Fast data for fitness 10 nov 2020

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This document discusses fast data for fitness applications using real-time health sensor data. It describes various data sources like heart rate, temperature, and GPS that can be ingested from sensors. It then outlines a data pipeline for fitness applications using technologies like Apache Kafka, Cloudera Flow, streaming analytics, and edge computing. This pipeline collects data from devices and sensors, processes it using tools like SQL and analytics, and delivers it to cloud and edge systems for applications.

Software

Fast Data for Fitness
Real-time Health Sensor Ingest
Timothy Spann - Principal DataFlow Field Engineer

© 2020 Cloudera, Inc. All rights reserved. 3
Timothy Spann
Data in Motion Principal Field Engineer
@PaasDev
DZone Zone Leader and Big Data MVB
Princeton NJ Future of Data Meetup
https://github.com/tspannhw https://www.datainmotion.dev/
https://meetup.com/futureofdata-princeton/

© 2020 Cloudera, Inc. All rights reserved. 4
Data Sources - From Images to Sub-Second Data Events
Heart Rate
Temperature
Blood Sugar
Altimeter
Blood Pressure
Weight
GPS
Live Video/Picture
AI for Pose Estimation
Improvement to Technique

© 2020 Cloudera, Inc. All rights reserved. 5
Data for Fitness+ Pipeline
Weather
Weather
Climate
Aggregates
Sensors
SQL
Analytics
Sources
Pollution
Wearables
REST

© 2020 Cloudera, Inc. All rights reserved. 6
Fitness+ Data Streaming Pipeline
Device Data
SensorsLogs
Weather
Sensors
Aggregates
Energy
SQL
Analytics
MiNiFi
Agent
Deep Learning
Classiﬁcation
Edge Private
Cloud
Multi-Public
Cloud

© 2020 Cloudera, Inc. All rights reserved. 7
Fast Fitness+ Data

© 2020 Cloudera, Inc. All rights reserved. 8
CLOUDERA FLOW AND EDGE MANAGEMENT
Enable easy ingestion, routing, management and delivery of any data anywhere (Edge, cloud, data
center) to any downstream system with built in end-to-end security and provenance
Advanced tooling to industrialize
ﬂow development (Flow Development
Life Cycle)
ACQUIRE
• Over 300 Prebuilt Processors
• Easy to build your own
• Parse, Enrich & Apply Schema
• Filter, Split, Merger & Route
• Throttle & Backpressure
FTP
SFTP
HL7
UDP
XML
HTTP
EMAIL
HTML
IMAGE
SYSLOG
PROCESS
HASH
MERGE
EXTRACT
DUPLICATE
SPLIT
ENCRYPT
TALL
EVALUATE
EXECUTE
GEOENRICH
SCAN
REPLACE
TRANSLATE
CONVERT
ROUTE TEXT
ROUTE CONTENT
ROUTE CONTEXT
ROUTE RATE
DISTRIBUTE LOAD
DELIVER
• Guaranteed Delivery
• Full data provenance from
acquisition to delivery
• Diverse, Non-Traditional Sources
• Eco-system integration
FTP
SFTP
HL7
UDP
XML
HTTP
EMAIL
HTML
IMAGE
SYSLOG

© 2019 Cloudera, Inc. All rights reserved. 9
Apache Kafka
• Highly reliable distributed
messaging system
• Decouple applications, enables
many-to-many patterns
• Publish-Subscribe semantics
• Horizontal scalability
• Eﬃcient implementation to
operate at speed with big data
volumes
• Organized by topic to support
several use cases
Source
System
Source
System
Source
System
Kafka
Fraud
Detection
Security
Systems
Real-Time
Monitoring
Source
System
Source
System
Source
System
Fraud
Detection
Security
Systems
Real-Time
Monitoring
Many-To-Many
Publish-Subscribe
Point-To-Point
Request-Response

© 2020 Cloudera, Inc. All rights reserved. 10
New features delivered with Cloudera Streaming Analytics (CSA) 1.2
Next Generation Streaming Analytics
Flink SQL Support
Agile Streaming App
Development using SQL
New Flink Atlas Hook
Capture operational Flink
app metadata and lineage
Single View of Flink Yarn Jobs
Improve Developer Experience
& operational visibility

© 2020 Cloudera, Inc. All rights reserved. 11
● https://www.cloudera.com/tutorials/building-a-sentiment-analysis-application/3.html
● https://blog.cloudera.com/benchmarking-niﬁ-performance-and-scalability/
● https://www.datainmotion.dev/2020/06/no-more-spaghetti-ﬂows.html
● https://www.datainmotion.dev/2020/06/the-rise-of-mega-edge-ﬂank.html
● https://www.datainmotion.dev/2020/01/cloudera-edge2ai-miniﬁ-java-agent-with.html
● 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
● https://www.datainmotion.dev/2020/06/unboxing-most-amazing-edge-ai-device.html
● https://www.datainmotion.dev/2019/08/edge-processing-with-jetson-nano-part-3.html
● https://www.datainmotion.dev/2020/04/predicting-sensor-readings-with-time.html
REFERENCES

© 2020 Cloudera, Inc. All rights reserved. 12
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28March2024-Codeless-Generative-AI-Pipelines https://www.meetup.com/futureofdata-princeton/events/299440871/ https://www.meetup.com/real-time-analytics-meetup-ny/events/299290822/ ******Note***** The event is seat-limited, therefore please complete your registration here. Only people completing the form will be able to attend. ----------------------- We're excited to invite you to join us in-person, for a Real-Time Analytics exploration! Join us for an evening of insights, networking as we delve into the OSS technologies shaping the field! Agenda: 05:30-06:00: Pizza and friends 06:00- 06:40: Codeless GenAI Pipelines with Flink, Kafka, NiFi 06:40- 07:20 Real-Time Analytics in the Corporate World: How Apache Pinot® Powers Industry Leaders 07:20-07:30 QNA Codeless GenAI Pipelines with Flink, Kafka, NiFi | Tim Spann, Cloudera Explore the power of real-time streaming with GenAI using Apache NiFi. Learn how NiFi simplifies data engineering workflows, allowing you to focus on creativity over technical complexities. I'll guide you through practical examples, showcasing NiFi's automation impact from ingestion to delivery. Whether you're a seasoned data engineer or new to GenAI, this talk offers valuable insights into optimizing workflows. Join us to unlock the potential of real-time streaming and witness how NiFi makes data engineering a breeze for GenAI applications! Real-Time Analytics in the Corporate World: How Apache Pinot® Powers Industry Leaders | Viktor Gamov, StarTree Explore how industry leaders like LinkedIn, Uber Eats, and Stripe are mastering real-time data with Viktor as your guide. Discover how Apache Pinot transforms data into actionable insights instantly. Viktor will showcase Pinot's features, including the Star-Tree Index, and explain why it's a game-changer in data strategy. This session is for everyone, from data geeks to business gurus, eager to uncover the future of tech. Join us and be wowed by the power of real-time analytics with Apache Pinot! ------- 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.

28March2024-Codeless-Generative-AI-Pipelines

Timothy Spann

https://princetonacm.acm.org/tcfpro/ 18th Annual IEEE IT Professional Conference (ITPC) Armstrong Hall at The College of New Jersey Friday, March 15th, 2024 | 10:00 AM to 5:00 PM IT Professional Conference at Trenton Computer Festival IEEE Information Technology Professional Conference on Friday, March 15th, 2024 TCFPro24 Building Real-Time Generative AI Pipelines Building Real-Time Generative AI Pipelines In this talk, Tim will delve into the exciting realm of building real-time generative AI pipelines with streaming capabilities. The discussion will revolve around the integration of cutting-edge technologies to create dynamic and responsive systems that harness the power of generative algorithms. From leveraging streaming data sources to implementing advanced machine learning models, the presentation will explore the key components necessary for constructing a robust real-time generative AI pipeline. Practical insights, use cases, and best practices will be shared, offering a comprehensive guide for developers and data scientists aspiring to design and implement dynamic AI systems in a streaming environment. Tim will show a live demo showing we can use Apache NiFi to provide a live chat between a person in Slack and several LLM models all orchestrated with Apache NiFi, Apache Kafka and Python. We will use RAG against Chroma and Pinecone vector data stores, Hugging Face and WatsonX.AI LLM, and add additional context with NiFi lookups of stocks, weather and other data streams in real-time. Timothy Spann Tim Spann is a Principal Developer Advocate in Data In Motion for Cloudera. He works with Apache NiFi, Apache Pulsar, Apache Kafka, 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.

TCFPro24 Building Real-Time Generative AI Pipelines

Timothy Spann

2024 Build Generative AI for Non-Profits

Timothy Spann

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/

2024 February 28 - NYC - Meetup Unlocking Financial Data with Real-Time Pipel...

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.

Conf42-Python-Building Apache NiFi 2.0 Python Processors

Timothy Spann

Conf42Python -Using Apache NiFi, Apache Kafka, RisingWave, and Apache Iceberg with Stock Data and LLM Abstract In this talk, we’ll discuss how to use Apache NiFi, Apache Kafka, RisingWave, and Apache Iceberg to process and analyze stock data. We demonstrated the ingestion, processing, and analysis of stock data. Additionally, we illustrated how to use an LLM to generate predictions from the analyzed data. Karin Wolok Developer Relations, Dev Marketing, and Community Programming @ Project Elevate Karin Wolok's LinkedIn account Karin Wolok's twitter account Tim Spann Principal Developer Advocate @ Cloudera Tim Spann's LinkedIn account Tim Spann's twitter account https://www.conf42.com/Python_2024_Karin_Wolok_Tim_Spann_nifi__kafka_risingwave_iceberg_llm

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

Timothy Spann

2024 Feb AI Meetup NYC GenAI_LLMs_ML_Data Codeless Generative AI Pipelines https://www.aicamp.ai/event/eventdetails/W2024022214 apache nifi llm generative ai gen ai ml dl machine learning apache kafka apache flink postgresql python AI Meetup (NYC): GenAI, LLMs, ML and Data Feb 22, 05:30 PM EST Welcome to the monthly in-person AI meetup in New York City, in collaboration with Microsoft. Join us for deep dive tech talks on AI, GenAI, LLMs and machine learning, food/drink, networking with speakers and fellow developers Agenda: * 5:30pm~6:00pm: Checkin, Food/drink and networking * 6:00pm~6:10pm: Welcome/community update * 6:10pm~8:30pm: Tech talks * 8:30pm: Q&A, Open discussion Tech Talk: Searching and Reasoning Over Multimedia Data with Vector Databases and LMMs Speaker: Zain Hasan (Weaviate LinkedIn) Abstract: In this talk, Zain Hasan will discuss how we can use open-source multimodal embedding models in conjunction with large generative multimodal models that can that can see, hear, read, and feel data(!), to perform cross-modal search(searching audio with images, videos with text etc.) and multimodal retrieval augmented generation (MM-RAG) at the billion-object scale with the help of open source vector databases. I will also demonstrate, with live code demos, how being able to perform this cross-modal retrieval in real-time can enables users to use LLMs that can reason over their enterprise multimodal data. This talk will revolve around how we can scale the usage of multimodal embedding and generative models in production. Tech Talk: Codeless Generative AI Pipelines Speaker: Timothy Spann (Cloudera LinkedIn) Abstract: Join us for an insightful talk on leveraging the power of real-time streaming tools, specifically Apache NiFi, to revolutionize GenAI data engineering. In this session, we’ll explore how the integration of Apache NiFi can automate the entire process of prompt building, making it a seamless and efficient task. Speakers/Topics: Stay tuned as we are updating speakers and schedules. If you have a keen interest in speaking to our community, we invite you to submit topics for consideration: Submit Topics Sponsors: We are actively seeking sponsors to support our community. Whether it is by offering venue spaces, providing food/drink, or cash sponsorship. Sponsors will have the chance to speak at the meetups, receive prominent recognition, and gain exposure to our extensive membership base of 20,000+ local or 300K+ developers worldwide. Venue: Microsoft NYC - Times Square, 11 Times Square, New York, NY 10036 Room Name: Central Park West 6501 Community on Slack/Discord - Event chat: chat and connect with speakers and attendees - Sharing blogs, events, job openings, projects collaborations Join Slack (search and join the #newyork channel) | Join Discord

2024 Feb AI Meetup NYC GenAI_LLMs_ML_Data Codeless Generative AI Pipelines

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.

DBA Fundamentals Group: Continuous SQL with Kafka and Flink

Timothy Spann

NY Open Source Data Meetup Feb 8 2024 Building Real-time Pipelines with FLaNK...

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

OSACon 2023_ Unlocking Financial Data with Real-Time Pipelines

Timothy Spann

Building Real-time Travel Alerts In this session, we will walk through how to build a complete streaming application to send alerts based on travel advisories from public data. We will also join in other data sources of relevance and push out alerts. We will show you how to build this streaming application with Apache NiFi, Apache Kafka, and Apache Flink and show you when/why/how, and what to build to maximize performance, productivity, and ease of development. Let's get streaming. Apache Flink Apache Kafka Apache NiFi FLaNK Stack Tim Spann Big Data Conference Europe 2023

Building Real-Time Travel Alerts

Timothy Spann

JConWorld: Continuous SQL with Kafka and Flink In this talk, I will walk through how someone can setup and run continous 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 over view will show hands-on techniques, tips and examples of how to do this. 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. https://www.datainmotion.dev/p/about-me.html https://dzone.com/users/297029/bunkertor.html https://www.youtube.com/channel/UCDIDMDfje6jAvNE8DGkJ3_w?view_as=subscriber

JConWorld_ Continuous SQL with Kafka and Flink

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

[EN]DSS23_tspann_Integrating LLM with Streaming Data Pipelines

Timothy Spann

Evolve 2023 NYC - Integrating AI Into Realtime Data Pipelines Demo

Timothy Spann

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

AIDevWorldApacheNiFi101

Timothy Spann

26Oct2023_ Adding Generative AI to Real-Time Streaming Pipelines_ NYC Meetup.pdf ## Details **Important** Please complete your registration in this short form. For on-site we have limited room, so please confirm if you are attending in-person in Manhattan, NYC. -------------------------------------------------------------------------------------------- We're at StarTree, excited to join forces with our friends at Cloudera, for a meetup that is all about The Latest in Real-Time Analytics: Generative AI and LLM, featuring Apache Pinot and Apache NiFi. Join us for an insightful discussion about cutting-edge analytics, meet the community in person, and catch up over drinks and snacks. What's the plan ? 05:30-06:00 Pizza and Networking 06:00-06:35 Adding Generative AI to Real-Time Streaming Pipelines | Tim Spann, Principal Developer Advocate, Cloudera 06:35-07:10 Apache Pinot and Kafka an excellent pairing for refined palates | Tim Veil, VP of Solutions Engineering and Enablement, StarTree 07:10-07:20 QNA 07:20- 07:30 More Snacks and Networking ;) **Important** Seats are limited Please complete your registration in this short form. Adding Generative AI to Real-Time Streaming Pipelines | Timothy Spann In this talk, Tim will discuss the basics of real-time streaming, walk through the tools used including Apache NiFi, Apache Kafka and Apache Flink and show how to build a real-time streaming pipeline that sends prompts to LLMs hosted by the likes of Hugging Face, IBM and Cloudera. He will also discuss where real-time data stores like Apache Pinot come into play. He will show a detailed demonstration of a few use cases involving different sources of data including Kafka, Medium Articles and interactive Question and Response in Slack. He will then show you how you can build your own and where areas of growth exist. 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. https://github.com/tspannhw/SpeakerProfile Apache Pinot and Kafka an excellent pairing for refined palates | Tim Veil The other Tim, Tim Veil, will dive into the history and architect

26Oct2023_Adding Generative AI to Real-Time Streaming Pipelines_ NYC Meetup

Timothy Spann

CoC23_ Looking at the New Features of Apache NiFi Apache NiFi has a lot of new features, processors and best practices that have arrived in the last year or so. I will walk through building flows using the latest tips, techniques and processor. I will and change a number of data flows utilizing the latest NiFi version and point out gotchas and some never dos. The deck will act as a take-away with notes, tips and guides to what we covered. ===> Any NiFi 1.23+ and 2.0 in progress features people want to see?

CoC23_ Looking at the New Features of Apache NiFi

Timothy Spann

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Test automation is a cornerstone of software development and quality assurance in today's rapidly evolving digital landscape. Its significance cannot be overstated. Businesses can enhance efficiency, productivity, and accelerate software delivery to market through automation, streamlining testing processes effectively. This comprehensive guide addresses the best practices for test automation in 2024. It offers a detailed checklist to empower you to optimize your automation efforts and maintain a competitive edge.

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Short Story: Unveiling the Reasoning Abilities of Large Language Models by Ke...

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In an era where security concerns are paramount, the integration of artificial intelligence (AI) into CCTV cameras has revolutionized surveillance capabilities. One of the most significant advancements is the ability to achieve real-time threat detection, enabling immediate responses to potential security breaches. This blog explores how AI is reshaping surveillance through real-time threat detection and the implications of this technology.

Optimizing AI for immediate response in Smart CCTV

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In the realm of real-time applications, Large Language Models (LLMs) have long dominated language-centric tasks, while tools like OpenCV have excelled in the visual domain. However, the future (maybe) lies in the fusion of LLMs and deep learning, giving birth to the revolutionary concept of Large Action Models (LAMs). Imagine a world where AI not only comprehends language but mimics human actions on technology interfaces. For example, the Rabbit r1 device presented at CES 2024, driven by an AI operating system and LAM, brings this vision to life. It executes complex commands, leveraging GUIs with unprecedented ease. In this presentation, join me on a journey as a software engineer tinkering with WebRTC, Janus, and LLM/LAMs. Together, we’ll evaluate the current state of these AI technologies, unraveling the potential they hold for shaping the future of real-time applications.

Unveiling the Tech Salsa of LAMs with Janus in Real-Time Applications

Alberto González Trastoy

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Software Quality Assurance Interview Questions

Arshad QA

Tech Tuesday-Harness the Power of Effective Resource Planning with OnePlan’s ...

OnePlan Solutions

How To Troubleshoot Collaboration Apps for the Modern Connected Worker

ThousandEyes

Data spaces in distributed environments should be allowed to evolve in agile ways providing data space owners with large flexibility about which data they store. Agility and heterogeneity, however, jeopardize data exchanges because representations may build on varying ontologies and data consumers may not rely on the semantic correctness of their queries in the context of semantically heterogeneous, evolving data spaces. Graph data spaces are one example of a powerful model for representing and querying data whose semantics may change over time. To assert and enforce conditions on individual graph data spaces, shape languages (e.g SHACL) have been developed. We investigate the question of how querying and programming can be guarded by reasoning over SHACL constraints in a distributed setting and we sketch a picture of how a future landscape based on semantically heterogeneous data spaces might look like.

Shapes for Sharing between Graph Data Spaces - and Epistemic Querying of RDF-...

Steffen Staab

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anilsa9823

Introducing MyIntelliAccount™ Cloud Accounting Software as a Service (SaaS) , the complete system for simplifying business accounting needs. MyIntelliAccount Cloud Accounting SaaS is an easy to understand and easy to use application and system designed for the Web with supporting applications for iOS and Android devices. Designed to work like a natural extension of your web browser, the user interface for MyIntelliAccount Cloud Accounting SaaS is intuitive and thoughtfully organized to help you easily navigate and access your business accounting information. Because our company takes the time to research, study, and understand the applications we develop, we are confident that once you use MyIntelliAccount Cloud Accounting SaaS , you will be asking yourself how you ever got along without it.

Try MyIntelliAccount Cloud Accounting Software As A Service Solution Risk Fre...

MyIntelliSource, Inc.

Investing in AI transformation today The modern business advantage: Uncovering deep insights with AI Organizations around the world have come to recognize AI as the transformative technology that enables them to gain real business advantage. AI’s ability to organize vast quantities of data allows those who implement it to uncover deep business insights, augment human expertise, drive operational efficiency, transform their products, and better serve their customers

Microsoft AI Transformation Partner Playbook.pdf

Willy Marroquin (WillyDevNET)

Unlocking the Future of AI Agents with Large Language Models

aagamshah0812

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Document contains steps to getting ups and running quickly with MyTimeClock Employee Scheduling and Time Keeping Cloud Software as a Service Solution, Web version. Try MyTimeClock or any of our other software packages risk-free by registering for a FREE ACCOUNT at https://register.myintellisource.com/. If you would like more information about our company or its software, follow us on Facebook, Instagram, LinkedIn, Twitter, or YouTube, visit our home page at https://www.myintellisource.com/, or send us an email at cs@myintellisource.com. Take care and have a great day.

Steps To Getting Up And Running Quickly With MyTimeClock Employee Scheduling ...

MyIntelliSource, Inc.

How To Use Server-Side Rendering with Nuxt.js

Andolasoft Inc

HR Software Buyers Guide in 2024 - HRSoftware.com

Fatema Valibhai

Fast data for fitness 10 nov 2020

1. Fast Data for Fitness Real-time Health Sensor Ingest Timothy Spann - Principal DataFlow Field Engineer

3. © 2020 Cloudera, Inc. All rights reserved. 3 Timothy Spann Data in Motion Principal Field Engineer @PaasDev DZone Zone Leader and Big Data MVB Princeton NJ Future of Data Meetup https://github.com/tspannhw https://www.datainmotion.dev/ https://meetup.com/futureofdata-princeton/

4. © 2020 Cloudera, Inc. All rights reserved. 4 Data Sources - From Images to Sub-Second Data Events Heart Rate Temperature Blood Sugar Altimeter Blood Pressure Weight GPS Live Video/Picture AI for Pose Estimation Improvement to Technique

6. © 2020 Cloudera, Inc. All rights reserved. 6 Fitness+ Data Streaming Pipeline Device Data SensorsLogs Weather Sensors Aggregates Energy SQL Analytics MiNiFi Agent Deep Learning Classiﬁcation Edge Private Cloud Multi-Public Cloud

8. © 2020 Cloudera, Inc. All rights reserved. 8 CLOUDERA FLOW AND EDGE MANAGEMENT Enable easy ingestion, routing, management and delivery of any data anywhere (Edge, cloud, data center) to any downstream system with built in end-to-end security and provenance Advanced tooling to industrialize ﬂow development (Flow Development Life Cycle) ACQUIRE • Over 300 Prebuilt Processors • Easy to build your own • Parse, Enrich & Apply Schema • Filter, Split, Merger & Route • Throttle & Backpressure FTP SFTP HL7 UDP XML HTTP EMAIL HTML IMAGE SYSLOG PROCESS HASH MERGE EXTRACT DUPLICATE SPLIT ENCRYPT TALL EVALUATE EXECUTE GEOENRICH SCAN REPLACE TRANSLATE CONVERT ROUTE TEXT ROUTE CONTENT ROUTE CONTEXT ROUTE RATE DISTRIBUTE LOAD DELIVER • Guaranteed Delivery • Full data provenance from acquisition to delivery • Diverse, Non-Traditional Sources • Eco-system integration FTP SFTP HL7 UDP XML HTTP EMAIL HTML IMAGE SYSLOG

9. © 2019 Cloudera, Inc. All rights reserved. 9 Apache Kafka • Highly reliable distributed messaging system • Decouple applications, enables many-to-many patterns • Publish-Subscribe semantics • Horizontal scalability • Eﬃcient implementation to operate at speed with big data volumes • Organized by topic to support several use cases Source System Source System Source System Kafka Fraud Detection Security Systems Real-Time Monitoring Source System Source System Source System Fraud Detection Security Systems Real-Time Monitoring Many-To-Many Publish-Subscribe Point-To-Point Request-Response

10. © 2020 Cloudera, Inc. All rights reserved. 10 New features delivered with Cloudera Streaming Analytics (CSA) 1.2 Next Generation Streaming Analytics Flink SQL Support Agile Streaming App Development using SQL New Flink Atlas Hook Capture operational Flink app metadata and lineage Single View of Flink Yarn Jobs Improve Developer Experience & operational visibility

11. © 2020 Cloudera, Inc. All rights reserved. 11 ● https://www.cloudera.com/tutorials/building-a-sentiment-analysis-application/3.html ● https://blog.cloudera.com/benchmarking-nifi-performance-and-scalability/ ● https://www.datainmotion.dev/2020/06/no-more-spaghetti-flows.html ● https://www.datainmotion.dev/2020/06/the-rise-of-mega-edge-flank.html ● https://www.datainmotion.dev/2020/01/cloudera-edge2ai-minifi-java-agent-with.html ● 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 ● https://www.datainmotion.dev/2020/06/unboxing-most-amazing-edge-ai-device.html ● https://www.datainmotion.dev/2019/08/edge-processing-with-jetson-nano-part-3.html ● https://www.datainmotion.dev/2020/04/predicting-sensor-readings-with-time.html REFERENCES

Fast data for fitness 10 nov 2020

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