The document provides an overview of Kafka & Couchbase integration patterns. It introduces Couchbase and Kafka, describes how Kafka Connect enables real-time data pipelines between data systems, and how the Couchbase Kafka connector integrates Couchbase with Kafka pipelines. Use cases for the connector include using Couchbase as a data source or sink within Kafka streams. The document concludes with demos of Couchbase as a source and sink using the connector.
Billions of Messages in Real Time: Why Paypal & LinkedIn Trust an Engagement ...confluent
(Bruno Simic, Solutions Engineer, Couchbase)
Breakout during Confluent’s streaming event in Munich. This three-day hands-on course focused on how to build, manage, and monitor clusters using industry best-practices developed by the world’s foremost Apache Kafka™ experts. The sessions focused on how Kafka and the Confluent Platform work, how their main subsystems interact, and how to set up, manage, monitor, and tune your cluster.
Billions of Messages in Real Time: Why Paypal & LinkedIn Trust an Engagement ...confluent
(Bruno Simic, Solutions Engineer, Couchbase)
Breakout during Confluent’s streaming event in Munich. This three-day hands-on course focused on how to build, manage, and monitor clusters using industry best-practices developed by the world’s foremost Apache Kafka™ experts. The sessions focused on how Kafka and the Confluent Platform work, how their main subsystems interact, and how to set up, manage, monitor, and tune your cluster.
Performance Tuning RocksDB for Kafka Streams’ State Storesconfluent
Performance Tuning RocksDB for Kafka Streams’ State Stores, Bruno Cadonna, Contributor to Apache Kafka & Software Developer at Confluent and Dhruba Borthakur, CTO & Co-founder Rockset
Meetup link: https://www.meetup.com/Berlin-Apache-Kafka-Meetup-by-Confluent/events/273823025/
Building a real-time pipeline from scratch that is able to handle billion+ transactions per day, store, analyze and visualize it all in real-time has never been easier. In this build-as-we-go talk, we’ll create a front-to-back architecture that does exactly that.
* we’ll start with a simple producer emitting a few messages and publishing them onto a Kafka queue
* on consuming end of the queue a Spark-based Streamliner process will pick them up and store in MemSQL
* ZoomData will connect to MemSQL for real-time visualization where we’ll be able to ask various questions and see answers change as data is flowing through the system
* we’ll quickly make the entire pipeline more complex by increasing the amount of data as well as complexity of the data, until reaching 100K transactions per second
As we walk through this demo, we will touch on cross data-center Kafka and MemSQL set-ups, speed limitations if any as well as echo back to real-life use cases of a similar set-up used in Goldman’s Asset Management division for the purposes of Portfolio Management & Trading.
Putting the Micro into Microservices with Stateful Stream Processingconfluent
How small can a microservice be? This talk will look at how Stateful Stream Processing is used to build truly autonomous, often minuscule services. With the distributed guarantees of Exactly Once Processing, Event Driven Services supported by Apache Kafka become reliable, fast and nimble, blurring the line between business system and big data pipeline.
How to build an event driven architecture with kafka and kafka connectLoi Nguyen
How to build an event driven architecture with Kafka & Kafka Connect?
Bài talk chia sẻ về quá trình 2 năm ứng dụng Kafka, Kafka Connect để chuyển đổi mô hình hệ thống của Vexere từ Monolithic thành Microservice, event driven
- Event driven architecture là gì?
- Làm thế nào để xây dựng 1 hệ thống event driven architecture một cách hiệu qủa bằng Kafka và Kafka Connect
onnect
- Các usercase hữu ích với Kafka & Kafka Connect
- Kinh nghiệm thực tế và các bài học rút ra
Serverless Architectures with AWS Lambda and MongoDB Atlas by Sig NarvaezData Con LA
Abstract:- It's easier than ever to power serverless architectures with managed database services like MongoDB Atlas. In this session, we will explore the rise of serverless architectures and how they've rapidly integrated into public and private cloud offerings. We will demonstrate how to build a simple REST API using AWS Lambda functions, create a highly available cluster in MongoDB Atlas, and connect both via VCP Peering. We will then simulate load and use the monitoring and scale features of MongoDB Atlas and use MongoDB Compass to browse our database.
Kafka error handling patterns and best practices | Hemant Desale and Aruna Ka...HostedbyConfluent
Transaction Banking from Goldman Sachs is a high volume, latency sensitive digital banking platform offering. We have chosen an event driven architecture to build highly decoupled and independent microservices in a cloud native manner and are designed to meet the objectives of Security, Availability Latency and Scalability. Kafka was a natural choice – to decouple producers and consumers and to scale easily for high volume processing. However, there are certain aspects that require careful consideration – handling errors and partial failures, managing downtime of consumers, secure communication between brokers and producers / consumers. In this session, we will present the patterns and best practices that helped us build robust event driven applications. We will also present our solution approach that has been reused across multiple application domains. We hope that by sharing our experience, we can establish a reference implementation that application developers can benefit from.
A stream processing platform is not an island unto itself; it must be connected to all of your existing data systems, applications, and sources. In this talk we will provide different options for integrating systems and applications with Apache Kafka, with a focus on the Kafka Connect framework and the ecosystem of Kafka connectors. We will discuss the intended use cases for Kafka Connect and share our experience and best practices for building large-scale data pipelines using Apache Kafka.
Operational Analytics on Event Streams in Kafkaconfluent
Speaker: Anirudh Ramanthan, Product Manager, Rockset
Tracking key events and analyzing these event streams are critical to many enterprises. We highlight how organizations are using Apache Kafka® as a fast, reliable event streaming platform alongside Rockset, a serverless search and analytics engine, to create stateful microservices to analyze their event streams.
In this talk, we will discuss a stateful microservices architecture, where events from multiple channels are collected and streamed into Kafka and continuously ingested into Rockset with no explicit schema or metadata specification required. Developers then use serverless compute frameworks, like AWS Lambda, in conjunction with serverless data management from Rockset to build microservices to derive insights on the data from Kafka. Organizations can leverage this pattern to support low-latency queries on event streams, providing immediate insight on their business.
Enabling Data Scientists to easily create and own Kafka Consumers | Stefan Kr...HostedbyConfluent
At Stitch Fix, we hire Full Stack Data Scientists (150+) and expect them to perform diverse functions: from conception to modeling to implementation to measurement. Since Kafka is the way we get event data, this inevitably means that a Data Scientist will need to write a Kafka consumer if they’re going to complete their implementation work. E.g. to transform some client data into features, or perform a model prediction, or allocate someone to an A/B test, etc. In this talk I’ll go over how we built an opinionated Kafka client to easily enable Data Scientists to deploy and own production Kafka consumers, by focusing on writing python functions rather than fighting pitfalls with Kafka.
How did we move the mountain? - Migrating 1 trillion+ messages per day across...HostedbyConfluent
Have you ever migrated Kafka clusters from one data center to another being completely transparent to client applications?
At PayPal, as part of a massive datacenter migration initiative, Kafka team successfully moved all PayPal Kafka traffic across data centers. This initiative involved migrating 20+ Kafka clusters (1000+ broker and zookeeper nodes), as well as 60+ mirrormaker groups which seamlessly handle Kafka traffic volumes as high as 1 trillion messages per day. Throughout the course of this migration, applications required no modification, encountered 0% service outage, 0% message loss and duplicated messages. The whole migration process was fully transparent to Kafka applications.
In this session, you will learn the strategies, techniques and tools the PayPal Kafka team has utilized for managing the migration process. You will also learn the lessons and pitfalls they experienced during this exercise, as well as the secret sauce of making the migration successful.
Real time Messages at Scale with Apache Kafka and CouchbaseWill Gardella
Kafka is a scalable, distributed publish subscribe messaging system that's used as a data transmission backbone in many data intensive digital businesses. Couchbase Server is a scalable, flexible document database that's fast, agile, and elastic. Because they both appeal to the same type of customers, Couchbase and Kafka are often used together.
This presentation from a meetup in Mountain View describes Kafka's design and why people use it, Couchbase Server and its uses, and the use cases for both together. Also covered is a description and demo of Couchbase Server writing documents to a Kafka topic and consuming messages from a Kafka topic. using the Couchbase Kafka Connector.
Stream your Operational Data with Apache Spark & Kafka into Hadoop using Couc...Data Con LA
Abstract:-
Tracking user events as they happen can challenge anyone providing real time user interaction. It can demand both huge scale and a lot of processing to support dynamic adjustment to targeting products and services. As the operational data store Couchbase data services are capable of processing tens of millions of updates a day. Streaming through systems such as Apache Spark and Kafka into Hadoop, information about these key events can be turned into deeper knowledge. We will review Lambda architectures deployed at sites like PayPal, Live Person and LinkedIn that leverage a Couchbase Data Pipeline.
Bio:-
Justin Michaels. With over 20 years experience in deploying mission critical systems, Justin Michaels industry experience covers capacity planning, architecture and industry vertical experience. Justin brings his passion for architecting, implementing and improving Couchbase to the community as a Solution Architect. His expertise involves both conventional application platforms as well as distributed data management systems. He regularly engages with existing and new Couchbase customers in performance reviews, architecture planning and best practice guidance.
Performance Tuning RocksDB for Kafka Streams’ State Storesconfluent
Performance Tuning RocksDB for Kafka Streams’ State Stores, Bruno Cadonna, Contributor to Apache Kafka & Software Developer at Confluent and Dhruba Borthakur, CTO & Co-founder Rockset
Meetup link: https://www.meetup.com/Berlin-Apache-Kafka-Meetup-by-Confluent/events/273823025/
Building a real-time pipeline from scratch that is able to handle billion+ transactions per day, store, analyze and visualize it all in real-time has never been easier. In this build-as-we-go talk, we’ll create a front-to-back architecture that does exactly that.
* we’ll start with a simple producer emitting a few messages and publishing them onto a Kafka queue
* on consuming end of the queue a Spark-based Streamliner process will pick them up and store in MemSQL
* ZoomData will connect to MemSQL for real-time visualization where we’ll be able to ask various questions and see answers change as data is flowing through the system
* we’ll quickly make the entire pipeline more complex by increasing the amount of data as well as complexity of the data, until reaching 100K transactions per second
As we walk through this demo, we will touch on cross data-center Kafka and MemSQL set-ups, speed limitations if any as well as echo back to real-life use cases of a similar set-up used in Goldman’s Asset Management division for the purposes of Portfolio Management & Trading.
Putting the Micro into Microservices with Stateful Stream Processingconfluent
How small can a microservice be? This talk will look at how Stateful Stream Processing is used to build truly autonomous, often minuscule services. With the distributed guarantees of Exactly Once Processing, Event Driven Services supported by Apache Kafka become reliable, fast and nimble, blurring the line between business system and big data pipeline.
How to build an event driven architecture with kafka and kafka connectLoi Nguyen
How to build an event driven architecture with Kafka & Kafka Connect?
Bài talk chia sẻ về quá trình 2 năm ứng dụng Kafka, Kafka Connect để chuyển đổi mô hình hệ thống của Vexere từ Monolithic thành Microservice, event driven
- Event driven architecture là gì?
- Làm thế nào để xây dựng 1 hệ thống event driven architecture một cách hiệu qủa bằng Kafka và Kafka Connect
onnect
- Các usercase hữu ích với Kafka & Kafka Connect
- Kinh nghiệm thực tế và các bài học rút ra
Serverless Architectures with AWS Lambda and MongoDB Atlas by Sig NarvaezData Con LA
Abstract:- It's easier than ever to power serverless architectures with managed database services like MongoDB Atlas. In this session, we will explore the rise of serverless architectures and how they've rapidly integrated into public and private cloud offerings. We will demonstrate how to build a simple REST API using AWS Lambda functions, create a highly available cluster in MongoDB Atlas, and connect both via VCP Peering. We will then simulate load and use the monitoring and scale features of MongoDB Atlas and use MongoDB Compass to browse our database.
Kafka error handling patterns and best practices | Hemant Desale and Aruna Ka...HostedbyConfluent
Transaction Banking from Goldman Sachs is a high volume, latency sensitive digital banking platform offering. We have chosen an event driven architecture to build highly decoupled and independent microservices in a cloud native manner and are designed to meet the objectives of Security, Availability Latency and Scalability. Kafka was a natural choice – to decouple producers and consumers and to scale easily for high volume processing. However, there are certain aspects that require careful consideration – handling errors and partial failures, managing downtime of consumers, secure communication between brokers and producers / consumers. In this session, we will present the patterns and best practices that helped us build robust event driven applications. We will also present our solution approach that has been reused across multiple application domains. We hope that by sharing our experience, we can establish a reference implementation that application developers can benefit from.
A stream processing platform is not an island unto itself; it must be connected to all of your existing data systems, applications, and sources. In this talk we will provide different options for integrating systems and applications with Apache Kafka, with a focus on the Kafka Connect framework and the ecosystem of Kafka connectors. We will discuss the intended use cases for Kafka Connect and share our experience and best practices for building large-scale data pipelines using Apache Kafka.
Operational Analytics on Event Streams in Kafkaconfluent
Speaker: Anirudh Ramanthan, Product Manager, Rockset
Tracking key events and analyzing these event streams are critical to many enterprises. We highlight how organizations are using Apache Kafka® as a fast, reliable event streaming platform alongside Rockset, a serverless search and analytics engine, to create stateful microservices to analyze their event streams.
In this talk, we will discuss a stateful microservices architecture, where events from multiple channels are collected and streamed into Kafka and continuously ingested into Rockset with no explicit schema or metadata specification required. Developers then use serverless compute frameworks, like AWS Lambda, in conjunction with serverless data management from Rockset to build microservices to derive insights on the data from Kafka. Organizations can leverage this pattern to support low-latency queries on event streams, providing immediate insight on their business.
Enabling Data Scientists to easily create and own Kafka Consumers | Stefan Kr...HostedbyConfluent
At Stitch Fix, we hire Full Stack Data Scientists (150+) and expect them to perform diverse functions: from conception to modeling to implementation to measurement. Since Kafka is the way we get event data, this inevitably means that a Data Scientist will need to write a Kafka consumer if they’re going to complete their implementation work. E.g. to transform some client data into features, or perform a model prediction, or allocate someone to an A/B test, etc. In this talk I’ll go over how we built an opinionated Kafka client to easily enable Data Scientists to deploy and own production Kafka consumers, by focusing on writing python functions rather than fighting pitfalls with Kafka.
How did we move the mountain? - Migrating 1 trillion+ messages per day across...HostedbyConfluent
Have you ever migrated Kafka clusters from one data center to another being completely transparent to client applications?
At PayPal, as part of a massive datacenter migration initiative, Kafka team successfully moved all PayPal Kafka traffic across data centers. This initiative involved migrating 20+ Kafka clusters (1000+ broker and zookeeper nodes), as well as 60+ mirrormaker groups which seamlessly handle Kafka traffic volumes as high as 1 trillion messages per day. Throughout the course of this migration, applications required no modification, encountered 0% service outage, 0% message loss and duplicated messages. The whole migration process was fully transparent to Kafka applications.
In this session, you will learn the strategies, techniques and tools the PayPal Kafka team has utilized for managing the migration process. You will also learn the lessons and pitfalls they experienced during this exercise, as well as the secret sauce of making the migration successful.
Real time Messages at Scale with Apache Kafka and CouchbaseWill Gardella
Kafka is a scalable, distributed publish subscribe messaging system that's used as a data transmission backbone in many data intensive digital businesses. Couchbase Server is a scalable, flexible document database that's fast, agile, and elastic. Because they both appeal to the same type of customers, Couchbase and Kafka are often used together.
This presentation from a meetup in Mountain View describes Kafka's design and why people use it, Couchbase Server and its uses, and the use cases for both together. Also covered is a description and demo of Couchbase Server writing documents to a Kafka topic and consuming messages from a Kafka topic. using the Couchbase Kafka Connector.
Stream your Operational Data with Apache Spark & Kafka into Hadoop using Couc...Data Con LA
Abstract:-
Tracking user events as they happen can challenge anyone providing real time user interaction. It can demand both huge scale and a lot of processing to support dynamic adjustment to targeting products and services. As the operational data store Couchbase data services are capable of processing tens of millions of updates a day. Streaming through systems such as Apache Spark and Kafka into Hadoop, information about these key events can be turned into deeper knowledge. We will review Lambda architectures deployed at sites like PayPal, Live Person and LinkedIn that leverage a Couchbase Data Pipeline.
Bio:-
Justin Michaels. With over 20 years experience in deploying mission critical systems, Justin Michaels industry experience covers capacity planning, architecture and industry vertical experience. Justin brings his passion for architecting, implementing and improving Couchbase to the community as a Solution Architect. His expertise involves both conventional application platforms as well as distributed data management systems. He regularly engages with existing and new Couchbase customers in performance reviews, architecture planning and best practice guidance.
Couchbase Connect 2016: Monitoring Production Deployments The Tools – LinkedInMichael Kehoe
Good monitoring can be the difference between a great night's sleep or hearing your phone go off at 2:37 a.m. because of a production outage. Couchbase Server provides a large number of metrics which can be overwhelming if you do not know the critical things to focus on or how to expose that information to your monitoring system. In this talk we will look at example production incidents, going in depth around specific things to monitor, and how this information can be used to find issues, work out root cause, and discover trends.
Couchbase and Apache Kafka - Bridging the gap between RDBMS and NoSQLDATAVERSITY
Thousands of companies, from Uber and Netflix to Goldman Sachs and Cisco, use Apache Kafka to transform and reshape their data architectures. Kafka is frequently used as the bridge between legacy RDBMS and new NoSQL database systems, effectively transforming SQL table data into JSON documents and vice versa. Many companies also use Kafka for business-critical applications that drive real-time stream processing and analytics, intersystem messaging, high-volume data ingestion, and operational metrics collection.
Couchbase and Kafka can be used together to address high throughput, distributed data management, and transformation challenges.
In this webinar we’ll explore:
Where Kafka fits into the big data ecosystem
How companies are using Kafka for both real-time processing and as a bus for data exchange
An example of how Kafka can bridge legacy RDBMS and new NoSQL database systems
Several real-world use case architectures
Rolling presentation during Couchbase Day. Including
Introduction to NoSQL
Why NoSQL?
Introduction to Couchbase
Couchbase Architecture
Single Node Operations
Cluster Operations
HA and DR
Availability and XDCR
Backup/Restore
Security
Developing with Couchbase
Couchbase SDKs
Couchbase Indexing
Couchbase GSI and Views
Indexing and Query
Couchbase Mobile
Couchbase Cloud No Equal (Rick Jacobs, Couchbase) Kafka Summit 2020HostedbyConfluent
This session will describe and demonstrate the longstanding integration between Couchbase Server and Apache Kafka and will include descriptions of both the mechanics of the integration and practical situations when combining these products is appropriate.
The Why, When, and How of NoSQL - A Practical ApproachDATAVERSITY
More and more Fortune 1000 companies like Marriott, Cars.com, Gannett, and PayPal are choosing NoSQL over relational databases like Oracle, SQL Server, and DB2 to power their web, mobile, and IoT applications. Why? Lower costs, higher performance and availability, better agility, and easier scalability. According to The Forrester Wave™: Big Data NoSQL, Q3 2016 report, “NoSQL is no longer an option.” Come see why.
This webinar is intended for developers, architects, and database engineers who are considering NoSQL as an alternative to relational databases. If you’re looking to add NoSQL to your environment, this webinar will show you how to get started and avoid potential pitfalls.
You’ll get practical advice, including:
•Key considerations in moving from relational to NoSQL
•How to identify applications that benefit most from NoSQL
•Data modeling and querying with NoSQL
•Migrating your data to NoSQL
•Best practices for making the switch
[db tech showcase Tokyo 2016] E22: Getting real time Oracle data into Kafka a...Insight Technology, Inc.
Kafka is quickly gaining momentum as a very popular and very fast messaging platform that is very good at integrating different types of data quickly. Kafka makes this data available as a real-time data stream for consumption by enterprise users.There is so much hidden data available in our Oracle databases. How can we turn the database inside out to make this data available real-time to Kafka along with the other data sources in our enterprise. This paper will present the use cases of Oracle real-time data streaming as well as an introduction into Kafka and how to use Oracle logical replication to get Oracle real time into Kafka. This paper will include a real life real time demo from Oracle into Kafka.
IBM Message Hub service in Bluemix - Apache Kafka in a public cloudAndrew Schofield
This talk was presented at the Kafka Meetup London meeting on 20 January 2016. You can find more information about Message Hub here: http://ibm.biz/message-hub-bluemix-catalog
Apache Kafka - Scalable Message Processing and more!Guido Schmutz
After a quick overview and introduction of Apache Kafka, this session cover two components which extend the core of Apache Kafka: Kafka Connect and Kafka Streams/KSQL.
Kafka Connects role is to access data from the out-side-world and make it available inside Kafka by publishing it into a Kafka topic. On the other hand, Kafka Connect is also responsible to transport information from inside Kafka to the outside world, which could be a database or a file system. There are many existing connectors for different source and target systems available out-of-the-box, either provided by the community or by Confluent or other vendors. You simply configure these connectors and off you go.
Kafka Streams is a light-weight component which extends Kafka with stream processing functionality. By that, Kafka can now not only reliably and scalable transport events and messages through the Kafka broker but also analyse and process these event in real-time. Interestingly Kafka Streams does not provide its own cluster infrastructure and it is also not meant to run on a Kafka cluster. The idea is to run Kafka Streams where it makes sense, which can be inside a “normal” Java application, inside a Web container or on a more modern containerized (cloud) infrastructure, such as Mesos, Kubernetes or Docker. Kafka Streams has a lot of interesting features, such as reliable state handling, queryable state and much more. KSQL is a streaming engine for Apache Kafka, providing a simple and completely interactive SQL interface for processing data in Kafka.
Slides: NoSQL Data Modeling Using JSON Documents – A Practical ApproachDATAVERSITY
After three decades of relational data modeling, everyone’s pretty comfortable with schemas, tables, and entity-relationships. As more and more Global 2000 companies choose NoSQL databases to power their Digital Economy applications, they need to think about how to best model their data. How do they move from a constrained, table-driven model to an agile, flexible data model based on JSON documents?
This webinar is intended for architects and application developers who want to learn about new JSON document data modeling approaches, techniques, and best practices. This webinar will show you how to get started building a JSON document data model, how to migrate a table-based data model to JSON documents, and how to optimize your design to enable fast query performance.
This webinar will provide practical, experience-based advice and best practices for modeling JSON documents, including:
- When to embed or not embed objects in your JSON document
- Data modeling using a practical data access pattern approach
- Indexing your JSON documents
- Querying your data using N1QL (SQL for JSON)
Kafka is primarily used to build real-time streaming data pipelines and applications that adapt to the data streams. It combines messaging, storage, and stream processing to allow storage and analysis of both historical and real-time data.
GSJUG: Mastering Data Streaming Pipelines 09May2023Timothy Spann
GSJUG: Mastering Data Streaming Pipelines 09May2023
https://www.meetup.com/futureofdata-princeton/events/293233881/
This is a repost from the Garden State Java Users Group Event.
Join me at
https://www.meetup.com/garden-state-java-user-group/events/293229660/
See: https://www.eventbrite.com/e/mastering-data-streaming-pipelines-tickets-627677218457?_ga=2.253257801.1787151623.1682868226-741104479.1678110925
Please note that registration via EventBrite is required to attend either in-person or online.
We are happy to announce that Tim Spann will be our special guest for the May 9, 2023 meeting!
Abstract:
In this session, Tim will show you some best practices that he has discovered over the last seven years in building data streaming applications including IoT, CDC, Logs, and more.
In his modern approach, we utilize several Apache 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, enhance events with NiFi enrichment. We build continuous queries against our topics with Flink SQL.
We will show where Java fits in as sources, enrichments, NiFi processors and sinks.
We hope to see you on May 9!
Speaker
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.
In this session, Tim will show you some best practices that he has discovered over the last seven years in building data streaming applications, including IoT, CDC, Logs, and more.
In his modern approach, we utilize several Apache 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, enhance events with NiFi enrichment. We build continuous queries against our topics with Flink SQL.
We will show where Java fits in as sources, enrichments, NiFi processors, and sinks.
https://www.eventbrite.com/e/mastering-data-streaming-pipelines-tickets-627677218457?_ga=2.253257801.178
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
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
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Talk about Data Platform, not just a database. Customers are building Service Endpoints and multiple types of applications and use cases per customer.
Memory-first architecture
Integrated, distributed caching tier
High performant, in-memory streaming across all database services
Full SQL query language (N1QL)
Best-in-class In-Memory Indexing
Active-Active global data replication
High speed Intra and Inter-cluster In-memory replication
Big Data integrations
Leverage In-Memory replication (Spark & Kafka)
Mobile
On-device local cache / storage (Offline & Peer-to-Peer operations)
Automatic multi-master replication
Full stack secure data management
at-most-once delivery means that for each message handed to the mechanism, that message is delivered zero or one times; in more casual terms it means that messages may be lost.
at-least-once delivery means that for each message handed to the mechanism potentially multiple attempts are made at delivering it, such that at least one succeeds; again, in more casual terms this means that messages may be duplicated but not lost.
exactly-once delivery means that for each message handed to the mechanism exactly one delivery is made to the recipient; the message can neither be lost nor duplicated.
https://dzone.com/articles/kafka-clients-at-most-once-at-least-once-exactly-o
The first one is the cheapest—highest performance, least implementation overhead—because it can be done in a fire-and-forget fashion without keeping state at the sending end or in the transport mechanism.
The second one requires retries to counter transport losses, which means keeping state at the sending end and having an acknowledgement mechanism at the receiving end.
The third is most expensive—and has consequently worst performance—because in addition to the second it requires state to be kept at the receiving end in order to filter out duplicate deliveries.
An at-most-once scenario happens when the commit interval has occurred, and that in turn triggers Kafka to automatically commit the last used offset. Meanwhile, let us say the consumer did not get a chance to complete the processing of the messages and consumer has crashed. Now when consumer restarts, it starts to receive messages from the last committed offset, in essence consumer could lose a few messages in between.
At-least-once scenario happens when consumer processes a message and commits the message into its persistent store and consumer crashes at that point. Meanwhile, let us say Kafka could not get a chance to commit the offset to the broker since commit interval has not passed. Now when the consumer restarts, it gets delivered with a few older messages from the last committed offset.
KEY POINT: Applications communicate directly to the services they need to fulfill the application request and the application does not need to be topology aware as the SDK has that already.
Single node type, services defined dynamically
One node acts the same as 100, just the services are spread out in the cluster
Query service accesses Index and Data to formulate response
All query and document access is topology aware and dynamically scalable
Develop with one node, deploy against multiple production nodes
The Couchbase SDK handles knowing about where in the cluster it needs to go to satisfy whatever the application is requesting, be I CRUD or cluster management.
KEY POINT: Applications communicate directly to the services they need to fulfill the application request and the application does not need to be topology aware as the SDK has that already.
Single node type, services defined dynamically
One node acts the same as 100, just the services are spread out in the cluster
Query service accesses Index and Data to formulate response
All query and document access is topology aware and dynamically scalable
Develop with one node, deploy against multiple production nodes
The Couchbase SDK handles knowing about where in the cluster it needs to go to satisfy whatever the application is requesting, be I CRUD or cluster management.
KEY POINT: Applications communicate directly to the services they need to fulfill the application request and the application does not need to be topology aware as the SDK has that already.
Single node type, services defined dynamically
One node acts the same as 100, just the services are spread out in the cluster
Query service accesses Index and Data to formulate response
All query and document access is topology aware and dynamically scalable
Develop with one node, deploy against multiple production nodes
The Couchbase SDK handles knowing about where in the cluster it needs to go to satisfy whatever the application is requesting, be I CRUD or cluster management.
KEY POINT: Applications communicate directly to the services they need to fulfill the application request and the application does not need to be topology aware as the SDK has that already.
Single node type, services defined dynamically
One node acts the same as 100, just the services are spread out in the cluster
Query service accesses Index and Data to formulate response
All query and document access is topology aware and dynamically scalable
Develop with one node, deploy against multiple production nodes
The Couchbase SDK handles knowing about where in the cluster it needs to go to satisfy whatever the application is requesting, be I CRUD or cluster management.
Image from http://docs.confluent.io/3.0.0/platform.html + couchbase logo instead of ERP :)
Link to Kafka intro video https://www.youtube.com/watch?v=wMLAlJimPzk ?
Source of image: https://www.confluent.io/product/compare/