Apache Cassandra overview
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Short overview of Apache Cassandra: features and benefits, use cases, architecture, CQL, data objects, etc.
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Introduction to Cassandra
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.
Presentation of Apache Cassandra
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".
Cassandra
This document provides an overview of the Cassandra NoSQL database. It begins with definitions of Cassandra and discusses its history and origins from projects like Bigtable and Dynamo. The document outlines Cassandra's architecture including its peer-to-peer distributed design, data partitioning, replication, and use of gossip protocols for cluster management. It provides examples of key features like tunable consistency levels and flexible schema design. Finally, it discusses companies that use Cassandra like Facebook and provides performance comparisons with MySQL.
NOSQL Database: Apache Cassandra
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?
An Overview of Apache Cassandra
Apache Cassandra is a free, distributed, open source, and highly scalable NoSQL database that is designed to handle large amounts of data across many commodity servers. It provides high availability with no single point of failure, linear scalability, and tunable consistency. Cassandra's architecture allows it to spread data across a cluster of servers and replicate across multiple data centers for fault tolerance. It is used by many large companies for applications that require high performance, scalability, and availability.
Cassandra architecture
The document compares Cassandra and PostgreSQL when deployed at scale. It outlines that Cassandra uses a peer-to-peer and masterless architecture with tunable consistency levels and can scale up and down easily. Cassandra also integrates tightly with Hadoop and offers the CQL query language similar to SQL. The document provides examples of basic SQL commands and their Cassandra equivalents using the CQL language.
Cassandra basics 2.0
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 point of failure, linear scalability and performance as nodes are added, and transparent elasticity allowing addition or removal of nodes without downtime. Data is partitioned and replicated across nodes using consistent hashing to balance loads and ensure availability in the event of failures. The write path sequentially appends data to commit logs and memtables which are periodically flushed to disk as SSTables, while the read path retrieves data from memtables and SSTables in parallel across replicas.
Cassandra an overview
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
Recommended
Introduction to Cassandra
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.
Presentation of Apache Cassandra
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".
Cassandra
This document provides an overview of the Cassandra NoSQL database. It begins with definitions of Cassandra and discusses its history and origins from projects like Bigtable and Dynamo. The document outlines Cassandra's architecture including its peer-to-peer distributed design, data partitioning, replication, and use of gossip protocols for cluster management. It provides examples of key features like tunable consistency levels and flexible schema design. Finally, it discusses companies that use Cassandra like Facebook and provides performance comparisons with MySQL.
NOSQL Database: Apache Cassandra
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?
An Overview of Apache Cassandra
Apache Cassandra is a free, distributed, open source, and highly scalable NoSQL database that is designed to handle large amounts of data across many commodity servers. It provides high availability with no single point of failure, linear scalability, and tunable consistency. Cassandra's architecture allows it to spread data across a cluster of servers and replicate across multiple data centers for fault tolerance. It is used by many large companies for applications that require high performance, scalability, and availability.
Cassandra architecture
The document compares Cassandra and PostgreSQL when deployed at scale. It outlines that Cassandra uses a peer-to-peer and masterless architecture with tunable consistency levels and can scale up and down easily. Cassandra also integrates tightly with Hadoop and offers the CQL query language similar to SQL. The document provides examples of basic SQL commands and their Cassandra equivalents using the CQL language.
Cassandra basics 2.0
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 point of failure, linear scalability and performance as nodes are added, and transparent elasticity allowing addition or removal of nodes without downtime. Data is partitioned and replicated across nodes using consistent hashing to balance loads and ensure availability in the event of failures. The write path sequentially appends data to commit logs and memtables which are periodically flushed to disk as SSTables, while the read path retrieves data from memtables and SSTables in parallel across replicas.
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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
Introduction to NoSQL & Apache Cassandra
An introduction to NoSQL databases and an overview of Apache Cassandra as a column family database.
Presentation I gave at Synechron Technologies
Apache Cassandra training. Overview and Basics
This document provides an overview of Apache Cassandra, including:
- Its history originating from Facebook's need to solve an inbox search problem.
- Its key features like high availability, linear scalability, fault tolerance and tunable consistency.
- Its architecture based on consistent hashing and a ring topology for data distribution.
- Its data model using keyspaces, column families, rows, and columns differently than a relational database.
- Examples of using the Cassandra CLI to create a schema, insert data, and perform queries.
Cassandra Architecture FTW
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.
Cassandra training
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.
Intro to Cassandra
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
Cassandra Day Atlanta 2015: Introduction to Apache Cassandra & DataStax Enter...
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 overview
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.
Evaluating Apache Cassandra as a Cloud Database
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.
Cassandra Database
A simple presentation about the Cassandra database, some example tools, and finally a demo with the strengths and weaknesses.
Cassandra ppt 2
This document provides instructions for downloading and configuring Apache Cassandra, including ensuring necessary properties are configured in the cassandra.yaml file. It also outlines how to use the Cassandra CQL shell to describe and interact with the cluster, keyspaces and tables. Finally, it mentions the DataStax tools DevCenter and OpsCenter for inserting and analyzing Cassandra data.
Introduction to Apache Cassandra
Introduction to Apache Cassandra (September 2014).
Design principles, replication, consistency, clusters, CQL.
Apache Cassandra @Geneva JUG 2013.02.26
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.
Introduction to Apache Cassandra
Casandra is a open-source, distributed, highly scalable and fault-tolerant database. It is a best choice for managing structured, semi-structured or unstructured data at a large amount.
Apache Cassandra at the Geek2Geek Berlin
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 background-and-architecture
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.
Cassandra tutorial
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 in the Real World
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.
Cassandra
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
This document provides an introduction to Apache Cassandra, a NoSQL distributed database. It discusses Cassandra's history and development by Facebook, key features including distributed architecture, data replication, fault tolerance, and linear scalability. It also compares relational and NoSQL databases, and lists some major companies that use Cassandra like Netflix, Apple, and eBay.
What is Apache Cassandra? | Apache Cassandra Tutorial | Apache Cassandra Intr...
** Apache Cassandra Certification Training: https://www.edureka.co/cassandra **
This Edureka tutorial on "What is Apache Cassandra" will give you a detailed introduction to the NoSQL database Apache Cassandra and it's various features. Learn why Cassandra is preferred over other Databases. You will also learn about the various elements of Cassandra Database with an interactive Industry based Use Case.
Talk About Apache Cassandra
Cassandra is a highly scalable, distributed, and fault-tolerant NoSQL database. It partitions data across nodes through consistent hashing of row keys, and replicates data for fault tolerance based on a replication factor. Cassandra provides tunable consistency levels for reads and writes. It uses a gossip protocol for node discovery and a commit log for write durability.
Apache Cassandra
Apache Cassandra is an open source NoSQL database that provides high performance and scalability across many servers. It was originally developed at Facebook in 2008 and released as an open source project on Google Code before becoming an Apache project in 2009. Cassandra uses a decentralized architecture and replication strategy to ensure there is no single point of failure and the system remains operational as long as one node remains up.
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This document provides an overview of Apache Cassandra, including:
- Its history originating from Facebook's need to solve an inbox search problem.
- Its key features like high availability, linear scalability, fault tolerance and tunable consistency.
- Its architecture based on consistent hashing and a ring topology for data distribution.
- Its data model using keyspaces, column families, rows, and columns differently than a relational database.
- Examples of using the Cassandra CLI to create a schema, insert data, and perform queries.
Cassandra Architecture FTW
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.
Cassandra training
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.
Intro to Cassandra
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
Cassandra Day Atlanta 2015: Introduction to Apache Cassandra & DataStax Enter...
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 overview
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.
Evaluating Apache Cassandra as a Cloud Database
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.
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A simple presentation about the Cassandra database, some example tools, and finally a demo with the strengths and weaknesses.
Cassandra ppt 2
This document provides instructions for downloading and configuring Apache Cassandra, including ensuring necessary properties are configured in the cassandra.yaml file. It also outlines how to use the Cassandra CQL shell to describe and interact with the cluster, keyspaces and tables. Finally, it mentions the DataStax tools DevCenter and OpsCenter for inserting and analyzing Cassandra data.
Introduction to Apache Cassandra
Introduction to Apache Cassandra (September 2014).
Design principles, replication, consistency, clusters, CQL.
Apache Cassandra @Geneva JUG 2013.02.26
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.
Introduction to Apache Cassandra
Casandra is a open-source, distributed, highly scalable and fault-tolerant database. It is a best choice for managing structured, semi-structured or unstructured data at a large amount.
Apache Cassandra at the Geek2Geek Berlin
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 background-and-architecture
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.
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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 in the Real World
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.
Cassandra
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
This document provides an introduction to Apache Cassandra, a NoSQL distributed database. It discusses Cassandra's history and development by Facebook, key features including distributed architecture, data replication, fault tolerance, and linear scalability. It also compares relational and NoSQL databases, and lists some major companies that use Cassandra like Netflix, Apple, and eBay.
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Apache Cassandra
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- Cassandra is an open source distributed database designed to handle large amounts of data across commodity servers.
- It was originally created at Facebook and is influenced by Amazon Dynamo and Google Bigtable.
- Cassandra uses a peer-to-peer distributed architecture with no single point of failure and supports replication across multiple data centers.
- It uses a column-oriented data model with tunable consistency levels and supports the Cassandra Query Language (CQL) which is similar to SQL.
- Major companies that use Cassandra include Facebook, Netflix, Twitter, IBM and more for its scalability, availability and flexibility.
cassandra
Apache Cassandra is a highly scalable, distributed database designed to handle large amounts of data across many servers with no single point of failure. It uses a peer-to-peer distributed system where data is replicated across multiple nodes for availability even if some nodes fail. Cassandra uses a column-oriented data model with dynamic schemas and supports fast writes and linear scalability.
Cassandra - A Basic Introduction Guide
Cassandra is a distributed database designed to handle large amounts of structured data across commodity servers. It provides linear scalability, fault tolerance, and high availability. Cassandra's architecture is masterless with all nodes equal, allowing it to scale out easily. Data is replicated across multiple nodes according to the replication strategy and factor for redundancy. Cassandra supports flexible and dynamic data modeling and tunable consistency levels. It is commonly used for applications requiring high throughput and availability, such as social media, IoT, and retail.
Webinar: ROI on Big Data - RDBMS, NoSQL or Both? A Simple Guide for Knowing H...
Big data doesn't mean big money. In fact, choosing a NoSQL solution will almost certainly save your business money, in terms of hardware, licensing, and total cost of ownership. What's more, choosing the correct technology for your use case will almost certainly increase your top line as well.
Big words, right? We'll back them up with customer case studies and lots of details.
This webinar will give you the basics for growing your business in a profitable way. What's the use of growing your top line but outspending any gains on cumbersome, ineffective, outdated IT? We'll take you through the specific use cases and business models that are the best fit for NoSQL solutions.
By the way, no prior knowledge is required. If you don't even know what RDBMS or NoSQL stand for, you are in the right place. Get your questions answered, and get your business on the right track to meeting your customers' needs in today's data environment.
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In today's data-driven world, organizations are faced with the challenge of efficiently processing and analyzing vast amounts of data to extract valuable insights. Apache Spark has emerged as a powerful tool for processing big data, offering speed, scalability, and ease of use. This project aims to leverage the capabilities of Spark to enhance data processing efficiency and empower organizations to derive meaningful insights from their data.Scalable Data Processing: Implement Spark to process large-scale datasets in a distributed computing environment, enabling parallel processing for enhanced scalability.
Real-time Data Analytics: Utilize Spark Streaming to perform real-time analytics on streaming data sources, enabling organizations to make timely decisions based on up-to-date information.
Advanced Analytics: Employ Spark's machine learning library (MLlib) to perform advanced analytics tasks such as predictive modeling, clustering, and classification, enabling organizations to uncover patterns and trends within their data.
Integration with Big Data Ecosystem: Integrate Spark seamlessly with other components of the big data ecosystem such as Hadoop, Kafka, and Cassandra, enabling seamless data ingestion, storage, and processing across different platforms.
Optimization and Performance Tuning: Implement optimization techniques such as partitioning, caching, and lazy evaluation to enhance the performance of Spark jobs and reduce processing time.
Methodology:
Data Exploration and Preparation: Explore and preprocess the dataset to handle missing values, outliers, and data inconsistencies, ensuring data quality and reliability.
Spark Environment Setup: Set up a Spark cluster either on-premises or on a cloud platform such as AWS or Azure, configuring the necessary resources and dependencies.
Development of Spark Applications: Develop Spark applications using Scala, Python, or Java to implement various data processing and analytics tasks according to the project requirements.
Testing and Validation: Test the Spark applications using sample datasets and validation techniques to ensure accuracy and reliability of the results.
Deployment and Integration: Deploy the Spark applications into production environment and integrate them with existing systems and workflows for seamless operation.
Deliverables:
Technical Documentation: Provide detailed documentation covering the project architecture, design decisions, implementation details, and deployment instructions.
Codebase: Deliver well-organized and documented codebase of the Spark applications developed during the project, along with unit tests and integration tests.
Performance Metrics: Present performance metrics and benchmarks demonstrating the efficiency and scalability of the Spark-based solution compared to traditional approaches.
Training and Support: Offer training sessions and support to the project stakeholders to enable them to effectively utilize and maintain the Spark-based solution.
Slides: Relational to NoSQL Migration
Join Principal Strategy Architect Ankit Patel to discuss the digital modernization journey many enterprises have taken from relational to NoSQL databases. In this webinar we will discuss the following:
• Why there is a need for digital modernization?
• What are the characteristics of the innovative data platform?
• What is NoSQL Apache Cassandra?
• How does DataStax innovate the NoSQL data platform?
• What are some of the challenges associated with digital modernization and migration?
Scaling Security on 100s of Millions of Mobile Devices Using Apache Kafka® an...
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Migrating Oracle database to Cassandra
This document discusses migrating Oracle databases to Cassandra. Cassandra offers lower costs, supports more data types, and can scale to handle large volumes of data across multiple data centers. It also allows for more flexible data modeling and built-in compression. The document compares Cassandra and Oracle on features, provides examples of companies using Cassandra, and outlines best practices for data modeling in Cassandra. It also discusses strategies for migrating data from Oracle to Cassandra including using loaders, Sqoop, and Spark.
Apache Cassandra Lunch #72: Databricks and Cassandra
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
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Follow Us and Reach Us At:
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Cassandra implementation for collecting data and presenting data
This document discusses Cassandra implementation for collecting and presenting data. It provides an overview of Cassandra, including why it was chosen, its architecture and data model. It describes how data is written to and read from Cassandra, and demonstrates the data model and graphing of data. Future uses of Cassandra are discussed.
AWS Big Data Landscape
This document provides an overview of big data concepts and architectures, as well as AWS big data services. It begins with introducing big data challenges around variety, volume, and velocity of data. It then covers the Hadoop ecosystem including HDFS, MapReduce, Hive, Pig and Spark. The document also discusses data lake architectures and how AWS services like S3, Glue, Athena, EMR, Redshift, QuickSight can be used to build them. Specific services covered in more detail include Kinesis, MSK, Glue, EMR and Redshift. Real-world examples of big data usage are also presented.
Real time analytics
Getting real-time analytics for devices/application/business monitoring from trillions of events and petabytes of data like companies Netflix, Uber, Alibaba, Paypal, Ebay, Metamarkets do.
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Apache Cassandra overview
- 1. Apache Cassandra overview by Taras Tymoshchuk, software developer at ElifTech
- 2. Introduction What is Apache Cassandra? Apache Cassandra™ is a free Distributed… High performance… Extremely scalable… Fault tolerant (i.e. no single point of failure)… post-relational database solution. Cassandra can serve as both real-time datastore (the “system of record”) for online/transactional applications, and as a read-intensive database for business intelligence systems.
- 3. Top Use Cases ● Internet of things applications – Cassandra is perfect for consuming lots of fast incoming data from devices, sensors and similar mechanisms that exist in many different locations. ● Product catalogs and retail apps – Cassandra is the database of choice for many retailers that need durable shopping cart protection, fast product catalog input and lookups, and similar retail app support. ● User activity tracking and monitoring – many media and entertainment companies use Cassandra to track and monitor the activity of their users’ interactions with their movies, music, website and online applications. ● Messaging – Cassandra serves as the database backbone for numerous mobile phone and messaging providers’ applications. ● Social media analytics and recommendation engines – many online companies, websites, and social media providers use Cassandra to ingest, analyze, and provide analysis and recommendations to their customers.
- 4. Key Cassandra Features and Benefits ● Gigabyte to Petabyte scalability ● Linear performance ● No SPOF ● Easy replication / data distribution ● Multi datacenter and cloud capable ● No need for separate caching layer ● Tunable data consistency ● Flexible schema design ● Data compaction ● CQL language (like SQL) ● Support for key languages and platforms ● No need for special hardware or software
- 5. Architecture Overview In Cassandra, all nodes play an identical role; there is no concept of a master node. Cassandra’s built-for-scale architecture means that it is capable of handling large amounts of data and thousands of concurrent users. Cassandra’s architecture also means that, unlike other master-slave or sharded systems, it has no single point of failure and therefore is capable of offering true continuous availability and uptime.
- 6. CQL Astyanix / Hector API: SliceQuery<string,string,string>query=... query.set Key (“x”) query.set Column Family (“y”) CQL: SELECT A FROM Y WHERE ID=”X”
- 8. Overview Cassandra data model COL1 VAL1 (TS1) COL2 VAL2 (TS2)KEY
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- 10. Reading Data
- 11. Rake ● Bad implemented range scan, Cassandra can not currently transfer data; ● Compaction backing a request; ● Many settings made on the cluster level, type, storage strategy and etc.; ● Counters.
- 12. Thank you for your attention! Find us at eliftech.com Have a question? Contact us: info@eliftech.com