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
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.
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
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.
AWS User Group Meetup Berlin - Kay Lerch on Apache NiFi (2016-04-19)Kay Lerch
My talk at yesterdays AWS Usergroup meetup in Berlin gave the audience an introduction to the concepts and features of Apache NiFi as well as to the capabilities of this product regarding integration of AWS IoT.
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
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.
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
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.
AWS User Group Meetup Berlin - Kay Lerch on Apache NiFi (2016-04-19)Kay Lerch
My talk at yesterdays AWS Usergroup meetup in Berlin gave the audience an introduction to the concepts and features of Apache NiFi as well as to the capabilities of this product regarding integration of AWS IoT.
Event-Driven Messaging and Actions using Apache Flink and Apache NiFiDataWorks Summit
At Comcast, our team has been architecting a customer experience platform which is able to react to near-real-time events and interactions and deliver appropriate and timely communications to customers. By combining the low latency capabilities of Apache Flink and the dataflow capabilities of Apache NiFi we are able to process events at high volume to trigger, enrich, filter, and act/communicate to enhance customer experiences. Apache Flink and Apache NiFi complement each other with their strengths in event streaming and correlation, state management, command-and-control, parallelism, development methodology, and interoperability with surrounding technologies. We will trace our journey from starting with Apache NiFi over three years ago and our more recent introduction of Apache Flink into our platform stack to handle more complex scenarios. In this presentation we will compare and contrast which business and technical use cases are best suited to which platform and explore different ways to integrate the two platforms into a single solution.
Introduction
This workshop is a hands-on session to quickly deploy Hadoop and Streaming on AWS / Azure / Google Cloud.
Cloudbreak simplifies the deployment of Hadoop in cloud environments. It enables the enterprise to quickly run big data workloads in the cloud while optimizing the use of cloud resources.
Format
A short introductory lecture about Cloudbreak. This is followed by a walk through and lab leveraging Hadoop and Streaming in the Cloud with Cloudbreak.
Objective
To provide a quick and short hands-on introduction to Hadoop on the cloud. Review key benefits of cluster deployment automation.
This lab will use Cloudbreak to quickly and effortlessly stand up Hadoop and Streaming clusters in a cloud provider of your choice. The lab shows the use of Ambari blueprints that are your declarative definitions of your Hadoop or Streaming clusters. Steps to dynamically change these blueprints and use external databases and external authentication sources and in essence showing a way to provide Shared Authentication, Authorization and Audit across ephemeral and long-lasting clusters. However it is not limited to only custom blueprints, the lab also shows how Cloudbreak provides easy to use custom scripts called recipes that can be executed before or after Ambari start or after cluster installation.
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 Acquisition Automation for NiFi in a Hybrid Cloud environment – the Path...DataWorks Summit
Liberty Global is one of the world’s largest international TV and broadband company, operating in multiple European countries, and with tens of millions of TV, broadband internet, telephony and mobile subscribers.
The Data Solutions team's journey started last year with a strategic project that aimed to implement a state of the art Hybrid Cloud Big Data platform. In this talk, the Manager and the Platform Architect are presenting the team’s data acquisition journey which begins with implementing NiFi flows with simple Get-Put pattern and, in its the final iteration, produces a solution capable of generating complex flows automatically, leading the path to the DataOps way of working.
Intelligently Collecting Data at the Edge – Intro to Apache MiNiFiDataWorks Summit
Description: 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 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.
Tracking Crime as It Occurs with Apache Phoenix, Apache HBase and Apache NiFiDataWorks Summit
Utilizing Apache NiFi we read various open data REST APIs and camera feeds to ingest crime and related data real-time streaming it into HBase and Phoenix tables. HBase makes an excellent storage option for our real-time time series data sources. We can immediately query our data utilizing Apache Zeppelin against Phoenix tables as well as Hive external tables to HBase.
Apache Phoenix tables also make a great option since we can easily put microservices on top of them for application usage. I have an example Spring Boot application that reads from our Philadelphia crime table for front-end web applications as well as RESTful APIs.
Apache NiFi makes it easy to push records with schemas to HBase and insert into Phoenix SQL tables.
Resources:
https://community.hortonworks.com/articles/54947/reading-opendata-json-and-storing-into-phoenix-tab.html
https://community.hortonworks.com/articles/56642/creating-a-spring-boot-java-8-microservice-to-read.html
https://community.hortonworks.com/articles/64122/incrementally-streaming-rdbms-data-to-your-hadoop.html
Connect Data and Devices with Apache NiFiData Works MD
Data Works MD November 2019 - https://www.meetup.com/DataWorks/events/265433970/
Video is available at https://youtu.be/JklA7FNUVhY
Connect Data and Devices with Apache NiFi
Apache NiFi is an easy to use, powerful, and reliable system to process and distribute data. It comes with a wonderful management UI, a large marketplace of standard Processors, and a great Open Source Community behind it. This session will show you how to move data across servers & networks. It will show you how to manipulate data, enrich data, and stream data through custom enrichment processors.
The talk is designed to walk you through the NiFi basics, while showing practical examples you can follow-along with. The examples will include showing how to perform data manipulation using a custom java processor, the ExecuteScript processor, with JavaScript and Python, and the JoltTransformData processor. Open-source tools, such as Jolt, jQ, and JsonPath will be demonstrated. Finally, it will show how you could prototype a REST service with Standard Processors! There will even be a light-bulb flashing from things happening in NiFi.
Ryan Hendrickson is a Senior Software Engineer and Director of Innovation who joined Clarity Business Solutions in 2015. He is a Software Project Co-Lead, participates in the Maryland Data Works Meetup, attended OSCON 2018, presented at CodeMash 2019, and enjoys working on his 1966 MGB.
Bill Farmer is VP of Engineering and senior software engineer at Clarity Business Solutions where he is responsible for bringing innovative opportunities to solve some of the customer’s hardest data problems. Bill has over twenty years experience building data processing and visualization systems across a variety of domains including finance, transportation, and government.
Elli Schwarz is a Senior Software Engineer at Clarity Business Solutions. He has 15 years experience developing Java applications, creating custom data processing solutions, and applying specialized data models and ontologies to facilitate data exchange. An Apache Nifi enthusiast, he enjoys using Nifi to performing complex ETL tasks for his clients.
Big data security challenges are bit different from traditional client-server applications and are distributed in nature, introducing unique security vulnerabilities. Cloud Security Alliance (CSA) has categorized the different security and privacy challenges into four different aspects of the big data ecosystem. These aspects are infrastructure security, data privacy, data management and, integrity and reactive security. Each of these aspects are further divided into following security challenges:
1. Infrastructure security
a. Secure distributed processing of data
b. Security best practices for non-relational data stores
2. Data privacy
a. Privacy-preserving analytics
b. Cryptographic technologies for big data
c. Granular access control
3. Data management
a. Secure data storage and transaction logs
b. Granular audits
c. Data provenance
4. Integrity and reactive security
a. Endpoint input validation/filtering
b. Real-time security/compliance monitoring
In this talk, we are going to refer above classification and identify existing security controls, best practices, and guidelines. We will also paint a big picture about how collective usage of all discussed security controls (Kerberos, TDE, LDAP, SSO, SSL/TLS, Apache Knox, Apache Ranger, Apache Atlas, Ambari Infra, etc.) can address fundamental security and privacy challenges that encompass the entire Hadoop ecosystem. We will also discuss briefly recent security incidents involving Hadoop systems.
Speakers
Krishna Pandey, Staff Software Engineer, Hortonworks
Kunal Rajguru, Premier Support Engineer, Hortonworks
Supercharge Your Real-time Event Processing with Neo4j's Streams Kafka Connec...HostedbyConfluent
Do your event streams use connected-data domains such as fraud detection, live logistics routing, or predicting network outages? How can you maintain the analysis and leverage those connections real-time?
Graph databases differ from traditional, tabular ones in that they treat connections between data as first class citizens. This means they are optimized for detecting and understanding these relationships – providing insight at speed and at scale.
By combining event streams from Kafka along with the power of the Neo4j graph database for interrogating and investigating connections, you make real-time, event-driven intelligent insight a reality.
Neo4j Streams integrates Neo4j with Apache Kafka event streams, to serve as a source of data, for instance Change Data Capture or a sink to ingest any kind of Kafka event into your graph. In this session we’ll show you how to get up and running with Neo4j Streams to show you how to sink and source between graphs and streams.
Avoiding Log Data Overload in a CI/CD System While Streaming 190 Billion Even...DataWorks Summit
Learn how Pure Storage engineering manages streaming 190B log events per day and makes use of that deluge of data in our continuous integration (CI) pipeline. Our test infrastructure runs over 70,000 tests per day creating a large triage problem that would require at least 20 triage engineers. Instead, Spark's flexible computing platform allows us to write a single application for both streaming and batch jobs to understand the state of our CI pipeline for our team of 3 triage engineers. Using encoded patterns, Spark indexes log data for real-time reporting (Streaming), uses Machine Learning for performance modeling and prediction (Batch job), and finds previous matches for newly encoded patterns (Batch job). Resource allocation in this mixed environment can be challenging; a containerized Spark cluster deployment, and disaggregated compute and storage layers allow us to programmatically shift compute resources between the streaming and batch applications.. This talk will go over design decisions to meet SLAs of streaming and batching in hardware, data layout, access patterns, and containers strategy. We will also go over the challenges, lessons learned, and best practices for similar data pipelines.
Speaker
Joshua Robinson, JOSHUA ROBINSON
Founding Engineer
Pure Storage
How is it that one system can query terabytes of data, yet still provide interactive query support? This talk will discuss two of the underlying technologies that allow Apache Hive to support fast query response, both on-premise in HDFS and in cloud object stores such as S3 and WASB.
LLAP was introduced in Hive 2.6. It provides standing processes that securely cache Hive’s columnar data and can do query processing without ever needing to start tasks in Hadoop. We will cover LLAP’s architecture, intended uses cases, and performance numbers for both on-premise and in the cloud.
The second technology is the integration of Hive with Apache Druid. Druid excels at low-latency, interactive queries over streaming data. Its method of storing data makes it very well suited for OLAP style queries. We will cover how Hive can be integrated with Druid to support real-time streaming of data from Kafka and OLAP queries.
Real-time Freight Visibility: How TMW Systems uses NiFi and SAM to create sub...DataWorks Summit
TMW Systems, A TRIMBLE Company, is the industry-leading transportation management software. 3PLs, brokers, distribution and supply operations, dedicated and private fleets, commercial carriers, and energy service providers rely on our transportation management systems, our fleet maintenance management software, or our routing and scheduling software to make them more efficient and profitable. Billions of data points exist in the trucking industry, and we at TMW Systems are pioneers of tracking millions of trucks, freights, and assets.
The architecture team at TMW leverages Nifi and SAM to deliver the immense volume of data in real-time. In this session, you will get a thorough understanding of all the streaming components. We have utilized Apache Kafka, Apache Nifi, and Streaming Analytics Manager to build our real-time data pipeline. We will also discuss the real-time event processing using SAM and Schema Registry. Lastly, we will show custom processors in Nifi and SAM that helped us with complex event processing.
Speaker
Krishna Potluri, TMW Systems, A Trimble Company, Big Data Architect
Donnie Wheat, Trimble, Senior Big Data Architect
Join us as we walk through examples of integrating Apache NiFi into existing enterprise ETL environments. We'll look at how to solve the challenges of integrating a real-time, interactive dataflow tool like NiFi into traditional ETL workflows, touching on common topics like design and deployment, version control, dataset testing, environment variables, and code promotion.
We will demonstrate how to manage changes to your NiFi data flows using SDLC approaches traditionally applied to batch processing scripts. We will walk you through the building blocks of achieving continuous integration and deployment (CI/CD) for your data flows. This includes examples of NiFi automation.
We will cover:
- Using the NiFi Command Line Interface (CLI) to script interaction with NiFi and NiFi Registry
- Utilizing NiPyApi, a Python module that provides an abstraction for interacting with the NiFi REST API, for advanced automation
- Setting up Docker-based integration test environments for NiFi
Lastly, we will demonstrate how to integrate these tools in SDLC workflows for NiFi flow versioning.
Speaker
Kevin Doran, Software Developer, Hortonworks
Extending Twitter's Data Platform to Google CloudDataWorks Summit
Twitter's Data Platform is built using multiple complex open source and in house projects to support Data Analytics on hundreds of petabytes of data. Our platform support storage, compute, data ingestion, discovery and management and various tools and libraries to help users for both batch and realtime analytics. Our DataPlatform operates on multiple clusters across different data centers to help thousands of users discover valuable insights. As we were scaling our Data Platform to multiple clusters, we also evaluated various cloud vendors to support use cases outside of our data centers. In this talk we share our architecture and how we extend our data platform to use cloud as another datacenter. We walk through our evaluation process, challenges we faced supporting data analytics at Twitter scale on cloud and present our current solution. Extending Twitter's Data platform to cloud was complex task which we deep dive in this presentation.
Enterprise IIoT Edge Processing with Apache NiFiTimothy Spann
April 5, 2018 IoT Fusion 2018 Conference in Philadelphia, PA hosted by Chariot Solutions. This talk is about Apache NiFi, MiniFi, Python, Deep Learning, NVidia Jetson TX1, Raspberry Pi, Apache MXNet, TensorFlow and how to run things at the edge and process in your big data center. http://iotfusion.net/session/ https://github.com/tspannhw/IoTFusion2018Talk
Big Data Day LA 2016/ Big Data Track - Building scalable enterprise data flow...Data Con LA
Connecting enterprise systems has always been a tough task. Modern IoT applications have exacerbated the issue by the need to integrate legacy systems with novel high velocity data streams. Various patterns like messaging, REST, etc. have been proposed, but they necessitate rearchitecting the integration layer which is extremely arduous. In this talk we will show you how to use Apache NiFi to solve your data integration, movement and ingestion problems. Next, we will examine how Apache NiFi can be used to construct durable, scalable and responsive IoT apps in conjunction with other stream processing and messaging frameworks.
Presentation gives more insight about what is Converged Infrastructure , types of Converged Infrastructure and its benefits. Also it provides details about various Converged Infrastructure vendors in market and their shares.
Event-Driven Messaging and Actions using Apache Flink and Apache NiFiDataWorks Summit
At Comcast, our team has been architecting a customer experience platform which is able to react to near-real-time events and interactions and deliver appropriate and timely communications to customers. By combining the low latency capabilities of Apache Flink and the dataflow capabilities of Apache NiFi we are able to process events at high volume to trigger, enrich, filter, and act/communicate to enhance customer experiences. Apache Flink and Apache NiFi complement each other with their strengths in event streaming and correlation, state management, command-and-control, parallelism, development methodology, and interoperability with surrounding technologies. We will trace our journey from starting with Apache NiFi over three years ago and our more recent introduction of Apache Flink into our platform stack to handle more complex scenarios. In this presentation we will compare and contrast which business and technical use cases are best suited to which platform and explore different ways to integrate the two platforms into a single solution.
Introduction
This workshop is a hands-on session to quickly deploy Hadoop and Streaming on AWS / Azure / Google Cloud.
Cloudbreak simplifies the deployment of Hadoop in cloud environments. It enables the enterprise to quickly run big data workloads in the cloud while optimizing the use of cloud resources.
Format
A short introductory lecture about Cloudbreak. This is followed by a walk through and lab leveraging Hadoop and Streaming in the Cloud with Cloudbreak.
Objective
To provide a quick and short hands-on introduction to Hadoop on the cloud. Review key benefits of cluster deployment automation.
This lab will use Cloudbreak to quickly and effortlessly stand up Hadoop and Streaming clusters in a cloud provider of your choice. The lab shows the use of Ambari blueprints that are your declarative definitions of your Hadoop or Streaming clusters. Steps to dynamically change these blueprints and use external databases and external authentication sources and in essence showing a way to provide Shared Authentication, Authorization and Audit across ephemeral and long-lasting clusters. However it is not limited to only custom blueprints, the lab also shows how Cloudbreak provides easy to use custom scripts called recipes that can be executed before or after Ambari start or after cluster installation.
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 Acquisition Automation for NiFi in a Hybrid Cloud environment – the Path...DataWorks Summit
Liberty Global is one of the world’s largest international TV and broadband company, operating in multiple European countries, and with tens of millions of TV, broadband internet, telephony and mobile subscribers.
The Data Solutions team's journey started last year with a strategic project that aimed to implement a state of the art Hybrid Cloud Big Data platform. In this talk, the Manager and the Platform Architect are presenting the team’s data acquisition journey which begins with implementing NiFi flows with simple Get-Put pattern and, in its the final iteration, produces a solution capable of generating complex flows automatically, leading the path to the DataOps way of working.
Intelligently Collecting Data at the Edge – Intro to Apache MiNiFiDataWorks Summit
Description: 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 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.
Tracking Crime as It Occurs with Apache Phoenix, Apache HBase and Apache NiFiDataWorks Summit
Utilizing Apache NiFi we read various open data REST APIs and camera feeds to ingest crime and related data real-time streaming it into HBase and Phoenix tables. HBase makes an excellent storage option for our real-time time series data sources. We can immediately query our data utilizing Apache Zeppelin against Phoenix tables as well as Hive external tables to HBase.
Apache Phoenix tables also make a great option since we can easily put microservices on top of them for application usage. I have an example Spring Boot application that reads from our Philadelphia crime table for front-end web applications as well as RESTful APIs.
Apache NiFi makes it easy to push records with schemas to HBase and insert into Phoenix SQL tables.
Resources:
https://community.hortonworks.com/articles/54947/reading-opendata-json-and-storing-into-phoenix-tab.html
https://community.hortonworks.com/articles/56642/creating-a-spring-boot-java-8-microservice-to-read.html
https://community.hortonworks.com/articles/64122/incrementally-streaming-rdbms-data-to-your-hadoop.html
Connect Data and Devices with Apache NiFiData Works MD
Data Works MD November 2019 - https://www.meetup.com/DataWorks/events/265433970/
Video is available at https://youtu.be/JklA7FNUVhY
Connect Data and Devices with Apache NiFi
Apache NiFi is an easy to use, powerful, and reliable system to process and distribute data. It comes with a wonderful management UI, a large marketplace of standard Processors, and a great Open Source Community behind it. This session will show you how to move data across servers & networks. It will show you how to manipulate data, enrich data, and stream data through custom enrichment processors.
The talk is designed to walk you through the NiFi basics, while showing practical examples you can follow-along with. The examples will include showing how to perform data manipulation using a custom java processor, the ExecuteScript processor, with JavaScript and Python, and the JoltTransformData processor. Open-source tools, such as Jolt, jQ, and JsonPath will be demonstrated. Finally, it will show how you could prototype a REST service with Standard Processors! There will even be a light-bulb flashing from things happening in NiFi.
Ryan Hendrickson is a Senior Software Engineer and Director of Innovation who joined Clarity Business Solutions in 2015. He is a Software Project Co-Lead, participates in the Maryland Data Works Meetup, attended OSCON 2018, presented at CodeMash 2019, and enjoys working on his 1966 MGB.
Bill Farmer is VP of Engineering and senior software engineer at Clarity Business Solutions where he is responsible for bringing innovative opportunities to solve some of the customer’s hardest data problems. Bill has over twenty years experience building data processing and visualization systems across a variety of domains including finance, transportation, and government.
Elli Schwarz is a Senior Software Engineer at Clarity Business Solutions. He has 15 years experience developing Java applications, creating custom data processing solutions, and applying specialized data models and ontologies to facilitate data exchange. An Apache Nifi enthusiast, he enjoys using Nifi to performing complex ETL tasks for his clients.
Big data security challenges are bit different from traditional client-server applications and are distributed in nature, introducing unique security vulnerabilities. Cloud Security Alliance (CSA) has categorized the different security and privacy challenges into four different aspects of the big data ecosystem. These aspects are infrastructure security, data privacy, data management and, integrity and reactive security. Each of these aspects are further divided into following security challenges:
1. Infrastructure security
a. Secure distributed processing of data
b. Security best practices for non-relational data stores
2. Data privacy
a. Privacy-preserving analytics
b. Cryptographic technologies for big data
c. Granular access control
3. Data management
a. Secure data storage and transaction logs
b. Granular audits
c. Data provenance
4. Integrity and reactive security
a. Endpoint input validation/filtering
b. Real-time security/compliance monitoring
In this talk, we are going to refer above classification and identify existing security controls, best practices, and guidelines. We will also paint a big picture about how collective usage of all discussed security controls (Kerberos, TDE, LDAP, SSO, SSL/TLS, Apache Knox, Apache Ranger, Apache Atlas, Ambari Infra, etc.) can address fundamental security and privacy challenges that encompass the entire Hadoop ecosystem. We will also discuss briefly recent security incidents involving Hadoop systems.
Speakers
Krishna Pandey, Staff Software Engineer, Hortonworks
Kunal Rajguru, Premier Support Engineer, Hortonworks
Supercharge Your Real-time Event Processing with Neo4j's Streams Kafka Connec...HostedbyConfluent
Do your event streams use connected-data domains such as fraud detection, live logistics routing, or predicting network outages? How can you maintain the analysis and leverage those connections real-time?
Graph databases differ from traditional, tabular ones in that they treat connections between data as first class citizens. This means they are optimized for detecting and understanding these relationships – providing insight at speed and at scale.
By combining event streams from Kafka along with the power of the Neo4j graph database for interrogating and investigating connections, you make real-time, event-driven intelligent insight a reality.
Neo4j Streams integrates Neo4j with Apache Kafka event streams, to serve as a source of data, for instance Change Data Capture or a sink to ingest any kind of Kafka event into your graph. In this session we’ll show you how to get up and running with Neo4j Streams to show you how to sink and source between graphs and streams.
Avoiding Log Data Overload in a CI/CD System While Streaming 190 Billion Even...DataWorks Summit
Learn how Pure Storage engineering manages streaming 190B log events per day and makes use of that deluge of data in our continuous integration (CI) pipeline. Our test infrastructure runs over 70,000 tests per day creating a large triage problem that would require at least 20 triage engineers. Instead, Spark's flexible computing platform allows us to write a single application for both streaming and batch jobs to understand the state of our CI pipeline for our team of 3 triage engineers. Using encoded patterns, Spark indexes log data for real-time reporting (Streaming), uses Machine Learning for performance modeling and prediction (Batch job), and finds previous matches for newly encoded patterns (Batch job). Resource allocation in this mixed environment can be challenging; a containerized Spark cluster deployment, and disaggregated compute and storage layers allow us to programmatically shift compute resources between the streaming and batch applications.. This talk will go over design decisions to meet SLAs of streaming and batching in hardware, data layout, access patterns, and containers strategy. We will also go over the challenges, lessons learned, and best practices for similar data pipelines.
Speaker
Joshua Robinson, JOSHUA ROBINSON
Founding Engineer
Pure Storage
How is it that one system can query terabytes of data, yet still provide interactive query support? This talk will discuss two of the underlying technologies that allow Apache Hive to support fast query response, both on-premise in HDFS and in cloud object stores such as S3 and WASB.
LLAP was introduced in Hive 2.6. It provides standing processes that securely cache Hive’s columnar data and can do query processing without ever needing to start tasks in Hadoop. We will cover LLAP’s architecture, intended uses cases, and performance numbers for both on-premise and in the cloud.
The second technology is the integration of Hive with Apache Druid. Druid excels at low-latency, interactive queries over streaming data. Its method of storing data makes it very well suited for OLAP style queries. We will cover how Hive can be integrated with Druid to support real-time streaming of data from Kafka and OLAP queries.
Real-time Freight Visibility: How TMW Systems uses NiFi and SAM to create sub...DataWorks Summit
TMW Systems, A TRIMBLE Company, is the industry-leading transportation management software. 3PLs, brokers, distribution and supply operations, dedicated and private fleets, commercial carriers, and energy service providers rely on our transportation management systems, our fleet maintenance management software, or our routing and scheduling software to make them more efficient and profitable. Billions of data points exist in the trucking industry, and we at TMW Systems are pioneers of tracking millions of trucks, freights, and assets.
The architecture team at TMW leverages Nifi and SAM to deliver the immense volume of data in real-time. In this session, you will get a thorough understanding of all the streaming components. We have utilized Apache Kafka, Apache Nifi, and Streaming Analytics Manager to build our real-time data pipeline. We will also discuss the real-time event processing using SAM and Schema Registry. Lastly, we will show custom processors in Nifi and SAM that helped us with complex event processing.
Speaker
Krishna Potluri, TMW Systems, A Trimble Company, Big Data Architect
Donnie Wheat, Trimble, Senior Big Data Architect
Join us as we walk through examples of integrating Apache NiFi into existing enterprise ETL environments. We'll look at how to solve the challenges of integrating a real-time, interactive dataflow tool like NiFi into traditional ETL workflows, touching on common topics like design and deployment, version control, dataset testing, environment variables, and code promotion.
We will demonstrate how to manage changes to your NiFi data flows using SDLC approaches traditionally applied to batch processing scripts. We will walk you through the building blocks of achieving continuous integration and deployment (CI/CD) for your data flows. This includes examples of NiFi automation.
We will cover:
- Using the NiFi Command Line Interface (CLI) to script interaction with NiFi and NiFi Registry
- Utilizing NiPyApi, a Python module that provides an abstraction for interacting with the NiFi REST API, for advanced automation
- Setting up Docker-based integration test environments for NiFi
Lastly, we will demonstrate how to integrate these tools in SDLC workflows for NiFi flow versioning.
Speaker
Kevin Doran, Software Developer, Hortonworks
Extending Twitter's Data Platform to Google CloudDataWorks Summit
Twitter's Data Platform is built using multiple complex open source and in house projects to support Data Analytics on hundreds of petabytes of data. Our platform support storage, compute, data ingestion, discovery and management and various tools and libraries to help users for both batch and realtime analytics. Our DataPlatform operates on multiple clusters across different data centers to help thousands of users discover valuable insights. As we were scaling our Data Platform to multiple clusters, we also evaluated various cloud vendors to support use cases outside of our data centers. In this talk we share our architecture and how we extend our data platform to use cloud as another datacenter. We walk through our evaluation process, challenges we faced supporting data analytics at Twitter scale on cloud and present our current solution. Extending Twitter's Data platform to cloud was complex task which we deep dive in this presentation.
Enterprise IIoT Edge Processing with Apache NiFiTimothy Spann
April 5, 2018 IoT Fusion 2018 Conference in Philadelphia, PA hosted by Chariot Solutions. This talk is about Apache NiFi, MiniFi, Python, Deep Learning, NVidia Jetson TX1, Raspberry Pi, Apache MXNet, TensorFlow and how to run things at the edge and process in your big data center. http://iotfusion.net/session/ https://github.com/tspannhw/IoTFusion2018Talk
Big Data Day LA 2016/ Big Data Track - Building scalable enterprise data flow...Data Con LA
Connecting enterprise systems has always been a tough task. Modern IoT applications have exacerbated the issue by the need to integrate legacy systems with novel high velocity data streams. Various patterns like messaging, REST, etc. have been proposed, but they necessitate rearchitecting the integration layer which is extremely arduous. In this talk we will show you how to use Apache NiFi to solve your data integration, movement and ingestion problems. Next, we will examine how Apache NiFi can be used to construct durable, scalable and responsive IoT apps in conjunction with other stream processing and messaging frameworks.
Presentation gives more insight about what is Converged Infrastructure , types of Converged Infrastructure and its benefits. Also it provides details about various Converged Infrastructure vendors in market and their shares.
Dynamic Hyper-Converged Future Proof Your Data CenterDataCore Software
IT organizations are continuously striving to reduce the amount of time and effort to deploy new resources for the business. Data center and remote office infrastructures are often complex and rigid to deploy, causing operational delays. As a result, many IT organizations are looking at a hyper-converged infrastructure.
Read this whitepaper to discover that a hyper-converged approach is flexible and easy to deploy and offers:
• Lower CAPEX because of lower up-front prices for infrastructure
• Lower OPEX through reductions in operational expenses and personnel
• Faster time-to-value for new business needs
There has been a lot of interest and buzz recently around hyperconvergence. It's the biggest IT shift since the rise of server virtualization. As with any budding space there is some confusion. This paper looks at the top 10 benefits of hyperconvergence and and also answers some frequently asked questions.
A buyer's guide to Hyper-Converged infrastructureEric Van 't Hoff
A very comprehensive buyer's guide for anyone considering to use a Hyper-Converged Infrastructure to modernize his/her Data-Center. The report is published by Computer Weekly and covers solutions from Dell EMC VxRail or XC, Nutanix, VMware vSAN ReadyNodes, HPE SimpliVity and Pivot3 to name just a few HCI leaders.
Enterprise data-centers are straining to keep pace with dynamic business demands, as well as to incorporate advanced technologies and architectures that aim to improve infrastructure performance
Cloud Technology and Virtualization
"Project Deliverable 4: Cloud Technology and Virtualization"
Christopher Nevels
Dr. Darcel Ford
CIS 590
11-24-13
Cloud Technology and Virtualization
There are many reasons companies and organizations are investing in server virtualization. Some of the reasons are financially motivated, while others address technical concerns. Server virtualization conserves space through consolidation. It's common practice to dedicate each server to a single application. If several applications only use a small amount of processing power, the network administrator can consolidate several machines into one server running multiple virtual environments. For companies that have hundreds or thousands of servers, the need for physical space can decrease significantly. Server virtualization provides a way for companies to practice redundancy without purchasing additional hardware. Redundancy refers to running the same application on multiple servers. It's a safety measure -- if a server fails for any reason, another server running the same application can take its place. This minimizes any interruption in service. It wouldn't make sense to build two virtual servers performing the same application on the same physical server. If the physical server were to crash, both virtual servers would also fail. In most cases, network administrators will create redundant virtual servers on different physical machines. Virtual servers offer programmers isolated, independent systems in which they can test new applications or operating systems. Rather than buying a dedicated physical machine, the network administrator can create a virtual server on an existing machine. Because each virtual server is independent in relation to all the other servers, programmers can run software without worrying about affecting other applications (Strickland 2013).
Cloud computing is ideal for small companies, as it’s cost-effective, saves time and energy, and it allows for a high level of customization. According to Forbes, a 2009 study found that cloud computing could save up to 67% of the lifecycle cost for server deployment on a large scale. Another study found that using cloud solutions generally results in higher investment returns (when compared to an on-site system). There are further cost saving benefits, such as less need for expensive hardware and software, and no need for physical networks or IT maintenance. Also, cloud systems are usually ‘pay-as-you-go’, so you only pay for what you use. There are no upfront investments, and IT requirements can be easily budgeted for. Also, various cloud services can either be added or scaled back, depending on where your business is, and how much growth is taking place. The cloud is also highly customizable: you can select what platform you want, which payroll software to use, and what email marketing tools you require – all from different vendors, and all individually configurable (K2 SEO 2013).
The c.
Virtualization is one of the hottest trends occurring in the IT industry. We dive into what virtualization is and why you should be thinking about implementing it into your network plan.
Hyperconvergence 101: A Crash Course in Redefining Your InfrastructureePlus
Is a hyperconverged infrastructure (HCI) the right choice for your data center? EMC partners with ePlus to help transform your data center. From assessment to implementation, ePlus can be a trusted guide to get your HCI solution up and running.
Best Compute Solutions, Backup Services, and Data Storage CenterSamidhaTakle1
Stop worrying about your data. SAID Technologies got you covered with the best Compute Solutions, Backup and Data Storage Center services.
For more details, visit : https://saidtechnologies.com/compute-storage-and-backup/
The process of virtualization enables the creation of virtual forms of servers, applications, networks and storage. The four main types of virtualization are network virtualization, storage virtualization, application virtualization and desktop virtualization.
A Complete Guide to Cloud Hosting by Cyfuture CloudAngela Addison
Our presentation "A Complete Guide to Cloud Hosting" offers a comprehensive overview of cloud hosting, exploring its key concepts, benefits, and various service models. It delves into the differences between public, private, and hybrid cloud environments, providing insights into scalability, cost-efficiency, and security. The guide also covers essential topics such as deployment strategies, cloud management tools, and best practices for optimizing performance. Whether you're a beginner looking to understand the basics or an IT professional seeking advanced knowledge, this presentation equips you with the information needed to make informed decisions about cloud hosting solutions.
Overview: Woolpack private cloud services
Enables Virtual Data Centers (VDCs)
User friendly Web based Graphical user interface for management
Robust functionality and High level of security
Simulation of various hardware configurations
Provision for huge number of Linux/Windows M/c
Management of multiple storage backend
Best in class integrated solution because of strategic Partnerships
Utilization of existing investments virtualization solutions
Low CAPEX, Low OPEX and Very High ROI
Charges only for the service and not the software
Web hosting is a service that is needed for rendering websites accessible over the Internet and can be of many types, which includes WordPress Hosting, that is meant exclusively as a hosting solution for WordPress sites.
HTS Dedicated Servers and HTS Dedicated Hosting are popular solutions for hosting websites, wherein both the services offer dedicated IP addresses to the hosted sites.
HTS Dedicated Servers and HTS Dedicated Hosting are popular solutions for hosting websites, wherein both the services offer dedicated IP addresses to the hosted sites.
Shared Hosting, Dedicated Hosting, VPS Hosting and WordPress Hosting are some of the most commonly used web hosting solutions to host different types of websites. Reseller Hosting offers a perfect hosting solution for starting the business of web hosting at the least expense.
The basic settings related to cPanel & WHM, such as nameservers or contact information, can be configured through this interface. All available setup settings are displayed by the system by default.
Essential Features in Web Hosting PlansHTS Hosting
Certain web hosting features, such as high uptime, fast page loads, 24/7 technical support, etc., are features that need to be present in every web hosting plan,in order for the web hosting service to be efficient.
VPS Hosting, which is a less expensive hosting alternative to availing a dedicated server, offers convenience with regard to server management through its Managed VPS Hosting service and full control over server management through its Self-managed VPS Hosting service.
Difference Between Managed VPS Hosting Self-Managed VPS HostingHTS Hosting
Managed VPS Hosting and Self-managed VPS Hosting are two different types of VPS Hosting services for hosting websites on Virtual Private Servers (VPS).
Web Hosting, Web Servers, Web Hosts and MoreHTS Hosting
The service of web hosting that is provided by web hosts, through various web hosting solutions, offers web server space for hosting websites and keeps sites up and running seamlessly.
A business site needs to be seamlessly accessible online at fast speed and securely. Hence, it is important that it is hosted through such a web hosting solution that meets these specific hosting requirements perfectly.
Reseller Hosting and Dedicated Web ServersHTS Hosting
Reseller Hosting is a web hosting service, whereas a dedicated server is a web server used in web hosting for storing and processing the files of a single site per server.
The system creates a tarball file (.tar.gz) every time a backup is created. It contains the compressed versions of the files of an account. The file format that is used by the system is, USERNAME.tar.gz. In it, “USERNAME” represents the username of the cPanel account.
HTS VPS (Virtual Private Servers) and HTS Dedicated Servers are two of the many services offered by HTS Hosting to its global customers for hosting their websites and storing their valuable data on the secure and fast web servers of HTS Hosting.
HTS Hosting, which is a globally preferred web hosting service provider, offers Basic, Advance, Business and Professional WordPress Hosting plans for the effective hosting of WordPress sites, at the most budget-friendly prices.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
2. Table of Contents
2
Hyperconvergence
Hypervisor
Hyperconverged Infrastructure
(HCI)
Difference between Converged
Infrastructure and
Hyperconverged Infrastructure
Benefits of Using
Hyperconverged Infrastructure
Key Users of Hyperconverged
Infrastructure (HCI)
Key Users: Virtual Servers
Key Users: SMEs and
Remote Offices
Key Users: Containers and
Kubernetes
Key Users: Analytics,
Machine Learning and AI
Key Users: For Disaster
Recovery
3. An IT framework in which computing, storage and networking are combined into a single
system is referred to as hyperconvergence. It aims at increasing scalability and reducing
complexity with regard to the functioning of a data center. Hyperconvergence ensures
convenient consumption by making use of multiple nodes that have been clustered for the
purpose of creating reservoirs of shared compute and storage resources.
A hypervisor is included in hyperconverged platforms to ensure virtualized networking,
virtualized computing and software-defined storage.
With regard to data center modernization, hyperconvergence is able to deliver public cloud
infrastructure’s agility without forsaking control over on-premises hardware.
3
Hyperconvergence
4. A hypervisor refers to a computer software, hardware or firmware that serves to create and
run virtual machines. The system on which one or multiple virtual machines are run by a
hypervisor is called a host machine. Each virtual machine is known as a guest machine.
A hypervisor provides a virtual operating platform to the guest operating systems and manages
its guest operating systems’ execution.
4
Hypervisor
5. A unified, software-defined system which integrates any traditional data center’s elements
(compute, storage, networking, management etc.) is known as a hyperconverged infrastructure.
Such an integrated solution makes use of x86 servers and software for the purpose of replacing
purpose-built hardware that is expensive.
5
Hyperconverged Infrastructure
(HCI)
7. A package of software that is preconfigured, and hardware, is made available in a single system
in converged infrastructure for the purpose of rendering management simpler. The storage,
compute and networking components in a converged infrastructure are discrete. Hence, these
can be separated.
In a hyperconverged infrastructure, its components cannot be separated. It entails virtual
implementation of software-defined elements that can be integrated seamlessly into the
hypervisor environment. Hyperconvergence not only enables more abstraction along with
enhanced levels of automation but also helps businesses with their capacity expansion by
deploying additional modules.
7
Difference between Converged Infrastructure and
Hyperconverged Infrastructure
8. There are many benefits that can be reaped by making a shift from legacy infrastructure to
hyperconverged infrastructure.The main benefits that inspire this switch are as follows-
Lower Costs
Ease of use and flexibility
Greater scalability
Resource efficiency
Enhanced and consistent performance
Increased productivity and efficiency of IT teams
Maximum ROI with regard to the infrastructure
Reduction in data centers’ footprint
8
Benefits of Using Hyperconverged Infrastructure
9. The benefits of hyperconverged infrastructure are mainly used by virtual servers and virtual
desktops; SMEs and remote offices; containers and Kubernetes; analytics, machine learning and
AI and for backup and disaster recovery.These will be discussed in the next few slides.
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Key Users of Hyperconverged Infrastructure (HCI)
10. The integration and ease of management provided by a hyperconverged infrastructure ensure
that it is widely used by virtual servers and virtual desktops.
To digress, servers are used by web hosting companies as well, for the purpose of storing files
of websites in order to deliver those files over the Internet for making the websites accessible.
Different terms are used for the most reliable web hosting providers, such as the “Best Cloud
Hosting Company”, the “Best Windows Shared Hosting Company”, the “Best Linux Dedicated
Server Hosting Company” etc.
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Key Users:Virtual Servers
11. With the aid of hyperconverged infrastructure organizations can improve the management of
their remote offices. SMEs (Small and Medium Enterprises) are less bothered about the
performance issues that affect HCI hardware. Hence, SMEs usually overlook it and opt for
hyperconverged infrastructure because of its ease of use.
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Key Users: SMEs and Remote Offices
12. Containers are best suited to utilize the benefits of HCI. This is especially true for data centers.
Containers do not require a hypervisor. Hence, these can function directly with the hardware
and the underlying OS (operating system).
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Key Users: Containers and Kubernetes
13. Hyperconverged infrastructure can be deployed quickly and scaled easily by adding nodes. This
renders it extremely suitable for analytics, machine learning and AI.
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Key Users:Analytics, Machine Learning
and AI
14. HCI is majorly used for backup as it is capable of supporting backup as well as disaster
recovery. For this purpose, a second hyper-converged system is used to ensure data duplication.
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Key Users: For Disaster Recovery