Continuous SQL with Apache Streaming (FLaNK and FLiP)
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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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This document provides an overview and summary of Apache Pulsar, a distributed streaming and messaging platform. It discusses Pulsar's benefits like data durability, scalability, geo-replication and multi-tenancy. It outlines key use cases like message queuing and data streaming. The document also summarizes Pulsar's architecture, subscriptions modes, connectors, and integration with other technologies like Apache Flink, Apache NiFi and MQTT. It highlights real-world customer implementations and provides demos of ingesting IoT data via Pulsar.
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This document summarizes Haitao Wang's experience working on streaming platforms at Alibaba and Microsoft. It describes Alibaba's data infrastructure challenges in handling large volumes of streaming data. It introduces Alibaba Blink, a distribution of Apache Flink that was developed to meet Alibaba's scale needs. Blink has achieved unprecedented throughput of 472 million events per second with latency of 10s of milliseconds. The document outlines improvements made in Blink's runtime, declarative SQL support, and use cases at Alibaba including real-time A/B testing, search index building, and online machine learning.
Python web conference 2022 apache pulsar development 101 with python (f li-...
This document provides an overview of using Apache Pulsar for Python development. It discusses Python producers, consumers, and schemas. It also covers connecting Pulsar to other technologies like MQTT, web sockets, and Kafka via Python. Pulsar Functions in Python are demonstrated. Examples of using Python with Pulsar on Raspberry Pi are provided. The document is presented by Tim Spann, a developer advocate at StreamNative, and includes information on his background and StreamNative's training resources.
Learning the basics of Apache NiFi for iot OSS Europe 2020
Learning the basics of Apache NiFi for iot OSS Europe 2020
Best practices in using NiFi, Hue, Impala, Kafka, Flink, CDSW for iot applications
Using apache mx net in production deep learning streaming pipelines
As a Data Engineer I am often tasked with taking Machine Learning and Deep Learning models into production, sometimes in the cloud and sometimes at the edge. I have developed Java code that allows us to run these models at the edge and as part of a sensor/webcam/images/data stream. I have developed custom interfaces in Apache NiFi to enable real-time classification against MXNet models directly through the Java API or through DJL.AI's Java interface. I will demo running models on NVIDIA Jetson Nanos and NVIDIA Xavier NX devices as well as in the cloud.
# Technologies Utilized:
# Apache MXNet, DJL.AI, NVIDIA Jetson Nano, NVIDIA Jetson XAVIER, Apache NiFi, MiNIFi, Java, Python.
Apache flink
Diego Pacheco is the principal software architect at Apache Flink, an open-source streaming dataflow engine for distributed computations over data streams. Flink supports both batch and stream processing with high throughput and low latency using Scala, Java, and Python. It provides capabilities for data distribution, communication, fault tolerance, and machine learning and graph processing using Gelly.
Using FLiP with influxdb for EdgeAI IoT at Scale
Using FLiP with influxdb for EdgeAI IoT at Scale
apache pulsar
influxdb
apache flink
streamnative
apache spark
apache nifi
FLiP(N) stack
Kafka as your Data Lake - is it Feasible? (Guido Schmutz, Trivadis) Kafka Sum...
For a long time we discuss how much data we can keep in Kafka. Can we store data forever or do we remove data after a while and maybe having the history in a data lake on Object Storage or HDFS? With the advent of Tiered Storage in Confluent Enterprise Platform, storing data much longer in Kafka is much very feasible. So can we replace a traditional data lake with just Kafka? Maybe at least for the raw data? But what about accessing the data, for example using SQL?
KSQL allows for processing data in a streaming fashion using an SQL like dialect. But what about reading all data of a topic? You can reset the offset and still use KSQL. But there is another family of products, so-called query engines for Big Data. They originate from the idea of reading Big Data sources such as HDFS, object storage or HBase, using the SQL language. Presto, Apache Drill and Dremio are the most popular solutions in that space. Lately these query engines also added support for Kafka topics as a source of data. With that you can read a topic as a table and join it with information available in other data sources. The idea of course is not real-time streaming analytics but batch analytics directly on the Kafka topic, without having to store it in a big data storage.
This talk answers, how well these tools support Kafka as a data source. What serialization formats do they support? Is there some form of predicate push-down supported or do we have to always read the complete topic? How performant is a query against a topic, compared to a query against the same data sitting in HDFS or an object store? And finally, will this allow us to replace our data lake or at least part of it by Apache Kafka?
Cloud lunch and learn real-time streaming in azure
Cloud lunch and learn real-time streaming in azure
Apache pulsar is an open source, cloud-native distributed messaging and streaming platform.
How Disney+ uses fast data ubiquity to improve the customer experience
Disney+ uses Amazon Kinesis to drive real-time actions like providing title recommendations for customers, sending events across microservices, and delivering logs for operational analytics to improve the customer experience. In this session, you learn how Disney+ built real-time data-driven capabilities on a unified streaming platform. This platform ingests billions of events per hour in Amazon Kinesis Data Streams, processes and analyzes that data in Amazon Kinesis Data Analytics for Apache Flink, and uses Amazon Kinesis Data Firehose to deliver data to destinations without servers or code. Hear how these services helped Disney+ scale its viewing experience to tens of millions of customers with the required quality and reliability.
Learn more about re:Invent 2020 at http://bit.ly/3c4NSdY
ApacheCon 2021: Apache NiFi 101- introduction and best practices
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
What's hot (20)
Incrementally streaming rdbms data to your data lake automagically
Incrementally streaming rdbms data to your data lake automagically
Automation + dev ops summit hail hydrate! from stream to lake
Automation + dev ops summit hail hydrate! from stream to lake
Using the FLaNK Stack for edge ai (flink, nifi, kafka, kudu)
Using the FLaNK Stack for edge ai (flink, nifi, kafka, kudu)
Cracking the nut, solving edge ai with apache tools and frameworks
Cracking the nut, solving edge ai with apache tools and frameworks
Select Star: Flink SQL for Pulsar Folks - Pulsar Summit NA 2021
Select Star: Flink SQL for Pulsar Folks - Pulsar Summit NA 2021
PortoTechHub - Hail Hydrate! From Stream to Lake with Apache Pulsar and Friends
PortoTechHub - Hail Hydrate! From Stream to Lake with Apache Pulsar and Friends
Apache Flink @ Alibaba - Seattle Apache Flink Meetup
Apache Flink @ Alibaba - Seattle Apache Flink Meetup
Python web conference 2022 apache pulsar development 101 with python (f li-...
Python web conference 2022 apache pulsar development 101 with python (f li-...
Learning the basics of Apache NiFi for iot OSS Europe 2020
Learning the basics of Apache NiFi for iot OSS Europe 2020
Using apache mx net in production deep learning streaming pipelines
Using apache mx net in production deep learning streaming pipelines
Kafka as your Data Lake - is it Feasible? (Guido Schmutz, Trivadis) Kafka Sum...
Kafka as your Data Lake - is it Feasible? (Guido Schmutz, Trivadis) Kafka Sum...
Cloud lunch and learn real-time streaming in azure
Cloud lunch and learn real-time streaming in azure
How Disney+ uses fast data ubiquity to improve the customer experience
How Disney+ uses fast data ubiquity to improve the customer experience
ApacheCon 2021: Apache NiFi 101- introduction and best practices
ApacheCon 2021: Apache NiFi 101- introduction and best practices
Similar to Continuous SQL with Apache Streaming (FLaNK and FLiP)
Real time cloud native open source streaming of any data to apache solr
Real time cloud native open source streaming of any data to apache solr
Utilizing Apache Pulsar and Apache NiFi we can parse any document in real-time at scale. We receive a lot of documents via cloud storage, email, social channels and internal document stores. We want to make all the content and metadata to Apache Solr for categorization, full text search, optimization and combination with other datastores. We will not only stream documents, but all REST feeds, logs and IoT data. Once data is produced to Pulsar topics it can instantly be ingested to Solr through Pulsar Solr Sink.
Utilizing a number of open source tools, we have created a real-time scalable any document parsing data flow. We use Apache Tika for Document Processing with real-time language detection, natural language processing with Apache OpenNLP, Sentiment Analysis with Stanford CoreNLP, Spacy and TextBlob. We will walk everyone through creating an open source flow of documents utilizing Apache NiFi as our integration engine. We can convert PDF, Excel and Word to HTML and/or text. We can also extract the text to apply sentiment analysis and NLP categorization to generate additional metadata about our documents. We also will extract and parse images that if they contain text we can extract with TensorFlow and Tesseract.
FLiP Into Trino
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
DATA SUMMIT 24 Building Real-Time Pipelines With FLaNK
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
Using the flipn stack for edge ai (flink, nifi, pulsar)
The document summarizes a presentation about using the FLiPN stack (Flink, NiFi, Pulsar) for edge AI. It discusses the key components - Apache Flink for stream processing, Apache Pulsar for messaging and streaming, and Apache NiFi for dataflow. It provides an overview of their features and benefits. It also demonstrates integrating these technologies with edge devices like NVIDIA Jetson boards and deploying the streaming pipelines to StreamNative Cloud.
Using the FLiPN Stack for Edge AI (Flink, NiFi, Pulsar) - Pulsar Summit Asia ...
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.
FLiPN provides a quick set of tools to build applications at any scale for any streaming and IoT use cases.
Tools
Apache Flink, Apache Pulsar, Apache NiFi, MiNiFi, Apache MXNet, DJL.AI
References
https://www.datainmotion.dev/2019/08/...
https://www.datainmotion.dev/2019/09/...
https://www.datainmotion.dev/2019/05/...
https://www.datainmotion.dev/2019/03/...
Get the presentation slides: https://www.slideshare.net/streamnati...
Subscribe to the StreamNative Newsletter for Apache Pulsar for more Pulsar content: https://share.hsforms.com/1IS56E-RvSV...
Get started with the on-demand Pulsar training by StreamNative Academy: https://www.academy.streamnative.io/
Flink in action
Artsem Semianenko (Adform) - "Flink in action или как приручить белочку"
Slides for presentation: https://www.youtube.com/watch?v=YSI5_RFlcPE
Source: https://github.com/art4ul/flink-demo
ApacheCon 2021 - Apache NiFi Deep Dive 300
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.
Ai dev world utilizing apache pulsar, apache ni fi and minifi for edgeai io...
Ai dev world utilizing apache pulsar, apache ni fi and minifi for edgeai iot at scale
streamnative
apache pulsar
Hail hydrate! from stream to lake using open source
(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
Tracking Crime as It Occurs with Apache Phoenix, Apache HBase and Apache NiFi
Utilizing Apache NiFi we read various open data REST APIs and camera feeds to ingest crime and related data real-time streaming it into HBase and Phoenix tables. HBase makes an excellent storage option for our real-time time series data sources. We can immediately query our data utilizing Apache Zeppelin against Phoenix tables as well as Hive external tables to HBase.
Apache Phoenix tables also make a great option since we can easily put microservices on top of them for application usage. I have an example Spring Boot application that reads from our Philadelphia crime table for front-end web applications as well as RESTful APIs.
Apache NiFi makes it easy to push records with schemas to HBase and insert into Phoenix SQL tables.
Resources:
https://community.hortonworks.com/articles/54947/reading-opendata-json-and-storing-into-phoenix-tab.html
https://community.hortonworks.com/articles/56642/creating-a-spring-boot-java-8-microservice-to-read.html
https://community.hortonworks.com/articles/64122/incrementally-streaming-rdbms-data-to-your-hadoop.html
Workshop híbrido: Stream Processing con Flink
El Stream processing es un requisito previo de la pila de data streaming, que impulsa aplicaciones y pipelines en tiempo real.
Permite una mayor portabilidad de datos, una utilización optimizada de recursos y una mejor experiencia del cliente al procesar flujos de datos en tiempo real.
En nuestro taller práctico híbrido, aprenderás cómo filtrar, unir y enriquecer fácilmente datos en tiempo real dentro de Confluent Cloud utilizando nuestro servicio Flink sin servidor.
Large scale stream processing with Apache Flink
In today’s world it’s no longer enough to build systems that process big volumes of information. We now need applications that can handle large continuous streams of data with very low latency so we can react to the ever-changing environment around us. To efficiently handle such problems we need to deploy a stream processing solution. During the talk we’ll explore one of the most popular frameworks for stream processing – Apache Flink. We’ll see what unique capabilities it provides and how they apply to some real world problems. And we’ll also explore how it works under the hood and how to get the scalable and fault-tolerant stream processing that Flink provides.
K. Tzoumas & S. Ewen – Flink Forward Keynote
This document provides information about the first conference on Apache Flink. It summarizes key aspects of the Apache Flink streaming engine, including its improved DataStream API, support for event time processing, high availability, and integration of batch and streaming capabilities. The document also outlines Flink's progress towards version 1.0, which will focus on defining public APIs and backwards compatibility, and outlines future plans such as enhancing usability features on top of the DataStream API.
Apache Kafka and API Management / API Gateway – Friends, Enemies or Frenemies...
Microservices became the new black in enterprise architectures. APIs provide functions to other applications or end users. Even if your architecture uses another pattern than microservices, like SOA (Service-Oriented Architecture) or Client-Server communication, APIs are used between the different applications and end users.
Apache Kafka plays a key role in modern microservice architectures to build open, scalable, flexible and decoupled real time applications. API Management complements Kafka by providing a way to implement and govern the full life cycle of the APIs.
This session explores how event streaming with Apache Kafka and API Management (including API Gateway and Service Mesh technologies) complement and compete with each other depending on the use case and point of view of the project team. The session concludes exploring the vision of event streaming APIs instead of RPC calls.
Apache Kafka and API Management / API Gateway – Friends, Enemies or Frenemies?
Microservices became the new black in enterprise architectures. APIs provide functions to other applications or end users. Even if your architecture uses another pattern than microservices, like SOA (Service-Oriented Architecture) or Client-Server communication, APIs are used between the different applications and end users.
Apache Kafka plays a key role in modern microservice architectures to build open, scalable, flexible and decoupled real time applications. API Management complements Kafka by providing a way to implement and govern the full life cycle of the APIs.
This session explores how event streaming with Apache Kafka and API Management (including API Gateway and Service Mesh technologies) complement and compete with each other depending on the use case and point of view of the project team. The session concludes exploring the vision of event streaming APIs instead of RPC calls.
Understand how event streaming with Kafka and Confluent complements tools and frameworks such as Kong, Mulesoft, Apigee, Envoy, Istio, Linkerd, Software AG, TIBCO Mashery, IBM, Axway, etc.
A Streaming API Data Exchange provides streaming replication between business units and companies. API Management with REST/HTTP is not appropriate for streaming data.
Scylla Summit 2022: FLiP Into Apache Pulsar Apps with ScyllaDB
In this session, Timothy Spann, Developer Advocate at StreamNative will introduce you to the world of Apache Pulsar and how to build real-time messaging and streaming applications with a variety of OSS libraries, schemas, languages, frameworks, and tools against ScyllaDB. He 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. He will share instructions on the few steps you need to get an environment ready to start building awesome apps. You will also learn how to quickly deploy an app to a production cloud cluster with StreamNative.
To watch all of the recordings hosted during Scylla Summit 2022 visit our website here: https://www.scylladb.com/summit.
StreamNative FLiP into scylladb - scylla summit 2022
StreamNative FLiP into scylladb - scylla summit 2022
Utilizing Apache Pulsar with Apache NiFi, Apache Flink, Apache Spark and Scylla for fast IoT application with MQTT and beyond.
Apache NiFi Crash Course - San Jose Hadoop Summit
This document provides an overview of Apache NiFi and dataflow. It begins with defining what dataflow is and the challenges of moving data effectively. It then introduces Apache NiFi, describing its key features like guaranteed delivery, data buffering, prioritized queuing, and data provenance. The document discusses NiFi's architecture including its use of FlowFiles to move data agnostically through processors. It also covers NiFi's extension points and integration with other systems. Finally, it describes a live demo use case of using NiFi to integrate real-time traffic data for urban planning.
Real time stock processing with apache nifi, apache flink and apache kafka
Real time stock processing with apache nifi, apache flink and apache kafka with Kafka Connect apps, SMM, NiFi Registry, Scheam Registry, Kafka topics, Flink SQL, NiFi
Using FLiP with InfluxDB for EdgeAI IoT at Scale 2022
Using FLiP with InfluxDB for EdgeAI IoT at Scale 2022
https://adtmag.com/webcasts/2021/12/influxdata-february-10.aspx?tc=page0
Using FLiP with InfluxDB for EdgeAI IoT at Scale
Date: Thursday, February 10th at 11am PT / 2pm ET
Join this webcast as Timothy from StreamNative takes you on a hands-on deep-dive using Pulsar, 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.
The team runs deep learning models on the edge devices, sends images, and captures real-time GPS and sensor data. Their low-coding IoT applications provide easy edge routing, transformation, data acquisition and alerting before they decide what data to stream in real-time to their data space. These edge applications classify images and sensor readings in real-time at the edge and then send Deep Learning results to Flink SQL and Apache NiFi for transformation, parsing, enrichment, querying, filtering and merging data to InfluxDB.
In this session you will learn how to:
Build an end-to-end streaming edge app
Pull messages from Pulsar topics and persists the messages to InfluxDB
Build a data stream for IoT with NiFi and InfluxDB
Use Apache Flink + Apache Pulsar
Timothy Spann, Developer Advocate, StreamNative
Tim Spann is a Developer Advocate at StreamNative where he works with Apache NiFi, MiniFi, Kafka, 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 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.
Similar to Continuous SQL with Apache Streaming (FLaNK and FLiP) (20)
Real time cloud native open source streaming of any data to apache solr
Real time cloud native open source streaming of any data to apache solr
DATA SUMMIT 24 Building Real-Time Pipelines With FLaNK
DATA SUMMIT 24 Building Real-Time Pipelines With FLaNK
Using the flipn stack for edge ai (flink, nifi, pulsar)
Using the flipn stack for edge ai (flink, nifi, pulsar)
Using the FLiPN Stack for Edge AI (Flink, NiFi, Pulsar) - Pulsar Summit Asia ...
Using the FLiPN Stack for Edge AI (Flink, NiFi, Pulsar) - Pulsar Summit Asia ...
Ai dev world utilizing apache pulsar, apache ni fi and minifi for edgeai io...
Ai dev world utilizing apache pulsar, apache ni fi and minifi for edgeai io...
Hail hydrate! from stream to lake using open source
Hail hydrate! from stream to lake using open source
Tracking Crime as It Occurs with Apache Phoenix, Apache HBase and Apache NiFi
Tracking Crime as It Occurs with Apache Phoenix, Apache HBase and Apache NiFi
Apache Kafka and API Management / API Gateway – Friends, Enemies or Frenemies...
Apache Kafka and API Management / API Gateway – Friends, Enemies or Frenemies...
Apache Kafka and API Management / API Gateway – Friends, Enemies or Frenemies?
Apache Kafka and API Management / API Gateway – Friends, Enemies or Frenemies?
Scylla Summit 2022: FLiP Into Apache Pulsar Apps with ScyllaDB
Scylla Summit 2022: FLiP Into Apache Pulsar Apps with ScyllaDB
StreamNative FLiP into scylladb - scylla summit 2022
StreamNative FLiP into scylladb - scylla summit 2022
Real time stock processing with apache nifi, apache flink and apache kafka
Real time stock processing with apache nifi, apache flink and apache kafka
Using FLiP with InfluxDB for EdgeAI IoT at Scale 2022
Using FLiP with InfluxDB for EdgeAI IoT at Scale 2022
More from Timothy Spann
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
by
Timothy Spann
Principal Developer Advocate
https://budapestdata.hu/2024/en/
https://budapestml.hu/2024/en/
tim.spann@zilliz.com
https://www.linkedin.com/in/timothyspann/
https://x.com/paasdev
https://github.com/tspannhw
https://www.youtube.com/@flank-stack
milvus
vector database
gen ai
generative ai
deep learning
machine learning
apache nifi
apache pulsar
apache kafka
apache flink
DSSML24_tspann_CodelessGenerativeAIPipelines
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Generative AI on Enterprise Cloud with NiFi and Milvus
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/
April 2024 - NLIT Cloudera Real-Time LLM Streaming 2024
https://www.fbcinc.com/e/nlit/agenda.aspx
Cloudera booth
data in motion
tim spann
seattle
April 2024
Real-Time AI Streaming - AI Max Princeton
AI Max Conference Princeton
https://www.startupgrind.com/events/details/startup-grind-princeton-presents-startup-grind-hosts-ai-max-summit/
Conf42-LLM_Adding Generative AI to Real-Time Streaming Pipelines
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
2024 XTREMEJ_ Building Real-time Pipelines with FLaNK_ A Case Study with Tra...
2024 XTREMEJ_ Building Real-time Pipelines with FLaNK_ A Case Study with Transit Data
https://xtremej.dev/2023/schedule/
Building Real-time Pipelines with FLaNK: A Case Study with Transit Data
Overview of the problem, the application (code walkthru and running), overview of FLaNK, introduction to NiFi, introduction to Kafka, and introduction to Flink.
28March2024-Codeless-Generative-AI-Pipelines
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.
TCFPro24 Building Real-Time Generative AI Pipelines
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.
2024 Build Generative AI for Non-Profits
2024_BuildGenerativeAIforNon-Profits
https://www.meetup.com/futureofdata-princeton/events/299515503/
https://www.meetup.com/applied-generative-artificial-intelligence-applications/events/299486639/
LLM
2024 February 28 - NYC - Meetup Unlocking Financial Data with Real-Time Pipel...
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/
Conf42-Python-Building Apache NiFi 2.0 Python Processors
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.
Conf42Python -Using Apache NiFi, Apache Kafka, RisingWave, and Apache Iceberg...
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
2024 Feb AI Meetup NYC GenAI_LLMs_ML_Data Codeless Generative AI Pipelines
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
DBA Fundamentals Group: Continuous SQL with Kafka and Flink
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.
NY Open Source Data Meetup Feb 8 2024 Building Real-time Pipelines with FLaNK...
NY Open Source Data Meetup Feb 8 2024 Building Real-time Pipelines with FLaNK_ A Case Study with Transit Data
OSACon 2023_ Unlocking Financial Data with Real-Time Pipelines
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
Building Real-Time Travel Alerts
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.
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Apache Kafka
Apache NiFi
FLaNK Stack
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Could you use a simple piece of Scala validation code (granted, a very simplistic one too!) that you can rewrite, now and again, to refresh your basic understanding of Applicative operators <*>, <*, *>?
The goal is not to write perfect code showcasing validation, but rather, to provide a small, rough-and ready exercise to reinforce your muscle-memory.
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Salesforce Healthcare CRM, implemented by VALiNTRY360, revolutionizes patient management by enhancing patient engagement, streamlining administrative processes, and improving care coordination. Its advanced analytics, robust security, and seamless integration with telehealth services ensure that healthcare providers can deliver personalized, efficient, and secure patient care. By automating routine tasks and providing actionable insights, Salesforce Healthcare CRM enables healthcare providers to focus on delivering high-quality care, leading to better patient outcomes and higher satisfaction. VALiNTRY360's expertise ensures a tailored solution that meets the unique needs of any healthcare practice, from small clinics to large hospital systems.
For more info visit us https://valintry360.com/solutions/health-life-sciences
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- 14. streamnative.io Connect with the Community & Stay Up-To-Date ● Join the Pulsar Slack channel - Apache-Pulsar.slack.com ● Follow @streamnativeio and @apache_pulsar on Twitter ● Subscribe to Monthly Pulsar Newsletter for major news, events, project updates, and resources in the Pulsar community
- 15. streamnative.io ● https://github.com/tspannhw/StreamingSQLExamples ● https://github.com/streamnative/pulsar-flink ● https://www.linkedin.com/pulse/2021-schedule-tim-spann/ ● https://github.com/tspannhw/SpeakerProfile/blob/main/2021/talks/20210729_HailHydrate!FromStream toLake_TimSpann.pdf ● https://streamnative.io/en/blog/release/2021-04-20-flink-sql-on-streamnative-cloud ● https://docs.streamnative.io/cloud/stable/compute/flink-sql Deeper Content @PaasDev https://www.pulsardeveloper.com/ timothyspann
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