This summer, coming to a server near you, Cassandra 3.0! Contributors and committers have been working hard on what is the most ambitious release to date. It’s almost too much to talk about, but we will dig into some of the most important, ground breaking features that you’ll want to use. Indexing changes that will make your applications faster and spark jobs more efficient. Storage engine changes to get even more density and efficiency from your nodes. Developer focused features like full JSON support and User Defined Functions. And finally, one of the most requested features, Windows support, has made it’s arrival. There is more, but you’ll just have to some see for yourself. Get your front row seat and don’t miss it!
A lot has changed since I gave one of these talks and man, has it been good. 2.0 brought us a lot of new CQL features and now with 2.1 we get even more! Let me show you some real life data models and those new features taking developer productivity to an all new high. User Defined Types, New Counters, Paging, Static Columns. Exciting new ways of making your app truly killer!
Apache Cassandra 2.0 is out - now there's no reason not to ditch that ol' legacy relational system for your important online applications. Cassandra 2.0 includes big impact features like Light Weight Transactions and Triggers. Do you know about the other new enhancements that got lost in the noise. Let's put the spotlight on all the things! Changes in memory management, file handling and internals. Low hype but they pack a big punch. While we were at it, we also did a bit of house cleaning.
Hear about how Coursera uses Cassandra as the core of its scalable online education platform. I'll discuss the strengths of Cassandra that we leverage, as well as some limitations that you might run into as well in practice.
In the second part of this talk, we'll dive into how best to effectively use the Datastax Java drivers. We'll dig into how the driver is architected, and use this understanding to develop best practices to follow. I'll also share a couple of interesting bug we've run into at Coursera.
Further discussion on Data Modeling with Apache Cassandra. Overview of formal data modeling techniques as well as practical. Real-world use cases and associated data models.
Functional data models are great, but how can you squeeze out more performance and make them awesome! Let's talk through some example models, go through the tuning steps and understand the tradeoffs. Many time's just a simple understanding of the underlying internals can make all the difference. I've helped some of the biggest companies in the world do this and I can help you. Do you feel the need for Cassandra 2.0 speed?
A lot has changed since I gave one of these talks and man, has it been good. 2.0 brought us a lot of new CQL features and now with 2.1 we get even more! Let me show you some real life data models and those new features taking developer productivity to an all new high. User Defined Types, New Counters, Paging, Static Columns. Exciting new ways of making your app truly killer!
Apache Cassandra 2.0 is out - now there's no reason not to ditch that ol' legacy relational system for your important online applications. Cassandra 2.0 includes big impact features like Light Weight Transactions and Triggers. Do you know about the other new enhancements that got lost in the noise. Let's put the spotlight on all the things! Changes in memory management, file handling and internals. Low hype but they pack a big punch. While we were at it, we also did a bit of house cleaning.
Hear about how Coursera uses Cassandra as the core of its scalable online education platform. I'll discuss the strengths of Cassandra that we leverage, as well as some limitations that you might run into as well in practice.
In the second part of this talk, we'll dive into how best to effectively use the Datastax Java drivers. We'll dig into how the driver is architected, and use this understanding to develop best practices to follow. I'll also share a couple of interesting bug we've run into at Coursera.
Further discussion on Data Modeling with Apache Cassandra. Overview of formal data modeling techniques as well as practical. Real-world use cases and associated data models.
Functional data models are great, but how can you squeeze out more performance and make them awesome! Let's talk through some example models, go through the tuning steps and understand the tradeoffs. Many time's just a simple understanding of the underlying internals can make all the difference. I've helped some of the biggest companies in the world do this and I can help you. Do you feel the need for Cassandra 2.0 speed?
Storing time series data with Apache CassandraPatrick McFadin
If you are looking to collect and store time series data, it's probably not going to be small. Don't get caught without a plan! Apache Cassandra has proven itself as a solid choice now you can learn how to do it. We'll look at possible data models and the the choices you have to be successful. Then, let's open the hood and learn about how data is stored in Apache Cassandra. You don't need to be an expert in distributed systems to make this work and I'll show you how. I'll give you real-world examples and work through the steps. Give me an hour and I will upgrade your time series game.
Introduction to data modeling with apache cassandraPatrick McFadin
Are you using relational databases and wonder how to get started with data modeling and Apache Cassandra? Here is a starting tour of how to get started. Translating from the knowledge you already have to the knowledge you need to effective with Cassandra development. We cover patterns and anti-patterns. Get going today!
At this meetup Patrick McFadin, Solutions Architect at DataStax, will be discussing the most recently added features in Apache Cassandra 2.0, including: Lightweight transactions, eager retries, improved compaction, triggers, and CQL cursors. He'll also be touching on time series data with Apache Cassandra.
Preview of Cassandra 2.2 and 3.0 features. Materialized views, user defined functions, user defined aggregations, new storage engine, rewritten hints, improved vnodes, native JSON support, updated garbage collector.
This is a two part talk in which we'll go over the architecture that enables Apache Cassandra’s linear scalability as well as how DataStax Drivers are able to take full advantage of it to provide developers with nicely designed and speedy clients extendable to the core.
Cassandra Day Atlanta 2015: Building Your First Application with Apache Cassa...DataStax Academy
You’ve heard the talks, followed the tutorials, and done the research. You are a font of Cassandra knowledge. Now it’s time to change the world! (Or at least build something to make your boss happy). In this talk we’ll walk through the process of building KillrVideo, an open source video sharing website where users can upload and share videos, rate them, comment on them, and more. By looking at a real application, we’ll talk about architectural decisions, how the application drives the data model, some pro tips when using the DataStax drivers, and some lessons learned from mistakes made along the way. You’ll leave this session ready to start building your next application (world-changing or otherwise) with Cassandra.
Cassandra 3.0 - JSON at scale - StampedeCon 2015StampedeCon
This session will explore the new features in Cassandra 3.0, starting with JSON support. Cassandra now allows storing JSON directly to Cassandra rows and vice versa, making it trivial to deploy Cassandra as a component in modern service-oriented architectures.
Cassandra 3.0 also delivers other enhancements to developer productivity: user defined functions let developers deploy custom application logic server side with any language conforming to the Java scripting API, including Javascript. Global indexes allow scaling indexed queries linearly with the size of the cluster, a first for open-source NoSQL databases.
Finally, we will cover the performance improvements in Cassandra 3.0 as well.
The first half of this presentation is an introduction to Apache Cassandra's architecture, highlighting its main features: distributed (masterless), replicated, multi data center.
The second half is focused on data modeling with Apache Cassandra, the differences with the relational way of doing data modeling and a few real examples, highlighting potential issues and providing alternatives.
Time series with Apache Cassandra - Long versionPatrick McFadin
Apache Cassandra has proven to be one of the best solutions for storing and retrieving time series data. This talk will give you an overview of the many ways you can be successful. We will discuss how the storage model of Cassandra is well suited for this pattern and go over examples of how best to build data models.
You've made a good career developing applications using a relational database. You know learning how to be a Cassandra developer is going to be a great skill to add. Now it's time to bridge those two things into reality. I was in your shoes and I can help. How do you work without ACID transactions? The data model looks similar but is so different! What are some of the bad things I should avoid? What are some of the traps I can fall into moving from a relational database? I hear these questions all the time. Let's spend some time to walk through each one and get you on track. Before you know it, you'll be going crazy on your next Cassandra based application!
About the Speaker
Patrick McFadin Chief Evangelist, DataStax
Patrick McFadin is one of the leading experts of Apache Cassandra and data modeling techniques. As the Chief Evangelist for Apache Cassandra and consultant for DataStax, he has helped build some of the largest and exciting deployments in production. Previous to DataStax, he was Chief Architect at Hobsons and an Oracle DBA/Developer for over 15 years.
Building your First Application with CassandraLuke Tillman
You’ve heard the talks, followed the tutorials, and done the research. You are a font of Cassandra knowledge. Now it’s time to change the world! (Or at least build something to make your boss happy). In this talk we’ll walk through the process of building KillrVideo, an open source video sharing website where users can upload and share videos, rate them, comment on them, and more. By looking at a real application, we’ll talk about architectural decisions, how the application drives the data model, some pro tips when using the DataStax drivers, and some lessons learned from mistakes made along the way. You’ll leave this session ready to start building your next application (world-changing or otherwise) with Cassandra.
Storing time series data with Apache CassandraPatrick McFadin
If you are looking to collect and store time series data, it's probably not going to be small. Don't get caught without a plan! Apache Cassandra has proven itself as a solid choice now you can learn how to do it. We'll look at possible data models and the the choices you have to be successful. Then, let's open the hood and learn about how data is stored in Apache Cassandra. You don't need to be an expert in distributed systems to make this work and I'll show you how. I'll give you real-world examples and work through the steps. Give me an hour and I will upgrade your time series game.
Introduction to data modeling with apache cassandraPatrick McFadin
Are you using relational databases and wonder how to get started with data modeling and Apache Cassandra? Here is a starting tour of how to get started. Translating from the knowledge you already have to the knowledge you need to effective with Cassandra development. We cover patterns and anti-patterns. Get going today!
At this meetup Patrick McFadin, Solutions Architect at DataStax, will be discussing the most recently added features in Apache Cassandra 2.0, including: Lightweight transactions, eager retries, improved compaction, triggers, and CQL cursors. He'll also be touching on time series data with Apache Cassandra.
Preview of Cassandra 2.2 and 3.0 features. Materialized views, user defined functions, user defined aggregations, new storage engine, rewritten hints, improved vnodes, native JSON support, updated garbage collector.
This is a two part talk in which we'll go over the architecture that enables Apache Cassandra’s linear scalability as well as how DataStax Drivers are able to take full advantage of it to provide developers with nicely designed and speedy clients extendable to the core.
Cassandra Day Atlanta 2015: Building Your First Application with Apache Cassa...DataStax Academy
You’ve heard the talks, followed the tutorials, and done the research. You are a font of Cassandra knowledge. Now it’s time to change the world! (Or at least build something to make your boss happy). In this talk we’ll walk through the process of building KillrVideo, an open source video sharing website where users can upload and share videos, rate them, comment on them, and more. By looking at a real application, we’ll talk about architectural decisions, how the application drives the data model, some pro tips when using the DataStax drivers, and some lessons learned from mistakes made along the way. You’ll leave this session ready to start building your next application (world-changing or otherwise) with Cassandra.
Cassandra 3.0 - JSON at scale - StampedeCon 2015StampedeCon
This session will explore the new features in Cassandra 3.0, starting with JSON support. Cassandra now allows storing JSON directly to Cassandra rows and vice versa, making it trivial to deploy Cassandra as a component in modern service-oriented architectures.
Cassandra 3.0 also delivers other enhancements to developer productivity: user defined functions let developers deploy custom application logic server side with any language conforming to the Java scripting API, including Javascript. Global indexes allow scaling indexed queries linearly with the size of the cluster, a first for open-source NoSQL databases.
Finally, we will cover the performance improvements in Cassandra 3.0 as well.
The first half of this presentation is an introduction to Apache Cassandra's architecture, highlighting its main features: distributed (masterless), replicated, multi data center.
The second half is focused on data modeling with Apache Cassandra, the differences with the relational way of doing data modeling and a few real examples, highlighting potential issues and providing alternatives.
Time series with Apache Cassandra - Long versionPatrick McFadin
Apache Cassandra has proven to be one of the best solutions for storing and retrieving time series data. This talk will give you an overview of the many ways you can be successful. We will discuss how the storage model of Cassandra is well suited for this pattern and go over examples of how best to build data models.
You've made a good career developing applications using a relational database. You know learning how to be a Cassandra developer is going to be a great skill to add. Now it's time to bridge those two things into reality. I was in your shoes and I can help. How do you work without ACID transactions? The data model looks similar but is so different! What are some of the bad things I should avoid? What are some of the traps I can fall into moving from a relational database? I hear these questions all the time. Let's spend some time to walk through each one and get you on track. Before you know it, you'll be going crazy on your next Cassandra based application!
About the Speaker
Patrick McFadin Chief Evangelist, DataStax
Patrick McFadin is one of the leading experts of Apache Cassandra and data modeling techniques. As the Chief Evangelist for Apache Cassandra and consultant for DataStax, he has helped build some of the largest and exciting deployments in production. Previous to DataStax, he was Chief Architect at Hobsons and an Oracle DBA/Developer for over 15 years.
Building your First Application with CassandraLuke Tillman
You’ve heard the talks, followed the tutorials, and done the research. You are a font of Cassandra knowledge. Now it’s time to change the world! (Or at least build something to make your boss happy). In this talk we’ll walk through the process of building KillrVideo, an open source video sharing website where users can upload and share videos, rate them, comment on them, and more. By looking at a real application, we’ll talk about architectural decisions, how the application drives the data model, some pro tips when using the DataStax drivers, and some lessons learned from mistakes made along the way. You’ll leave this session ready to start building your next application (world-changing or otherwise) with Cassandra.
Cassandra Summit 2014: Highly Scalable Web Application in the Cloud with Cass...DataStax Academy
Presenters, L
Putting together a cloud based web application that allows end users to upload, encode, manage and distribute video media files is not a difficult task these days. Especially with the number of related frameworks and services available, ready to be used or consumed. The situation gets more complex when the expected traffic is in the millions-of-users range, globally distributed, and requiring detailed monitoring for usage. Using this scenario, in this session you will learn how to use the recently updated Datastax C# Cassandra driver, how to deploy a multi-datacenter Cassandra cluster using the Microsoft Azure platform that can be accessed from different programming languages, and how to leverage existing cloud services to perform some of the tasks associated with this use case.
Cassandra Day London 2015: Building Your First Application in Apache CassandraDataStax Academy
Speaker(s): Luke Tillman, Apache Cassandra Language Evangelist at DataStax
You’ve heard the talks, followed the tutorials, and done the research. You are a font of Cassandra knowledge. Now it’s time to change the world! (Or at least build something to make your boss happy). In this talk we’ll walk through the process of building KillrVideo, an open source video sharing website where users can upload and share videos, rate them, comment on them, and more. By looking at a real application, we’ll talk about architectural decisions, how the application drives the data model, some pro tips when using the DataStax drivers, and some lessons learned from mistakes made along the way. You’ll leave this session ready to start building your next application (world-changing or otherwise) with Cassandra.
Cassandra Day Chicago 2015: Building Your First Application with Apache Cassa...DataStax Academy
Speaker(s): Luke Tillman, Apache Cassandra Language Evangelist at DataStax
You’ve heard the talks, followed the tutorials, and done the research. You are a font of Cassandra knowledge. Now it’s time to change the world! (Or at least build something to make your boss happy). In this talk we’ll walk through the process of building KillrVideo, an open source video sharing website where users can upload and share videos, rate them, comment on them, and more. By looking at a real application, we’ll talk about architectural decisions, how the application drives the data model, some pro tips when using the DataStax drivers, and some lessons learned from mistakes made along the way. You’ll leave this session ready to start building your next application (world-changing or otherwise) with Cassandra.
Creating a Python Microservice Tier in Four Sprints with Cassandra, Kafka, an...Jeffrey Carpenter
Retracing the journey to create a Python implementation of the KillrVideo microservice tier in four sprints. In this talk we’ll consider:
- Advice on API design and infrastructure selection for microservice architectures
- The case for Python as a viable language for microservice implementations
- Best practices for integrating Apache Cassandra, Apache Kafka, and DataStax Enterprise Graph in Python applications
- Lessons learned from livestreaming a graph recommendation engine implementation on Twitch
In this webinar, we review the benefits of deploying a microservices architecture with Cassandra as your backbone in order to ensure your applications become incredibly reliable. We discuss in detail:
- How to create microservices in Node.js with ExpressJs and Seneca
- Tuning the Node.js driver for Cassandra: error handling, load balancing and degrees of parallelism
- Additional best practices to ensure your systems are highly performant and available
The sample service is available on GitHub: https://github.com/jorgebay/killr-service
CDI portable extensions are one of greatest features of Java EE allowing the platform to be extended in a clean and portable way. But allowing extension is just part of the story. CDI opens the door to a whole new eco-system for Java EE, but it’s not the role of the specification to create these extensions.
Apache DeltaSpike is the project that leads this brand new eco-system by providing useful extension modules for CDI applications as well as tools to ease the creation of new ones.
In this session, we’ll start by presenting the DeltaSpike toolbox and show how it helps you to develop for CDI. Then we’ll describe the major extensions included in DeltaSpike, including 'configuration', 'scheduling' and 'data'.
CDI portable extensions are one of greatest features of Java EE allowing the platform to be extended in a clean and portable way. But allowing extension is just part of the story. CDI opens the door to a whole new eco-system for Java EE, but it’s not the role of the specification to create these extensions.
Apache DeltaSpike is the project that leads this brand new eco-system by providing useful extension modules for CDI applications as well as tools to ease the creation of new ones.
In this session, we’ll start by presenting the DeltaSpike toolbox and show how it helps you to develop for CDI. Then we’ll describe the major extensions included in Deltaspike, including 'configuration', 'scheduling' and 'data'.
View IT operations as a flow of data (Sources of Truth) thru work-cells (automation processes) to deliver value to the customer.
There should be only one source of truth for every piece of configuration data.
Device configurations are poor source of truth.
Third normal form? That’s so 20th century. Learn the newest techniques to make your Cassandra database sing from the rafters in performance and scalability. AND it uses concepts that you already know and apply every day. You can do this. This is the must-see half hour of your professional life! These developers found a new way to work with databases. First you will be shocked, then you will be inspired!
Pivotal Platform - December Release A First LookVMware Tanzu
Join Dan Baskette and Jared Ruckle for a first look at the latest Pivotal Platform capabilities with demos and expert Q&A. Attend this session and learn how you can put these new updates to work for your enterprise. We’ll review these highlights in more depth:
● Pivotal Spring Cloud Gateway: The cloud-native gateway developers love
● App developers can deploy user-provided sidecars with a buildpack (beta)
● Pivotal Cloud Cache 1.10 takes performance even higher
● The new RabbitMQ open source release comes to Pivotal Platform
We’ll also review new capabilities to help .NET developers go faster, and enhancements for platform observability.
Presenters :Jared Ruckle and Dan Baskette, Pivotal
How to help your editor love your vue component libraryPiotr Tomiak
For web developers, one of the best things about Vue is its flexibility. Various ways to develop components, powerful mixins, and the freedom to choose a build system are just some of the advantages of Vue.
In this talk I'll look at Vue component libraries from our perspective as IDE developers and discuss some challenges we're facing with such flexibility. How to find all the available components in a library? What types their props have? Where are the docs? These are just some of the questions we have to answer.
Finally, I'll tell the story of how we ended up creating web-types, a format for describing component libraries. I'll demonstrate how it can help editors and documentation generators and how to use it.
Managing large volumes of data isn’t trivial and needs a plan. Fast Data is how we describe the nature of data in a heavily consumer-driven world. Fast in. Fast out. Is your data infrastructure ready? You will learn some important reference architectures for large-scale data problems. The three main areas are covered:
Organize - Manage the incoming data stream and ensure it is processed correctly and on time. No data left behind.
Process - Analyze volumes of data you receive in near real-time or in a batch. Be ready for fast serving in your application.
Store - Reliably store data in the data models to support your application. Never accept downtime or slow response times.
If you’re involved in open source work in or around a business, you will inevitably have the discussion, “Is this open source or proprietary?” Do not take this moment lightly. This seemingly easy question is met with strong opinions on both sides. Friendships have been lost. Companies have suffered. It’s as close to religious warfare as we can get in the tech world.
It’s time to call a truce.
There are plenty of valid arguments on both sides. Patrick McFadin outlines the pros and cons of each. Using example scenarios of projects that must decide whether or not they’ll be open source, Patrick explores objective ways to make a decision without descending into chaos and name calling. Even without a completely objective picture, understanding both sides of the argument can help keep you on track and civil. Patrick has been involved in OSS for more years than he likes to admit and would love for his past mistakes to benefit you.
Topics include:
- Key questions to ask to help guide your decision
- Reasons for choosing OSS
- Reasons for staying strictly proprietary
- Considerations for mixing OSS and proprietary models
- Transitioning from one model to the other
An Introduction to time series with Team ApachePatrick McFadin
We as an industry are collecting more data every year. IoT, web, and mobile applications send torrents of bits to our data centers that have to be processed and stored, even as users expect an always-on experience—leaving little room for error. Patrick McFadin explores how successful companies do this every day using the powerful Team Apache: Apache Kafka, Spark, and Cassandra.
Patrick walks you through organizing a stream of data into an efficient queue using Apache Kafka, processing the data in flight using Apache Spark Streaming, storing the data in a highly scaling and fault-tolerant database using Apache Cassandra, and transforming and finding insights in volumes of stored data using Apache Spark.
Topics include:
- Understanding the right use case
- Considerations when deploying Apache Kafka
- Processing streams with Apache Spark Streaming
- A deep dive into how Apache Cassandra stores data
- Integration between Cassandra and Spark
- Data models for time series
- Postprocessing without ETL using Apache Spark on Cassandra
You’ve heard all of the hype, but how can SMACK work for you? In this all-star lineup, you will learn how to create a reactive, scaling, resilient and performant data processing powerhouse. We will go through the basics of Akka, Kafka and Mesos and then deep dive into putting them together in an end2end (and back again) distrubuted transaction. Distributed transactions mean producers waiting for one or more of consumers to respond. On the backend, you will see how Apache Cassandra and Spark can be combined to add the incredibly scaling storage and data analysis needed for fast data pipelines. With these technologies as a foundation, you have the assurance that scale is never a problem and uptime is default.
Help! I want to contribute to an Open Source project but my boss says no.Patrick McFadin
You love using Open Source Software. It's done right by you and now you want to contribute back. You get your patch all ready and… the boss says no! Don't feel alone. Enterprises everywhere are trying to figure this out. I'll walk you through what actually risks exist to businesses and how you can help manage them. Maybe armed with some information your boss will say... yes!
Analyzing Time Series Data with Apache Spark and CassandraPatrick McFadin
You have collected a lot of time series data so now what? It's not going to be useful unless you can analyze what you have. Apache Spark has become the heir apparent to Map Reduce but did you know you don't need Hadoop? Apache Cassandra is a great data source for Spark jobs! Let me show you how it works, how to get useful information and the best part, storing analyzed data back into Cassandra. That's right. Kiss your ETL jobs goodbye and let's get to analyzing. This is going to be an action packed hour of theory, code and examples so caffeine up and let's go.
A Cassandra + Solr + Spark Love Triangle Using DataStax EnterprisePatrick McFadin
Wait! Back away from the Cassandra 2ndary index. It’s ok for some use cases, but it’s not an easy button. "But I need to search through a bunch of columns to look for the data and I want to do some regression analysis… and I can’t model that in C*, even after watching all of Patrick McFadins videos. What do I do?” The answer, dear developer, is in DSE Search and Analytics. With it’s easy Solr API and Spark integration so you can search and analyze data stored in your Cassandra database until your heart’s content. Take our hand. WE will show you how.
Apache cassandra and spark. you got the the lighter, let's start the firePatrick McFadin
Introduction to analyzing Apache Cassandra data using Apache Spark. This includes data models, operations topics and the internal on how Spark interfaces with Cassandra.
Owning time series with team apache Strata San Jose 2015Patrick McFadin
Break out your laptops for this hands-on tutorial is geared around understanding the basics of how Apache Cassandra stores and access time series data. We’ll start with an overview of how Cassandra works and how that can be a perfect fit for time series. Then we will add in Apache Spark as a perfect analytics companion. There will be coding as a part of the hands on tutorial. The goal will be to take a example application and code through the different aspects of working with this unique data pattern. The final section will cover the building of an end-to-end data pipeline to ingest, process and store high speed, time series data.
Nike Tech Talk: Double Down on Apache Cassandra and SparkPatrick McFadin
Apache Cassandra has proven to be one of the best solutions for storing and retrieving time series data at high velocity and high volume. This talk will give you an overview of the many ways you can be successful by introducing Apache Cassandra concepts. We will discuss how the storage model of Cassandra is well suited for this pattern and go over examples of how best to build data models. There will also be examples of how you can use Apache Spark along with Apache Cassandra to create a real time data analytics platform. It’s so easy, you will be shocked and ready to try it yourself.
Apache Cassandra is a popular choice for a wide variety of application persistence needs. There are many design choices that can effect uptime and performance. In this talk we'll look at some of the many things to consider from a single server to multiple data centers. Basic understanding of Cassandra features coupled with client driver features can be a very powerful combination. This talk will be an introduction but will deep dive into the technical details of how Cassandra works.
Making money with open source and not losing your soul: A practical guidePatrick McFadin
We now live in a world where Open Source Software is as generally accepted as any commercial software. This doesn’t mean that there are lack of commercial aspects for OSS, because I’m here to tell you, Open Source is a perfectly viable business model. Don't worry! You don't have to sell your soul to the suits on Wall Street and give up on the core values of open source to make it work. I'm employed by a company that (hopefully) embodies these values with a lot of success. I’ve also interviewed many business leaders in Open Source companies. Let me share some of what I’ve learned so you too can be successful. The topics I will be covering:
- Picking the right open source license
- Business models for monetizing open source
- Engaging the community in a mutually beneficial way
- Competing with commercial alternatives
- The selling process (yes, we have to talk about that)
Building Antifragile Applications with Apache CassandraPatrick McFadin
Even with the best infrastructure, failures will occur without warning and are almost guaranteed. Building applications that can resist this fact of life can be both art and science. In this talk, I'll try to eliminate the art portion and focus more on the science. Starting at high level architecture decisions, I will take you through each layer and finally down to actual application code. Using Cassandra as the back end database, we can build layers of fault tolerance that will leave end users completely unaware of the underlying chaos that could be occurring. With a little planning, we can say goodbye to the Fail Whale and the fragility of the traditional RDBMS. Topics will include:
- Application strategies to utilize active-active, diverse, datacenters
- Replicating data with the highest integrity and maximum resilience
- Utilizing Cassandra's built-in fault tolerance
- Architecture of private, cloud or hybrid based applications
- Application driver techniques when using Cassandra
A 30 minute talk I did at Cassandra Dublin and Cassandra London. Just some things I've learned along the way as I've helped some of the largest users of Cassandra be successful. Learn form other peoples mistakes!
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
4. User Defined Functions
• Counter table
• User clicks on a number of stars
• rating_counter = How many clicks
• rating_total = Cumulative amount of stars
4
CREATE TABLE video_rating (
videoid uuid,
rating_counter counter,
rating_total counter,
PRIMARY KEY (videoid)
);
5. User Defined Functions
5
CREATE TABLE video_rating (
videoid uuid,
rating_counter counter,
rating_total counter,
PRIMARY KEY (videoid)
);
public long getRatingForVideo(UUID videoId) {
BoundStatement bs =
getRatingByVideoPreparedStatement.bind(videoId);
ResultSet rs = session.execute(bs);
Row row = rs.one();
// Get the count and total rating for the video
long total = row.getLong("rating_total");
long count = row.getLong("rating_counter");
// Divide the total by the count and return an average
return (total / count);
}
6. User Defined Functions
6
CREATE TABLE video_rating (
videoid uuid,
rating_counter counter,
rating_total counter,
PRIMARY KEY (videoid)
);
public long getRatingForVideo(UUID videoId) {
BoundStatement bs =
getRatingByVideoPreparedStatement.bind(videoId);
ResultSet rs = session.execute(bs);
Row row = rs.one();
// Get the count and total rating for the video
long total = row.getLong("rating_total");
long count = row.getLong("rating_counter");
// Divide the total by the count and return an average
return (total / count);
}
Application code?
7. User Defined Functions
7
CREATE OR REPLACE FUNCTION averageRating ( rating_counter counter, rating_total counter )
RETURNS Float
LANGUAGE java
AS '
return Float.valueOf(rating_total.floatValue() / rating_counter.floatValue());
';
Function Name CQL TypeObject return type
Java Code
8. User Defined Functions
• Add to your CQL statement!
8
> SELECT averageRating(rating_counter, rating_total) AS avg
FROM video_rating
WHERE videoid = 99051fe9-6a9c-46c2-b949-38ef78858dd0;
videoid | rating_counter | rating_total
--------------------------------------+----------------+--------------
99051fe9-6a9c-46c2-b949-38ef78858dd0 | 3 | 12
avg
-----
4
9. User Defined Functions - Fine print
• “Pure” functions
• Nothing outside of input parameters
• Return types are only objects. No primitives
• Method signatures on parameter type
9
10. User Defined Function Language Support
• Java
• JavaScript
10
• Scala
• Groovy
• Jython
• JRuby
Primary Languages
Optional Languages
11. JSON Support
• Table to store a video
• TYPE to store metadata
11
CREATE TYPE video_metadata (
height int,
width int,
video_bit_rate set<text>,
encoding text
);
CREATE TABLE videos (
videoid uuid,
userid uuid,
name varchar,
description varchar,
location text,
location_type int,
preview_thumbnails map<text,text>,
tags set<varchar>,
metadata set <frozen<video_metadata>>,
added_date timestamp,
PRIMARY KEY (videoid)
);
12. JSON Support
12
INSERT INTO videos (videoid, name, userid, description, location, location_type,
preview_thumbnails, tags, added_date, metadata)
VALUES (49f64d40-7d89-4890-b910-dbf923563a33,'The World''s Next Top Data Model',
9761d3d7-7fbd-4269-9988-6cfd4e188678,
'Third in a three part series for Cassandra Data Modeling','http://www.youtube.com/watch?
v=HdJlsOZVGwM',1,
{'YouTube':'http://www.youtube.com/watch?v=HdJlsOZVGwM'},{'cassandra','data
model','examples','instruction'},'2013-06-11 11:00:00',
{{ height: 480, width: 640, encoding: 'MP4', video_bit_rate: {'1000kbs', '400kbs'}}});
Decompose into standard insert
OR!
13. JSON Support
13
INSERT INTO videos JSON
'{
"videoid":"99051fe9-6a9c-46c2-b949-38ef78858dd0",
"added_date":"2012-06-01 08:00:00.000",
"description":"My cat likes to play the piano! So funny.",
"location":"/us/vid/b3/b3a76c6b-7c7f-4af6-964f-803a9283c401",
"location_type":1,
"metadata":[
{
"height":480,
"width":640,
"video_bit_rate":[
"1000kbs",
"400kbs"
],
"encoding":"MP4"
}
],
"name":"My funny cat",
"preview_thumbnails":{
"10":"/us/vid/b3/b3a76c6b-7c7f-4af6-964f-803a9283c401"
},
"tags":[
"cats",
"lol",
"piano"
],
"userid":"d0f60aa8-54a9-4840-b70c-fe562b68842b"
}';
One block of JSON
OR!
14. JSON Support
14
INSERT INTO videos (videoid, name, userid, description, location, location_type, preview_thumbnails, tags,
added_date, metadata)
VALUES (99051fe9-6a9c-46c2-b949-38ef78858dd0,'My funny cat',d0f60aa8-54a9-4840-b70c-fe562b68842b,
'My cat likes to play the piano! So funny.','/us/vid/b3/b3a76c6b-7c7f-4af6-964f-803a9283c401',1,
{'10':'/us/vid/b3/b3a76c6b-7c7f-4af6-964f-803a9283c401'},{'cats','piano','lol'},'2012-06-01 08:00:00',
fromJson('
[{
"height":480,
"width":640,
"video_bit_rate":[
"1000kbs",
"400kbs"
],
"encoding":"MP4"
}]
')
);
Just a block at a time
20. More Indexes!
• Partial Indexes - Postponed until 3.1
• Functional Indexes - using a UDF in an index
20
CREATE INDEX ON user_rating averageRating(rating_counter, rating_total);
22. Hints to Raw Files
• Pre 3.0 hints stored in table
• Create load on entire write path
• …and read path
• …and compaction
22
CREATE TABLE system.hints (
target_id uuid,
hint_id timeuuid,
message_version int,
mutation blob,
PRIMARY KEY (target_id, hint_id, message_version)
) WITH COMPACT STORAGE
AND CLUSTERING ORDER BY (hint_id ASC, message_version ASC);
23. Hints to Raw Files
• Hints now written to a local file
• Replays direct from disk
• Bulk streamed to endpoints
23
CREATE TABLE system.hints (
target_id uuid,
hint_id timeuuid,
message_version int,
mutation blob,
PRIMARY KEY (target_id, hint_id, message_version)
) WITH COMPACT STORAGE
AND CLUSTERING ORDER BY (hint_id ASC, message_version ASC);
24. Windows Compatibility - The Problem
• Java file management on Windows is… different
• File delete’s are not possible
• Hard links - Broke
• Snapshots - Broke
• Memory Mapped I/O - Broke
24
25. Windows Compatibility - 3.0
• Re-tooling of critical file functions
• Extensive use of FILE_SHARE_DELETE from JDK7
• Launch now in PowerShell
• CCM now supports windows
25
26. Storage Engine Changes
• Now infamous CASSANDRA-8099
• Technical debt from Thrift
• Move from Thrift centric to CQL centric storage
26
27. Pre 3.0 Storage Engine Format
27
2005:12:1:102005:12:1:92005:12:1:82005:12:1:7
5F22A0BC
Partition Key Clustering Columns
F2B3652CFFB3652D7AB3652C
PRIMARY KEY (userId,added_date,videoId)
A12378E55F5A32
37. Commit Log Compression
• Segments are compressed by time interval
• Higher throughput under high writes
37
38. Commit Log Compression
• Segments are compressed by time interval
• Higher throughput under high writes
38
39. Commit Log Compression
• Segments are compressed by time interval
• Higher throughput under high writes
39
40. Smaller but significant changes
• Direct buffer decompression of reads
• Avoiding memory allocation on Index Summary search
• Repair concurrency improvements
• Optimal CRC32 implementation at runtime
40
42. Role Based Access Control
• Expands on User based auth in 1.2
• Requires the internal auth to be enabled
42
CREATE ROLE supervisor;
GRANT MODIFY ON user_credentials TO supervisor;
43. When will it ship?
43
Maybe June
When 8099 is finished, it ships