Database ingest with Apache NiFi and MiNiFi
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This document discusses data ingestion using Apache NiFi and MiNiFi. Apache NiFi is a dataflow system that allows for reliable and secure transfer of data between systems. It is used for ingesting data from sources into analytic platforms and preparing data through actions like format conversion and parsing. Apache MiNiFi is a subproject that deploys smaller agents to collect data at the edge and push it to NiFi in the data center. The document provides an overview of NiFi's capabilities like guaranteed delivery, buffering, security, and clustering support.
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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.
Timothy Spann
Principal Developer Advocate for Data in Motion @ Cloudera
OSSNA Building Modern Data Streaming Apps
OSSNA
Building Modern Data Streaming Apps
https://ossna2023.sched.com/event/1Jt05/virtual-building-modern-data-streaming-apps-with-open-source-timothy-spann-streamnative
Timothy Spann
Cloudera
Principal Developer Advocate
Data in Motion
In my session, I will show you some best practices I have discovered over the last seven years in building data streaming applications, including IoT, CDC, Logs, and more. In my modern approach, we utilize several open-source frameworks to maximize all the best features. 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 make continuous queries against our topics with Flink SQL. We will stream data into various open-source data stores, including Apache Iceberg, Apache Pinot, and others. We use the best streaming tools for the current applications with the open source stack - FLiPN. https://www.flipn.app/ Updates: This will be in-person with live coding based on feedback from the crowd. This will also include new data stores, new sources, and data relevant to and from the Vancouver area. This will also include updates to the platforms and inclusion of Apache Iceberg, Apache Pinot and some other new tech.
https://github.com/tspannhw/SpeakerProfile Tim Spann is a Principal Developer Advocate for Cloudera. He works with Apache Kafka, Apache Flink, Flink SQL, Apache NiFi, MiniFi, 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, messaging, streaming technologies, and Java programming. Previously, he was a 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 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.
Timothy J Spann
Cloudera
Principal Developer Advocate
Hightstown, NJ
Websitehttps://datainmotion.dev/
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
Timothy Spann [StreamNative] | Using FLaNK with InfluxDB for EdgeAI IoT at Sc...
Using FLaNK with InfluxDB for EdgeAI IoT at Scale
Timothy from StreamNative take you on a hands-on deep-dive on 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 run deep learning models on the edge devices and send images, and capture 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 real-time to their data space. These edge applications classify images and sensor readings 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.
Moving to hyper-converged? Don’t forget about data protection.
It's no surprise that hyper-converged Infrastructure has become one of today's fastest growing data center deployments. With a software-centric architecture that tightly integrates compute, storage, networking, and virtualization resources, it's an attractive option for a variety of new and existing workloads, including traditional tier 1 virtualization and scale out architectures. This session will provide a close look at how Veritas NetBackup supports these new architectures – along with some important questions you must consider as you design backup and recovery for hyper-converged solutions.
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Apache NiFi, Apache Flink, Apache Kafka
Timothy Spann
Principal Developer Advocate
Cloudera
Data in Motion
https://budapestdata.hu/2023/en/speakers/timothy-spann/
Timothy Spann
Principal Developer Advocate
Cloudera (US)
LinkedIn · GitHub · datainmotion.dev
June 8 · Online · English talk
Building Modern Data Streaming Apps with NiFi, Flink and Kafka
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 Kafka. From there we build streaming ETL with Apache Flink SQL. We will stream data into Apache Iceberg.
We use the best streaming tools for the current applications with FLaNK. flankstack.dev
BIO
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.
OpenSource and the Cloud ApacheCon.pptx
This document discusses Twitter's adoption of open source technologies and how it is evolving with extending infrastructure support for the cloud. It provides details on Twitter's use of open source technologies like Apache Hadoop, Apache Spark and Apache Kafka at scale for data processing, storage and analytics. It also discusses Twitter's cloud journey, challenges in areas like metadata integration, data replication at scale, security and tooling for easy onboarding of cloud services. Lastly, it covers topics like a focus on standards, challenges of multi-cloud, and whether to choose all cloud or a hybrid approach going forward.
Big Data Day LA 2015 - Always-on Ingestion for Data at Scale by Arvind Prabha...
While the last few years have seen great advancements in computing paradigms for big data stores, there remains one critical bottleneck in this architecture - the ingestion process. Instead of immediate insights into the data, a poor ingestion process can cause headaches and problems to no end. On the other hand, a well-designed ingestion infrastructure should give you real-time visibility into how your systems are functioning at any given time. This can significantly increase the overall effectiveness of your ad-campaigns, fraud-detection systems, preventive-maintenance systems, or other critical applications underpinning your business.
In this session we will explore various modes of ingest including pipelining, pub-sub, and micro-batching, and identify the use-cases where these can be applied. We will present this in the context of open source frameworks such as Apache Flume, Kafka, among others that can be used to build related solutions. We will also present when and how to use multiple modes and frameworks together to form hybrid solutions that can address non-trivial ingest requirements with little or no extra overhead. Through this discussion we will drill-down into details of configuration and sizing for these frameworks to ensure optimal operations and utilization for long-running deployments.
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
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Cox Communications, one of the largest network providers in the U.S., is primarily focused on ensuring network security and providing better service to customers including:
• Real-time monitoring of IP security traffic to identify and alert the unusual network activities across interfaces within an organization
• Enrich the security team with capabilities to determine the source and destination of traffic, class of service, and the causes of congestion on NetFlow data
Challenges:
Data related to Network Security includes more granular streaming data. The major challenge lies in having an unified platform to perform data cleansing, transformation, analytics and reporting on this huge streaming datasets. With the growing network traffic, there is an exponential growth with the associated data. There is a need for Scalable framework to handle these datasets and derive useful information out of data. Along with data processing, data retrieval also plays a major role for better analysis. Currently Data processing was done in daily batch using manual python scripts and with implementation of custom data structures which were specific to use cases. There was a need for more generic and unified framework to provide automated real time end to end solution to obtain high performing, more granular business results.
Solution:
Automation of this process has opportunities on several fronts, notably, providing consistency, repeat-ability, and modernization of OLAP analytics on enterprise big data platform. Reports can be generated easier and faster with the underlying OLAP engine.
• Modern Big Data Platform provides the necessary tool and infrastructure to land, cleanse, process Real time stream data processing and enriching data using the ecosystem components like Spark, Kafka, Hive
• Impressively faster OLAP analytics using Hive LLAP and Druid Integration
• Simple and faster reporting using Superset
All of the necessary components under one roof of Hortonworks Hadoop Platform.
An end-to-end solution using Big Data platform produced faster and repeatable results with sub second query results.
Value Additions by above solution:
• Deliver ultra-fast SQL analytics that can be consumed from the BI tool by security engineering team to get accelerated business results
• Opportunity for business users to explore and visualize real time streaming datasets with integration for various data sources and build dashboards for different slices
• Capability to run BI queries in just milliseconds over 1TB dataset
• High granular permission model on security datasets that allow intricate rules on accessibility for the datasets
xGem Data Stream Processing
This document provides an overview of data stream processing. It discusses what streaming data is and examples like IoT sensors, social media, and website monitoring. It also outlines the typical components of a streaming data pipeline including collecting and ingesting data from various sources, processing the data in real-time, storing the processed data, and serving it for analytics, search, and dashboards. Key streaming technologies mentioned include Apache Kafka, Apache NiFi, and various stream processing frameworks. It also introduces Stishovite as a console for managing an entire streaming data platform built from open source components.
Devfest uk & ireland using apache nifi with apache pulsar for fast data on-r...
Devfest uk & ireland using apache nifi with apache pulsar for fast data on-ramp 2022
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.
https://www.devfest-uki.com/schedule
https://linktr.ee/tspannhw
Unconference Round Table Notes
Unconference Round Table Notes
The future of real-time stream processing
WASM (Web Assembly)
Petabyte, 5000 Node Clusters, Smart Hyper Scaling
Multi-language support (Python, Rust, Kotlin, Golang, Carbon, JVM)
Machine Learning, Deep Learning, AI and Advanced Math
Low Code Development like Apache NiFi, DataFlow Designer, SQL
Dynamic Hybrid Deployment
Citizen Stream Engineer
IoT, Edge Streaming and Hybrid Edge Streaming
Java 20, 21; Java Loom Virtual Threading
Ultra low latency, trillions of events per second, massive RAM/network
Current challenges of real-time stream processing and proposed solutions
Deployment, Automation and Scaling
Choosing right project/sizing for use case
Simple Event Processing vs Complex Event Processing
Leveraging existing applications
Developer Skills
Self management and monitoring
Cost issues -> autoscaling, optimizing, performance, hybrid deployment
Performance / Benchmarking real-time stream processing
Kafka/Pulsar: https://openmessaging.cloud/docs/benchmarks/
NiFi: https://blog.cloudera.com/benchmarking-nifi-performance-and-scalability/
Flink: https://github.com/ververica/flink-sql-benchmark
Hazelcast: https://hazelcast.com/press-release/hazelcast-demonstrates-cloud-efficiency-real-time-stream-processing-of-one-billion-events-per-second/
Current trends of real-time stream processing in 2023
Current challenges of real-time stream processing and proposed solutions
Performance / Benchmarking real-time stream processing
The future of real-time stream processing
Current trends of real-time stream processing in 2023
Lightweight serverless
Hazelcast
SQL
Flink
Kafka or Pulsar as Messaging Hub
Java 17+
Managed Clusters, Containers and Environments
Real-Time Analytics
Fast Storage Options
Machine Data 101
Who should attend? Beginner - New to Splunk and have not used it before.
Description: Machine-generated data is one of the fastest growing and complex areas of big data. It's also one of the most valuable, containing a definitive record of all user transactions, customer behavior, machine behavior, security threats, fraudulent activity and more. Join us as we explore the basics of machine data analysis and highlight techniques to help you turn your organization’s machine data into valuable insights. This introductory workshop includes a hands-on(bring your laptop) demonstration of Splunk’s technology and covers use cases both inside and outside IT. Learn why more than 13,000 customers in over 110 countries use Splunk to make business, government, and education more efficient, secure, and profitable.
Big Data for Security - DNS Analytics
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Database ingest with Apache NiFi and MiNiFi
- 1. Data Ingestion with Apache NiFi and MiNiFi Lucian Neghina Big Data Architect by Developer for Developers
- 2. ➔ Periodically batch ingestion ➔ Incremental ingestion ➔ Over 100 servers ➔ No access to the servers ➔ Push access only ➔ Networking failure ➔ Secure communication Challenges
- 3. Data Flow
- 4. Simplistic view of Data Flow
- 5. Realistic view of Data Flow
- 6. Apache NiFi NiFi is based on Flow Based Programming ➔ Data agnostic ➔ Guaranteed data delivery ➔ Data Buffering / Back-Pressure ➔ Prioritized queuing ➔ Secure (SSL, HTTPS, encrypted content...) ➔ Data provenance ➔ Support push and pull models ➔ Designed for extension ➔ Clustering
- 7. Apache NiFi What is NiFi used for? ➔ Reliable and secure transfer of data between systems ➔ Delivery of data from sources to analytic platforms ➔ Enrichment and preparation of data: ◆ Conversion between formats ◆ Extraction /Parsing ◆ Routing decisions What is NiFi NOT used for? ➔ Distributed computation ➔ Complex Event Processing ➔ Long term data storage
- 8. Apache MiNiFi Subproject of Apache NiFi ➔ Agent model to collect data at the edge ➔ Smaller footprint ➔ Design & Deploy ➔ Warm re-deploys NiFi lives in the data center MiNiFi lives as close to where data is born
- 9. System Architecture Physical Store ... Data Center Server Cluster
- 10. Data Flow - Server-side (NiFi)
- 11. Data Flow - Agent-side (MiNiFi)
- 12. Thank you ● NiFi Template https://github.com/nflucian/nifi/raw/master/DemoNiFi.xml ● twitter: @nflucian ● github: github.com/nflucian