This document discusses Cassandra and Hadoop. It describes how Netflix used Cassandra to store user and usage data across multiple data centers and Amazon Web Services regions. Cassandra provided fast writes and reads at scale. The document also discusses how Cassandra can be used as the data store for Hadoop, providing analytics on logs and metrics data. Cassandra offers operational simplicity and high availability through its peer-to-peer and tunable consistency models.
qconsf 2013: Top 10 Performance Gotchas for scaling in-memory Algorithms - Sr...Sri Ambati
Top 10 Performance Gotchas in scaling in-memory Algorithms
Abstract:
Math Algorithms have primarily been the domain of desktop data science. With the success of scalable algorithms at Google, Amazon, and Netflix, there is an ever growing demand for sophisticated algorithms over big data. In this talk, we get a ringside view in the making of the world's most scalable and fastest machine learning framework, H2O, and the performance lessons learnt scaling it over EC2 for Netflix and over commodity hardware for other power users.
Top 10 Performance Gotchas is about the white hot stories of i/o wars, S3 resets, and muxers, as well as the power of primitive byte arrays, non-blocking structures, and fork/join queues. Of good data distribution & fine-grain decomposition of Algorithms to fine-grain blocks of parallel computation. It's a 10-point story of the rage of a network of machines against the tyranny of Amdahl while keeping the statistical properties of the data and accuracy of the algorithm.
Track: Scalability, Availability, and Performance: Putting It All Together
Time: Wednesday, 11:45am - 12:35pm
Compendium of my Brisk, Cassandra & Hadoop talks of the Summer 2011 - Delivered at JavaOne2011. I like the content in this one personally as it touches, Usecase driven intro to Cassandra, NoSQL followed by Intro to hadoop - MapReduce, HDFS internals, NameNode and JobTrackers. And how Brisk decomposes the Single point of failures in HDFS while providing a single form for Realtime & Batch storage and processing.
(And it seemed enjoyable to the audience in attendance)
SFJava, SFNoSQL, SFMySQL, Marakana & Microsoft come together for a presentation evening of three NoSQL technologies - Apache Cassandra, Mongodb, Hadoop.
This talk lays out a few talking points for Apache Cassandra.
Brisk - Truly peer-to-peer hadoop.
Brisk is an open-source Hadoop & Hive distribution that uses Apache Cassandra for its core services and storage. Brisk makes it possible to run Hadoop MapReduce on top of CassandraFS, an HDFS-compatible storage layer. By replacing HDFS with CassandraFS, users leverage MapReduce jobs on Cassandra’s peer-to-peer, fault-tolerant and scalable architecture.
With CassandraFS all nodes are peers. Data files can be loaded through any node in the cluster and any node can serve as the JobTracker for MapReduce jobs. Hive MetaStore is stored & accessed as just another column family (table) on the distributed data store. Brisk makes Hadoop truly peer-to-peer.
We demonstrate visualisation & monitoring of Brisk using OpsCenter. The operational simplicity of cassandra’s multi-datacenter & multi-region aware replication makes Brisk well-suited for a rich set of Applications and usecases. And by being able to store and isolate hdfs & online data within the same data cluster, Brisk makes analytics possible without ETL!
LA Scalability Talk, Mahalo
May 31.2011
qconsf 2013: Top 10 Performance Gotchas for scaling in-memory Algorithms - Sr...Sri Ambati
Top 10 Performance Gotchas in scaling in-memory Algorithms
Abstract:
Math Algorithms have primarily been the domain of desktop data science. With the success of scalable algorithms at Google, Amazon, and Netflix, there is an ever growing demand for sophisticated algorithms over big data. In this talk, we get a ringside view in the making of the world's most scalable and fastest machine learning framework, H2O, and the performance lessons learnt scaling it over EC2 for Netflix and over commodity hardware for other power users.
Top 10 Performance Gotchas is about the white hot stories of i/o wars, S3 resets, and muxers, as well as the power of primitive byte arrays, non-blocking structures, and fork/join queues. Of good data distribution & fine-grain decomposition of Algorithms to fine-grain blocks of parallel computation. It's a 10-point story of the rage of a network of machines against the tyranny of Amdahl while keeping the statistical properties of the data and accuracy of the algorithm.
Track: Scalability, Availability, and Performance: Putting It All Together
Time: Wednesday, 11:45am - 12:35pm
Compendium of my Brisk, Cassandra & Hadoop talks of the Summer 2011 - Delivered at JavaOne2011. I like the content in this one personally as it touches, Usecase driven intro to Cassandra, NoSQL followed by Intro to hadoop - MapReduce, HDFS internals, NameNode and JobTrackers. And how Brisk decomposes the Single point of failures in HDFS while providing a single form for Realtime & Batch storage and processing.
(And it seemed enjoyable to the audience in attendance)
SFJava, SFNoSQL, SFMySQL, Marakana & Microsoft come together for a presentation evening of three NoSQL technologies - Apache Cassandra, Mongodb, Hadoop.
This talk lays out a few talking points for Apache Cassandra.
Brisk - Truly peer-to-peer hadoop.
Brisk is an open-source Hadoop & Hive distribution that uses Apache Cassandra for its core services and storage. Brisk makes it possible to run Hadoop MapReduce on top of CassandraFS, an HDFS-compatible storage layer. By replacing HDFS with CassandraFS, users leverage MapReduce jobs on Cassandra’s peer-to-peer, fault-tolerant and scalable architecture.
With CassandraFS all nodes are peers. Data files can be loaded through any node in the cluster and any node can serve as the JobTracker for MapReduce jobs. Hive MetaStore is stored & accessed as just another column family (table) on the distributed data store. Brisk makes Hadoop truly peer-to-peer.
We demonstrate visualisation & monitoring of Brisk using OpsCenter. The operational simplicity of cassandra’s multi-datacenter & multi-region aware replication makes Brisk well-suited for a rich set of Applications and usecases. And by being able to store and isolate hdfs & online data within the same data cluster, Brisk makes analytics possible without ETL!
LA Scalability Talk, Mahalo
May 31.2011
Lessons from Cassandra & Spark (Matthias Niehoff & Stephan Kepser, codecentri...DataStax
We built an application based on the principles of CQRS and Event Sourcing using Cassandra and Spark. During the project we encountered a number of challenges and problems with Cassandra and the Spark Connector.
In this talk we want to outline a few of those problems and our actions to solve them. While some problems are specific to CQRS and Event Sourcing applications most of them are use case independent.
About the Speakers
Matthias Niehoff IT-Consultant, codecentric AG
works as an IT-Consultant at codecentric AG in Germany. His focus is on big data & streaming applications with Apache Cassandra & Apache Spark. Yet he does not lose track of other tools in the area of big data. Matthias shares his experiences on conferences, meetups and usergroups.
Stephan Kepser Senior IT Consultant and Data Architect, codecentric AG
Dr. Stephan Kepser is an expert on cloud computing and big data. He wrote a couple of journal articles and blog posts on subjects of both fields. His interests reach from legal questions to questions of architecture and design of cloud computing and big data systems to technical details of NoSQL databases.
Big Data Day LA 2015 - Sparking up your Cassandra Cluster- Analytics made Awe...Data Con LA
After a brief technical introduction to Apache Cassandra we'll then go into the exciting world of Apache Spark integration, and learn how you can turn your transactional datastore into an analytics platform. Apache Spark has taken the Hadoop world by storm (no pun intended!), and is widely seen as the replacement to Hadoop Map Reduce. Apache Spark coupled with Cassandra are perfect allies, Cassandra does the distributed data storage, Spark does the distributed computation.
Cassandra on Mesos Across Multiple Datacenters at Uber (Abhishek Verma) | C* ...DataStax
Traditionally, machines were statically partitioned across the different services at Uber. In an effort to increase the machine utilization, Uber has recently started transitioning most of its services, including the storage services, to run on top of Mesos. This presentation will describe the initial experience building and operating a framework for running Cassandra on top of Mesos running across multiple datacenters at Uber. This framework automates several Cassandra operations such as node repairs, addition of new nodes and backup/restore. It improves efficiency by co-locating CPU-intensive services as well as multiple Cassandra nodes on the same Mesos agent. It handles failure and restart of Mesos agents by using persistent volumes and dynamic reservations. This talk includes statistics about the number of Cassandra clusters in production, time taken to start a new cluster, add a new node, detect a node failure; and the observed Cassandra query throughput and latency.
About the Speaker
Abhishek Verma Software Engineer, Uber
Dr. Abhishek Verma is currently working on running Cassandra on top of Mesos at Uber. Prior to this, he worked on BorgMaster at Google and was the first author of the Borg paper published in Eurosys 2015. He received an MS in 2010 and a PhD in 2012 in Computer Science from the University of Illinois at Urbana-Champaign, during which he authored more than 20 publications in conferences, journals and books and presented tens of talks.
Optimizing Your Cluster with Coordinator Nodes (Eric Lubow, SimpleReach) | Ca...DataStax
With the addition of vnodes (Virtual Nodes), Cassandra users were able to gain a few benefits as a result of streaming when it came to bootstrapping and decommissioning nodes. On the flip side, having to route requests on larger clusters became a lot more intensive of a workload for all nodes that were then forced to act coordinator nodes. By setting up a tier of proxy nodes, we were able to have our cluster of 50 nodes perform with a 300% improvement on average in a mixed workload environment. This is an explanation of what we did, how we did it, and why it works.
About the Speaker
Eric Lubow CTO, SimpleReach
Eric Lubow is CTO of SimpleReach, where he builds highly-scalable distributed systems for processing analytics data. Eric is also a DataStax MVP for Cassandra, and co-author of Practical Cassandra. In his spare time, Eric is a skydiver, motorcycle rider, mixed martial artist, and dog dad.
Detail behind the Apache Cassandra 2.0 release and what is new in it including Lightweight Transactions (compare and swap) Eager retries, Improved compaction, Triggers (experimental) and more!
• CQL cursors
Cassandra Day Atlanta 2015: Introduction to Apache Cassandra & DataStax Enter...DataStax Academy
This is a crash course introduction to Cassandra. You'll step away understanding how it's possible to to utilize this distributed database to achieve high availability across multiple data centers, scale out as your needs grow, and not be woken up at 3am just because a server failed. We'll cover the basics of data modeling with CQL, and understand how that data is stored on disk. We'll wrap things up by setting up Cassandra locally, so bring your laptops.
Introduction to Cassandra: Replication and ConsistencyBenjamin Black
A short introduction to replication and consistency in the Cassandra distributed database. Delivered April 28th, 2010 at the Seattle Scalability Meetup.
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.
Lessons from Cassandra & Spark (Matthias Niehoff & Stephan Kepser, codecentri...DataStax
We built an application based on the principles of CQRS and Event Sourcing using Cassandra and Spark. During the project we encountered a number of challenges and problems with Cassandra and the Spark Connector.
In this talk we want to outline a few of those problems and our actions to solve them. While some problems are specific to CQRS and Event Sourcing applications most of them are use case independent.
About the Speakers
Matthias Niehoff IT-Consultant, codecentric AG
works as an IT-Consultant at codecentric AG in Germany. His focus is on big data & streaming applications with Apache Cassandra & Apache Spark. Yet he does not lose track of other tools in the area of big data. Matthias shares his experiences on conferences, meetups and usergroups.
Stephan Kepser Senior IT Consultant and Data Architect, codecentric AG
Dr. Stephan Kepser is an expert on cloud computing and big data. He wrote a couple of journal articles and blog posts on subjects of both fields. His interests reach from legal questions to questions of architecture and design of cloud computing and big data systems to technical details of NoSQL databases.
Big Data Day LA 2015 - Sparking up your Cassandra Cluster- Analytics made Awe...Data Con LA
After a brief technical introduction to Apache Cassandra we'll then go into the exciting world of Apache Spark integration, and learn how you can turn your transactional datastore into an analytics platform. Apache Spark has taken the Hadoop world by storm (no pun intended!), and is widely seen as the replacement to Hadoop Map Reduce. Apache Spark coupled with Cassandra are perfect allies, Cassandra does the distributed data storage, Spark does the distributed computation.
Cassandra on Mesos Across Multiple Datacenters at Uber (Abhishek Verma) | C* ...DataStax
Traditionally, machines were statically partitioned across the different services at Uber. In an effort to increase the machine utilization, Uber has recently started transitioning most of its services, including the storage services, to run on top of Mesos. This presentation will describe the initial experience building and operating a framework for running Cassandra on top of Mesos running across multiple datacenters at Uber. This framework automates several Cassandra operations such as node repairs, addition of new nodes and backup/restore. It improves efficiency by co-locating CPU-intensive services as well as multiple Cassandra nodes on the same Mesos agent. It handles failure and restart of Mesos agents by using persistent volumes and dynamic reservations. This talk includes statistics about the number of Cassandra clusters in production, time taken to start a new cluster, add a new node, detect a node failure; and the observed Cassandra query throughput and latency.
About the Speaker
Abhishek Verma Software Engineer, Uber
Dr. Abhishek Verma is currently working on running Cassandra on top of Mesos at Uber. Prior to this, he worked on BorgMaster at Google and was the first author of the Borg paper published in Eurosys 2015. He received an MS in 2010 and a PhD in 2012 in Computer Science from the University of Illinois at Urbana-Champaign, during which he authored more than 20 publications in conferences, journals and books and presented tens of talks.
Optimizing Your Cluster with Coordinator Nodes (Eric Lubow, SimpleReach) | Ca...DataStax
With the addition of vnodes (Virtual Nodes), Cassandra users were able to gain a few benefits as a result of streaming when it came to bootstrapping and decommissioning nodes. On the flip side, having to route requests on larger clusters became a lot more intensive of a workload for all nodes that were then forced to act coordinator nodes. By setting up a tier of proxy nodes, we were able to have our cluster of 50 nodes perform with a 300% improvement on average in a mixed workload environment. This is an explanation of what we did, how we did it, and why it works.
About the Speaker
Eric Lubow CTO, SimpleReach
Eric Lubow is CTO of SimpleReach, where he builds highly-scalable distributed systems for processing analytics data. Eric is also a DataStax MVP for Cassandra, and co-author of Practical Cassandra. In his spare time, Eric is a skydiver, motorcycle rider, mixed martial artist, and dog dad.
Detail behind the Apache Cassandra 2.0 release and what is new in it including Lightweight Transactions (compare and swap) Eager retries, Improved compaction, Triggers (experimental) and more!
• CQL cursors
Cassandra Day Atlanta 2015: Introduction to Apache Cassandra & DataStax Enter...DataStax Academy
This is a crash course introduction to Cassandra. You'll step away understanding how it's possible to to utilize this distributed database to achieve high availability across multiple data centers, scale out as your needs grow, and not be woken up at 3am just because a server failed. We'll cover the basics of data modeling with CQL, and understand how that data is stored on disk. We'll wrap things up by setting up Cassandra locally, so bring your laptops.
Introduction to Cassandra: Replication and ConsistencyBenjamin Black
A short introduction to replication and consistency in the Cassandra distributed database. Delivered April 28th, 2010 at the Seattle Scalability Meetup.
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.
Breakthrough OLAP performance with Cassandra and SparkEvan Chan
Find out about breakthrough architectures for fast OLAP performance querying Cassandra data with Apache Spark, including a new open source project, FiloDB.
Lambda Architecture with Spark Streaming, Kafka, Cassandra, Akka, ScalaHelena Edelson
Scala Days, Amsterdam, 2015: Lambda Architecture - Batch and Streaming with Spark, Cassandra, Kafka, Akka and Scala; Fault Tolerance, Data Pipelines, Data Flows, Data Locality, Akka Actors, Spark, Spark Cassandra Connector, Big Data, Asynchronous data flows. Time series data, KillrWeather, Scalable Infrastructure, Partition For Scale, Replicate For Resiliency, Parallelism
Isolation, Data Locality, Location Transparency
Apache Cassandra Lunch #72: Databricks and CassandraAnant Corporation
In Cassandra Lunch #72, we will discuss how we can use Databricks with Cassandra.
Accompanying Blog: https://blog.anant.us/apache-cassandra-lunch-72-databricks-and-cassandra
Accompanying YouTube: https://youtu.be/5zCN27KHADo
Sign Up For Our Newsletter: http://eepurl.com/grdMkn
Join Cassandra Lunch Weekly at 12 PM EST Every Wednesday: https://www.meetup.com/Cassandra-DataStax-DC/events/
Cassandra.Link:
https://cassandra.link/
Follow Us and Reach Us At:
Anant:
https://www.anant.us/
Awesome Cassandra:
https://github.com/Anant/awesome-cassandra
Cassandra.Lunch:
https://github.com/Anant/Cassandra.Lunch
Email:
solutions@anant.us
LinkedIn:
https://www.linkedin.com/company/anant/
Twitter:
https://twitter.com/anantcorp
Eventbrite:
https://www.eventbrite.com/o/anant-1072927283
Facebook:
https://www.facebook.com/AnantCorp/
Join The Anant Team:
https://www.careers.anant.us
C* Summit 2013: Netflix Open Source Tools and Benchmarks for Cassandra by Adr...DataStax Academy
Netflix has updated and added new tools and benchmarks for Cassandra in the last year. In this talk we will cover the latest additions and recipes for the Astyanax Java client, updates to Priam to support Cassandra 1.2 Vnodes, plus newly released and upcoming tools that are all part of the NetflixOSS platform. Following on from the Cassandra on SSD on AWS benchmark that was run live during the 2012 Summit, we've been benchmarking a large write intensive multi-region cluster to see how far we can push it. Cassandra is the data storage and global replication foundation for the Cloud Native architecture that runs Netflix streaming for 36 Million users. Netflix is also offering a Cloud Prize for open source contributions to NetflixOSS, and there are ten categories including Best Datastore Integration and Best Contribution to Performance Improvements, with $10K cash and $5K of AWS credits for each winner. We'd like to pay you to use our free software!
Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open-source and horizontally scalable. The original intention has been modern web-scale databases. The movement began early 2009 and is growing rapidly. Often more characteristics apply such as: schema-free, easy replication support, simple API, eventually consistent / BASE (not ACID), a huge amount of data and more. So the misleading term "nosql" (the community now translates it mostly with "not only sql") should be seen as an alias to something like the definition above.
Fast and Simplified Streaming, Ad-Hoc and Batch Analytics with FiloDB and Spa...Helena Edelson
O'Reilly Webcast with Myself and Evan Chan on the new SNACK Stack (playoff of SMACK) with FIloDB: Scala, Spark Streaming, Akka, Cassandra, FiloDB and Kafka.
A Tale of Three Apache Spark APIs: RDDs, DataFrames, and Datasets with Jules ...Databricks
Of all the developers’ delight, none is more attractive than a set of APIs that make developers productive, that are easy to use, and that are intuitive and expressive. Apache Spark offers these APIs across components such as Spark SQL, Streaming, Machine Learning, and Graph Processing to operate on large data sets in languages such as Scala, Java, Python, and R for doing distributed big data processing at scale. In this talk, I will explore the evolution of three sets of APIs-RDDs, DataFrames, and Datasets-available in Apache Spark 2.x. In particular, I will emphasize three takeaways: 1) why and when you should use each set as best practices 2) outline its performance and optimization benefits; and 3) underscore scenarios when to use DataFrames and Datasets instead of RDDs for your big data distributed processing. Through simple notebook demonstrations with API code examples, you’ll learn how to process big data using RDDs, DataFrames, and Datasets and interoperate among them. (this will be vocalization of the blog, along with the latest developments in Apache Spark 2.x Dataframe/Datasets and Spark SQL APIs: https://databricks.com/blog/2016/07/14/a-tale-of-three-apache-spark-apis-rdds-dataframes-and-datasets.html)
This presentation explains how to get started with Apache Cassandra to provide a scale out, fault tolerant backend for inventory storage on OpenSimulator.
The deck describes ScyllaDB's flagship product - a drop and replacement alternative to Apache Cassandra at 10X the speed. ScyllaDB innovative design relies on shard-per-core, own caching and c++ to deliver blazing and consistent performance. Check the deck on how this was achieved.
Top 10 Performance Gotchas for scaling in-memory Algorithms.srisatish ambati
Top 10 Data Parallelism and Model Parallelism lessons from scaling H2O.
"Math Algorithms have primarily been the domain of desktop data science. With the success of scalable algorithms at Google, Amazon, and Netflix, there is an ever growing demand for sophisticated algorithms over big data. In this talk, we get a ringside view in the making of the world's most scalable and fastest machine learning framework, H2O, and the performance lessons learnt scaling it over EC2 for Netflix and over commodity hardware for other power users.
Top 10 Performance Gotchas is about the white hot stories of i/o wars, S3 resets, and muxers, as well as the power of primitive byte arrays, non-blocking structures, and fork/join queues. Of good data distribution & fine-grain decomposition of Algorithms to fine-grain blocks of parallel computation. It's a 10-point story of the rage of a network of machines against the tyranny of Amdahl while keeping the statistical properties of the data and accuracy of the algorithm."
SF Java presentation of jvm goes to big data.
“Slowly yet surely the JVM is going to Big Data! In this fun filled presentation we see what pieces of Java & JVM triumph or unravel in the battle for performance at high scale!”
Concurrency is the currency of scale on multi-core & the new generation of collections and non-blocking hashmaps are well worth the time taking a deep dive into. We take a quick look at the next gen serialization techniques as well as implementation pitfalls around UUID. The achilles' heel for JVM remains Garbage Collection: a deep dive into the internals of the memory model, common GC algorithms and their tuning knobs is always a big draw. EC2 & cloud present us with a virtualized & unchartered territory for scaling the JVM.
We will leave some room for Q&A or fill it up with any asynchronous I/O that might queue up during the talk. A round of applause will be due to the various tools that are essentials for Java performance debugging.
invited netflix talk: JVM issues in the age of scale! We take an under the hood look at java locking, memory model, overheads, serialization, uuid, gc tuning, CMS, ParallelGC, java.
Caching in Java - A review of different caching vendors (Oracle Coherence, Apache Cassandra, Infinispan, Ehcache/Terracotta, etc) and limitations presented by the underlying Java Platform.
Presented at RedHat Summit 2010, Boston
Speakers: SriSatish Ambati, Performance Engg
Manik Surtani, InfiniSpan Lead
Presentation details from RH Summit:
How to Stop Worrying & Start Caching in Java
SriSatish Ambati — Performance & Partner Engineer, Azul Systems, Inc.
Manik Surtani — Principal Software Engineer, Red Hat
Application data caching has come of age as distributed and large cache clusters are now common. The next generation of applications that depend on efficient caching has come into being and data and cache size explosion has set in.
In this session, Azul Systems’ SriSatish Ambati and Red Hat’s Manik Surtani will survey performance characteristics of different cache algorithms, their implementations (e.g., implementing a 200Gb data cache size), and how well they work in practical JVM deployments. In each scenario, they will present patterns of architecture that scale, and demonstrate where read and write performance stands in the context of increasing cache sizes and concurrency.
Throughout this discussion, they will recognize several villains, including heap fragmentation, long-lived objects, multi-VM communication, socket handlers, and queue managers. SriSatish and Manik will take a fun-filled “whodunit” approach to portray the roles played by each villain in killing cache performance.
http://www.redhat.com/promo/summit/2010/sessions/jboss.html
JavaOne 2010: Top 10 Causes for Java Issues in Production and What to Do When...srisatish ambati
Top 10 Causes for Java Issues in Production and What to Do When Things Go Wrong
JavaOne 2010.
Abstract: It's Friday evening and you hear the first rumble . . . one java node has become slightly unresponsive. You lookup the process, get a thread dump, and for good measure restart it at 8 p.m. Saturday afternoon is when you realize that other nodes have caught the flu and you get the ugly call from the customer. In a matter of hours, you're on that conference bridge with support groups of different packages and Java vendors and one of your uberarchitects. Yes, production instances are up and down, and restarting like there's no tomorrow. Here's an accumulated compendium of the op 10 things that can cause Java production heartburn and what to do when your Java production is on fire. And yes, please have your tools belt on.
Speaker(s):
Cliff Click, Azul Systems, Distinguished Engineer
SriSatish Ambati, Azul Systems, Performance Engineer
ApacheCon2010: Cache & Concurrency Considerations in Cassandra (& limits of JVM)srisatish ambati
Cache & Concurrency considerations for a high performance Cassandra deployment.
SriSatish Ambati
Cassandra has hit it's stride as a distributed java NoSQL database! It's fast, it's in-memory, it's scalable, it's seda; It's eventually consistent model makes it practical for the large & growing volumes of unstructured data usecases. It is also time to run it through the filters of performance analysis. For starters it runs on the java virtual machine and inherits the capabilities and culpabilities of the platform. This presentation reviews the runtime architecture, cache behavior & performance of a real-world workload on Cassandra. We blend existing system & jvm tools to get a quick overview & a breakdown of hotspots in the get, put & update operations. We highlight the role played by garbage collection & fragmentation due to long lived objects; We investigate lock contention in the data structures under concurrent usage. Cassandra uses UDP for management & TCP for data: we look at robustness of the communication patterns during high spikes and cluster-wide events. We review Non-Blocking Hashmap modifications to Cassandra that improve concurrency & amplify performance of this frontrunner in the NoSQL space
ApacheCon2010 NA
Wed, 03 November 2010 15:00
cassandra
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
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
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
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.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into 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.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
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