The Cassandra Distributed Database

An introduction to the Apache Cassandra database, as presented at the Northern Illinois Coders user group on 20141022.

This document provides an agenda and introduction for a presentation on Apache Cassandra and DataStax Enterprise. The presentation covers an introduction to Cassandra and NoSQL, the CAP theorem, Apache Cassandra features and architecture including replication, consistency levels and failure handling. It also discusses the Cassandra Query Language, data modeling for time series data, and new features in DataStax Enterprise like Spark integration and secondary indexes on collections. The presentation concludes with recommendations for getting started with Cassandra in production environments.

Cassandra is a distributed database designed to handle large amounts of data across commodity servers. It aims for high availability with no single points of failure. Data is distributed across nodes and replicated for redundancy. Cassandra uses a decentralized design with peer-to-peer communication and an eventually consistent model. It requires denormalized data models and queries to be defined prior to data structure.

Cassandra is a distributed database that is especially well-suited for handling large volumes of writes and data across many servers. It provides high availability through replication and tunable consistency levels. The document discusses Cassandra's architecture including its use of a ring topology, log-structured storage, and data model using a partition key and clustering columns. It also explains how Cassandra can be used as part of a polyglot persistence strategy along with complementary technologies like Spark and DSE Analytics.

This document provides an introduction to Cassandra, including key details about its history, supported versions, scalability, data model, and use cases. Cassandra is an open source distributed database management system designed to handle large amounts of data across many commodity servers. It provides high availability with no single points of failure and linear scalability across commodity hardware. Cassandra is optimized for fast reads on large datasets based on predefined keys or indexes and is well-suited for applications with heavy write loads like time series data, messaging, and fraud detection.

This document discusses evaluating Apache Cassandra as a cloud database. It provides an overview of DataStax, the commercial leader in Apache Cassandra. DataStax delivers database products and services based on Cassandra. Cassandra is a free, distributed, high performance, and extremely scalable database that can serve as both a real-time and read-intensive database. The document outlines how Cassandra stacks up against key attributes of a cloud database such as transparent elasticity, scalability, high availability, and more. It encourages readers to download Cassandra to try in their own environments.

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

This document outlines an online course on Cassandra that covers its key concepts and features. The course contains 8 modules that progress from introductory topics to more advanced ones like integrating Cassandra with Hadoop. It teaches students how to model and query data in Cassandra, configure and maintain Cassandra clusters, and build a sample application. The course includes live classes, recordings, quizzes, assignments, and an online certification exam to help students learn Cassandra.

Apache Cassandra is a highly scalable, multi-datacenter database that provides massive scalability, high performance, reliability and availability without single points of failure. It is operations and developer friendly with simple design, exposed metrics, and tools like OpsCenter and DevCenter. Cassandra is used by many large companies including Netflix to store film metadata and user ratings, La Poste to store parcel distribution metadata, and Spotify to store over 1 billion playlists.

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.

Cassandra is a highly scalable, eventually consistent, distributed, structured columnfamily store with no single points of failure, initially open-sourced by Facebook and now part of the Apache Incubator. These slides are from Jonathan Ellis's OSCON 09 talk: http://en.oreilly.com/oscon2009/public/schedule/detail/7975

Apache Cassandra is a scalable distributed hash map that stores data across multiple commodity servers. It provides high availability with no single point of failure and scales horizontally as more servers are added. Cassandra uses an eventually consistent model and tunable consistency levels. Data is organized into keyspaces containing column families with rows and columns.

This document provides an overview and introduction to Cassandra including: - An agenda that outlines the topics covered in the overview including architecture, data modeling differences from RDBMS, and CQL. - Recommended resources for learning more about Cassandra including documentation, video courses, books, and articles. - Requirements that Cassandra aims to meet for database management including scaling, uptime, performance, and cost. - Key aspects of Cassandra including being open source, distributed, decentralized, scalable, fault tolerant, and using a flexible data model. - Examples of large companies that use Cassandra in production including Apple, Netflix, eBay, and others handling large datasets.

Cassandra is a distributed, decentralized, wide column store NoSQL database modeled after Amazon's Dynamo and Google's Bigtable. It provides high availability with no single point of failure, elastic scalability and tunable consistency. Cassandra uses consistent hashing to partition and distribute data across nodes, vector clocks to track data versions for consistency, and Merkle trees to detect and repair inconsistencies between replicas.

An introduction to NoSQL databases and an overview of Apache Cassandra as a column family database. Presentation I gave at Synechron Technologies

With Apache Cassandra being a massively scalable open source NoSQL database and with the amount of data that we create and copy annually which is doubling in size every two years, it is expected to reach 44 zettabytes, or 44 trillion gigabytes, we can assume that sooner or later a DBA will be handling a Cassandra database in their shop. This beginner/intermediate-level session will take you through my journey of an Oracle DBA and my first 100 days of starting to administer a Cassandra Cluster, show several demos and all the roadblocks and the success I had along this path.

This document provides an overview of Apache Cassandra and Datastax Enterprise. It discusses what Cassandra is, how it is used across different industries, its key features like scalability and availability. It also covers Cassandra terminology, data distribution, replication strategies, consistency levels, and how reads and writes work in Cassandra.

1) The document discusses Cassandra, a NoSQL database. It provides an overview of Cassandra's history and features. 2) Cassandra was originally developed at Facebook and is now an open source project. It is based on concepts from Bigtable and Dynamo. 3) The document covers Cassandra's data model, architecture including use of gossip protocols and consistency levels, and compares it with relational databases.

At Spotify, we see failure as an opportunity to learn. During the two years we've used Cassandra in our production environment, we have learned a lot. This session touches on some of the exciting design anti-patterns, performance killers and other opportunities to lose a finger that are at your disposal with Cassandra.

The document discusses Cassandra concepts, patterns, and anti-patterns. It begins with an agenda that covers choosing NoSQL, Cassandra concepts based on Dynamo and Bigtable, and patterns and anti-patterns of use. It then delves into Cassandra concepts such as consistent hashing, vector clocks, gossip protocol, hinted handoff, read repair, and consistency levels. It also discusses Bigtable concepts like sparse column-based data model, SSTables, commit log, and memtables. Finally, it outlines several patterns and anti-patterns of Cassandra use.

Cassandra's data model is more flexible than typically assumed. Cassandra allows tuning of consistency levels to balance availability and consistency. It can be made consistently when certain replication conditions are met. Cassandra uses a row-oriented model where rows are uniquely identified by keys and group columns and super columns. Super column families allow grouping columns under a common name and are often used for denormalizing data. Cassandra's data model is query-based rather than domain-based. It focuses on answering questions through flexible querying rather than storing predefined objects. Design patterns like materialized views and composite keys can help support different types of queries.

"Buy It Now! Cassandra at eBay" talk at Cassandra Summit 2013 This session will cover various use cases for Cassandra at eBay. It’ll start with overview of eBay’s heterogeneous data platform comprised of SQL & NoSQL databases, and where Cassandra fits into that. For each use case, Jay will go into detail of system design, data model & multi-datacenter deployment. To conclude, Jay will summarize the best practices that guide Cassandra utilization at eBay. http://www.datastax.com/company/news-and-events/events/cassandrasummit2013

CQL is the query language for Apache Cassandra that provides an SQL-like interface. The document discusses the evolution from the older Thrift RPC interface to CQL and provides examples of modeling tweet data in Cassandra using tables like users, tweets, following, followers, userline, and timeline. It also covers techniques like denormalization, materialized views, and batch loading of related data to optimize for common queries.

Cassandra is a distributed database management system designed to handle large amounts of data across many commodity servers. It provides high availability with no single points of failure and linear scalability as nodes are added. Cassandra uses a peer-to-peer distributed architecture and tunable consistency levels to achieve high performance and availability without requiring strong consistency. It is based on Amazon's Dynamo and Google's Bigtable papers and provides a combination of their features.

This document discusses mobile optimization technologies. It begins with an introduction to market trends in mobile data usage and the growth of 4G/LTE networks. It then covers several technologies for optimizing mobile content delivery, including TCP optimization, front-end optimization (FEO) of HTML and images, and mobile CDNs. Performance tests are presented comparing the impact of FEO and image optimization as well as analyzing packet loss rates with and without TCP tuning for bandwidth-limited users. The goal is to improve quality of experience for mobile users through optimizations at various levels of the networking stack.

This document provides an overview and introduction to Cassandra, an open source distributed database management system designed to handle large amounts of data across many commodity servers. It discusses Cassandra's origins from influential papers on Bigtable and Dynamo, its properties including flexibility, scalability and high availability. The document also covers Cassandra's data model using keyspaces and column families, its consistency options, API including Thrift and language drivers, and provides examples of usage for an address book app and storing timeseries data.

1. The document discusses Cassandra Query Language (CQL), a new structured query language for Apache Cassandra that is similar to SQL. 2. CQL aims to provide a simpler alternative to Cassandra's existing Thrift API, which is difficult for clients to use and unstable due to its tight coupling to Cassandra's internal APIs. 3. The document outlines some benefits of CQL compared to the Thrift API, such as requiring less client-side abstraction and being more intuitive through its use of a familiar query/data model.

Cassandra is a highly scalable, open-source distributed database designed to handle large amounts of structured data across many servers. It provides high availability with no single point of failure and was created by Facebook to power search on their messaging platform. Cassandra uses a decentralized peer-to-peer architecture and replicates data across multiple data centers for fault tolerance. It emphasizes performance and scalability over more complex query options and does not support features like joins typically found in relational databases. Companies like Netflix and Hulu use Cassandra for its availability, scalability, and ability to span large clusters with minimal maintenance.

Mercy Natalia Angulo Pinillos es una profesora que trabaja en el Instituto Educativo Antonio José de Sucre en el Valle del Cauca. Ella tiene títulos de Normalista Bachiller, Normalista Superior, Licenciada en Ciencias Naturales y Especialista en Informática Educativa. Imparte clases de grado primero y preescolar y su principal dificultad es el transporte. En su tiempo libre le gusta leer y visitar amigos. Sus aspiraciones son aplicar mejor las TIC en su labor docente y ampliar su conocimiento sobre cómo utilizar las herramientas

Cassandra presentation given at the 3rd annual Palmetto Open Source Software Conference (POSSCON 2010).