Let's build a simple ingest to cloud datawarehouse with low code

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Let's build a simple ingest to cloud datawarehouse with low code From DevOps Stage 2020 Using NiFi to load data warehouse, cloud data, iot, sensors, rest, csv, network, events, logs and all types of data into various sql, nosql, cloud and on premise data stores. principal dataflow field engineer tim spann

Technology

Let's Build a Simple Ingest to Cloud Data
Warehouse With Low Code
Timothy Spann
Principal DataFlow Field Engineer
Cloudera
@PaasDev

3
Future of Data - Princeton (Global Youtube)
@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 ...

4
Speakers
Tim Spann
Principal DataFlow Field Engineer
@PaasDev
DZone Zone Leader and Big Data MVB
Princeton NJ Future of Data Meetup
https://github.com/tspannhw
https://www.datainmotion.dev/

© 2020 Cloudera, Inc. All rights reserved. 5
STORAGE LAYER
prices
CFM REFERENCE ARCHITECTURE
Apache NiFi
Apache Kafka
DATA SYNDICATION
SERVICE BY KAFKA
Kafka Topic
iot
DATA FLOW APPS
POWERED BY NIFI
Apache Impala
Deep Learning & Machine
Learning
MODEL EXECUTION
REST

© 2019 Cloudera, Inc. All rights reserved. 6
Trillions of Messages to SQL Databases and Data Warehouses
PROCESS DELIVER
HASH
MERGE
EXTRACT
DUPLICATE
SPLIT
ROUTE TEXT
ROUTE CONTENT
ROUTE CONTEXT
CONTROL RATE
DISTRIBUTE LOAD
GEOENRICH
SCAN
REPLACE
TRANSLATE
CONVERT
ENCRYPT
TALL
EVALUATE
EXECUTE

7
Reference Architecture
Files to RDBMS
Source Files Read Files Cloudera DataFlow
Pipeline
PutDatabaseRecord or
PutKudu or
PutHbaseRecord or
PutHiveStreaming

© 2019 Cloudera, Inc. All rights reserved. 8
Messages to Databases

‘QueryRecord’ Processor
https://medium.com/@abdelkrim.hadjidj/democratizing-niﬁ-record-processors-with-automatic-schemas-inferenc
e-4f2b2794c427

ADVANCED XML PROCESSING
https://www.datainmotion.dev/2019/03/advanced-xml-processing-with-apache.html
https://pierrevillard.com/2018/06/28/niﬁ-1-7-xml-reader-writer-and-forkrecord-processor/

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Live Demo Jam Expands: The Leading-Edge Streaming Data Platform with NiFi, Kafka, and Flink Timothy Spann Twitter - @PaasDev // Blog: www.datainmotion.dev Frequent speaker at major conferences and events. Principal DataFlow Field Engineer for streaming around Apache NiFi, NiFi Registry, MiNiFi, Kafka, Kafka Connect, Kafka Streams, Flink, Flink SQL, SMM, SRM, SR and EFM. Previously at E&Y, HPE, Pivotal & Hortonworks Question #1 What is the most difficult part of an Edge Flow? Gateway Agent Edge Data Collection Processing Data https://github.com/tspannhw/DemoJam2021 https://github.com/tspannhw/CloudDemo2021

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ApacheCon 2021: Apache NiFi 101- introduction and best practices Thursday 14:10 UTC Apache NiFi 101: Introduction and Best Practices Timothy Spann 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 DZone Zone Leader and Big Data MVB @PaasDev https://github.com/tspannhw https://www.datainmotion.dev/ https://github.com/tspannhw/SpeakerProfile https://dev.to/tspannhw https://sessionize.com/tspann/ https://www.slideshare.net/bunkertor

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ApacheCon 2021 Apache Deep Learning 302 Tuesday 18:00 UTC Apache Deep Learning 302 Timothy Spann This talk will discuss and show examples of using Apache Hadoop, Apache Kudu, Apache Flink, Apache Hive, Apache MXNet, Apache OpenNLP, Apache NiFi and Apache Spark for deep learning applications. This is the follow up to previous talks on Apache Deep Learning 101 and 201 and 301 at ApacheCon, Dataworks Summit, Strata and other events. As part of this talk, the presenter will walk through using Apache MXNet Pre-Built Models, integrating new open source Deep Learning libraries with Python and Java, as well as running real-time AI streams from edge devices to servers utilizing Apache NiFi and Apache NiFi - MiNiFi. This talk is geared towards Data Engineers interested in the basics of architecting Deep Learning pipelines with open source Apache tools in a Big Data environment. The presenter will also walk through source code examples available in github and run the code live on Apache NiFi and Apache Flink clusters. 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. * https://github.com/tspannhw/ApacheDeepLearning302/ * https://github.com/tspannhw/nifi-djl-processor * https://github.com/tspannhw/nifi-djlsentimentanalysis-processor * https://github.com/tspannhw/nifi-djlqa-processor * https://www.linkedin.com/pulse/2021-schedule-tim-spann/

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(VIRTUAL) Hail Hydrate! From Stream to Lake Using Open Source - Timothy J Spann, StreamNative https://osselc21.sched.com/event/lAPi?iframe=no 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. https://osselc21.sched.com/event/lAPi/virtual-hail-hydrate-from-stream-to-lake-using-open-source-timothy-j-spann-streamnative

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FLiP Into Trino FLiP into Trino. Flink Pulsar Trino Pulsar SQL (Trino/Presto) Remember the days when you could wait until your batch data load was done and then you could run some simple queries or build stale dashboards? Those days are over, today you need instant analytics as the data is streaming in real-time. You need universal analytics where that data is. I will show you how to do this utilizing the latest cloud native open source tools. In this talk we will utilize Trino, Apache Pulsar, Pulsar SQL and Apache Flink to analyze instantly data from IoT, sensors, transportation systems, Logs, REST endpoints, 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 how to use Pulsar SQL to run analytics on live data. Tim Spann Developer Advocate StreamNative David Kjerrumgaard Developer Advocate StreamNative https://www.starburst.io/info/trinosummit/ https://github.com/tspannhw/FLiP-Into-Trino/blob/main/README.md https://github.com/tspannhw/StreamingAnalyticsUsingFlinkSQL/tree/main/src/main/java select * from pulsar."public/default"."weather"; Apache Pulsar plus Trio = fast analytics at scale

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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.

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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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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

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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?

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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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CoC23_Utilizing Real-Time Transit Data for Travel Optimization @PaasDev www.datainmotion.dev github.com/tspannhw medium.com/@tspann Principal Developer Advocate Princeton Future of Data Meetup ex-Pivotal, ex-Hortonworks, ex-StreamNative, ex-PwC, ex-EY, ex-HPE. Apache NiFi x Apache Kafka x Apache Flink There are a lot of factors involved in determining how you can find our way around and avoid delays, bad weather,dangers and expenses. In this talk I will focus on public transport in the largest transit system in the United States, the MTA, which is focused around New York City. Utilizing public and semi-public data feeds, this can be extended to most city and metropolitan areas around the world. As a personal example, I live in New Jersey and this is an extremely useful use of open source and public data. Once I am notified that I need to travel to Manhattan, I need to start my data streams flowing. Most of the data sources are REST feeds that are ingested by Apache NiFi to transform, convert, enrich and finalize it for usage in streaming tables with Flink SQL, but also keep that same contract with Kafka consumers, Iceberg tables and other users of this data. I do not need to many user interfaces to interopt with the system as I want my final decision sent in a Slack message to me and then I’ll get moving. Along the way data will be visible in NiFi lineage, Kafka topic views, Flink SQL output, REST output and Iceberg tables. Apache NiFi, Apache Kafka, Apache OpenNLP, Apache Tika, Apache Flink, Apache Avro, Apache Parquet, Apache Iceberg. https://github.com/tspannhw/FLaNK-MTA/tree/main https://medium.com/@tspann/finding-the-best-way-around-7491c76ca4cb https://medium.com/@tspann/open-source-streaming-talks-in-progress-3e75af8848b0 https://medium.com/@tspann/watching-airport-traffic-in-real-time-32c522a6e386

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Meetup - Brasil - Data In Motion - 2023 September 19 Meetup - Brasil - Data In Motion - 2023 September 19 https://www.meetup.com/cloudera-brasil-meetup/events/296072006/ https://github.com/tspannhw/FLaNK-SaoPauloBrazil Data in Motion: Overview e Novidades do NiFi, Kafka e Flink Apresentador: Tim Spann - Principal DIM Specialist and Developer Advocate Tuesday, September 19, 2023 at 6:30 PM to Tuesday, September 19, 2023 at 9:00 PM BRT Add to calendar WeWork Sala Comercial & Coworking Paulista Av. Paulista, 2537 · São Paulo, SP DZone Zone Leader and Big Data MVB Princeton and NYC Future of Data Meetups ex-Pivotal Field Engineer ex-StreamNative ex-PwC https://github.com/tspannhw https://twitter.com/PaaSDev https://www.datainmotion.dev/ https://medium.com/@tspann Intro to NiFi Intro to Kafka Intro to Flink Together as FLaNK Demos Q&A Apresentador: Tim Spann - Principal DIM Specialist and Developer Advocate

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Effective data discovery is crucial for maintaining compliance and mitigating risks in today's rapidly evolving privacy landscape. However, traditional manual approaches often struggle to keep pace with the growing volume and complexity of data. Join us for an insightful webinar where industry leaders from TrustArc and Privya will share their expertise on leveraging AI-powered solutions to revolutionize data discovery. You'll learn how to: - Effortlessly maintain a comprehensive, up-to-date data inventory - Harness code scanning insights to gain complete visibility into data flows leveraging the advantages of code scanning over DB scanning - Simplify compliance by leveraging Privya's integration with TrustArc - Implement proven strategies to mitigate third-party risks Our panel of experts will discuss real-world case studies and share practical strategies for overcoming common data discovery challenges. They'll also explore the latest trends and innovations in AI-driven data management, and how these technologies can help organizations stay ahead of the curve in an ever-changing privacy landscape.

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Let's build a simple ingest to cloud datawarehouse with low code

1. Let's Build a Simple Ingest to Cloud Data Warehouse With Low Code Timothy Spann Principal DataFlow Field Engineer Cloudera @PaasDev

3. 3 Future of Data - Princeton (Global Youtube) @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 ...

4. 4 Speakers Tim Spann Principal DataFlow Field Engineer @PaasDev DZone Zone Leader and Big Data MVB Princeton NJ Future of Data Meetup https://github.com/tspannhw https://www.datainmotion.dev/

5. © 2020 Cloudera, Inc. All rights reserved. 5 STORAGE LAYER prices CFM REFERENCE ARCHITECTURE Apache NiFi Apache Kafka DATA SYNDICATION SERVICE BY KAFKA Kafka Topic iot DATA FLOW APPS POWERED BY NIFI Apache Impala Deep Learning & Machine Learning MODEL EXECUTION REST

6. © 2019 Cloudera, Inc. All rights reserved. 6 Trillions of Messages to SQL Databases and Data Warehouses PROCESS DELIVER HASH MERGE EXTRACT DUPLICATE SPLIT ROUTE TEXT ROUTE CONTENT ROUTE CONTEXT CONTROL RATE DISTRIBUTE LOAD GEOENRICH SCAN REPLACE TRANSLATE CONVERT ENCRYPT TALL EVALUATE EXECUTE

7. 7 Reference Architecture Files to RDBMS Source Files Read Files Cloudera DataFlow Pipeline PutDatabaseRecord or PutKudu or PutHbaseRecord or PutHiveStreaming

9. ‘QueryRecord’ Processor https://medium.com/@abdelkrim.hadjidj/democratizing-niﬁ-record-processors-with-automatic-schemas-inferenc e-4f2b2794c427

10. ADVANCED XML PROCESSING https://www.datainmotion.dev/2019/03/advanced-xml-processing-with-apache.html https://pierrevillard.com/2018/06/28/niﬁ-1-7-xml-reader-writer-and-forkrecord-processor/

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