Apache Cassandra is a free and open source distributed database management system that is highly scalable and designed to manage large amounts of structured data. It provides high availability with no single point of failure. Cassandra uses a decentralized architecture and is optimized for scalability and availability without compromising performance. It distributes data across nodes and data centers and replicates data for fault tolerance.
Apache Cassandra is a highly scalable second-generation distributed database, bringing together Dynamo's fully distributed design and Bigtable's ColumnFamily-based data model.
This presentation, given at FOSDEM in 2010, provides a brief summary of cassandra's history, a high-level overview of the architecture and data model, and showcases some real life use-cases.
Agenda
- What is NOSQL?
- Motivations for NOSQL?
- Brewer’s CAP Theorem
- Taxonomy of NOSQL databases
- Apache Cassandra
- Features
- Data Model
- Consistency
- Operations
- Cluster Membership
- What Does NOSQL means for RDBMS?
This is a presentation of the popular NoSQL database Apache Cassandra which was created by our team in the context of the module "Business Intelligence and Big Data Analysis".
Basic Introduction to Cassandra with Architecture and strategies.
with big data challenge. What is NoSQL Database.
The Big Data Challenge
The Cassandra Solution
The CAP Theorem
The Architecture of Cassandra
The Data Partition and Replication
Apache Cassandra is a highly scalable second-generation distributed database, bringing together Dynamo's fully distributed design and Bigtable's ColumnFamily-based data model.
This presentation, given at FOSDEM in 2010, provides a brief summary of cassandra's history, a high-level overview of the architecture and data model, and showcases some real life use-cases.
Agenda
- What is NOSQL?
- Motivations for NOSQL?
- Brewer’s CAP Theorem
- Taxonomy of NOSQL databases
- Apache Cassandra
- Features
- Data Model
- Consistency
- Operations
- Cluster Membership
- What Does NOSQL means for RDBMS?
This is a presentation of the popular NoSQL database Apache Cassandra which was created by our team in the context of the module "Business Intelligence and Big Data Analysis".
Basic Introduction to Cassandra with Architecture and strategies.
with big data challenge. What is NoSQL Database.
The Big Data Challenge
The Cassandra Solution
The CAP Theorem
The Architecture of Cassandra
The Data Partition and Replication
I don't think it's hyperbole when I say that Facebook, Instagram, Twitter & Netflix now define the dimensions of our social & entertainment universe. But what kind of technology engines purr under the hoods of these social media machines?
Here is a tech student's perspective on making the paradigm shift to "Big Data" using innovative models: alphabet blocks, nesting dolls, & LEGOs!
Get info on:
- What is Cassandra (C*)?
- Installing C* Community Version on Amazon Web Services EC2
- Data Modelling & Database Design in C* using CQL3
- Industry Use Cases
The Apache Cassandra database is the right choice when you need scalability and high availability without compromising performance. Linear scalability and proven fault-tolerance on commodity hardware or cloud infrastructure make it the perfect platform for mission-critical data.Cassandra's support for replicating across multiple datacenters is best-in-class, providing lower latency for your users and the peace of mind of knowing that you can survive regional outages.
http://tyfs.rocks
Archaic database technologies just don't scale under the always on, distributed demands of modern IOT, mobile and web applications. We'll start this Intro to Cassandra by discussing how its approach is different and why so many awesome companies have migrated from the cold clutches of the relational world into the warm embrace of peer to peer architecture. After this high-level opening discussion, we'll briefly unpack the following:
• Cassandra's internal architecture and distribution model
• Cassandra's Data Model
• Reads and Writes
C* Summit 2013: Cassandra at eBay Scale by Feng Qu and Anurag JambhekarDataStax Academy
We have seen rapid adoption of C* at eBay in past two years. We have made tremendous efforts to integrate C* into existing database platforms, including Oracle, MySQL, Postgres, MongoDB, XMP etc.. We also scale C* to meet business requirement and encountered technical challenges you only see at eBay scale, 100TB data on hundreds of nodes. We will share our experience of deployment automation, managing, monitoring, reporting for both Apache Cassandra and DataStax enterprise.
I don't think it's hyperbole when I say that Facebook, Instagram, Twitter & Netflix now define the dimensions of our social & entertainment universe. But what kind of technology engines purr under the hoods of these social media machines?
Here is a tech student's perspective on making the paradigm shift to "Big Data" using innovative models: alphabet blocks, nesting dolls, & LEGOs!
Get info on:
- What is Cassandra (C*)?
- Installing C* Community Version on Amazon Web Services EC2
- Data Modelling & Database Design in C* using CQL3
- Industry Use Cases
The Apache Cassandra database is the right choice when you need scalability and high availability without compromising performance. Linear scalability and proven fault-tolerance on commodity hardware or cloud infrastructure make it the perfect platform for mission-critical data.Cassandra's support for replicating across multiple datacenters is best-in-class, providing lower latency for your users and the peace of mind of knowing that you can survive regional outages.
http://tyfs.rocks
Archaic database technologies just don't scale under the always on, distributed demands of modern IOT, mobile and web applications. We'll start this Intro to Cassandra by discussing how its approach is different and why so many awesome companies have migrated from the cold clutches of the relational world into the warm embrace of peer to peer architecture. After this high-level opening discussion, we'll briefly unpack the following:
• Cassandra's internal architecture and distribution model
• Cassandra's Data Model
• Reads and Writes
C* Summit 2013: Cassandra at eBay Scale by Feng Qu and Anurag JambhekarDataStax Academy
We have seen rapid adoption of C* at eBay in past two years. We have made tremendous efforts to integrate C* into existing database platforms, including Oracle, MySQL, Postgres, MongoDB, XMP etc.. We also scale C* to meet business requirement and encountered technical challenges you only see at eBay scale, 100TB data on hundreds of nodes. We will share our experience of deployment automation, managing, monitoring, reporting for both Apache Cassandra and DataStax enterprise.
This is a preliminary study and the objective of this study is to make simple distributed database system with some basic tutorials. Cassandra is a distributed database from Apache that is highly scalable and designed to accomplish very large amounts of organized data. Without having a single point of failure, it offers high accessibility. This report highlights with a basic outline of Cassandra trailed by its architecture, installation, and significant classes and interfaces. Subsequently, it proceeds to cover how to perform operations such as CREATE, ALTER, UPDATE, and DELETE on KEYSPACES, TABLES, and INDEXES using CQLSH using C#/.NET Client with a sample program done by ASP.NET(C#).
The project is focussed on Comparison Between HBASE and CASSANDRA using YCSB. It is a data storage and management project performed at National College Of Ireland
I have examined the performance of two databases - HBase and Cassandra in terms of their scalability, security, performance and compared the results thus obtained through different operations on the Ubuntu interface.
CASSANDRA A DISTRIBUTED NOSQL DATABASE FOR HOTEL MANAGEMENT SYSTEMIJCI JOURNAL
Apache Cassandra is a distributed storage system for managing very large amounts of structured data.
Cassandra provides highly available service with no single point of failure. Cassandra aims to run on top
of an infrastructure of hundreds of nodes possibly spread across different data centers with small and large
components fail continuously. Cassandra manages the persistent state in the face of the failures which
drives the reliability and scalability of the software systems. Cassandra does not support a full relational
data model because it resembles a database and shares many design and implementation strategies. In this
paper, discuss an implementation of Cassandra as Hotel Management System application. Cassandra
system was designed to run on cheap commodity hardware. Cassandra provides high write throughput and
read efficiency.
The No SQL Principles and Basic Application Of Casandra ModelRishikese MR
The slides discuss various matters of the No SQL and casandra Models, the slide gives a complete picture of the both topics and its relations. Also it discuss the merits and demerits of the topics and its features and examples are also described.
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.
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.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
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.
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.
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/
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!
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.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Leading Change strategies and insights for effective change management pdf 1.pdf
Cassandra tutorial
1. Cassandra Tutorial
Apache Cassandra is a free open source
and distributed database management
system.It is highly scalable and designed
to manage very large amounts of
structured data. It provides high
availability with no single point of failure.
2. NoSQLDatabase
• A NoSQL database (sometimes called as Not Only SQL) is a
database that provides a mechanism to store and retrieve data other
than the tabular relations used in relational databases. These
databases are schema-free, support easy replication, have simple
API, eventually consistent, and can handle huge amounts of data.
• The primary objective of a NoSQL database is to have
• simplicity of design,
• horizontal scaling
• finer control over availability.
• NoSql databases use different data structures compared to
relational databases. It makes some operations faster in NoSQL. The
suitability of a given NoSQL database depends on the problem it
must solve.
3. • Apache Cassandra is an open source distributed database
system that is designed for storing and managing large
amounts of data across commodity servers. Cassandra can
serve as both a real-time operational data store for online
transactional applications and a read-intensive database for
large-scale business intelligence systems.
• Originally created for facebook, Cassandra is designed to have
peer to peer symmetric nodes, instead of master or named
nodes, to ensure there can never be a single point of failure
Cassandra automatically partitions data across all the nodes
in the database cluster, but the administrator has the power to
determine what data will be replicated and how many copies
of the data will be created.
4. Features of Cassandra
• Cassandra Features:
• Elastic scalability - Cassandra is highly scalable; it allows to add more hardware to
accommodate more customers and more data as per requirement.
• Always on architecture - Cassandra has no single point of failure and it is continuously
available for business-critical applications that cannot afford a failure.
• Fast linear-scale performance - Cassandra is linearly scalable, i.e., it increases your
throughput as you increase the number of nodes in the cluster. Therefore it maintains a
quick response time.
• Flexible data storage - Cassandra accommodates all possible data formats including:
structured, semi-structured, and unstructured. It can dynamically accommodate changes to
your data structures according to your need.
• Easy data distribution - Cassandra provides the flexibility to distribute data where you
need by replicating data across multiple data centers.
• Transaction support - Cassandra supports properties like Atomicity, Consistency,
Isolation, and Durability (ACID).
• Fast writes - Cassandra was designed to run on cheap commodity hardware. It performs
blazingly fast writes and can store hundreds of terabytes of data, without sacrificing the
read efficiency.
5. Components of Cassandra
• Cassandra uses the Gossip Protocol in the background to allow the nodes
to communicate with each other and detect any faulty nodes in the cluster.
• The key components of Cassandra are as follows −
• Node − It is the place where data is stored.
• Data center − It is a collection of related nodes.
• Cluster − A cluster is a component that contains one or more data centers.
• Commit log − The commit log is a crash-recovery mechanism in
Cassandra. Every write operation is written to the commit log.
• Mem-table − A mem-table is a memory-resident data structure. After
commit log, the data will be written to the mem-table. Sometimes, for a
single-column family, there will be multiple mem-tables.
• SSTable − It is a disk file to which the data is flushed from the mem-table
when its contents reach a threshold value.
• Bloom filter − These are nothing but quick, nondeterministic, algorithms
for testing whether an element is a member of a set. It is a special kind of
cache. Bloom filters are accessed after every query.
6. Apache Cassandra data types
• Apache Cassandra NoSQL DBMS supports the most
common data types, including ASCII, bigint, BLOB,
Boolean, counter, decimal, double, float, int, text,
timestamp, UUID, VARCHAR and varint.
• Cassandra's data model offers the convenience of
column indexes with the performance of log-
structured updates, strong support for
denormalization and materialized views, and built-
in caching.
• Data access is performed using Cassandra Query
Language (CQL), which resembles SQL.
7. Cassandra Query Language
• Users can access Cassandra through its nodes using
Cassandra Query Language (CQL). CQL treats the
database (Keyspace) as a container of tables.
Programmers use cqlsh: a prompt to work with CQL or
separate application language drivers.
• Clients approach any of the nodes for their read-write
operations. That node (coordinator) plays a proxy
between the client and the nodes holding the data.
8. • Data storage in Cassandra is row-oriented, meaning that
all contents of a row are serialized together on disk.
Every row of columns has its unique key. Each row can
hold up to 2 billion columns .Furthermore, each row
must fit onto a single server, because data is partitioned
solely by row-key.
• To understand why databases like Cassandra, HBase and
BigTable (I’ll call them DSS, Distributed Storage
Services, from now on) were designed the way they are,
we’ll first have to understand what they were built to be
used for.
9. • DSS(A decision support system (DSS) is a computer-based
information system that supports business or organizational
decision-making activities. were designed to handle enormous
amounts of data, stored in billions of rows on large clusters.
Relational databases incorporate a lot of things that make it hard to
efficiently distribute them over multiple machines. DSS simply
remove some or all of these ties. No operations are allowed, that
require scanning extensive parts of the dataset, meaning no JOINS
or rich-queries
• Cassandra is a NoSQL Column family implementation supporting
the Big Table data model using the architectural aspects introduced
by Amazon Dynamo.
10. column family
• Cassandra consists of many storage nodes and stores each row
within a single storage node. Within each row, Cassandra
always stores columns sorted by their column names. Using
this sort order, Cassandra supports slice queries where given a
row, users can retrieve a subset of its columns falling within a
given column name range. For example, a slice query with
range tag0 to tag9999 will get all the columns whose names
fall between tag0 and tag9999.
• Keyspace – a group of many column families together. It is
only a logical grouping of column families and provides an
isolated scope for names.
• Finally, super columns reside within a column family that
groups several columns under a one key.
11. • Cassandra provides very fast writes, and they are actually
faster than reads where it can transfer data about 80-
360MB/sec per node. It achieves this using two
techniques.Cassandra keeps most of the data within memory
at the responsible node, and any updates are done in the
memory and written to the persistent storage (file system) in a
lazy fashion. To avoid losing data, however, Cassandra writes
all transactions to a commit log in the disk. Unlike updating
data items in the disk, writes to commit logs are append-only
and, therefore, avoid rotational delay while writing to the
disk. For more information on disk-drive performance
characteristics, see Resources.
12. • Unless writes have requested full consistency, Cassandra writes data to enough
nodes without resolving any data inconsistencies where it resolves
inconsistencies only at the first read. This process is called "read repair.“
• Healing from failure is manual
• If a node in a Cassandra cluster has failed, the cluster will continue to work if
you have replicas. Full recovery, which is to redistribute data and compensate
for missing replicas, is a manual operation through a command line tool
called node tool. Also, while the manual operation happens, the system will be
unavailable.
• It remembers deletes
• Cassandra is designed such that it continues to work without a problem even if a
node goes down (or gets disconnected) and comes back later. A consequence is
this complicates data deletions. For example, assume a node is down. While
down, a data item has been deleted in replicas. When the unavailable node
comes back on, it will reintroduce the deleted data item at the syncing process
unless Cassandra remembers that data item has been deleted.