Mm.. FLaNK Stack (MiNiFi MXNet Flink NiFi Kudu Kafka)

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Mm.. FLaNK Stack (MiNiFi MXNet Flink NiFi Kudu Kafka) A quick discussion and demo of the FLaNK stack. Streaming development with Apache NiFi, Apache Kafka, Apache Flink and friends. Dec 2019, Timothy Spann, Field Engineer, Data in Motion Princeton Meetup 10-dec-2019 https://www.meetup.com/futureofdata-princeton/events/266496424/ Hosted By PGA Fund at: https://pga.fund/coworking-space/ Princeton Growth Accelerator 5 Independence Way, 4th Floor, Princeton, NJ

Data & Analytics

Introduction to the Mm.. FLaNK
Stack
Timothy Spann

© 2019 Cloudera, Inc. All rights reserved. 2
Welcome to Future of Data - Princeton
@PaasDev
https://www.meetup.com/futureofdata-princeton/
From Big Data to AI to Streaming to Containers to
Cloud to Analytics to Cloud Storage to Fast Data to
Machine Learning to Microservices to ...

© 2019 Cloudera, Inc. All rights reserved. 3
Today’s Lead
Who am I?
Data in Motion Field Engineer
@PaasDev
DZone Zone Leader and Big Data MVB;
Princeton NJ Future of Data Meetup;
ex-Pivotal Field Engineer;
Author of Apache Kafka RefCard
https://github.com/tspannhw
https://www.datainmotion.dev/

https://github.com/tspannhw/MmFLaNK
https://www.datainmotion.dev/2019/11/introducing-mm-ﬂank-apache-ﬂink-stack.html

MiNiFim.. FLaNK
● https://www.datainmotion.dev/2019/03/using-raspberry-pi-3b-w
ith-apache-niﬁ.html
● https://www.datainmotion.dev/2019/05/cloudera-edge-manage
ment-introduction.html
● https://www.datainmotion.dev/2019/11/running-demo-apache-ﬂ
ink-application.html
● https://www.datainmotion.dev/2019/11/learning-apache-ﬂink-1
9.html
● https://www.datainmotion.dev/2019/10/migrating-apache-ﬂume
-ﬂows-to-apache_42.html

APACHE MINIFI C++ 0.6.0
Key New Features
Feature Description Apache JIRA
NiFi JNI Bindings Ability to run any native NiFi processor via JNI. Makes all
NiFi processors available to MiNiFi C++
MINIFICPP-740
Native Python
Processors
Ability to write native MiNiFi C++ processors in Python MINIFICPP-750
PublishKafka Ability to write to secured Kafka instance MINIFICPP-731
CoAP Support Ability to communicate with EFM server over CoAP.
Drastically reduces the network impact of heartbeats
Windows Support Ability to install MiNiFi via a MSI

MiNiFi C++ - MiNiFi IoT AI
•
••
•
https://www.datainmotion.dev/2019/08/rapid-iot-development-with-cloudera.html

Apache MXNet Native Processor for Apache NiFi
This is a beta, community release by me using the new beta Java API for Apache MXNet.
https://github.com/tspannhw/niﬁ-mxnetinference-processor
https://community.hortonworks.com/articles/229215/apache-niﬁ-processor-for-apache-mxnet-ssd-single.html
https://www.youtube.com/watch?v=Q4dSGPvqXSA

● https://www.slideshare.net/bunkertor/apache-deep-learning-101-apach
econ-montreal-2018-v031
● https://www.slideshare.net/bunkertor/apache-deep-learning-202-washi
ngton-dc-dws-2019
● https://www.slideshare.net/bunkertor/apache-deep-learning-201-barce
lona-dws-march-2019

• Cloud ready
• Python, C++, Scala, R, Julia, Matlab, MXNet.js and Perl Support
• Experienced team (XGBoost)
• AWS, Microsoft, NVIDIA, Baidu, Intel
• Apache Incubator Project
• Run distributed on YARN and Spark
• In my early tests, faster than TensorFlow. (Try this yourself)
• Runs on Raspberry PI, NVidia Jetson Nano and other constrained devices
https://mxnet.incubator.apache.org/how_to/cloud.html
https://github.com/apache/incubator-mxnet/tree/1.3.1/example
https://gluon-cv.mxnet.io/api/model_zoo.html
https://elinux.org/Jetson_Nano

• Great documentation
• Crash Course
• Gluon (Open API), GluonCV, GluonNLP
• Keras (One API Many Runtime Options)
• Great Python Interaction. Java and Scala APIs!
• Open Source Model Server Available
• ONNX (Open Neural Network Exchange Format) Support for AI Models
• Now in Version 1.5.1!
• Rich Model Zoo!
• Math Kernel Library and NVidia CUDA Optimizations
• TensorBoard compatible
http://mxnet.incubator.apache.org/ http://gluon.mxnet.io/https://onnx.ai
pip3.7 install -U keras-mxnet
https://gluon-nlp.mxnet.io
pip3.7 install --upgrade mxnet
pip3.7 install gluonnlp pip3.7 install gluoncv
pip3.7 install mxnet-mkl>=1.5.1 --upgrade

Instance Segmentation: Mask RCNN with GluonCV
Mask RCNN model trained on COCO dataset with ResNet-50 backbone

https://github.com/tspannhw/MmFLaNK/blob/master/IoTKafka.java

© 2019 Cloudera, Inc. All rights reserved. 19
Flink is a Distributed Data Processing System

© 2019 Cloudera, Inc. All rights reserved. 24
TH N Y U

Using the FLaNK Stack for edge ai (flink, nifi, kafka, kudu) ntroducing the FLaNK stack which combines Apache Flink, Apache NiFi, Apache Kafka and Apache Kudu to build fast applications for IoT, AI, rapid ingest. FLaNK provides a quick set of tools to build applications at any scale for any streaming and IoT use cases. https://www.flankstack.dev/ Tools Apache Flink, Apache Kafka, Apache NiFi, MiNiFi, Apache MXNet, Apache Kudu, Apache Impala, Apache HDFS References https://www.datainmotion.dev/2019/08/rapid-iot-development-with-cloudera.html https://www.datainmotion.dev/2019/09/powering-edge-ai-for-sensor-reading.html https://www.datainmotion.dev/2019/05/dataworks-summit-dc-2019-report.html https://www.datainmotion.dev/2019/03/using-raspberry-pi-3b-with-apache-nifi.html Track Community and Industry Impact

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Introduction to Apache NiFi 1.10 Parameters, Stateless, RetryFlowFile, Backpressure prediction, parquetreader, parquetwriter, postslack, remote inputport in process group. Dec 2019, Timothy Spann, Field Engineer, Data in Motion Princeton Meetup 10-dec-2019 https://www.meetup.com/futureofdata-princeton/events/266496424/ Hosted By PGA Fund at: https://pga.fund/coworking-space/ Princeton Growth Accelerator 5 Independence Way, 4th Floor, Princeton, NJ

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18 aug2021 Continuous SQL with Apache Streaming (FLaNK and FLiP) https://emamo.com/event/worldfestival-2021/s/pro-talk-continuous-sql-with-flink-WR115a In this talk, I will walk through how someone can set up and run continuous SQL queries against Pulsar topics utilizing Apache Flink. We will walk through creating Pulsar topics, schemas and publishing data. We will then cover consuming Pulsar data, joining Pulsar topics and inserting new events into Pulsar topics as they arrive. This basic overview will show hands-on techniques, tips and examples of how to do this using Pulsar tools. https://github.com/tspannhw/FLiP-IoT https://github.com/tspannhw/SpeakerProfile/tree/main/2021/talks

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AIDEVDAY_ Data-in-Motion to Supercharge AI https://www.meetup.com/futureofdata-newyork/events/295376737/ Lightning Talk 2: Data-in-Motion to Supercharge AI Speaker: Timothy Spann @Cloudera Abstract: A quick look at the current state of real-time streaming for powering both data ingest and transformation to provide training and enhancement data to models. Also how to use streaming to feed a pipeline of data against your models or models hosted a HuggingFace or elsewhere. https://huggingface.co/bigscience/bloom https://www.aicamp.ai/event/eventdetails/W2023082314 Apache NiFi, Apache Kafka, Apache Flink, HuggingFace, WatsonX.AI, REST API, Cloudera Machine Learning (CML), Bloom, Deep Learning, AI

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21-September-2021 - ApacheCon - Tuesday 17:10 UTC Apache NIFi Deep Dive 300 * https://github.com/tspannhw/EverythingApacheNiFi * https://github.com/tspannhw/FLiP-ApacheCon2021 * https://www.datainmotion.dev/2020/06/no-more-spaghetti-flows.html * https://github.com/tspannhw/FLiP-IoT * https://github.com/tspannhw/FLiP-Energy * https://github.com/tspannhw/FLiP-SOLR * https://github.com/tspannhw/FLiP-EdgeAI * https://github.com/tspannhw/FLiP-CloudQueries * https://github.com/tspannhw/FLiP-Jetson * https://www.linkedin.com/pulse/2021-schedule-tim-spann/ Tuesday 17:10 UTC Apache NIFi Deep Dive 300 Timothy Spann For Data Engineers who have flows already in production, I will dive deep into best practices, advanced use cases, performance optimizations, tips, tricks, edge cases, and interesting examples. This is a master class for those looking to learn quickly things I have picked up after years in the field with Apache NiFi in production. This will be interactive and I encourage questions and discussions. You will take away examples and tips in slides, github, and articles. This talk will cover: Load Balancing Parameters and Parameter Contexts Stateless vs Stateful NiFi Reporting Tasks NiFi CLI NiFi REST Interface DevOps Advanced Record Processing Schemas RetryFlowFile Lookup Services RecordPath Expression Language Advanced Error Handling Techniques Tim Spann is a Developer Advocate @ StreamNative where he works with Apache NiFi, Apache Pulsar, Apache Flink, 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 Field Engineer at Cloudera, a senior solutions architect at AirisData and a senior field engineer at Pivotal. 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.

Conf42-LLM_Adding Generative AI to Real-Time Streaming Pipelines

Timothy Spann

Conf42-LLM_Adding Generative AI to Real-Time Streaming Pipelines https://www.youtube.com/watch?v=Yeua8NlzQ3Y https://www.conf42.com/Large_Language_Models_LLMs_2024_Tim_Spann_generative_ai_streaming Adding Generative AI to Real-Time Streaming Pipelines Abstract Let’s build streaming pipelines that convert streaming events into prompts, call LLMs, and process the results. Summary Tim Spann: My talk is adding generative AI to real time streaming pipelines. I'm going to discuss a couple of different open source technologies. We'll touch on Kafka, Nifi, Flink, Python, Iceberg. All the slides, all the code and GitHub are out there. Llm, if you didn't know, is rapidly evolving. There's a lot of different ways to interact with models. That enrichment, transformation, processing really needs tools. The amount of models and projects and software that are available is massive. Nifi supports hundreds of different inputs and can convert them on the fly. Great way to distribute your data quickly to whoever needs it without duplication, without tight coupling. Fun to find new things to integrate into. So what we can do is, well, I want to get a meetup chat going. I have a processor here that just listens for events as they come from slack. And then I'm going to clean it up, add a couple fields and push that out to slack. Every model is a little bit of different tweaking. Nifi acts as a whole website. And as you see here, it can be get, post, put, whatever you want. We send that response back to flink and it shows up here. Thank you for attending this talk. I'm going to be speaking at some other events very shortly. Transcript This transcript was autogenerated. To make changes, submit a PR. Hi, Tim Spann here. My talk is adding generative AI to real time streaming pipelines, and we're here for the large language model conference at Comp 42, which is always a nice one, great place to be. I'm going to discuss a couple of different open source technologies that work together to enable you to build real time pipelines using large language models. So we'll touch on Kafka, Nifi, Flink, Python, Iceberg, and I'll show you a little bit of each one in the demos. I've been working with data machine learning, streaming IoT, some other things for a number of years, and you could contact me at any of these places, whether Twitter or whatever it's called, some different blogs, or in person at my meetups and at different conferences around the world. I do a weekly newsletter, cover streaming ML, a lot of LLM, open source, Python, Java, all kinds of fun stuff, as I mentioned, do a bunch of different meetups. They are not just in the east coast of the US, they are available virtually live, and I also put them on YouTube, and if you need them somewhere else, let me know. We publish all the slides, all the code and GitHub. Everything you need is out there. Let's get into the talk. Llm, if you didn't know, is rapidly evolving. While you're typing down the things that you use, it

Conf42-Python-Building Apache NiFi 2.0 Python Processors

Timothy Spann

Conf42-Python-Building Apache NiFi 2.0 Python Processors https://www.conf42.com/Python_2024_Tim_Spann_apache_nifi_2_processors Building Apache NiFi 2.0 Python Processors Abstract Let’s enhance real-time streaming pipelines with smart Python code. Adding code for vector databases and LLM. Summary Tim Spann: I'm going to be talking today, be building Apache 9520 Python processors. One of the main purposes of supporting Python in the streaming tool Apache Nifi is to interface with new machine learning and AI and Gen AI. He says Python is a real game changer for Cloudera. You're just going to add some metadata around it. It's a great way to pass a file along without changing it too substantially. We really need you to have Python 310 and again JDK 21 on your machine. You got to be smart about how you use these models. There are a ton of python processors available. You can use them in multiple ways. We're still in the early world of Python processors, so now's the time to start putting yours out there. Love to see a lot of people write their own. When we are parsing documents here, again, this is the Python one I'm picking PDF. Lots of different things you could do. If you're interested on writing your own python code for Apache Nifi, definitely reach out and thank.

Overview of Apache Flink: the 4G of Big Data Analytics Frameworks

DataWorks Summit/Hadoop Summit

Overview of Apache Fink: the 4 G of Big Data Analytics Frameworks

Slim Baltagi

Slides of my talk at the Hadoop Summit Europe in Dublin, Ireland on April 13th, 2016. The talk introduces Apache Flink as both a multi-purpose Big Data analytics framework and real-world streaming analytics framework. It is focusing on Flink's key differentiators and suitability for streaming analytics use cases. It also shows how Flink enables novel use cases such as distributed CEP (Complex Event Processing) and querying the state by behaving like a key value data store.

Overview of Apache Fink: The 4G of Big Data Analytics Frameworks

Slim Baltagi

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Building Real-Time Pipelines With FLaNK Timothy Spann, Principal Developer Advocate, Streaming - Cloudera Future of Data meetup, startup grind, AI Camp The combination of Apache Flink, Apache NiFi, and Apache Kafka for building real-time data processing pipelines is extremely powerful, as demonstrated by this case study using the FLaNK-MTA project. The project leverages these technologies to process and analyze real-time data from the New York City Metropolitan Transportation Authority (MTA). FLaNK-MTA demonstrates how to efficiently collect, transform, and analyze high-volume data streams, enabling timely insights and decision-making. Apache NiFi Apache Kafka Apache Flink Apache Iceberg LLM Generative AI Slack Postgresql

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Timothy Spann

Gen AI on Enterprise Cloud Apache NiFi Milvus Apache Kafka Apache Flink Cloudera Machine Learning Cloudera DataFlow https://medium.com/@tspann/building-a-milvus-connector-for-nifi-34372cb3c7fa https://www.meetup.com/futureofdata-princeton/events/300737266/ https://lu.ma/q7pcfyjn?source=post_page-----34372cb3c7fa--------------------------------&tk=TTyakY If you're interested in working with Generative AI on the cloud, this virtual workshop is for you. Tim Spann from Cloudera and Yujian Tang from Zilliz will cover how you can implement your own GenAI workflows on the cloud at enterprise scale. 9:00 - 9:05: Intro 9:05 - 9:15: What is Milvus 9:15 - 9:25: Cloudera Development Platform 9:25 - 10:00: Demo Location https://www.youtube.com/watch?v=IfWIzKsoHnA https://github.com/tspannhw/SpeakerProfile https://www.linkedin.com/in/yujiantang/

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TCFPro24 Building Real-Time Generative AI Pipelines

Timothy Spann

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2024 February 28 - NYC - Meetup Unlocking Financial Data with Real-Time Pipelines https://www.meetup.com/futureofdata-newyork/events/298660453/ Unlocking Financial Data with Real-Time Pipelines (Flink Analytics on Stocks with SQL ) By Timothy Spann Financial institutions thrive on accurate and timely data to drive critical decision-making processes, risk assessments, and regulatory compliance. However, managing and processing vast amounts of financial data in real-time can be a daunting task. To overcome this challenge, modern data engineering solutions have emerged, combining powerful technologies like Apache Flink, Apache NiFi, Apache Kafka, and Iceberg to create efficient and reliable real-time data pipelines. In this talk, we will explore how this technology stack can unlock the full potential of financial data, enabling organizations to make data-driven decisions swiftly and with confidence. Introduction: Financial institutions operate in a fast-paced environment where real-time access to accurate and reliable data is crucial. Traditional batch processing falls short when it comes to handling rapidly changing financial markets and responding to customer demands promptly. In this talk, we will delve into the power of real-time data pipelines, utilizing the strengths of Apache Flink, Apache NiFi, Apache Kafka, and Iceberg, to unlock the potential of financial data. I will be utilizing NiFi 2.0 with Python and Vector Databases. Timothy Spann Principal Developer Advocate, Cloudera Tim Spann is a Principal Developer Advocate in Data In Motion for Cloudera. He works with Apache NiFi, Apache Kafka, Apache Pulsar, Apache Flink, Flink SQL, Apache Pinot, Trino, Apache Iceberg, DeltaLake, Apache Spark, Big Data, IoT, Cloud, AI/DL, machine learning, and deep learning. Tim has over ten years of experience with the IoT, big data, distributed computing, messaging, streaming technologies, and Java programming. Previously, he was a Developer Advocate at StreamNative, Principal DataFlow Field Engineer at Cloudera, a Senior Solutions Engineer at Hortonworks, 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 & NYC on Big Data, Cloud, IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as ApacheCon, DeveloperWeek, Pulsar Summit and many more. He holds a BS and MS in computer science. https://twitter.com/PaaSDev https://www.linkedin.com/in/timothyspann/ https://medium.com/@tspann https://github.com/tspannhw/FLiPStackWeekly/

Conf42Python -Using Apache NiFi, Apache Kafka, RisingWave, and Apache Iceberg...

Timothy Spann

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2024 Feb AI Meetup NYC GenAI_LLMs_ML_Data Codeless Generative AI Pipelines

Timothy Spann

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DBA Fundamentals Group: Continuous SQL with Kafka and Flink

Timothy Spann

DBA Fundamentals Group: Continuous SQL with Kafka and Flink 20-Feb-2024 In this talk, I will walk through how someone can set up and run continuous SQL queries against Kafka topics utilizing Apache Flink. We will walk through creating Kafka topics, schemas, and publishing data. We will then cover consuming Kafka data, joining Kafka topics, and inserting new events into Kafka topics as they arrive. This basic overview will show hands-on techniques, tips, and examples of how to do this. Tim Spann Tim Spann is the Principal Developer Advocate for Data in Motion @ Cloudera where he works with Apache Kafka, Apache Flink, Apache NiFi, Apache Iceberg, 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 Developer Advocate at StreamNative, Principal Field Engineer at Cloudera, a Senior Solutions Architect at AirisData and a senior field engineer at Pivotal. 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.

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OSACon 2023_ Unlocking Financial Data with Real-Time Pipelines

Timothy Spann

OSACon 2023_ Unlocking Financial Data with Real-Time Pipelines Unlocking Financial Data with Real-Time Pipelines Financial institutions thrive on accurate and timely data to drive critical decision-making processes, risk assessments, and regulatory compliance. However, managing and processing vast amounts of financial data in real-time can be a daunting task. To overcome this challenge, modern data engineering solutions have emerged, combining powerful technologies like Apache Flink, Apache NiFi, Apache Kafka, and Iceberg to create efficient and reliable real-time data pipelines. In this talk, we will explore how this technology stack can unlock the full potential of financial data, enabling organizations to make data-driven decisions swiftly and with confidence. Introduction: Financial institutions operate in a fast-paced environment where real-time access to accurate and reliable data is crucial. Traditional batch processing falls short when it comes to handling rapidly changing financial markets and responding to customer demands promptly. In this talk, we will delve into the power of real-time data pipelines, utilizing the strengths of Apache Flink, Apache NiFi, Apache Kafka, and Iceberg, to unlock the potential of financial data. Key Points to be Covered: Introduction to Real-Time Data Pipelines: a. The limitations of traditional batch processing in the financial domain. b. Understanding the need for real-time data processing. Apache Flink: Powering Real-Time Stream Processing: a. Overview of Apache Flink and its role in real-time stream processing. b. Use cases for Apache Flink in the financial industry. c. How Flink enables fast, scalable, and fault-tolerant processing of streaming financial data. Apache Kafka: Building Resilient Event Streaming Platforms: a. Introduction to Apache Kafka and its role as a distributed streaming platform. b. Kafka's capabilities in handling high-throughput, fault-tolerant, and real-time data streaming. c. Integration of Kafka with financial data sources and consumers. Apache NiFi: Data Ingestion and Flow Management: a. Overview of Apache NiFi and its role in data ingestion and flow management. b. Data integration and transformation capabilities of NiFi for financial data. c. Utilizing NiFi to collect and process financial data from diverse sources. Iceberg: Efficient Data Lake Management: a. Understanding Iceberg and its role in managing large-scale data lakes. b. Iceberg's schema evolution and table-level metadata capabilities. c. How Iceberg simplifies data lake management in financial institutions. Real-World Use Cases: a. Real-time fraud detection using Flink, Kafka, and NiFi. b. Portfolio risk analysis with Iceberg and Flink. c. Streamlined regulatory reporting leveraging all four technologies. Best Practices and Considerations: a. Architectural considerations when building real-time financial data pipelines. b. Ensuring data integrity, security, and compliance in real-time pipelines. c. Scalability an

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Timothy Spann

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Timothy Spann

[EN]DSS23_tspann_Integrating LLM with Streaming Data Pipelines https://dssconf.pl/en/#agenda-section Integrating LLM with Streaming Data Pipelines Timothy Spann, Principal Developer Advocate, Cloudera APACHE NIFI, APACHE FLINK, APACHE KAFKA, LLM, HUGGINGFACE, REST, STREAMING LESS In this talk and demo I will walk through how to add LLMs to your streaming pipelines by integration through Apache NiFi. https://github.com/tspannhw/FLaNK-watsonx.ai Cloudera streaming llm generative ai slack to slack

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AIDevWorld 23 Apache NiFi 101 Introduction and Best Practices https://sched.co/1RoAO Timothy Spann, Cloudera, Principal Developer Advocate In this talk, we will walk step by step through Apache NiFi from the first load to first application. I will include slides, articles and examples to take away as a Quick Start to utilizing Apache NiFi in your real-time dataflows. I will help you get up and running locally on your laptop, Docker or in CDP Public Cloud. Wednesday November 1, 2023 12:00pm - 12:25pm PDT VIRTUAL AI DevWorld -- Main Stage https://app.hopin.com/events/api-world-2023-ai-devworld/stages Retail & E-Commerce AI (Industry AI Conference) Session Type OPEN TALK Track or Conference Retail & E-Commerce AI (Industry AI Conference), Industry AI Conference, VIRTUAL, Tensorflow & PyTorch & Open Source Frameworks (AI/ML Engineering Conference), AI/ML Engineering Conference, AI DevWorld In-Person/Virtual Virtual, Virtual Exclusive apache nifi Timothy Spann Cloudera Principal Developer Advocate for Data in Motion Tim Spann is the Principal Developer Advocate for Data in Motion @ Cloudera where he works with Apache Kafka, Apache Flink, Apache NiFi, Apache Iceberg, 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 Developer Advocate at StreamNative, Principal Field Engineer at Cloudera, a Senior Solutions Architect at AirisData and a senior field engineer at Pivotal. 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. cloudera dataflow

CoC23_ Let’s Monitor The Conditions at the Conference

Timothy Spann

CoC23_ Let’s Monitor The Conditions at the Conference At home, I monitor the temperature, humidity, gas levels, ozone, air quality, and other features around my desk.Let’s bring this to the different spots around the conference including lunch tables, vendor booths, hotel rooms, and more. I need to know about these readings now, not when I get back home from the conference. We need to get these sensor readings immediately in case we need to turn on a fan or move to another area. We will also see if my talk produces a lot of hot air!?!??My setup is pretty simple, a raspberry pi, a breakout garden sensor mount, and as many sensors as I can carry. All code and parameters will be available ASF-licensed in github.The software stack is Python and Java, MQTT, HTML, JQuery, and Apache NiFi, Apache Kafka and Apache Flink.

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Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...

Subhajit Sahu

Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.

Adjusting OpenMP PageRank : SHORT REPORT / NOTES

Subhajit Sahu

For massive graphs that fit in RAM, but not in GPU memory, it is possible to take advantage of a shared memory system with multiple CPUs, each with multiple cores, to accelerate pagerank computation. If the NUMA architecture of the system is properly taken into account with good vertex partitioning, the speedup can be significant. To take steps in this direction, experiments are conducted to implement pagerank in OpenMP using two different approaches, uniform and hybrid. The uniform approach runs all primitives required for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid approach runs certain primitives in sequential mode (i.e., sumAt, multiply).

Q1’2024 Update: MYCI’s Leap Year Rebound

Oppotus

Adjusting primitives for graph : SHORT REPORT / NOTES

Subhajit Sahu

Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is Multiply with different modes (map) 1. Performance of sequential execution based vs OpenMP based vector multiply. 2. Comparing various launch configs for CUDA based vector multiply. Sum with different storage types (reduce) 1. Performance of vector element sum using float vs bfloat16 as the storage type. Sum with different modes (reduce) 1. Performance of sequential execution based vs OpenMP based vector element sum. 2. Performance of memcpy vs in-place based CUDA based vector element sum. 3. Comparing various launch configs for CUDA based vector element sum (memcpy). 4. Comparing various launch configs for CUDA based vector element sum (in-place). Sum with in-place strategies of CUDA mode (reduce) 1. Comparing various launch configs for CUDA based vector element sum (in-place).

哪里卖(usq毕业证书)南昆士兰大学毕业证研究生文凭证书托福证书原版一模一样

axoqas

原版定制【Q微信:741003700】《(usq毕业证书)南昆士兰大学毕业证研究生文凭证书》【Q微信:741003700】成绩单、雅思、外壳、留信学历认证永久存档查询，采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。【业务选择办理准则】一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【Q微信741003700】文凭即可二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信741003700】即可三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才【关于价格问题（保证一手价格）】我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子我给客户的都是第一手的代理价格，因为我想坦诚对待大家不想跟大家在价格方面浪费时间对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。

一比一原版(UPenn毕业证)宾夕法尼亚大学毕业证成绩单

ewymefz

UPenn毕业证【微信95270640】办理宾夕法尼亚大学毕业证原版一模一样、UPenn毕业证制作【Q微信95270640】《宾夕法尼亚大学毕业证购买流程》《UPenn成绩单制作》宾夕法尼亚大学毕业证书UPenn毕业证文凭宾夕法尼亚大学本科毕业证书,学历学位认证如何办理【留学国外学位学历认证、毕业证、成绩单、大学Offer、雅思托福代考、语言证书、学生卡、高仿教育部认证等一切高仿或者真实可查认证服务】代办国外（海外）英国、加拿大、美国、新西兰、澳大利亚、新西兰等国外各大学毕业证、文凭学历证书、成绩单、学历学位认证真实可查。办国外宾夕法尼亚大学宾夕法尼亚大学硕士学位证成绩单教育部学历学位认证留信认证大使馆认证留学回国人员证明修改成绩单信封申请学校offer录取通知书在读证明offer letter。快速办理高仿国外毕业证成绩单： 1宾夕法尼亚大学毕业证+成绩单+留学回国人员证明+教育部学历认证（全套留学回国必备证明材料给父母及亲朋好友一份完美交代）; 2雅思成绩单托福成绩单OFFER在读证明等留学相关材料（申请学校转学甚至是申请工签都可以用到）。 3.毕业证 #成绩单等全套材料从防伪到印刷从水印到钢印烫金高精仿度跟学校原版100%相同。专业服务请勿犹豫联系我！联系人微信号：95270640诚招代理：本公司诚聘当地代理人员如果你有业余时间有兴趣就请联系我们。国外宾夕法尼亚大学宾夕法尼亚大学硕士学位证成绩单办理过程： 1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）； 2开始安排制作毕业证成绩单电子图； 3毕业证成绩单电子版做好以后发送给您确认； 4毕业证成绩单电子版您确认信息无误之后安排制作成品； 5成品做好拍照或者视频给您确认； 6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。我们在哪里父母对我们的爱和思念为我们的生命增加了光彩给予我们自由追求的力量生活的力量我们也不忘感恩正因为这股感恩的线牵着我们使我们在一年的结束时刻义无反顾的踏上了回家的旅途人们常说父母恩最难回报愿我能以当年爸爸妈妈对待小时候的我们那样耐心温柔地对待我将渐渐老去的父母体谅他们以反哺之心奉敬父母以感恩之心孝顺父母哪怕只为父母换洗衣服为父母喂饭送汤按摩酸痛的腰背握着父母的手扶着他们一步一步地慢慢散步.娃

The Building Blocks of QuestDB, a Time Series Database

javier ramirez

Talk Delivered at Valencia Codes Meetup 2024-06. Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds. It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.

一比一原版(UofM毕业证)明尼苏达大学毕业证成绩单

ewymefz

UofM毕业证【微信95270640】办文凭{明尼苏达大学毕业证}Q微Q微信95270640UofM毕业证书成绩单/学历认证UofM Diploma未毕业、挂科怎么办？+QQ微信：Q微信95270640-大学Offer（申请大学）、成绩单（申请考研）、语言证书、在读证明、使馆公证、办真实留信网认证、真实大使馆认证、学历认证办国外明尼苏达大学明尼苏达大学毕业证假文凭教育部学历学位认证留信认证大使馆认证留学回国人员证明修改成绩单信封申请学校offer录取通知书在读证明offer letter。快速办理高仿国外毕业证成绩单： 1明尼苏达大学毕业证+成绩单+留学回国人员证明+教育部学历认证（全套留学回国必备证明材料给父母及亲朋好友一份完美交代）; 2雅思成绩单托福成绩单OFFER在读证明等留学相关材料（申请学校转学甚至是申请工签都可以用到）。 3.毕业证 #成绩单等全套材料从防伪到印刷从水印到钢印烫金高精仿度跟学校原版100%相同。专业服务请勿犹豫联系我！联系人微信号：95270640诚招代理：本公司诚聘当地代理人员如果你有业余时间有兴趣就请联系我们。国外明尼苏达大学明尼苏达大学毕业证假文凭办理过程： 1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）； 2开始安排制作毕业证成绩单电子图； 3毕业证成绩单电子版做好以后发送给您确认； 4毕业证成绩单电子版您确认信息无误之后安排制作成品； 5成品做好拍照或者视频给您确认； 6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。有一次山娃坐在门口写作业写着写着竟伏在桌上睡着了迷迷糊糊中山娃似乎听到了父亲的脚步声当他晃晃悠悠站起来时才诧然发现一位衣衫破旧的妇女挎着一只硕大的蛇皮袋手里拎着长铁钩正站在门口朝黑色的屋内张望不好坏人小偷山娃一怔却也灵机一动立马仰起头双手拢在嘴边朝楼上大喊：“爸爸爸——有人找——那人一听朝山娃尴尬地笑笑悻悻地走了山娃立马“嘭的一声将铁门锁死心却咚咚地乱跳当山娃跟父亲说起这事时父亲很吃惊抚摸着山娃母

一比一原版(爱大毕业证书)爱丁堡大学毕业证如何办理

g4dpvqap0

毕业原版【微信:41543339】【(爱大毕业证书)爱丁堡大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】【业务选择办理准则】一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才留信网服务项目： 1、留学生专业人才库服务（留信分析） 2、国（境）学习人员提供就业推荐信服务 3、留学人员区块链存储服务 → 【关于价格问题（保证一手价格）】我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子我给客户的都是第一手的代理价格，因为我想坦诚对待大家不想跟大家在价格方面浪费时间对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！

Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...

John Andrews

SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation" Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults Description: Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project. Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas

一比一原版(Coventry毕业证书)考文垂大学毕业证如何办理

74nqk8xf

毕业原版【微信:41543339】【(Coventry毕业证书)考文垂大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】【业务选择办理准则】一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才留信网服务项目： 1、留学生专业人才库服务（留信分析） 2、国（境）学习人员提供就业推荐信服务 3、留学人员区块链存储服务 → 【关于价格问题（保证一手价格）】我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子我给客户的都是第一手的代理价格，因为我想坦诚对待大家不想跟大家在价格方面浪费时间对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！

Best best suvichar in gujarati english meaning of this sentence as Silk road ...

AbhimanyuSinha9

Everything you wanted to know about LIHTC

Roger Valdez

Influence of Marketing Strategy and Market Competition on Business Plan

jerlynmaetalle

Criminal IP - Threat Hunting Webinar.pdf

Criminal IP

一比一原版(UofS毕业证书)萨省大学毕业证如何办理

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一比一原版(UIUC毕业证)伊利诺伊大学|厄巴纳-香槟分校毕业证如何办理

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UIUC毕业证offer【微信95270640】☀《伊利诺伊大学|厄巴纳-香槟分校毕业证购买》GoogleQ微信95270640《UIUC毕业证模板办理》加拿大文凭、本科、硕士、研究生学历都可以做,二、业务范围： ★、全套服务：毕业证、成绩单、化学专业毕业证书伪造《伊利诺伊大学|厄巴纳-香槟分校大学毕业证》Q微信95270640《UIUC学位证书购买》 (诚招代理)办理国外高校毕业证成绩单文凭学位证,真实使馆公证（留学回国人员证明）真实留信网认证国外学历学位认证雅思代考国外学校代申请名校保录开请假条改GPA改成绩ID卡 1.高仿业务:【本科硕士】毕业证,成绩单（GPA修改）,学历认证（教育部认证）,大学Offer,,ID,留信认证,使馆认证,雅思,语言证书等高仿类证书； 2.认证服务: 学历认证（教育部认证）,大使馆认证（回国人员证明）,留信认证（可查有编号证书）,大学保录取,雅思保分成绩单。 3.技术服务：钢印水印烫金激光防伪凹凸版设计印刷激凸温感光标底纹镭射速度快。办理伊利诺伊大学|厄巴纳-香槟分校伊利诺伊大学|厄巴纳-香槟分校毕业证offer流程： 1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）； 2开始安排制作毕业证成绩单电子图； 3毕业证成绩单电子版做好以后发送给您确认； 4毕业证成绩单电子版您确认信息无误之后安排制作成品； 5成品做好拍照或者视频给您确认； 6快递给客户（国内顺丰国外DHLUPS等快读邮寄） -办理真实使馆公证（即留学回国人员证明） -办理各国各大学文凭（世界名校一对一专业服务,可全程监控跟踪进度） -全套服务：毕业证成绩单真实使馆公证真实教育部认证。让您回国发展信心十足！（详情请加一下文凭顾问+微信:95270640）欢迎咨询！的鬼地方父亲的家在高楼最底屋最下面很矮很黑是很不显眼的地下室父亲的家安在别人脚底下须绕过高楼旁边的垃圾堆下八个台阶才到父亲的家很狭小除了一张单人床和一张小方桌几乎没有多余的空间山娃一下子就联想起学校的男小便处山娃很想笑却怎么也笑不出来山娃很迷惑父亲的家除了一扇小铁门连窗户也没有墓穴一般阴森森有些骇人父亲的城也便成了山娃的城父亲的家也便成了山娃的家父亲让山娃呆在屋里做作业看电视最多只能在门口透透气间

做(mqu毕业证书)麦考瑞大学毕业证硕士文凭证书学费发票原版一模一样

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原版定制【Q微信:741003700】《(mqu毕业证书)麦考瑞大学毕业证硕士文凭证书》【Q微信:741003700】成绩单、雅思、外壳、留信学历认证永久存档查询，采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。【业务选择办理准则】一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【Q微信741003700】文凭即可二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信741003700】即可三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才【关于价格问题（保证一手价格）】我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子我给客户的都是第一手的代理价格，因为我想坦诚对待大家不想跟大家在价格方面浪费时间对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。

原版制作(swinburne毕业证书)斯威本科技大学毕业证毕业完成信一模一样

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学校原件一模一样【微信：741003700 】《(swinburne毕业证书)斯威本科技大学毕业证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。本公司拥有海外各大学样板无数，能完美还原。 1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700 【主营项目】一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！二.真实使馆公证(即留学回国人员证明,不成功不收费) 三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度) 如果您处于以下几种情况： ◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】 ◇面对父母的压力，希望尽快拿到； ◇不清楚认证流程以及材料该如何准备； ◇回国时间很长，忘记办理； ◇回国马上就要找工作，办给用人单位看； ◇企事业单位必须要求办理的 ◇需要报考公务员、购买免税车、落转户口 ◇申请留学生创业基金留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才

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一比一原版(UofS毕业证书)萨省大学毕业证如何办理

一比一原版(UIUC毕业证)伊利诺伊大学|厄巴纳-香槟分校毕业证如何办理

做(mqu毕业证书)麦考瑞大学毕业证硕士文凭证书学费发票原版一模一样

原版制作(swinburne毕业证书)斯威本科技大学毕业证毕业完成信一模一样

Mm.. FLaNK Stack (MiNiFi MXNet Flink NiFi Kudu Kafka)

1. Introduction to the Mm.. FLaNK Stack Timothy Spann

2. © 2019 Cloudera, Inc. All rights reserved. 2 Welcome to Future of Data - Princeton @PaasDev https://www.meetup.com/futureofdata-princeton/ From Big Data to AI to Streaming to Containers to Cloud to Analytics to Cloud Storage to Fast Data to Machine Learning to Microservices to ...

3. © 2019 Cloudera, Inc. All rights reserved. 3 Today’s Lead Who am I? Data in Motion Field Engineer @PaasDev DZone Zone Leader and Big Data MVB; Princeton NJ Future of Data Meetup; ex-Pivotal Field Engineer; Author of Apache Kafka RefCard https://github.com/tspannhw https://www.datainmotion.dev/

4. https://github.com/tspannhw/MmFLaNK https://www.datainmotion.dev/2019/11/introducing-mm-ﬂank-apache-ﬂink-stack.html

5. MiNiFim.. FLaNK ● https://www.datainmotion.dev/2019/03/using-raspberry-pi-3b-w ith-apache-nifi.html ● https://www.datainmotion.dev/2019/05/cloudera-edge-manage ment-introduction.html ● https://www.datainmotion.dev/2019/11/running-demo-apache-fl ink-application.html ● https://www.datainmotion.dev/2019/11/learning-apache-flink-1 9.html ● https://www.datainmotion.dev/2019/10/migrating-apache-flume -flows-to-apache_42.html

6. MiNiFim.. FLaNK

7. MiNiFim.. FLaNK

8. APACHE MINIFI C++ 0.6.0 Key New Features Feature Description Apache JIRA NiFi JNI Bindings Ability to run any native NiFi processor via JNI. Makes all NiFi processors available to MiNiFi C++ MINIFICPP-740 Native Python Processors Ability to write native MiNiFi C++ processors in Python MINIFICPP-750 PublishKafka Ability to write to secured Kafka instance MINIFICPP-731 CoAP Support Ability to communicate with EFM server over CoAP. Drastically reduces the network impact of heartbeats Windows Support Ability to install MiNiFi via a MSI

9. MiNiFi C++ - MiNiFi IoT AI • •• • https://www.datainmotion.dev/2019/08/rapid-iot-development-with-cloudera.html

10. Apache MXNet Native Processor for Apache NiFi This is a beta, community release by me using the new beta Java API for Apache MXNet. https://github.com/tspannhw/niﬁ-mxnetinference-processor https://community.hortonworks.com/articles/229215/apache-niﬁ-processor-for-apache-mxnet-ssd-single.html https://www.youtube.com/watch?v=Q4dSGPvqXSA

11. ● https://www.slideshare.net/bunkertor/apache-deep-learning-101-apach econ-montreal-2018-v031 ● https://www.slideshare.net/bunkertor/apache-deep-learning-202-washi ngton-dc-dws-2019 ● https://www.slideshare.net/bunkertor/apache-deep-learning-201-barce lona-dws-march-2019

12. • Cloud ready • Python, C++, Scala, R, Julia, Matlab, MXNet.js and Perl Support • Experienced team (XGBoost) • AWS, Microsoft, NVIDIA, Baidu, Intel • Apache Incubator Project • Run distributed on YARN and Spark • In my early tests, faster than TensorFlow. (Try this yourself) • Runs on Raspberry PI, NVidia Jetson Nano and other constrained devices https://mxnet.incubator.apache.org/how_to/cloud.html https://github.com/apache/incubator-mxnet/tree/1.3.1/example https://gluon-cv.mxnet.io/api/model_zoo.html https://elinux.org/Jetson_Nano

13. • Great documentation • Crash Course • Gluon (Open API), GluonCV, GluonNLP • Keras (One API Many Runtime Options) • Great Python Interaction. Java and Scala APIs! • Open Source Model Server Available • ONNX (Open Neural Network Exchange Format) Support for AI Models • Now in Version 1.5.1! • Rich Model Zoo! • Math Kernel Library and NVidia CUDA Optimizations • TensorBoard compatible http://mxnet.incubator.apache.org/ http://gluon.mxnet.io/https://onnx.ai pip3.7 install -U keras-mxnet https://gluon-nlp.mxnet.io pip3.7 install --upgrade mxnet pip3.7 install gluonnlp pip3.7 install gluoncv pip3.7 install mxnet-mkl>=1.5.1 --upgrade

14. Instance Segmentation: Mask RCNN with GluonCV Mask RCNN model trained on COCO dataset with ResNet-50 backbone

15.

16. https://github.com/tspannhw/MmFLaNK/blob/master/IoTKafka.java

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Mm.. FLaNK Stack (MiNiFi MXNet Flink NiFi Kudu Kafka)

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Mm.. FLaNK Stack (MiNiFi MXNet Flink NiFi Kudu Kafka)