The document compares SQL and NoSQL databases. SQL databases follow ACID properties and are good for applications requiring consistency, but do not scale well. NoSQL databases sacrifice consistency for scalability and availability. Data is modeled flexibly in NoSQL as documents, collections, or key-value pairs without predefined schemas. Examples include embedding or linking in MongoDB and using tables and items in DynamoDB. The CAP theorem explains the tradeoff between consistency, availability, and partition tolerance that NoSQL databases face. The conclusion is that SQL works for consistency while NoSQL scales, and a hybrid approach can be optimized.
“not only SQL.”
NoSQL databases are databases store data in a format other than relational tables.
NoSQL databases or non-relational databases don’t store relationship data well.
The rising interest in NoSQL technology over the last few years resulted in an increasing number of evaluations and comparisons among competing NoSQL technologies From survey we create a concise and up-to-date comparison of NoSQL engines, identifying their most beneficial use from the software engineer point of view.
“not only SQL.”
NoSQL databases are databases store data in a format other than relational tables.
NoSQL databases or non-relational databases don’t store relationship data well.
The rising interest in NoSQL technology over the last few years resulted in an increasing number of evaluations and comparisons among competing NoSQL technologies From survey we create a concise and up-to-date comparison of NoSQL engines, identifying their most beneficial use from the software engineer point of view.
NoSQL is known as Not only SQL database, provides a mechanism for storage and retrieval of data.
In this section is discussing about two data models.
Aggregate Data Models
Distribution Data Models
Key-Value data model, Document data model, Column-family stores and Graph database are come under Aggregate data Models
Distribution data Models are Sharding, Master-slave replication and Peer-peer replication
NoSQL, as many of you may already know, is basically a database used to manage huge sets of unstructured data, where in the data is not stored in tabular relations like relational databases. Most of the currently existing Relational Databases have failed in solving some of the complex modern problems like:
• Continuously changing nature of data - structured, semi-structured, unstructured and polymorphic data.
• Applications now serve millions of users in different geo-locations, in different timezones and have to be up and running all the time, with data integrity maintained
• Applications are becoming more distributed with many moving towards cloud computing.
NoSQL plays a vital role in an enterprise application which needs to access and analyze a massive set of data that is being made available on multiple virtual servers (remote based) in the cloud infrastructure and mainly when the data set is not structured. Hence, the NoSQL database is designed to overcome the Performance, Scalability, Data Modelling and Distribution limitations that are seen in the Relational Databases.
What is NoSQL? How does it come to the picture? What are the types of NoSQL? Some basics of different NoSQL types? Differences between RDBMS and NoSQL. Pros and Cons of NoSQL.
What is MongoDB? What are the features of MongoDB? Nexus architecture of MongoDB. Data model and query model of MongoDB? Various MongoDB data management techniques. Indexing in MongoDB. A working example using MongoDB Java driver on Mac OSX.
NoSQL is known as Not only SQL database, provides a mechanism for storage and retrieval of data.
In this section is discussing about two data models.
Aggregate Data Models
Distribution Data Models
Key-Value data model, Document data model, Column-family stores and Graph database are come under Aggregate data Models
Distribution data Models are Sharding, Master-slave replication and Peer-peer replication
NoSQL, as many of you may already know, is basically a database used to manage huge sets of unstructured data, where in the data is not stored in tabular relations like relational databases. Most of the currently existing Relational Databases have failed in solving some of the complex modern problems like:
• Continuously changing nature of data - structured, semi-structured, unstructured and polymorphic data.
• Applications now serve millions of users in different geo-locations, in different timezones and have to be up and running all the time, with data integrity maintained
• Applications are becoming more distributed with many moving towards cloud computing.
NoSQL plays a vital role in an enterprise application which needs to access and analyze a massive set of data that is being made available on multiple virtual servers (remote based) in the cloud infrastructure and mainly when the data set is not structured. Hence, the NoSQL database is designed to overcome the Performance, Scalability, Data Modelling and Distribution limitations that are seen in the Relational Databases.
What is NoSQL? How does it come to the picture? What are the types of NoSQL? Some basics of different NoSQL types? Differences between RDBMS and NoSQL. Pros and Cons of NoSQL.
What is MongoDB? What are the features of MongoDB? Nexus architecture of MongoDB. Data model and query model of MongoDB? Various MongoDB data management techniques. Indexing in MongoDB. A working example using MongoDB Java driver on Mac OSX.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
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.
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).
3. Agenda
• Some database theory
• Data Modelling in SQL databases
• ACID transactions
• Why NoSQL?
• Data Modelling in NoSQL databases
• CAP theorem
4. Database and
its Types
• A database is an organized collection of
data stored and accessed electronically.
Small databases can be stored on a file
system, while large databases are hosted
on computer clusters or cloud storage
• Types of databases- Relational (SQL
DBs) and Non-Relational (NoSQL DBs)
Relational Databases Non-Relational Databases
6. ACID Transactions
Atomic: All operations in a transaction will
succeed or every operation has to roll back.
Consistent : On the completion of a transaction,
the database is structurally sound.
Isolated: Any two transactions are not
interfering and appear to run sequentially.
Durable: Result of applying a transaction is
permanent even in case of a failure.
Because of ACID properties , Relational DBs are
used with applications which require high accuracy
and consistency eg Retail and Financial applications
7. Data Modeling in Relational Databases
Conceptual
Data Model
Logical
Data Model
Physical
Data Model
EDW
Mart
Mart
OLTP
OLTP
OLTP
OLTP
OLTP
10. Why NoSQL?
• Data Format- NoSQL databases support wide variety
of very large complex, semi-structured or
unstructured data.
• Performance – The schema of RDBMS is highly
normalized and requires the use of multiple joins,
which doesn’t performs well with large amount of
data.
• Scalability - Existing RDBMS solutions require scale
up, which is limited and not really scalable when
dealing with exponential growth of data.
• Availability – NoSQL databases are highly available
even in case of power failures due to implementation
of distributed systems.
• Accommodating - The schema in NoSQL databases is
not fixed and pre-defined. It depends on the user
access patterns. NoSQL databases can easily
accommodate frequent changes in data structure.
13. MongoDB- Key Concepts
• Data stored in JSON like documents
• A MongoDB Database contains collections and each collection
contains documents
• Unlike RDBMS, a pre-defined schema for a collection is optional,
hence flexible data structures.
• Maintains backup copies of the database instance
18. Linking v/s Embedding?
• Embedding is storing the related data within a document
that is frequently accessed together. This is also called
denormalized data model.
• Linking, also known as referencing means referencing data
of one collection into another. This is also called
normalized data model.
19. Data Modeling in DynamoDB
• DynamoDB is a fully managed database service on AWS, that can handle complex access patterns like time
series data or even geospatial data.
• Key Concepts-
Data model in the form of tables
Data stored in the form of items (key-value attributes)
Primary Key (mandatory Partition Key and optional Sort key )
Data Types- Scalar (number, string etc.) & Multi-valued (sets)
21. CAP Theorem
• Consistency - All users see the same
data at the same time.
• Availability – The system is going to
respond to every incoming request
with a success or failure.
• Partition Tolerance – The system
continues to function as expected even
in case of failure of a part of system.
22.
23. Summary
• SQL- works great, isn’t
scalable for large data
• NoSQL- works great,
isn’t suitable for
everyone
• SQL + NoSQL- Optimized
solution
![About me
{
name: “Garima Hasija”,
company: [
“Wood Mackenzie”, “Tata Consultancy Services”, “Infosys
Consulting”
],
title: “Senior Data Modeler”,
past_exp: [
“Data Modeler”, “Data Analyst”, “ETL Developer”
],
linkedin: “https://www.linkedin.com/in/garima-hasija-aa474b173/”
}](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)