data stream processing systems.
https://db.cs.cmu.edu/events/quarantine-db-talk-2020-confluent-ksqldb-a-stream-relational-database-system/
This document provides an overview of Apache NiFi and dataflow. It begins with an introduction to the challenges of moving data effectively within and between systems. It then discusses Apache NiFi's key features for addressing these challenges, including guaranteed delivery, data buffering, prioritized queuing, and data provenance. The document outlines NiFi's architecture and components like repositories and extension points. It also previews a live demo and invites attendees to further discuss Apache NiFi at a Birds of a Feather session.
Apache Kafka becoming the message bus to transfer huge volumes of data from various sources into Hadoop.
It's also enabling many real-time system frameworks and use cases.
Managing and building clients around Apache Kafka can be challenging. In this talk, we will go through the best practices in deploying Apache Kafka
in production. How to Secure a Kafka Cluster, How to pick topic-partitions and upgrading to newer versions. Migrating to new Kafka Producer and Consumer API.
Also talk about the best practices involved in running a producer/consumer.
In Kafka 0.9 release, we’ve added SSL wire encryption, SASL/Kerberos for user authentication, and pluggable authorization. Now Kafka allows authentication of users, access control on who can read and write to a Kafka topic. Apache Ranger also uses pluggable authorization mechanism to centralize security for Kafka and other Hadoop ecosystem projects.
We will showcase open sourced Kafka REST API and an Admin UI that will help users in creating topics, re-assign partitions, Issuing
Kafka ACLs and monitoring Consumer offsets.
OpenShift is a Platform-as-a-Service that provides development environments on demand using containers. It automates application lifecycles including build, deploy, and retirement. OpenShift uses containers to package applications and dependencies in a portable way. Red Hat addresses concerns around adopting containers at scale through OpenShift, which provides security, scalability, integration, management and certification capabilities. OpenShift runs on a user's choice of infrastructure and orchestrates applications across nodes using Kubernetes.
0-60: Tesla's Streaming Data Platform ( Jesse Yates, Tesla) Kafka Summit SF 2019confluent
Tesla ingests trillions of events every day from hundreds of unique data sources through our streaming data platform. Find out how we developed a set of high-throughput, non-blocking primitives that allow us to transform and ingest data into a variety of data stores with minimal development time. Additionally, we will discuss how these primitives allowed us to completely migrate the streaming platform in just a few months. Finally, we will talk about how we scale team size sub-linearly to data volumes, while continuing to onboard new use cases.
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.
The document discusses new features in Apache Hadoop 3, including HDFS erasure coding which reduces storage overhead, YARN federation which improves scalability, and the Application Timeline Server which provides improved visibility into application performance. It also covers HDFS multi standby NameNodes which enhances high availability, and the future directions of Hadoop including object storage with Ozone and running HDFS on cloud infrastructure.
Real-Life Use Cases & Architectures for Event Streaming with Apache KafkaKai Wähner
Streaming all over the World: Real-Life Use Cases & Architectures for Event Streaming with Apache Kafka.
Learn about various case studies for event streaming with Apache Kafka across industries. The talk explores architectures for real-world deployments from Audi, BMW, Disney, Generali, Paypal, Tesla, Unity, Walmart, William Hill, and more. Use cases include fraud detection, mainframe offloading, predictive maintenance, cybersecurity, edge computing, track&trace, live betting, and much more.
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.
Hadoop 3.0 has been years in the making, and now it's finally arriving. Andrew Wang and Daniel Templeton offer an overview of new features, including HDFS erasure coding, YARN Timeline Service v2, YARN federation, and much more, and discuss current release management status and community testing efforts dedicated to making Hadoop 3.0 the best Hadoop major release yet.
This document summarizes Netflix's use of Kafka in their data pipeline. It discusses how Netflix evolved from using S3 and EMR to introducing Kafka and Kafka producers and consumers to handle 400 billion events per day. It covers challenges of scaling Kafka clusters and tuning Kafka clients and brokers. Finally, it outlines Netflix's roadmap which includes contributing to open source projects like Kafka and testing failure resilience.
Designing Event-Driven Applications with Apache NiFi, Apache Flink, Apache Spark
DevNexus 2022 Atlanta
https://devnexus.com/presentations/7150/
This talk is a quick overview of the How, What and WHY of Apache Pulsar, Apache Flink and Apache NiFi. I will show you how to design event-driven applications that scale the cloud native way.
This talk was done live in person at DevNexus across from the booth in room 311
Tim Spann
Tim Spann is a Developer Advocate for StreamNative. He works with StreamNative Cloud, Apache Pulsar, Apache Flink, Flink SQL, Apache NiFi, MiniFi, Apache MXNet, TensorFlow, Apache Spark, big data, the IoT, machine learning, and deep learning. Tim has over a decade of experience with the IoT, big data, distributed computing, streaming technologies, and Java programming. Previously, he was a Principal DataFlow Field Engineer at Cloudera, a Senior Solutions Architect at AirisData, a Senior Field Engineer at Pivotal and a Team Leader at HPE. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton on big data, the IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as IoT Fusion, Strata, ApacheCon, Data Works Summit Berlin, DataWorks Summit Sydney, and Oracle Code NYC. He holds a BS and MS in computer science.
Grafana Loki: like Prometheus, but for LogsMarco Pracucci
Loki is a horizontally-scalable, highly-available log aggregation system inspired by Prometheus. It is designed to be very cost-effective and easy to operate, as it does not index the contents of the logs, but rather labels for each log stream.
In this talk, we will introduce Loki, its architecture and the design trade-offs in an approachable way. We’ll both cover Loki and Promtail, the agent used to scrape local logs to push to Loki, including the Prometheus-style service discovery used to dynamically discover logs and attach metadata from applications running in a Kubernetes cluster.
Finally, we’ll show how to query logs with Grafana using LogQL - the Loki query language - and the latest Grafana features to easily build dashboards mixing metrics and logs.
Apache Pinot Case Study: Building Distributed Analytics Systems Using Apache ...HostedbyConfluent
The document describes Apache Pinot, an open source distributed real-time analytics platform used at LinkedIn. It discusses the challenges of building user-facing real-time analytics systems at scale. It initially describes LinkedIn's use of Apache Kafka for ingestion and Apache Pinot for queries, but notes challenges with Pinot's initial Kafka consumer group-based approach for real-time ingestion, such as incorrect results, limited scalability, and high storage overhead. It then presents Pinot's new partition-level consumption approach which addresses these issues by taking control of partition assignment and checkpointing, allowing for independent and flexible scaling of individual partitions across servers.
Dynamically Scaling Data Streams across Multiple Kafka Clusters with Zero Fli...Flink Forward
Flink Forward San Francisco 2022.
Flink consumers read from Kafka as a scalable, high throughput, and low latency data source. However, there are challenges in scaling out data streams where migration and multiple Kafka clusters are required. Thus, we introduced a new Kafka source to read sharded data across multiple Kafka clusters in a way that conforms well with elastic, dynamic, and reliable infrastructure. In this presentation, we will present the source design and how the solution increases application availability while reducing maintenance toil. Furthermore, we will describe how we extended the existing KafkaSource to provide mechanisms to read logical streams located on multiple clusters, to dynamically adapt to infrastructure changes, and to perform transparent cluster migrations and failover.
by
Mason Chen
The Top 5 Apache Kafka Use Cases and Architectures in 2022Kai Wähner
This document discusses the top 5 use cases and architectures for data in motion in 2022. It describes:
1) The Kappa architecture as an alternative to the Lambda architecture that uses a single stream to handle both real-time and batch data.
2) Hyper-personalized omnichannel experiences that integrate customer data from multiple sources in real-time to provide personalized experiences across channels.
3) Multi-cloud deployments using Apache Kafka and data mesh architectures to share data across different cloud platforms.
4) Edge analytics that deploy stream processing and Kafka brokers at the edge to enable low-latency use cases and offline functionality.
5) Real-time cybersecurity applications that use streaming data
This document provides an overview of Apache NiFi, an open source system for automated data flows. It discusses how NiFi originated from NSA technology and Flow-Based Programming. It describes NiFi's core concepts like FlowFiles, Processors, and Connections. It also covers how NiFi can be used for messaging and data distribution tasks like acquiring, processing, and storing massive amounts of data from various sources in a secure and scalable way. The document concludes with use cases for NiFi and a demonstration of moving log data between systems using NiFi's visual interface and tracking capabilities.
Data ingestion and distribution with apache NiFiLev Brailovskiy
In this session, we will cover our experience working with Apache NiFi, an easy to use, powerful, and reliable system to process and distribute a large volume of data. The first part of the session will be an introduction to Apache NiFi. We will go over NiFi main components and building blocks and functionality.
In the second part of the session, we will show our use case for Apache NiFi and how it's being used inside our Data Processing infrastructure.
As organizations pursue Big Data initiatives to capture new opportunities for data-driven insights, data governance has become table stakes both from the perspective of external regulatory compliance as well as business value extraction internally within an enterprise. This session will introduce Apache Atlas, a project that was incubated by Hortonworks along with a group of industry leaders across several verticals including financial services, healthcare, pharma, oil and gas, retail and insurance to help address data governance and metadata needs with an open extensible platform governed under the aegis of Apache Software Foundation. Apache Atlas empowers organizations to harvest metadata across the data ecosystem, govern and curate data lakes by applying consistent data classification with a centralized metadata catalog.
In this talk, we will present the underpinnings of the architecture of Apache Atlas and conclude with a tour of governance capabilities within Apache Atlas as we showcase various features for open metadata modeling, data classification, visualizing cross-component lineage and impact. We will also demo how Apache Atlas delivers a complete view of data movement across several analytic engines such as Apache Hive, Apache Storm, Apache Kafka and capabilities to effectively classify, discover datasets.
ksqlDB: A Stream-Relational Database Systemconfluent
Speaker: Matthias J. Sax, Software Engineer, Confluent
ksqlDB is a distributed event streaming database system that allows users to express SQL queries over relational tables and event streams. The project was released by Confluent in 2017 and is hosted on Github and developed with an open-source spirit. ksqlDB is built on top of Apache Kafka®, a distributed event streaming platform. In this talk, we discuss ksqlDB’s architecture that is influenced by Apache Kafka and its stream processing library, Kafka Streams. We explain how ksqlDB executes continuous queries while achieving fault tolerance and high vailability. Furthermore, we explore ksqlDB’s streaming SQL dialect and the different types of supported queries.
Matthias J. Sax is a software engineer at Confluent working on ksqlDB. He mainly contributes to Kafka Streams, Apache Kafka's stream processing library, which serves as ksqlDB's execution engine. Furthermore, he helps evolve ksqlDB's "streaming SQL" language. In the past, Matthias also contributed to Apache Flink and Apache Storm and he is an Apache committer and PMC member. Matthias holds a Ph.D. from Humboldt University of Berlin, where he studied distributed data stream processing systems.
https://db.cs.cmu.edu/events/quarantine-db-talk-2020-confluent-ksqldb-a-stream-relational-database-system/
This document provides an overview of Apache NiFi and dataflow. It begins with an introduction to the challenges of moving data effectively within and between systems. It then discusses Apache NiFi's key features for addressing these challenges, including guaranteed delivery, data buffering, prioritized queuing, and data provenance. The document outlines NiFi's architecture and components like repositories and extension points. It also previews a live demo and invites attendees to further discuss Apache NiFi at a Birds of a Feather session.
Apache Kafka becoming the message bus to transfer huge volumes of data from various sources into Hadoop.
It's also enabling many real-time system frameworks and use cases.
Managing and building clients around Apache Kafka can be challenging. In this talk, we will go through the best practices in deploying Apache Kafka
in production. How to Secure a Kafka Cluster, How to pick topic-partitions and upgrading to newer versions. Migrating to new Kafka Producer and Consumer API.
Also talk about the best practices involved in running a producer/consumer.
In Kafka 0.9 release, we’ve added SSL wire encryption, SASL/Kerberos for user authentication, and pluggable authorization. Now Kafka allows authentication of users, access control on who can read and write to a Kafka topic. Apache Ranger also uses pluggable authorization mechanism to centralize security for Kafka and other Hadoop ecosystem projects.
We will showcase open sourced Kafka REST API and an Admin UI that will help users in creating topics, re-assign partitions, Issuing
Kafka ACLs and monitoring Consumer offsets.
OpenShift is a Platform-as-a-Service that provides development environments on demand using containers. It automates application lifecycles including build, deploy, and retirement. OpenShift uses containers to package applications and dependencies in a portable way. Red Hat addresses concerns around adopting containers at scale through OpenShift, which provides security, scalability, integration, management and certification capabilities. OpenShift runs on a user's choice of infrastructure and orchestrates applications across nodes using Kubernetes.
0-60: Tesla's Streaming Data Platform ( Jesse Yates, Tesla) Kafka Summit SF 2019confluent
Tesla ingests trillions of events every day from hundreds of unique data sources through our streaming data platform. Find out how we developed a set of high-throughput, non-blocking primitives that allow us to transform and ingest data into a variety of data stores with minimal development time. Additionally, we will discuss how these primitives allowed us to completely migrate the streaming platform in just a few months. Finally, we will talk about how we scale team size sub-linearly to data volumes, while continuing to onboard new use cases.
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.
The document discusses new features in Apache Hadoop 3, including HDFS erasure coding which reduces storage overhead, YARN federation which improves scalability, and the Application Timeline Server which provides improved visibility into application performance. It also covers HDFS multi standby NameNodes which enhances high availability, and the future directions of Hadoop including object storage with Ozone and running HDFS on cloud infrastructure.
Hadoop 3.0 will include major new features like HDFS erasure coding for improved storage efficiency and YARN support for long running services and Docker containers to improve resource utilization. However, it will maintain backwards compatibility and a focus on testing given the importance of compatibility for existing Hadoop users. The release is targeted for late 2017 after several alpha and beta stages.
Explaining how we leverage Hadoop and Spark to transform Coca-Cola East Japan information system and create new insight for the business like predicting the necessary to refill in the 500,000 vending machines managed by Coca-Cola East Japan, or supporting reporting activities by storing & aggregating the most granular data in Hadoop.
Apache Hadoop 3.0 is coming! As the next major release, it attracts everyone's attention as show case several bleeding-edge technologies and significant features across all components of Apache Hadoop, include: Erasure Coding in HDFS, Multiple Standby NameNodes, YARN Timeline Service v2, JNI-based shuffle in MapReduce, Apache Slider integration and Service Support as First Class Citizen, Hadoop library updates and client-side class path isolation, etc.
In this talk, we will update the status of Hadoop 3 especially the releasing work in community and then go deep diving on new features included in Hadoop 3.0. As a new major release, Hadoop 3 would also include some incompatible changes - we will go through most of these changes and explore its impact to existing Hadoop users and operators. In the last part of this session, we will continue to discuss ongoing efforts in Hadoop 3 age and show the big picture that how big data landscape could be largely influenced by Hadoop 3.
The document discusses the Virtual Data Connector project which aims to leverage Apache Atlas and Apache Ranger to provide unified metadata and access governance across data sources. Key points include:
- The project aims to address challenges of understanding, governing, and controlling access to distributed data through a centralized metadata catalog and policies.
- Apache Atlas provides a scalable metadata repository while Apache Ranger enables centralized access governance. The project will integrate these using a virtualization layer.
- Enhancements to Atlas and Ranger are proposed to better support the project's goals around a unified open metadata platform and metadata-driven governance.
- An initial minimum viable product will be built this year with the goal of an open, collaborative ecosystem around shared
Intelligently Collecting Data at the Edge - Intro to Apache 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.
Expected prior knowledge / intended audience: developers and data flow managers should have 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.
Speaker
Andy LoPresto, Sr. Member of Technical Staff, Hortonworks
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.
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.
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.
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.
HDF Powered by Apache NiFi IntroductionMilind Pandit
The document discusses Apache NiFi and its role in managing enterprise data flows, providing an overview of NiFi's key features and capabilities for reliable data transfer, preparation, and routing. It also demonstrates how NiFi is used in common use cases and provides examples of building simple data flows in NiFi to ingest, filter, and deliver data.
Introduction to Apache NiFi - Seattle Scalability MeetupSaptak Sen
The document introduces Apache NiFi, an open source tool for data flow. It discusses how data from the Internet of Things is growing faster than can be consumed and highlights Apache NiFi's ability to securely collect, process and distribute this data in motion. The key concepts of Apache NiFi are described as managing the flow of information, ensuring data provenance, and securing the control and data planes. Example use cases are provided and the document demonstrates Apache NiFi's visual interface for creating data flows between processors to ingest, transform and output data in real-time.
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.
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.
This document provides an overview of real-time processing capabilities on Hortonworks Data Platform (HDP). It discusses how a trucking company uses HDP to analyze sensor data from trucks in real-time to monitor for violations and integrate predictive analytics. The company collects data using Kafka and analyzes it using Storm, HBase and Hive on Tez. This provides real-time dashboards as well as querying of historical data to identify issues with routes, trucks or drivers. The document explains components like Kafka, Storm and HBase and how they enable a unified YARN-based architecture for multiple workloads on a single HDP cluster.
Internet of Things Crash Course Workshop at Hadoop SummitDataWorks Summit
This document provides an overview of how a trucking company can use Hortonworks Data Platform (HDP) to gain insights from real-time streaming data generated by sensors in its trucks. The company wants to monitor trucks for locations, violations, and other events. HDP allows the company to ingest streaming data from trucks using Kafka and analyze it in real-time with Storm for alerts or serve it to applications with HBase. The company can also run interactive queries on historical data with Hive and Tez. All of this is run on a single HDP cluster for consistent governance, security, and operations across batch and real-time workloads.
Building a modern end-to-end open source Big Data reference applicationDataWorks Summit
In this talk, Edgar Orendain walks through a modern real-time streaming application serving as a reference framework for developing a big data pipeline, complete with a broad range of use cases and powerful reusable core components.
Modern applications can ingest data and leverage analytics in real-time. These analytics are based on machine learning models typically built using historical big data. This reference application provides examples of connecting data-in-motion analytics to your application based on Big Data.
We review code, best practices and considerations involved when integrating different components into a complete data platform. From IoT sensor data collection, to flow management, real-time stream processing and analytics, through to machine learning and prediction, this reference project aims to help developers seed their own open source solutions – fast.
Real-Time Processing in Hadoop for IoT Use Cases - Phoenix HUGskumpf
The document discusses real-time processing in Hadoop using the Hortonworks Data Platform (HDP). It provides an overview of using HDP for real-time streaming analytics in a logistics scenario. Example applications and architectures are presented, including using Kafka for ingesting sensor data, Storm for stream processing, and HBase for real-time querying. Demos will also illustrate integrating predictive analytics into streaming scenarios.
Hortonworks DataFlow & Apache Nifi @Oslo Hadoop Big DataMats Johansson
This document provides an overview of Hortonworks DataFlow, which is powered by Apache NiFi. It discusses how the growth of IoT data is outpacing our ability to consume it and how NiFi addresses the new requirements around collecting, securing and analyzing data in motion. Key features of NiFi are highlighted such as guaranteed delivery, data provenance, and its ability to securely manage bidirectional data flows in real-time. Common use cases like predictive analytics, compliance and IoT optimization are also summarized.
The document discusses real-time processing in Hadoop and provides an overview of streaming architectures using the Hortonworks Data Platform (HDP). It includes two demos, the first showing a basic streaming scenario and the second integrating predictive analytics. The document aims to introduce HDP's capabilities for real-time streaming and predictive analytics and demonstrate them through examples relevant to logistics companies.
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.
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.
Storm Demo Talk - Colorado Springs May 2015Mac Moore
The document discusses real-time processing capabilities in Hadoop and Hortonworks Data Platform (HDP). It begins with an introduction to Hortonworks and an overview of real-time streaming architectures on HDP. It then demonstrates streaming capabilities with and without predictive analytics additions. The document highlights how HDP provides a centralized architecture and open data platform to enable real-time and batch processing of any type of data for analytics applications.
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.
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.
Similar to Hadoop Summit Tokyo Apache NiFi Crash Course (20)
This document discusses running Apache Spark and Apache Zeppelin in production. It begins by introducing the author and their background. It then covers security best practices for Spark deployments, including authentication using Kerberos, authorization using Ranger/Sentry, encryption, and audit logging. Different Spark deployment modes like Spark on YARN are explained. The document also discusses optimizing Spark performance by tuning executor size and multi-tenancy. Finally, it covers security features for Apache Zeppelin like authentication, authorization, and credential management.
This document discusses Spark security and provides an overview of authentication, authorization, encryption, and auditing in Spark. It describes how Spark leverages Kerberos for authentication and uses services like Ranger and Sentry for authorization. It also outlines how communication channels in Spark are encrypted and some common issues to watch out for related to Spark security.
This document discusses using a data science platform to enable digital diagnostics in healthcare. It provides an overview of healthcare data sources and Yale/YNHH's data science platform. It then describes the data science journey process using a clinical laboratory use case as an example. The goal is to use big data and machine learning to improve diagnostic reproducibility, throughput, turnaround time, and accuracy for laboratory testing by developing a machine learning algorithm and real-time data processing pipeline.
This document discusses using Apache Spark and MLlib for text mining on big data. It outlines common text mining applications, describes how Spark and MLlib enable scalable machine learning on large datasets, and provides examples of text mining workflows and pipelines that can be built with Spark MLlib algorithms and components like tokenization, feature extraction, and modeling. It also discusses customizing ML pipelines and the Zeppelin notebook platform for collaborative data science work.
This document compares the performance of Hive and Spark when running the BigBench benchmark. It outlines the structure and use cases of the BigBench benchmark, which aims to cover common Big Data analytical properties. It then describes sequential performance tests of Hive+Tez and Spark on queries from the benchmark using a HDInsight PaaS cluster, finding variations in performance between the systems. Concurrency tests are also run by executing multiple query streams in parallel to analyze throughput.
The document discusses modern data applications and architectures. It introduces Apache Hadoop, an open-source software framework for distributed storage and processing of large datasets across clusters of commodity hardware. Hadoop provides massive scalability and easy data access for applications. The document outlines the key components of Hadoop, including its distributed storage, processing framework, and ecosystem of tools for data access, management, analytics and more. It argues that Hadoop enables organizations to innovate with all types and sources of data at lower costs.
This document provides an overview of data science and machine learning. It discusses what data science and machine learning are, including extracting insights from data and computers learning without being explicitly programmed. It also covers Apache Spark, which is an open source framework for large-scale data processing. Finally, it discusses common machine learning algorithms like regression, classification, clustering, and dimensionality reduction.
This document provides an overview of Apache Spark, including its capabilities and components. Spark is an open-source cluster computing framework that allows distributed processing of large datasets across clusters of machines. It supports various data processing workloads including streaming, SQL, machine learning and graph analytics. The document discusses Spark's APIs like DataFrames and its libraries like Spark SQL, Spark Streaming, MLlib and GraphX. It also provides examples of using Spark for tasks like linear regression modeling.
Many Organizations are currently processing various types of data and in different formats. Most often this data will be in free form, As the consumers of this data growing it’s imperative that this free-flowing data needs to adhere to a schema. It will help data consumers to have an expectation of about the type of data they are getting and also they will be able to avoid immediate impact if the upstream source changes its format. Having a uniform schema representation also gives the Data Pipeline a really easy way to integrate and support various systems that use different data formats.
SchemaRegistry is a central repository for storing, evolving schemas. It provides an API & tooling to help developers and users to register a schema and consume that schema without having any impact if the schema changed. Users can tag different schemas and versions, register for notifications of schema changes with versions etc.
In this talk, we will go through the need for a schema registry and schema evolution and showcase the integration with Apache NiFi, Apache Kafka, Apache Storm.
There is increasing need for large-scale recommendation systems. Typical solutions rely on periodically retrained batch algorithms, but for massive amounts of data, training a new model could take hours. This is a problem when the model needs to be more up-to-date. For example, when recommending TV programs while they are being transmitted the model should take into consideration users who watch a program at that time.
The promise of online recommendation systems is fast adaptation to changes, but methods of online machine learning from streams is commonly believed to be more restricted and hence less accurate than batch trained models. Combining batch and online learning could lead to a quickly adapting recommendation system with increased accuracy. However, designing a scalable data system for uniting batch and online recommendation algorithms is a challenging task. In this talk we present our experiences in creating such a recommendation engine with Apache Flink and Apache Spark.
DeepLearning is not just a hype - it outperforms state-of-the-art ML algorithms. One by one. In this talk we will show how DeepLearning can be used for detecting anomalies on IoT sensor data streams at high speed using DeepLearning4J on top of different BigData engines like ApacheSpark and ApacheFlink. Key in this talk is the absence of any large training corpus since we are using unsupervised machine learning - a domain current DL research threats step-motherly. As we can see in this demo LSTM networks can learn very complex system behavior - in this case data coming from a physical model simulating bearing vibration data. Once draw back of DeepLearning is that normally a very large labaled training data set is required. This is particularly interesting since we can show how unsupervised machine learning can be used in conjunction with DeepLearning - no labeled data set is necessary. We are able to detect anomalies and predict braking bearings with 10 fold confidence. All examples and all code will be made publicly available and open sources. Only open source components are used.
QE automation for large systems is a great step forward in increasing system reliability. In the big-data world, multiple components have to come together to provide end-users with business outcomes. This means, that QE Automations scenarios need to be detailed around actual use cases, cross-cutting components. The system tests potentially generate large amounts of data on a recurring basis, verifying which is a tedious job. Given the multiple levels of indirection, the false positives of actual defects are higher, and are generally wasteful.
At Hortonworks, we’ve designed and implemented Automated Log Analysis System - Mool, using Statistical Data Science and ML. Currently the work in progress has a batch data pipeline with a following ensemble ML pipeline which feeds into the recommendation engine. The system identifies the root cause of test failures, by correlating the failing test cases, with current and historical error records, to identify root cause of errors across multiple components. The system works in unsupervised mode with no perfect model/stable builds/source-code version to refer to. In addition the system provides limited recommendations to file/open past tickets and compares run-profiles with past runs.
Improving business performance is never easy! The Natixis Pack is like Rugby. Working together is key to scrum success. Our data journey would undoubtedly have been so much more difficult if we had not made the move together.
This session is the story of how ‘The Natixis Pack’ has driven change in its current IT architecture so that legacy systems can leverage some of the many components in Hortonworks Data Platform in order to improve the performance of business applications. During this session, you will hear:
• How and why the business and IT requirements originated
• How we leverage the platform to fulfill security and production requirements
• How we organize a community to:
o Guard all the players, no one gets left on the ground!
o Us the platform appropriately (Not every problem is eligible for Big Data and standard databases are not dead)
• What are the most usable, the most interesting and the most promising technologies in the Apache Hadoop community
We will finish the story of a successful rugby team with insight into the special skills needed from each player to win the match!
DETAILS
This session is part business, part technical. We will talk about infrastructure, security and project management as well as the industrial usage of Hive, HBase, Kafka, and Spark within an industrial Corporate and Investment Bank environment, framed by regulatory constraints.
HBase is a distributed, column-oriented database that stores data in tables divided into rows and columns. It is optimized for random, real-time read/write access to big data. The document discusses HBase's key concepts like tables, regions, and column families. It also covers performance tuning aspects like cluster configuration, compaction strategies, and intelligent key design to spread load evenly. Different use cases are suitable for HBase depending on access patterns, such as time series data, messages, or serving random lookups and short scans from large datasets. Proper data modeling and tuning are necessary to maximize HBase's performance.
There has been an explosion of data digitising our physical world – from cameras, environmental sensors and embedded devices, right down to the phones in our pockets. Which means that, now, companies have new ways to transform their businesses – both operationally, and through their products and services – by leveraging this data and applying fresh analytical techniques to make sense of it. But are they ready? The answer is “no” in most cases.
In this session, we’ll be discussing the challenges facing companies trying to embrace the Analytics of Things, and how Teradata has helped customers work through and turn those challenges to their advantage.
In this talk, we will present a new distribution of Hadoop, Hops, that can scale the Hadoop Filesystem (HDFS) by 16X, from 70K ops/s to 1.2 million ops/s on Spotiy's industrial Hadoop workload. Hops is an open-source distribution of Apache Hadoop that supports distributed metadata for HSFS (HopsFS) and the ResourceManager in Apache YARN. HopsFS is the first production-grade distributed hierarchical filesystem to store its metadata normalized in an in-memory, shared nothing database. For YARN, we will discuss optimizations that enable 2X throughput increases for the Capacity scheduler, enabling scalability to clusters with >20K nodes. We will discuss the journey of how we reached this milestone, discussing some of the challenges involved in efficiently and safely mapping hierarchical filesystem metadata state and operations onto a shared-nothing, in-memory database. We will also discuss the key database features needed for extreme scaling, such as multi-partition transactions, partition-pruned index scans, distribution-aware transactions, and the streaming changelog API. Hops (www.hops.io) is Apache-licensed open-source and supports a pluggable database backend for distributed metadata, although it currently only support MySQL Cluster as a backend. Hops opens up the potential for new directions for Hadoop when metadata is available for tinkering in a mature relational database.
In high-risk manufacturing industries, regulatory bodies stipulate continuous monitoring and documentation of critical product attributes and process parameters. On the other hand, sensor data coming from production processes can be used to gain deeper insights into optimization potentials. By establishing a central production data lake based on Hadoop and using Talend Data Fabric as a basis for a unified architecture, the German pharmaceutical company HERMES Arzneimittel was able to cater to compliance requirements as well as unlock new business opportunities, enabling use cases like predictive maintenance, predictive quality assurance or open world analytics. Learn how the Talend Data Fabric enabled HERMES Arzneimittel to become data-driven and transform Big Data projects from challenging, hard to maintain hand-coding jobs to repeatable, future-proof integration designs.
Talend Data Fabric combines Talend products into a common set of powerful, easy-to-use tools for any integration style: real-time or batch, big data or master data management, on-premises or in the cloud.
While you could be tempted assuming data is already safe in a single Hadoop cluster, in practice you have to plan for more. Questions like: "What happens if the entire datacenter fails?, or "How do I recover into a consistent state of data, so that applications can continue to run?" are not a all trivial to answer for Hadoop. Did you know that HDFS snapshots are handling open files not as immutable? Or that HBase snapshots are executed asynchronously across servers and therefore cannot guarantee atomicity for cross region updates (which includes tables)? There is no unified and coherent data backup strategy, nor is there tooling available for many of the included components to build such a strategy. The Hadoop distributions largely avoid this topic as most customers are still in the "single use-case" or PoC phase, where data governance as far as backup and disaster recovery (BDR) is concerned are not (yet) important. This talk first is introducing you to the overarching issue and difficulties of backup and data safety, looking at each of the many components in Hadoop, including HDFS, HBase, YARN, Oozie, the management components and so on, to finally show you a viable approach using built-in tools. You will also learn not to take this topic lightheartedly and what is needed to implement and guarantee a continuous operation of Hadoop cluster based solutions.
Hadoop Distributed File System (HDFS) evolves from a MapReduce-centric storage system to a generic, cost-effective storage infrastructure where HDFS stores all data of inside the organizations. The new use case presents a new sets of challenges to the original HDFS architecture. One challenge is to scale the storage management of HDFS - the centralized scheme within NameNode becomes a main bottleneck which limits the total number of files stored. Although a typical large HDFS cluster is able to store several hundred petabytes of data, it is inefficient to handle large amounts of small files under the current architecture.
In this talk, we introduce our new design and in-progress work that re-architects HDFS to attack this limitation. The storage management is enhanced to a distributed scheme. A new concept of storage container is introduced for storing objects. HDFS blocks are stored and managed as objects in the storage containers instead of being tracked only by NameNode. Storage containers are replicated across DataNodes using a newly-developed high-throughput protocol based on the Raft consensus algorithm. Our current prototype shows that under the new architecture the storage management of HDFS scales 10x better, demonstrating that HDFS is capable of storing billions of files.
This document discusses optimizing Apache Spark machine learning workloads on OpenPOWER platforms. It provides an overview of Spark, machine learning, and deep learning. It then discusses how OpenPOWER systems are well-suited for these workloads due to features like high memory bandwidth, large caches, and GPU support. The document outlines various techniques for tuning Spark performance on OpenPOWER, such as configuration of executors, cores, memory, and storage levels. It also presents examples analyzing the performance of a matrix factorization machine learning application under different Spark configurations.
QA or the Highway - Component Testing: Bridging the gap between frontend appl...zjhamm304
These are the slides for the presentation, "Component Testing: Bridging the gap between frontend applications" that was presented at QA or the Highway 2024 in Columbus, OH by Zachary Hamm.
What is an RPA CoE? Session 2 – CoE RolesDianaGray10
In this session, we will review the players involved in the CoE and how each role impacts opportunities.
Topics covered:
• What roles are essential?
• What place in the automation journey does each role play?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
ScyllaDB is making a major architecture shift. We’re moving from vNode replication to tablets – fragments of tables that are distributed independently, enabling dynamic data distribution and extreme elasticity. In this keynote, ScyllaDB co-founder and CTO Avi Kivity explains the reason for this shift, provides a look at the implementation and roadmap, and shares how this shift benefits ScyllaDB users.
This talk will cover ScyllaDB Architecture from the cluster-level view and zoom in on data distribution and internal node architecture. In the process, we will learn the secret sauce used to get ScyllaDB's high availability and superior performance. We will also touch on the upcoming changes to ScyllaDB architecture, moving to strongly consistent metadata and tablets.
From Natural Language to Structured Solr Queries using LLMsSease
This talk draws on experimentation to enable AI applications with Solr. One important use case is to use AI for better accessibility and discoverability of the data: while User eXperience techniques, lexical search improvements, and data harmonization can take organizations to a good level of accessibility, a structural (or “cognitive” gap) remains between the data user needs and the data producer constraints.
That is where AI – and most importantly, Natural Language Processing and Large Language Model techniques – could make a difference. This natural language, conversational engine could facilitate access and usage of the data leveraging the semantics of any data source.
The objective of the presentation is to propose a technical approach and a way forward to achieve this goal.
The key concept is to enable users to express their search queries in natural language, which the LLM then enriches, interprets, and translates into structured queries based on the Solr index’s metadata.
This approach leverages the LLM’s ability to understand the nuances of natural language and the structure of documents within Apache Solr.
The LLM acts as an intermediary agent, offering a transparent experience to users automatically and potentially uncovering relevant documents that conventional search methods might overlook. The presentation will include the results of this experimental work, lessons learned, best practices, and the scope of future work that should improve the approach and make it production-ready.
Northern Engraving | Nameplate Manufacturing Process - 2024Northern Engraving
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
Northern Engraving | Modern Metal Trim, Nameplates and Appliance PanelsNorthern Engraving
What began over 115 years ago as a supplier of precision gauges to the automotive industry has evolved into being an industry leader in the manufacture of product branding, automotive cockpit trim and decorative appliance trim. Value-added services include in-house Design, Engineering, Program Management, Test Lab and Tool Shops.
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
"Scaling RAG Applications to serve millions of users", Kevin GoedeckeFwdays
How we managed to grow and scale a RAG application from zero to thousands of users in 7 months. Lessons from technical challenges around managing high load for LLMs, RAGs and Vector databases.
Session 1 - Intro to Robotic Process Automation.pdfUiPathCommunity
👉 Check out our full 'Africa Series - Automation Student Developers (EN)' page to register for the full program:
https://bit.ly/Automation_Student_Kickstart
In this session, we shall introduce you to the world of automation, the UiPath Platform, and guide you on how to install and setup UiPath Studio on your Windows PC.
📕 Detailed agenda:
What is RPA? Benefits of RPA?
RPA Applications
The UiPath End-to-End Automation Platform
UiPath Studio CE Installation and Setup
💻 Extra training through UiPath Academy:
Introduction to Automation
UiPath Business Automation Platform
Explore automation development with UiPath Studio
👉 Register here for our upcoming Session 2 on June 20: Introduction to UiPath Studio Fundamentals: https://community.uipath.com/events/details/uipath-lagos-presents-session-2-introduction-to-uipath-studio-fundamentals/
Lee Barnes - Path to Becoming an Effective Test Automation Engineer.pdfleebarnesutopia
So… you want to become a Test Automation Engineer (or hire and develop one)? While there’s quite a bit of information available about important technical and tool skills to master, there’s not enough discussion around the path to becoming an effective Test Automation Engineer that knows how to add VALUE. In my experience this had led to a proliferation of engineers who are proficient with tools and building frameworks but have skill and knowledge gaps, especially in software testing, that reduce the value they deliver with test automation.
In this talk, Lee will share his lessons learned from over 30 years of working with, and mentoring, hundreds of Test Automation Engineers. Whether you’re looking to get started in test automation or just want to improve your trade, this talk will give you a solid foundation and roadmap for ensuring your test automation efforts continuously add value. This talk is equally valuable for both aspiring Test Automation Engineers and those managing them! All attendees will take away a set of key foundational knowledge and a high-level learning path for leveling up test automation skills and ensuring they add value to their organizations.
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Keywords: AI, Containeres, Kubernetes, Cloud Native
Event Link: https://meine.doag.org/events/cloudland/2024/agenda/#agendaId.4211
In reality, dataflows move all over. Data is moved and stored in multiple places – sometimes interim, sometimes longterm. Data is procesed in different places, and then moved again. Complicated, convoluted, messy.
Kafka
Reads events in memory and write to distributed log
NiFi: simple event processing
Spark: complex event processing
Build predictive model from Historical insights.
Deploy predictive model for real-time insights.
Can put NiFi on a Gateway server but probably don’t want to mess with a UI on ever single one
Maybe not best fit
Let me get the key parts of NiFi close to where data begins and provide bidrectional communication
NiFi lives in the data center. Give it an enterprise server or a cluster of them.
MiNiFi lives close to where data is born and may be a guest on that device or system
Framework – put a new wrapper on the framework, or in maven terms, we kept the underlying modules and wrote minifi-framework-core replacing nifi-framework-core
Talking about MiNiFi-Java, Cpp version also exists
Initiates with ./bin/nifi.sh start
user, only need bootstrap and config.yml
nifi.properties and flow.xml are implementation details
Smart Cities
Monitor:
Public transportation vehicles
Pedestrian levels
Optimize public transit duration and walking routes
Source:
http://www.libelium.com/resources/top_50_iot_sensor_applications_ranking/