Cassandra is a highly scalable, distributed NoSQL database that is designed to handle large amounts of data across commodity servers while providing high availability without single points of failure. It uses a peer-to-peer distributed system where each node acts as both a client and server, allowing it to remain operational as long as one node remains active. Cassandra's data model consists of keyspaces that contain tables with rows and columns. Data is replicated across multiple nodes for fault tolerance.
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
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#).
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
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
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.
1) Apache Cassandra in term of CAP Theorem
2) What makes Apache Cassandra "Available"?
3) How Apache Cassandra ensures data consistency?
4) Cassandra advantages and disadvantages
5) Frameworks/libraries to access Apache Cassandra + performance comparison
A quick tour in 16 slides of Amazon's Redshift clustered, massively parallel database.
Find out what differentiates it from the other database products Amazon has, including SimpleDB, DynamoDB and RDS (MySQL, SQL Server and Oracle).
Learn how it stores data on disk in a columnar format and how this relates to performance and interesting compression techniques.
Contrast the difference between Redshift and a MySQL instance and discover how the clustered architecture may help to dramatically reduce query time.
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 from the trenches: migrating Netflix (update)Jason Brown
Update talk on Cassandra at Netflix, presented at the Silicon Valley NoSQL meetup on 9 Feb 2012. Includes an introduction to Astyanax, an open source cassandra client written in java.
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 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
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.
1) Apache Cassandra in term of CAP Theorem
2) What makes Apache Cassandra "Available"?
3) How Apache Cassandra ensures data consistency?
4) Cassandra advantages and disadvantages
5) Frameworks/libraries to access Apache Cassandra + performance comparison
A quick tour in 16 slides of Amazon's Redshift clustered, massively parallel database.
Find out what differentiates it from the other database products Amazon has, including SimpleDB, DynamoDB and RDS (MySQL, SQL Server and Oracle).
Learn how it stores data on disk in a columnar format and how this relates to performance and interesting compression techniques.
Contrast the difference between Redshift and a MySQL instance and discover how the clustered architecture may help to dramatically reduce query time.
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 from the trenches: migrating Netflix (update)Jason Brown
Update talk on Cassandra at Netflix, presented at the Silicon Valley NoSQL meetup on 9 Feb 2012. Includes an introduction to Astyanax, an open source cassandra client written in java.
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.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
2. What is Cassandra
• Apache Cassandra is highly scalable, high performance, distributed NoSQL database.
Cassandra is designed to handle huge amount of data across many commodity servers,
providing high availability without a single point of failure.
• Cassandra has a distributed architecture which is capable to handle a huge amount of
data. Data is placed on different machines with more than one replication factor to attain a
high availability without a single point of failure.
3. Features of Cassandra
There are a lot of outstanding technical features which makes Cassandra very popular.
Following is a list of some popular features of Cassandra:
• High Scalability
Cassandra is highly scalable which facilitates you to add more hardware to attach more
customers and more data as per requirement.
• Rigid Architecture
Cassandra has not a 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. It increases your throughput because it facilitates you to
increase the number of nodes in the cluster. Therefore it maintains a quick response time.
• Fault tolerant
Cassandra is fault tolerant. Suppose, there are 4 nodes in a cluster, here each node has a
copy of same data. If one node is no longer serving then other three nodes can served as per
request.
4. Cassandra Architecture
Cassandra was designed to handle big data workloads across multiple nodes without a
single point of failure. It has a peer-to-peer distributed system across its nodes, and data is
distributed among all the nodes in a cluster.
•In Cassandra, each node is independent and at the same time interconnected to other
nodes. All the nodes in a cluster play the same role.
•Every node in a cluster can accept read and write requests, regardless of where the data is
actually located in the cluster.
•In the case of failure of one node, Read/Write requests can be served from other nodes in
the network.
5. Write Operations
Every write activity of nodes is captured by the commit logs written in the nodes. Later the data
will be captured and stored in the mem-table. Whenever the mem-table is full, data will be
written into the SStable data file. All writes are automatically partitioned and replicated
throughout the cluster. Cassandra periodically consolidates the SSTables, discarding
unnecessary data.
6. Read Operations
In Read operations, Cassandra gets values from the mem-table and checks the bloom filter to
find the appropriate SSTable which contains the required data.
There are three types of read request that is sent to replicas by coordinators.
•Direct request
•Digest request
•Read repair request
The coordinator sends direct request to one of the replicas. After that, the coordinator sends the
digest request to the number of replicas specified by the consistency level and checks if the
returned data is an updated data.
7.
8. Data Replication in Cassandra
In Cassandra, nodes in a cluster act as replicas for a given piece of data. If some of the nodes
are responded with an out-of-date value, Cassandra will return the most recent value to the
client. After returning the most recent value, Cassandra performs a read repair in the
background to update the stale values.
See the following image to understand the schematic view of how Cassandra uses data
replication among the nodes in a cluster to ensure no single point of failure.
9. Components of Cassandra
•Node: A Cassandra node is a place where data is stored.
•Data center: Data center is a collection of related nodes.
•Cluster: A cluster is a component which contains one or more data centers.
•Commit log: In Cassandra, the commit log is a crash-recovery mechanism. 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.
12. Cassandra Data Model Components
Cassandra data model provides a mechanism for data storage. The components of Cassandra
data model are keyspaces , tables, and columns.
Keyspaces
Cassandra data model consists of keyspaces at the highest level. Keyspaces are the containers
of data, similar to the schema or database in a relational database. Typically, keyspaces contain
many tables.
Tables
Within the keyspaces, the tables are defined. Tables are also referred to as Column Families in the
earlier versions of Cassandra. Tables contain a set of columns and a primary key, and they store
data in a set of rows.
Columns
Columns define the structure of data in a table. Each column has an associated type, such as
integer, text, double, and Boolean.
13. •A keyspace needs to be defined before creating tables, as there is no default keyspace.
•A keyspace can contain any number of tables, and a table belongs only to one keyspace. This
represents a one-to-many relationship.
•Replication is specified at the keyspace level. For example, replication of three implies that each
data row in the keyspace will have three copies.
•Further, you need to specify the replication factor during the creation of keyspace. However, the
replication factor can be modified later.
14. Tables
A table contains data in the horizontal and vertical formats, referred to as rows and columns
respectively. Some of the features of tables are:
•Tables have multiple rows and columns. As mentioned earlier, a table is also called Column
Family in the earlier versions of Cassandra.
•It is still referred to as column family in some of the error messages and documents of
Cassandra.
•It is important to define a primary key for a table.
15. Columns
Column represents a single piece of data in Cassandra and has a type defined.
Some of its features are:
•Columns consist of various types, such as integer, big integer, text, float, double, and Boolean.
•Cassandra also provides collection types such as set, list, and map.
•Further, column values have an associated time stamp representing the time of update.
•This timestamp can be retrieved using the function writetime.