Cassandra is a distributed, column-oriented database designed to be highly scalable and fault-tolerant. It distributes data across nodes based on the partitioner, replicates data based on the replication strategy, and achieves consistency between replicas using a combination of hinted handoffs and read repair during reads and writes. Keyspaces contain column families which store rows of columns in a flexible schema-less data model that scales horizontally by adding more nodes.
Big Data & NoSQL - EFS'11 (Pavlo Baron)Pavlo Baron
That's the slides of my half day workshop at the EFS'11 in Stuttgart where I covered some theoretical aspects of NoSQL data stores relevant for dealing with large data amounts
https://mloey.github.io/courses/security2017.html
https://www.youtube.com/watch?v=td_8AM80DUA&list=PLKYmvyjH53q13_6aS4VwgXU0Nb_4sjwuf&index=2&t=37s
We will discuss the following: Symmetric Encryption, Substitution Techniques, Caesar Cipher, Monoalphabetic Cipher, Playfair Cipher, Hill Cipher
In this whole idea of v symmetric cipher model and also cryptography and cryptanalytics, also substitution techniques and transposition techniques and steganography.
In cryptography, a one-time pad (OTP) is an encryption technique that cannot be cracked if used correctly. In this technique, a plaintext is paired with a random ...
Big Data & NoSQL - EFS'11 (Pavlo Baron)Pavlo Baron
That's the slides of my half day workshop at the EFS'11 in Stuttgart where I covered some theoretical aspects of NoSQL data stores relevant for dealing with large data amounts
https://mloey.github.io/courses/security2017.html
https://www.youtube.com/watch?v=td_8AM80DUA&list=PLKYmvyjH53q13_6aS4VwgXU0Nb_4sjwuf&index=2&t=37s
We will discuss the following: Symmetric Encryption, Substitution Techniques, Caesar Cipher, Monoalphabetic Cipher, Playfair Cipher, Hill Cipher
In this whole idea of v symmetric cipher model and also cryptography and cryptanalytics, also substitution techniques and transposition techniques and steganography.
In cryptography, a one-time pad (OTP) is an encryption technique that cannot be cracked if used correctly. In this technique, a plaintext is paired with a random ...
We rubyists historically haven’t been in the habit of thinking about concurrency but the reality is that our thread-unsafe code often works by sheer luck. There are different implementations of Ruby with their own semantics that can unearth challenging and unexpected concurrency bugs in our code. We have to become more accustomed to writing threadsafe code in order to anticipate these potential surprises, especially in light of the rise in popularity of JRuby.
I will discuss approaches to writing threadsafe code in this talk, with a specific focus on performance considerations and testing. I'll start by explaining some basic concurrency concepts, describe methods for handling shared mutable data, and touch on the subtleties of concurrency primitives (Mutex, ConditionVariable). Hair-raising, real-world bugs will be used throughout the presentation to illustrate specific concurrency issues and techniques for solving them.
Overview of Cassandra architecture. Learn about how data is read and written into a Cassandra cluster. Internal gossip protocol. Some key data structure Cassandra uses like bloom filters, consistent hashing.
Webinar Back to Basics 3 - Introduzione ai Replica SetMongoDB
Un set di repliche in MongoDB è un gruppo di processi che mantengono copie dei dati su diversi server di database. Assicurano ridondanza e disponibilità elevata e sono la base di tutte le distribuzioni in produzione di MongoDB.
Why and How to use Onion Networking - #EMFCamp2018Alec Muffett
Outlining the hows and whys of using Onion Networking to connect apps, devices and tools securely over the Internet, without suffering blocks, NAT issues, or many forms of security woe.
When it Absolutely, Positively, Has to be There: Reliability Guarantees in Ka...confluent
In the financial industry, losing data is unacceptable. Financial firms are adopting Kafka for their critical applications. Kafka provides the low latency, high throughput, high availability, and scale that these applications require. But can it also provide complete reliability? As a system architect, when asked “Can you guarantee that we will always get every transaction,” you want to be able to say “Yes” with total confidence.
In this session, we will go over everything that happens to a message – from producer to consumer, and pinpoint all the places where data can be lost – if you are not careful. You will learn how developers and operation teams can work together to build a bulletproof data pipeline with Kafka. And if you need proof that you built a reliable system – we’ll show you how you can build the system to prove this too.
DIY: A distributed database cluster, or: MySQL ClusterUlf Wendel
Live from the International PHP Conference 2013: MySQL Cluster is a distributed, auto-sharding database offering 99,999% high availability. It runs on Rasperry PI as well as on a cluster of multi-core machines. A 30 node cluster was able to deliver 4.3 billion (not million) read transactions per second in 2012. Take a deeper look into the theory behind all the MySQL replication/clustering solutions (including 3rd party) and learn how they differ.
Cassandra SF 2015 - Repeatable, Scalable, Reliable, Observable Cassandraaaronmorton
Slides from my talk at Cassandra Summit 2015
http://cassandrasummit-datastax.com/agenda/repeatable-scalable-reliable-observable-cassandra/
thelastpickle.com
Cassandra sf 2015 - Steady State Data Size With Compaction, Tombstones, and TTL aaronmorton
Slides from my talk at Cassandra Summit 2015
http://cassandrasummit-datastax.com/agenda/steady-state-data-size-with-compaction-tombstones-and-ttl/
thelastpickle.com
My talk from http://wdcnz.com 2012.
I took a brief look at Cassandra and then stepped through building a twitter clone. Very rough code is at https://github.com/amorton/wdcnz-2012-site
Building a distributed Key-Value store with Cassandraaaronmorton
Slides from my talk at Kiwi Pycon in 2010.
Covers why we chose Cassandra, overview of it's feature and data model, and how we implemented our application.