Using JPA applications in the era of NoSQL: Introducing Hibernate OGMPT.JUG
Sanne Grinovero presented on Hibernate Object/Grid Mapper (OGM), which provides an object-oriented interface for NoSQL databases using JPA. OGM stores entities as serialized tuples and uses Lucene/Hibernate Search for querying. It reuses Hibernate Core and is targeted at Infinispan but also works with other NoSQL databases. The goals are to encourage new data usage patterns with a familiar programming model and ease of use while pushing NoSQL exploration in enterprises.
This document provides an introduction to Apache Kafka, an open-source distributed event streaming platform. It discusses Kafka's history as a project originally developed by LinkedIn, its use cases like messaging, activity tracking and stream processing. It describes key Kafka concepts like topics, partitions, offsets, replicas, brokers and producers/consumers. It also gives examples of how companies like Netflix, Uber and LinkedIn use Kafka in their applications and provides a comparison to Apache Spark.
Introducing Apache Kafka and why it is important to Oracle, Java and IT profe...Lucas Jellema
Events are playing an increasingly important role in modern application architecture. They represent fast, streaming data, they fuel the interaction between microservices, they are at the core of CQRS and event sourcing. Apache Kafka has quickly emerged as the de facto standard event platform: open source, cross technology, reliable and extremely scalable and available on any platform, in Docker and from the major cloud platforms- including Oracle Cloud’s Event Hub service. This session explains the what, why and how of Apache Kafka. What role does it play, how is it used and what are challenges and tricks for real life applications. How does it fit in with Oracle Database and Fusion Middleware and with Oracle Public Cloud? In several demos, Kafka is seen at work - in real time streaming event analysis through KSQL, in CQRS and microservices scenarios and with user interfaces updated in real time through events and HTML5 server sent events.
This presentation includes a demonstration of remote database synchronization through Twitter.
Fundamentals and Architecture of Apache KafkaAngelo Cesaro
Fundamentals and Architecture of Apache Kafka.
This presentation explains Apache Kafka's architecture and internal design giving an overview of Kafka internal functions, including:
Brokers, Replication, Partitions, Producers, Consumers, Commit log, comparison over traditional message queues.
Apache Kafka is an open-source distributed event streaming platform used for building real-time data pipelines and streaming apps. It was developed by LinkedIn in 2011 to solve problems with data integration and processing. Kafka uses a publish-subscribe messaging model and is designed to be fast, scalable, and durable. It allows both streaming and storage of data and acts as a central data backbone for large organizations.
Apache Kafka is an open-source distributed event streaming platform used for building real-time data pipelines and streaming apps. It was developed by LinkedIn in 2011 to solve problems with data integration and processing. Kafka uses a publish-subscribe messaging model and is designed to be fast, scalable, and durable. It allows both streaming and storage of data and acts as a central data backbone for large organizations.
Agile Lab is an Italian company that specializes in leveraging innovative technologies like machine learning, big data, and artificial intelligence to satisfy customers' objectives. They have over 50 specialists with deep experience in production environments. The company believes in investing in its team through conferences, R&D projects, and welfare benefits. They also release open source frameworks on GitHub and share knowledge through meetups in Milan and Turin.
Removing dependencies between services: Messaging and Apache KafkaDaniel Muñoz Garrido
The document discusses using a message broker like Apache Kafka to share information between services. It notes the drawbacks of direct calls between services, including lack of knowledge of external services and complex data consistency. Using a message broker allows services to focus on their own logic and publishes information without needing to know consumer throughput. It then provides an overview of Kafka, including that it is a distributed streaming platform that uses topics and partitions to store records with offsets and timestamps. Producers write to and consumers read from Kafka in parallel consumer groups. Real world examples are given of using Kafka for transactions across updating web services and ensuring consistent states.
Using JPA applications in the era of NoSQL: Introducing Hibernate OGMPT.JUG
Sanne Grinovero presented on Hibernate Object/Grid Mapper (OGM), which provides an object-oriented interface for NoSQL databases using JPA. OGM stores entities as serialized tuples and uses Lucene/Hibernate Search for querying. It reuses Hibernate Core and is targeted at Infinispan but also works with other NoSQL databases. The goals are to encourage new data usage patterns with a familiar programming model and ease of use while pushing NoSQL exploration in enterprises.
This document provides an introduction to Apache Kafka, an open-source distributed event streaming platform. It discusses Kafka's history as a project originally developed by LinkedIn, its use cases like messaging, activity tracking and stream processing. It describes key Kafka concepts like topics, partitions, offsets, replicas, brokers and producers/consumers. It also gives examples of how companies like Netflix, Uber and LinkedIn use Kafka in their applications and provides a comparison to Apache Spark.
Introducing Apache Kafka and why it is important to Oracle, Java and IT profe...Lucas Jellema
Events are playing an increasingly important role in modern application architecture. They represent fast, streaming data, they fuel the interaction between microservices, they are at the core of CQRS and event sourcing. Apache Kafka has quickly emerged as the de facto standard event platform: open source, cross technology, reliable and extremely scalable and available on any platform, in Docker and from the major cloud platforms- including Oracle Cloud’s Event Hub service. This session explains the what, why and how of Apache Kafka. What role does it play, how is it used and what are challenges and tricks for real life applications. How does it fit in with Oracle Database and Fusion Middleware and with Oracle Public Cloud? In several demos, Kafka is seen at work - in real time streaming event analysis through KSQL, in CQRS and microservices scenarios and with user interfaces updated in real time through events and HTML5 server sent events.
This presentation includes a demonstration of remote database synchronization through Twitter.
Fundamentals and Architecture of Apache KafkaAngelo Cesaro
Fundamentals and Architecture of Apache Kafka.
This presentation explains Apache Kafka's architecture and internal design giving an overview of Kafka internal functions, including:
Brokers, Replication, Partitions, Producers, Consumers, Commit log, comparison over traditional message queues.
Apache Kafka is an open-source distributed event streaming platform used for building real-time data pipelines and streaming apps. It was developed by LinkedIn in 2011 to solve problems with data integration and processing. Kafka uses a publish-subscribe messaging model and is designed to be fast, scalable, and durable. It allows both streaming and storage of data and acts as a central data backbone for large organizations.
Apache Kafka is an open-source distributed event streaming platform used for building real-time data pipelines and streaming apps. It was developed by LinkedIn in 2011 to solve problems with data integration and processing. Kafka uses a publish-subscribe messaging model and is designed to be fast, scalable, and durable. It allows both streaming and storage of data and acts as a central data backbone for large organizations.
Agile Lab is an Italian company that specializes in leveraging innovative technologies like machine learning, big data, and artificial intelligence to satisfy customers' objectives. They have over 50 specialists with deep experience in production environments. The company believes in investing in its team through conferences, R&D projects, and welfare benefits. They also release open source frameworks on GitHub and share knowledge through meetups in Milan and Turin.
Removing dependencies between services: Messaging and Apache KafkaDaniel Muñoz Garrido
The document discusses using a message broker like Apache Kafka to share information between services. It notes the drawbacks of direct calls between services, including lack of knowledge of external services and complex data consistency. Using a message broker allows services to focus on their own logic and publishes information without needing to know consumer throughput. It then provides an overview of Kafka, including that it is a distributed streaming platform that uses topics and partitions to store records with offsets and timestamps. Producers write to and consumers read from Kafka in parallel consumer groups. Real world examples are given of using Kafka for transactions across updating web services and ensuring consistent states.
Ten reasons to choose Apache Pulsar over Apache Kafka for Event Sourcing_Robe...StreamNative
More and more developer want to build cloud-native distributed application or microservices by making use of high performing, cloud-agnostic messaging technology for maximum decoupling. The only thing we do not want is the hassle of managing the complex message infrasturcture needed for the job, or the risk of getting into a vendor lock-in. Generally developers know Apache Kafka, but for event sourcing or the CQRS pattern Kafka is not really suitable. In this talk I will give you at least ten reasons why to choose Pulsar over Kafka for event sourcing and data consensus.
Unleashing Real-time Power with Kafka.pptxKnoldus Inc.
Unlock the potential of real-time data streaming with Kafka in this session. Learn the fundamentals, architecture, and seamless integration with Scala, empowering you to elevate your data processing capabilities. Perfect for developers at all levels, this hands-on experience will equip you to harness the power of real-time data streams effectively.
Kafka's basic terminologies, its architecture, its protocol and how it works.
Kafka at scale, its caveats, guarantees and use cases offered by it.
How we use it @ZaprMediaLabs.
What is Kafka & why is it Important? (UKOUG Tech17, Birmingham, UK - December...Lucas Jellema
Fast data arrives in real time and potentially high volume. Rapid processing, filtering and aggregation is required to ensure timely reaction and actual information in user interfaces. Doing so is a challenge, make this happen in a scalable and reliable fashion is even more interesting. This session introduces Apache Kafka as the scalable event bus that takes care of the events as they flow in and Kafka Streams and KSQL for the streaming analytics. Both Java and Node applications are demonstrated that interact with Kafka and leverage Server Sent Events and WebSocket channels to update the Web UI in real time. User activity performed by the audience in the Web UI is processed by the Kafka powered back end and results in live updates on all clients.
This presentation includes a demonstration of remote database synchronization through Twitter.
Apache Storm is a distributed, real-time computational framework used to process unbounded streams of data from sources like messaging systems or databases. It allows building topologies with spouts that act as data sources and bolts that perform computations. Data flows between nodes as tuples through streams. Apache Kafka is a distributed publish-subscribe messaging system that stores feeds of messages in topics, allowing producers to write data and consumers to read it.
Everyone in the Scala world is using or looking into using Akka for low-latency, scalable, distributed or concurrent systems. I'd like to share my story of developing and productionizing multiple Akka apps, including low-latency ingestion and real-time processing systems, and Spark-based applications.
When does one use actors vs futures?
Can we use Akka with, or in place of, Storm?
How did we set up instrumentation and monitoring in production?
How does one use VisualVM to debug Akka apps in production?
What happens if the mailbox gets full?
What is our Akka stack like?
I will share best practices for building Akka and Scala apps, pitfalls and things we'd like to avoid, and a vision of where we would like to go for ideal Akka monitoring, instrumentation, and debugging facilities. Plus backpressure and at-least-once processing.
Data Models and Consumer Idioms Using Apache Kafka for Continuous Data Stream...Erik Onnen
The document discusses Urban Airship's use of Apache Kafka for processing continuous data streams. It describes how Urban Airship uses Kafka for analytics, operational data, and presence data. Producers write device data to Kafka topics, and consumers create indexes from the data in databases like HBase and write to operational data warehouses. The document also covers Kafka concepts, best use cases, limitations, and examples of data structures for storing device metadata in Kafka streams.
Apache Kafka is a distributed streaming platform. It provides a high-throughput distributed messaging system that can handle trillions of events daily. Many large companies use Kafka for application logging, metrics collection, and powering real-time analytics. The current version is 0.8.2 and upcoming versions will include a new consumer, security features, and support for transactions.
Using Kafka as a Database For Real-Time Transaction Processing | Chad Preisle...HostedbyConfluent
You have learned about Kafka event sourcing with streams and using Kafka as a database, but you may be having a tough time wrapping your head around what that means and what challenges you will face. Kafka’s exactly once semantics, data retention rules, and stream DSL make it a great database for real-time transaction processing. This talk will focus on how to use Kafka events as a database. We will talk about using KTables vs GlobalKTables, and how to apply them to patterns we use with traditional databases. We will go over a real-world example of joining events against existing data and some issues to be aware of. We will finish covering some important things to remember about state stores, partitions, and streams to help you avoid problems when your data sets become large.
Kafka & Storm - FifthElephant 2015 by @bhaskerkode, HelpshiftBhasker Kode
The document discusses how Kafka's key distinguishing feature is its published protocol specification that defines how clients communicate with Kafka brokers. This allows different clients to integrate with Kafka by simply implementing the protocol over TCP, without relying on a specific client library. It also enables the ecosystem to develop rapidly due to wide adoption. The protocol focuses on efficiency through techniques like zero-copy transfer of message data directly from kernel space to sockets.
Apache Kafka is a distributed streaming platform used for building real-time data pipelines and streaming apps. It provides a unified, scalable, and durable platform for handling real-time data feeds. Kafka works by accepting streams of records from one or more producers and organizing them into topics. It allows both storing and forwarding of these streams to consumers. Producers write data to topics which are replicated across clusters for fault tolerance. Consumers can then read the data from the topics in the order it was produced. Major companies like LinkedIn, Yahoo, Twitter, and Netflix use Kafka for applications like metrics, logging, stream processing and more.
Apache Kafka is a distributed publish-subscribe messaging system that can handle high volumes of data and enable messages to be passed from one endpoint to another. It uses a distributed commit log that allows messages to be persisted on disk for durability. Kafka is fast, scalable, fault-tolerant, and guarantees zero data loss. It is used by companies like LinkedIn, Twitter, and Netflix to handle high volumes of real-time data and streaming workloads.
Kafka is an open source messaging system that can handle massive streams of data in real-time. It is fast, scalable, durable, and fault-tolerant. Kafka is commonly used for stream processing, website activity tracking, metrics collection, and log aggregation. It supports high throughput, reliable delivery, and horizontal scalability. Some examples of real-time use cases for Kafka include website monitoring, network monitoring, fraud detection, and IoT applications.
Kafka Streams: The Stream Processing Engine of Apache KafkaEno Thereska
This document discusses Kafka Streams, which is the stream processing engine of Apache Kafka. It provides an overview of Kafka Streams and how it can be used to build real-time applications and services. Some key features of Kafka Streams include its declarative programming model using the Kafka Streams DSL, ability to perform continuous computations on data streams and tables, and building event-driven microservices without external real-time processing frameworks. The document also provides examples of how to build applications that perform operations like joins, aggregations and filtering using the Kafka Streams API.
https://www.learntek.org/blog/apache-kafka/
https://www.learntek.org/
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses.
https://www.learntek.org/
https://www.learntek.org/blog/apache-kafka/
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses.
Apache Kafka - Scalable Message-Processing and more !Guido Schmutz
Independent of the source of data, the integration of event streams into an Enterprise Architecture gets more and more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analysed, often with many consumers or systems interested in all or part of the events. How can me make sure that all these event are accepted and forwarded in an efficient and reliable way? This is where Apache Kafaka comes into play, a distirbuted, highly-scalable messaging broker, build for exchanging huge amount of messages between a source and a target.
This session will start with an introduction into Apache and presents the role of Apache Kafka in a modern data / information architecture and the advantages it brings to the table. Additionally the Kafka ecosystem will be covered as well as the integration of Kafka in the Oracle Stack, with products such as Golden Gate, Service Bus and Oracle Stream Analytics all being able to act as a Kafka consumer or producer.
Ten reasons to choose Apache Pulsar over Apache Kafka for Event Sourcing_Robe...StreamNative
More and more developer want to build cloud-native distributed application or microservices by making use of high performing, cloud-agnostic messaging technology for maximum decoupling. The only thing we do not want is the hassle of managing the complex message infrasturcture needed for the job, or the risk of getting into a vendor lock-in. Generally developers know Apache Kafka, but for event sourcing or the CQRS pattern Kafka is not really suitable. In this talk I will give you at least ten reasons why to choose Pulsar over Kafka for event sourcing and data consensus.
Unleashing Real-time Power with Kafka.pptxKnoldus Inc.
Unlock the potential of real-time data streaming with Kafka in this session. Learn the fundamentals, architecture, and seamless integration with Scala, empowering you to elevate your data processing capabilities. Perfect for developers at all levels, this hands-on experience will equip you to harness the power of real-time data streams effectively.
Kafka's basic terminologies, its architecture, its protocol and how it works.
Kafka at scale, its caveats, guarantees and use cases offered by it.
How we use it @ZaprMediaLabs.
What is Kafka & why is it Important? (UKOUG Tech17, Birmingham, UK - December...Lucas Jellema
Fast data arrives in real time and potentially high volume. Rapid processing, filtering and aggregation is required to ensure timely reaction and actual information in user interfaces. Doing so is a challenge, make this happen in a scalable and reliable fashion is even more interesting. This session introduces Apache Kafka as the scalable event bus that takes care of the events as they flow in and Kafka Streams and KSQL for the streaming analytics. Both Java and Node applications are demonstrated that interact with Kafka and leverage Server Sent Events and WebSocket channels to update the Web UI in real time. User activity performed by the audience in the Web UI is processed by the Kafka powered back end and results in live updates on all clients.
This presentation includes a demonstration of remote database synchronization through Twitter.
Apache Storm is a distributed, real-time computational framework used to process unbounded streams of data from sources like messaging systems or databases. It allows building topologies with spouts that act as data sources and bolts that perform computations. Data flows between nodes as tuples through streams. Apache Kafka is a distributed publish-subscribe messaging system that stores feeds of messages in topics, allowing producers to write data and consumers to read it.
Everyone in the Scala world is using or looking into using Akka for low-latency, scalable, distributed or concurrent systems. I'd like to share my story of developing and productionizing multiple Akka apps, including low-latency ingestion and real-time processing systems, and Spark-based applications.
When does one use actors vs futures?
Can we use Akka with, or in place of, Storm?
How did we set up instrumentation and monitoring in production?
How does one use VisualVM to debug Akka apps in production?
What happens if the mailbox gets full?
What is our Akka stack like?
I will share best practices for building Akka and Scala apps, pitfalls and things we'd like to avoid, and a vision of where we would like to go for ideal Akka monitoring, instrumentation, and debugging facilities. Plus backpressure and at-least-once processing.
Data Models and Consumer Idioms Using Apache Kafka for Continuous Data Stream...Erik Onnen
The document discusses Urban Airship's use of Apache Kafka for processing continuous data streams. It describes how Urban Airship uses Kafka for analytics, operational data, and presence data. Producers write device data to Kafka topics, and consumers create indexes from the data in databases like HBase and write to operational data warehouses. The document also covers Kafka concepts, best use cases, limitations, and examples of data structures for storing device metadata in Kafka streams.
Apache Kafka is a distributed streaming platform. It provides a high-throughput distributed messaging system that can handle trillions of events daily. Many large companies use Kafka for application logging, metrics collection, and powering real-time analytics. The current version is 0.8.2 and upcoming versions will include a new consumer, security features, and support for transactions.
Using Kafka as a Database For Real-Time Transaction Processing | Chad Preisle...HostedbyConfluent
You have learned about Kafka event sourcing with streams and using Kafka as a database, but you may be having a tough time wrapping your head around what that means and what challenges you will face. Kafka’s exactly once semantics, data retention rules, and stream DSL make it a great database for real-time transaction processing. This talk will focus on how to use Kafka events as a database. We will talk about using KTables vs GlobalKTables, and how to apply them to patterns we use with traditional databases. We will go over a real-world example of joining events against existing data and some issues to be aware of. We will finish covering some important things to remember about state stores, partitions, and streams to help you avoid problems when your data sets become large.
Kafka & Storm - FifthElephant 2015 by @bhaskerkode, HelpshiftBhasker Kode
The document discusses how Kafka's key distinguishing feature is its published protocol specification that defines how clients communicate with Kafka brokers. This allows different clients to integrate with Kafka by simply implementing the protocol over TCP, without relying on a specific client library. It also enables the ecosystem to develop rapidly due to wide adoption. The protocol focuses on efficiency through techniques like zero-copy transfer of message data directly from kernel space to sockets.
Apache Kafka is a distributed streaming platform used for building real-time data pipelines and streaming apps. It provides a unified, scalable, and durable platform for handling real-time data feeds. Kafka works by accepting streams of records from one or more producers and organizing them into topics. It allows both storing and forwarding of these streams to consumers. Producers write data to topics which are replicated across clusters for fault tolerance. Consumers can then read the data from the topics in the order it was produced. Major companies like LinkedIn, Yahoo, Twitter, and Netflix use Kafka for applications like metrics, logging, stream processing and more.
Apache Kafka is a distributed publish-subscribe messaging system that can handle high volumes of data and enable messages to be passed from one endpoint to another. It uses a distributed commit log that allows messages to be persisted on disk for durability. Kafka is fast, scalable, fault-tolerant, and guarantees zero data loss. It is used by companies like LinkedIn, Twitter, and Netflix to handle high volumes of real-time data and streaming workloads.
Kafka is an open source messaging system that can handle massive streams of data in real-time. It is fast, scalable, durable, and fault-tolerant. Kafka is commonly used for stream processing, website activity tracking, metrics collection, and log aggregation. It supports high throughput, reliable delivery, and horizontal scalability. Some examples of real-time use cases for Kafka include website monitoring, network monitoring, fraud detection, and IoT applications.
Kafka Streams: The Stream Processing Engine of Apache KafkaEno Thereska
This document discusses Kafka Streams, which is the stream processing engine of Apache Kafka. It provides an overview of Kafka Streams and how it can be used to build real-time applications and services. Some key features of Kafka Streams include its declarative programming model using the Kafka Streams DSL, ability to perform continuous computations on data streams and tables, and building event-driven microservices without external real-time processing frameworks. The document also provides examples of how to build applications that perform operations like joins, aggregations and filtering using the Kafka Streams API.
https://www.learntek.org/blog/apache-kafka/
https://www.learntek.org/
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses.
https://www.learntek.org/
https://www.learntek.org/blog/apache-kafka/
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses.
Apache Kafka - Scalable Message-Processing and more !Guido Schmutz
Independent of the source of data, the integration of event streams into an Enterprise Architecture gets more and more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analysed, often with many consumers or systems interested in all or part of the events. How can me make sure that all these event are accepted and forwarded in an efficient and reliable way? This is where Apache Kafaka comes into play, a distirbuted, highly-scalable messaging broker, build for exchanging huge amount of messages between a source and a target.
This session will start with an introduction into Apache and presents the role of Apache Kafka in a modern data / information architecture and the advantages it brings to the table. Additionally the Kafka ecosystem will be covered as well as the integration of Kafka in the Oracle Stack, with products such as Golden Gate, Service Bus and Oracle Stream Analytics all being able to act as a Kafka consumer or producer.
Similar to Learning Apache Kafka Theory 101 w/ My Little Ponies (20)
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
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
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
2. Agenda
• What is Apache Kafka?
• What can I use event streaming for?
• Where is Apache Kafka used?
• Kafka Theory 101 Overview
And maybe a pony or two.
4. What is Apache Kafka?
• It is an 🙌 open-source 🙌 event streaming platform.
• Simply put, it is a way of moving data between systems.
• (i.e. between applications, and servers)
5. 3 key capabilities for event streaming:
• To publish (write) and subscribe to (read) streams of events, including
continuous import/export of data from other systems.
• To store streams of events durably and reliably for as long as a dev needs.
• To process streams of events as they occur or retrospectively.
7. What can I use event streaming for?
• To process payments and financial transactions in real-time
• stock exchanges
• banks
• insurances
• To track and monitor cars, trucks, fleets, and shipments in real-time
• logistics
• automotive industry
• To continuously capture and analyze sensor data from IoT devices/equipment
• factories
• wind parks
8. Event streaming – cont’d…
• To collect and immediately react to customer interactions and orders
• retail
• hotel and travel industry
• mobile applications
• To connect, store, and make available data produced by different divisions of
a company.
• To serve as the foundation for data platforms, event-driven architectures,
and microservices.
10. Where is Apache Kafka used?
• Netflix is using Kafka to apply
recommendations in real-time while
you're watching TV shows.
• Uber uses Kafka to gather user, taxi
and trip data in real-time to compute
and forecast demand and pricing in
real-time.
• LinkedIn uses Kafka to prevent spam in
their platform, collect user
interactions and make better
connection recommendations.
12. T is for Topics and Twilight Sparkle
• A particular stream of data.
• A topic is identified by its name.
• Topics are split into partitions.
• Each partition is ordered.
• Each message within a partition gets an
incremental id, called offset.
• Central protagonist of the show
• Most intellectual member of the Mane Six
• Her cutie mark represents her talent
for magic and her love
for books and knowledge.
0 1 2 3 4
0 1 2 3
0 1 2 3 4 5 6
Partition 0
Partition 1
Partition 2
13. Topic Replication
Topic Replication is the process to offer fail-over
capability for a topic.
• If one broker (holds topics and partitions) is
down, the other broker can serve the data.
• This replication factor defines the number of
copies of a topic in a Kafka cluster (made up of
multiple Kafka brokers).
• Kafka stores messages in topics that
are partitioned and replicated across
multiple brokers in a cluster.
Broker 2
Broker 1
Partition 0
Topic A
Partition 1
Topic B
Partition 0
Topic A
14. B is for Brokers and Babs Seed
• Holds topics and partitions.
• Each broker is identified with its ID (integer).
• Each broker contains certain topic partitions.
• After connecting to any broker, you're
connected to an entire cluster.
• Apple Bloom's cousin from Manehattan.
• Former member of the Cutie Mark
Crusaders
15. P is for Partitions and Pinkie Pie
• An ordered, immutable record sequence.
• Once data is written to a partition, it can't be
changed (immutable).
• Data is assigned randomly to a partition, unless a key
is provided.
• At any one time, only ONE broker can be a leader for
a given partition.
• That leader can receive and serve data for a
partition.
• The other brokers will just be passive
replicas and synchronize the data.
• Each partition is going to have one leader, and
multiple ISR (in-sync replica).
• Baker at Sugarcube Corner.
• Toothless pet alligator, Gummy.
• Represents the element of laughter.
16. P is also for Producers and Pound Cake
• How do we get data in Kafka?
• Producers write data to topics (which are made up of
partitions).
• Producers automatically know to which broker and
partition to write to.
• Producers Message Keys: producers can choose to
send a key with a message. (i.e. string, number, .etc)
• If a key is sent, all messages for that key will
always go to the same partition.
• A key is sent if you need message ordering for a
specific field (i.e. truck_id).
• Parents are surprised to find out that
Pound Cake is a male Pegasus, and his twin
foal sister (Pumpkin Cake) is a female
unicorn… even though their parents are
both Earth ponies.
17. C is for Consumers and Princess Celestia
• How do we read data in Kafka?
• They read data from a topic (identified by name).
• Consumers know which broker to read from.
• In case of broker failures, consumers know how to recover.
• Data is read in order within each partition.
• Consumer Groups: consumers read data in consumer
groups.
• Each consumer within a group will read directly from
exclusive partitions.
• If you have more consumers than partitions, some will
be inactive.
• Most magical pony.
• Responsible for raising the sun to create
light in Equestria.
• Over 1,000 years old!
18. Z is for Zookeeper and Zecora
• Keeps track of status of the Kafka cluster nodes.
• Also keeps track of Kafka topics and partitions.
• Currently, Apache Kafka® uses Apache ZooKeeper™ to
store its metadata (i.e. location of partitions, the
configuration of topics).
⚠️ Apache Kafka is removing the Apache ZooKeeper
Dependency. ⚠️
• In 2019, they outlined a plan to break this
dependency and bring metadata management back
into Kafka itself.
• Female zebra shaman and herbalist.
• Always speaks in rhyme.
20. What now, developer?
• To get hands-on experience, follow the Quickstart.
• To learn more about Apache Kafka, check out the developer docs.
• Books and academic papers!