Timothy Spann
Principal Developer Advocate for Milvus @ Zilliz
Princeton, New Jersey, United States
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Tim Spann is the Principal Developer Advocate for Milvus @ Zilliz where he works with Milvus, Towhee, Attu, Generative AI, ML, Hugging Face, 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 Principal Developer Advocate at Cloudera, 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://medium.com/@tspann
https://www.youtube.com/@FLaNK-Stack
https://www.datainmotion.dev/p/about-me.html
https://dzone.com/users/297029/bunkertor.html
Awards
Area of Expertise
Topics
Adding Generative AI to Real-Time Streaming Pipelines
In this talk I walk through various use cases where bringing real-time data to LLM solves some interesting problems.
In one case we use Apache NiFi to provide a live chat between a person in Slack and several LLM models all orchestrated via NiFi and Kafka. In another case NiFi ingests live travel data and feeds it to HuggingFace and OLLAMA LLM models for summarization. I also do live chatbot. We also augment LLM prompts and results with live data streams. All with ASF projects. I call this pattern FLaNK AI.
https://github.com/tspannhw/FLaNK-HuggingFace-BLOOM-LLM
https://medium.com/@tspann/mixtral-generative-sparse-mixture-of-experts-in-dataflows-59744f7d28a9
https://medium.com/@tspann/building-an-llm-bot-for-meetups-and-conference-interactivity-c211ea6e3b61
Building Real-time Pipelines with FLaNK: A Case Study with Transit Data
In this session, we will explore the powerful combination of Apache Flink, Apache NiFi, and Apache Kafka for building real-time data processing pipelines. We will present a case study using the FLaNK-MTA project, which leverages these technologies to process and analyze real-time data from the New York City Metropolitan Transportation Authority (MTA). By integrating Flink, NiFi, and Kafka, FLaNK-MTA demonstrates how to efficiently collect, transform, and analyze high-volume data streams, enabling timely insights and decision-making.
Takeaways:
Understanding the integration of Apache Flink, Apache NiFi, and Apache Kafka for real-time data processing
Insights into building scalable and fault-tolerant data processing pipelines
Best practices for data collection, transformation, and analytics with FLaNK-MTA as a reference
Knowledge of use cases and potential business impact of real-time data processing 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 and performance optimization techniques.
Conclusion: In this talk, we will demonstrate the power of combining Apache Flink, Apache NiFi, Apache Kafka, and Iceberg to unlock financial data's true potential. Attendees will gain insights into how these technologies can empower financial institutions to make informed decisions, respond to market changes swiftly, and comply with regulations effectively. Join us to explore the world of real-time data pipelines and revolutionize financial data management.
Empowering IoT with Real-time Stream Processing: Flink, NiFi, and Pulsar
The rapid growth of the Internet of Things (IoT) has generated an enormous volume of data that organizations must harness to gain valuable insights and drive actionable outcomes. To address the challenges of processing IoT data at scale, this talk proposal aims to explore the powerful combination of Apache Flink, Apache NiFi, and Apache Pulsar. We will delve into how these cutting-edge technologies can empower IoT applications with real-time stream processing, seamless data integration, and reliable message queuing.
Building a Full Lifecycle Streaming Data Pipeline
In this talk, we will delve into the process of building a full lifecycle streaming data pipeline using Apache Airflow, Apache Kafka, and Apache Iceberg. We will cover the key features and capabilities of each tool, and demonstrate how they can be integrated to create a robust and efficient pipeline for handling real-time streaming data.
By combining the power of Apache Kafka, Apache Airflow, Apache NiFi and Apache Iceberg, developers can build a full lifecycle streaming data pipeline that is capable of efficiently handling real-time data at scale. This talk will provide a comprehensive overview of how to utilize these tools to build a reliable and effective streaming data pipeline.
Building a Real-Time IoT Application with Apache Pulsar and Apache Pinot
We will walk step-by-step with live code and demos on how to build a real-time IoT application with Pinot + Pulsar.
First, we stream sensor data from an edge device monitoring location conditions to Pulsar via a Python application.
We have our Apache Pinot "realtime" table connected to Pulsar via the pinot-pulsar stream ingestion connector.
Our data streams into the stream, and we visualize it with Superset.
https://medium.com/@tspann/building-a-real-time-iot-application-with-apache-pulsar-and-apache-pinot-1e3baf8c1824
Source Code
https://github.com/tspannhw/pulsar-thermal-pinot
Reference
https://docs.pinot.apache.org/basics/data-import/pinot-stream-ingestion/apache-pulsar
https://dev.startree.ai/docs/pinot/recipes/pulsar
Sink Your Teeth into Streaming at Any Scale
Using the low-latency Apache Pulsar we can build up millions of streams of concurrent data and join them in real time with Apache Flink. We need an ultra-low latency database that can support these workloads to build next-generation IoT, financial and instant analytical transit applications
By sinking data into ScyllaDB we enable amazingly fast applications that can grow to any size and join with existing data sources.
The next generation of apps is being built now, you must choose the right low-latency scalable platform for these massively data-intensive applications. ScyllaDB + Pulsar + Flink is that platform. Choose Open, Choose Fast, and Make the right choice.
Building Modern Data Streaming Apps
In my session, I will show you some best practices I have discovered over the last 7 years in building data streaming applications including IoT, CDC, Logs, and more.
In my modern approach, we utilize several open-source frameworks to maximize the best features of all. We often start with Apache NiFi as the orchestrator of streams flowing into Apache Pulsar. From there we build streaming ETL with Apache Spark and enhance events with Pulsar Functions for ML and enrichment. We build continuous queries against our topics with Flink SQL. We will stream data into ScyllaDB.
We use the best streaming tools for the current applications with FLiPN and FLaNK. https://www.flipn.app/
Deploying Machine Learning Models with Pulsar Functions
In this talk I will present a technique for deploying machine learning models to provide real-time predictions using Apache Pulsar Functions. In order to provide a prediction in real-time, the model usually receives a single data point from the caller, and is expected to provide an accurate prediction within a few milliseconds.
Throughout this talk, I will demonstrate the steps required to deploy a fully-trained ML that predicts the delivery time for a food delivery service based upon real-time traffic information, the customer's location, and the restaurant that will be fulfilling the order.
Architecting Your First Event Driven Serverless Streaming Applications
Once you have built a topic in Apache Pulsar, you will quickly see the need to build event-driven applications. This can require a lot of decisions on what framework to use, where to run it, how to deploy it, and how to manage these applications.
I will walk you through step-by-step in building Pulsar Functions which is the easy way to design, test, develop, integrate, deploy, monitor, and manage serverless streaming applications in Java and Python.
Together we will build a full application as an Apache Pulsar function and enjoy the power of running it in the cloud for IoT events and add any routing, transformation, or machine learning that we need to accomplish our business requirements.
BUILD ML ENHANCED EVENT STREAMING APPLICATIONS WITH JAVA MICROSERVICES
In this talk we will walk through how to build event streaming applications as functions running in with cloud native messaging via Apache Pulsar that run on near infinite scale in any cloud, docker or K8. We will show you have to deploy ML functions to transform real-time data for IoT, Streaming Analytics and many other use cases. After this talk you will be able to build Java microservices with ease and deploy them anywhere utilizing the open source unified streaming and messaging platform, Apache Pulsar. Finally, we will show you have to add dashboards with Web Sockets, no code data sinks, integrate with Apache NiFi data pipelines, SQL Reports with Apache Spark and finally continuous ETL with Apache Flink. I have built many of these applications for many organizations as part of the FLiPN Stack. Let's build next generation applications today regardless if your data is REST APIs, Sensors, Logs, NoSQL Sources, Events or Database tables.
https://github.com/tspannhw?tab=repositories&q=FLiP&type=source
Building FLiPN Stack Edge AI Applications
Introducing the FLiPN stack which combines Apache Flink, Apache NiFi, Apache Pulsar and other Apache tools to build fast applications for IoT, AI, rapid ingest with Java, C#, Python or Golang.
FLiPN provides a quick set of tools to build applications at any scale for any streaming and IoT use cases.
Apache Pulsar enables Java applications to communicate asynchronously at any scale, geo-replicate and interact with non JVM applications. Pulsar also acts as a function mesh to run Java functions as a FaaS triggered by Events. All of this is open source and includes an integrated Schema Registry with support for JSON, Avro, Text and ProtoBuf schemas.
Tools
Java, Golang, Python, C#, Apache Flink, Apache Pulsar, Apache NiFi, MiNiFi, Apache MXNet, DJL.AI
References
https://streamnative.io/blog/engineering/2021-11-17-building-edge-applications-with-apache-pulsar/
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/2021/11/producing-and-consuming-pulsar-messages.html
Apache Pulsar Development 101 with Python
In this session I will get you started with real-time cloud native streaming programming with Python.
We will start off with a gentle introduction to Apache Pulsar and setting up your first easy standalone cluster. We will then l show you how to produce and consume message to Pulsar using several different Python libraries including Python client, websockets, MQTT and even Kafka.
After this session you will building real-time streaming and messaging applications with Python.
Ingesting Data at Scale into Elasticsearch with Apache Pulsar
One of the best things about Elasticsearch is its ability to handle large amounts of data and serve this data with sub-millisecond latency, which makes it an ideal platform to run analytics workloads. But like any purpose-built database, there are always trade-offs to consider. Elasticsearch's case is how to load the data continuously and at scale. A way to solve this problem is by using a buffer layer that can store and forward events to Elasticsearch. Apache Pulsar provides a great alternative to implement this layer.
This talk will explain how Pulsar can implement data ingestion, validation, aggregation, and storage and push this data to Elasticsearch using the sink connector. It will provide the necessary knowledge for you to ingest any data of data, such as logs, sensor data, and streaming events into Elasticsearch for analytics and visualization.
FLiP Into Apache Pulsar Apps with MongoDB
In this session, I will introduce you to the world of Apache Pulsar and how to build real-time messaging and streaming application with a variety of OSS libraries, schemas, languages, frameworks and tools against MongoDB. We will show you all the options from MQTT, Web Sockets, Java, Golang, Python, NodeJS, Apache NiFi, Kafka on Pulsar, Pulsar protocol and more. You will FLiP your lid on how much you learn in a short time. I will send out instructions on the few steps you need to get an environment ready to start building awesome apps. We'll also show you how to quickly deploy an app to a production cloud cluster with StreamNative.
Utilizing Apache Kafka, Apache NiFi and MiNiFi for EdgeAI IoT at Scale
A hands-on deep dive on using Apache Kafka, Kafka Streams, Apache NiFi + Edge Flow Manager + MiniFi Agents with Apache MXNet, OpenVino, TensorFlow Lite, and other Deep Learning Libraries on the actual edge devices including Raspberry Pi with Movidius 2, Google Coral TPU and NVidia Jetson Nano. We run deep learning models on the edge devices and send images, capture real-time GPS and sensor data. With our low coding IoT applications providing easy edge routing, transformation, data acquisition and alerting before we decide what data to stream real-time to our data space. These edge applications classify images and sensor readings real-time at the edge and then send Deep Learning results to Kafka Streams and Apache NiFi for transformation, parsing, enrichment, querying, filtering and merging data to various Apache data stores including Apache Kudu and Apache HBase.
https://www.datainmotion.dev/2019/08/updating-machine-learning-models-at.html
Using Apache NiFi with Apache Pulsar for Fast Data On-Ramp
As the Pulsar communities grows, more and more connectors will be added. To enhance the availability of sources and sinks and to make use of the greater Apache Streaming community, joining forces between Apache NiFi and Apache Pulsar is a perfect fit. Apache NiFi also adds the benefits of ELT, ETL, data crunching, transformation, validation and batch data processing. Once data is ready to be an event, NiFi can launch it into Pulsar at light speed.
I will walk through how to get started, some use cases and demos and answer questions.
Hail Hydrate! From Stream to Lake with Pulsar and Friends
A cloud data lake that is empty is not useful to anyone.
How can you quickly, scalably and reliably fill your cloud data lake with diverse sources of data you already have and new ones you never imagined you needed. Utilizing open source tools from Apache, the FLiP stack enables any data engineer, programmer or analyst to build reusable modules with low or no code. FLiP utilizes Apache NiFi, Apache Pulsar, Apache Flink and MiNiFi agents to load CDC, Logs, REST, XML, Images, PDFs, Documents, Text, semistructured data, unstructured data, structured data and a hundred data sources you could never dream of streaming before.
I will teach you how to fish in the deep end of the lake and return a data engineering hero. Let's hope everyone is ready to go from 0 to Petabyte hero.
FLiP Stack for Cloud Data Lakes
Utilizing an all Apache stack for Rapid Data Lake Population and querying utilizing Apache Flink, Apache Pulsar and Apache NiFi.
We can quickly stream data to and from any datalake, data lake house, lakehouse, database or any datamart regardless of cloud or size. FLiP allows for Java and Python developers to build scalable solutions that span messaging and streaming in cloud native fashion with full monitoring.
Apache Pulsar with MQTT for Edge Computing
Today we will span from edge to any and all clouds to support data collection, real-time streaming, sensor ingest, edge computing, IoT use cases and edge AI. Apache Pulsar allows us to build computing at the edge and produce and consume messages at scale in any IoT, hybrid or cloud environment. Apache Pulsar supports MoP which allows for MQTT protocol to be used for high speed messaging.
We will teach you to quickly build scalable open source streaming applications regardless of if you are running in containers, pods, edge devices, VMs, on-premise servers, moving vehicles and any cloud.
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.
Apache NiFi 101: Introduction and Best Practices
https://www.datainmotion.dev/2020/06/no-more-spaghetti-flows.html
https://github.com/tspannhw/EverythingApacheNiFi
https://www.datainmotion.dev/2020/12/basic-understanding-of-cloudera-flow.html
https://www.datainmotion.dev/2020/10/top-25-use-cases-of-cloudera-flow.html
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.
I will cover:
Terminology
Flow Files
Version Control
Repositories
Basic Record Processing
Provenance
Backpressure
Prioritizers
System Diagnostics
Processors
Process Groups
Scheduling and Cron
Bulletin Board
Relationships
Routing
Tasks
Networking
Basic Cluster Architecture
Listeners
Controller Services
Remote Ports
Handling Errors
Funnels
Real-Time Streaming in Any and All Clouds, Hybrid and Beyond
Description
Today, data is being generated from devices and containers living at the edge of networks, clouds and data centers. We need to run business logic, analytics and deep learning at the scale and as events arrive.
Tools:
Apache Flink, Apache Kafka, Apache NiFi, MiNiFi, DJL.ai Apache MXNet.
References:
https://www.datainmotion.dev/2019/11/introducing-mm-flank-apache-flink-stack.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
Source Code: https://github.com/tspannhw/MmFLaNK
Tags
AI + Machine Learning Databases Developer Tools Hybrid Integration Internet of Things
Real-Time Streaming in Azure
Today, data is being generated from devices and containers living at the edge of networks, clouds and data centers. We need to run business logic, analytics and deep learning at the scale and as events arrive.
Tools:
Apache Flink, Apache Kafka, Apache NiFi, MiNiFi, DJL.ai Apache MXNet.
References:
https://www.datainmotion.dev/2019/11/introducing-mm-flank-apache-flink-stack.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
Source Code: https://github.com/tspannhw/MmFLaNK
Pack Your Bags, We’re Going on a Data Journey!
This three-hour workshop is aimed at organizations who have (or are about to) embark(ed) on their data journey, and are looking for guidance on best practices, tools, and recommendations on navigating through the full data science lifecycle from collection to visualization.
Participants will be exposed to a variety of speakers and data experts to illuminate the critical elements that go into making their data journey a success. The session will kick off with a keynote speaker that will provide an overview of the data journey, followed by a hands-on demonstration highlighting the various personas needed in a data team participating in this journey. The demo will also showcase some of the open-source tools used by experts in the field, while using datasets and use cases relevant to nonprofits. Finally, participants will rotate between breakout sessions to further explore each of these tools and personas, and to give them an opportunity to speak with data specialists who can help address their specific data questions and challenges.
Participants will leave this interactive workshop armed with a stronger understanding and a roadmap to embark on their data journey successfully. We will also be incorporating best practices and learnings from our successful workshop at NetHope 2019.
Cracking the Nut, Solving Edge AI with Apache Tools and Frameworks
Today, data is being generated from devices and containers living at the edge of networks, clouds and data centers. We need to run business logic, analytics and deep learning at the edge before we start our real-time streaming flows. Fortunately using the all Apache Mm FLaNK stack we can do this with ease! Streaming AI Powered Analytics From the Edge to the Data Center is now a simple use case. With MiNiFi we can ingest the data, do data checks, cleansing, run machine learning and deep learning models and route our data in real-time to Apache NiFi and/or Apache Kafka for further transformations and processing. Apache Flink will provide our advanced streaming capabilities fed real-time via Apache Kafka topics. Apache MXNet models will run both at the edge and in our data centers via Apache NiFi and MiNiFi. Our final data will be stored in Apache Kudu via Apache NiFi for final SQL analytics.
Tools:
Apache Flink, Apache Kafka, Apache NiFi, MiNiFi, DJL.ai Apache MXNet, Apache Kudu, Apache Impala, Apache HDFS
References:
https://www.datainmotion.dev/2019/11/introducing-mm-flank-apache-flink-stack.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
Source Code: https://github.com/tspannhw/MmFLaNK
Using the Mm FLaNK Stack for Edge AI (Flink, NiFi, Kafka, Kudu)
Introducing 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
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