MongoDB is NoSQL database which is based on documents.
Some basic commands and architecture of MongoDB are explained, also concepts like Sharding and map/reduce are represented too.
Using semi-structured data in modern applicationsMariaDB plc
JSON is the de facto standard for consuming and producing data from web, mobile and IoT apps, but relational databases are required for reliability – enforcing data integrity and providing durability with transactions. They're not mutually exclusive. MariaDB TX introduced SQL functions for validating, indexing and querying JSON documents – and for returning relational data as JSON documents, or JSON documents as relational data. In this session you will learn how to read, write, index and query semi-structured/non-traditional data with hands-on examples.
Using semi-structured data in modern applicationsMariaDB plc
JSON is the de facto standard for consuming and producing data from web, mobile and IoT apps, but relational databases are required for reliability – enforcing data integrity and providing durability with transactions. They're not mutually exclusive. MariaDB TX introduced SQL functions for validating, indexing and querying JSON documents – and for returning relational data as JSON documents, or JSON documents as relational data. In this session you will learn how to read, write, index and query semi-structured/non-traditional data with hands-on examples.
Comparison between mongo db and cassandra using ycsbsonalighai
Performed YCSB benchmarking test to check the performances of MongoDB and Cassandra for different workloads and a million opcounts and generated a report discussing clear insights.
Pros and Cons of MongoDB in Web DevelopmentNirvana Canada
Databases are available in plenty, and choosing the right one for your organization is a challenging task. In this blog, we will specifically focus on MongoDB and its pros and cons for web development.
Compare and contrast big data processing platforms RDBMS, Hadoop, and Spark. pros and cons of each platform are discussed. Business use cases are also included.
A database management system (or DBMS) is essentially nothing more than a computerized data-keeping system. Users of the system are given facilities to perform several kinds of operations on such a system for either manipulation of the data in the database or the management of the database structure itself.
Database Concepts to understand how DBMS works and develop expertise in available DBMS software like ORACLE.
Let me know if you have any questions.
Thanks
The webinar was conducted by Bhuvan Gandhi and Vishwas Ganatra on 22-23 August, 2020. It was powered by Encode - The coding club of PDPU.
Bhuvan Gandhi - https://github.com/bmg02/database-workshop-encode
Vishwas Ganatra - https://github.com/vishwasganatra/Encode-database-workshop
Key aspects of big data storage and its architectureRahul Chaturvedi
This paper helps understand the tools and technologies related to a classic BigData setting. Someone who reads this paper, especially Enterprise Architects, will find it helpful in choosing several BigData database technologies in a Hadoop architecture.
Modeling data and best practices for the Azure Cosmos DB.Mohammad Asif
Azure Cosmos DB is Microsoft's globally distributed, multi-model database service. In this session we covered ,modeling of data using NOSQL cosmos database and how it's helpful for distributed application to maintain high availability ,scaling in multiple region and throughput.
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
Comparison between mongo db and cassandra using ycsbsonalighai
Performed YCSB benchmarking test to check the performances of MongoDB and Cassandra for different workloads and a million opcounts and generated a report discussing clear insights.
Pros and Cons of MongoDB in Web DevelopmentNirvana Canada
Databases are available in plenty, and choosing the right one for your organization is a challenging task. In this blog, we will specifically focus on MongoDB and its pros and cons for web development.
Compare and contrast big data processing platforms RDBMS, Hadoop, and Spark. pros and cons of each platform are discussed. Business use cases are also included.
A database management system (or DBMS) is essentially nothing more than a computerized data-keeping system. Users of the system are given facilities to perform several kinds of operations on such a system for either manipulation of the data in the database or the management of the database structure itself.
Database Concepts to understand how DBMS works and develop expertise in available DBMS software like ORACLE.
Let me know if you have any questions.
Thanks
The webinar was conducted by Bhuvan Gandhi and Vishwas Ganatra on 22-23 August, 2020. It was powered by Encode - The coding club of PDPU.
Bhuvan Gandhi - https://github.com/bmg02/database-workshop-encode
Vishwas Ganatra - https://github.com/vishwasganatra/Encode-database-workshop
Key aspects of big data storage and its architectureRahul Chaturvedi
This paper helps understand the tools and technologies related to a classic BigData setting. Someone who reads this paper, especially Enterprise Architects, will find it helpful in choosing several BigData database technologies in a Hadoop architecture.
Modeling data and best practices for the Azure Cosmos DB.Mohammad Asif
Azure Cosmos DB is Microsoft's globally distributed, multi-model database service. In this session we covered ,modeling of data using NOSQL cosmos database and how it's helpful for distributed application to maintain high availability ,scaling in multiple region and throughput.
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
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).
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Show drafts
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
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.
Quantitative Data AnalysisReliability Analysis (Cronbach Alpha) Common Method...2023240532
Quantitative data Analysis
Overview
Reliability Analysis (Cronbach Alpha)
Common Method Bias (Harman Single Factor Test)
Frequency Analysis (Demographic)
Descriptive Analysis
2. Outlines
What is NoSQL
MongoDB
Advantages and Disadvantages
Data Model and Security
Basic Command
MongoDB Architecture
Protections and Security
Versions
5. MongoDB introduced by Gen10 in 2007
MongoDB is documental-based database
MongoDB is open source
6.
7. CharactrisityMongoDB
Each field could be used as index and a field could
possesses two index
Implemented with C, C++ and Javascript
Supports text search for textual fields
Contains general API and Drivers
Supports Journaling
Maintains big binary fill with help of GridFS
MongoDB is scale out with help of Sharding
Support Replication
8. MongoDB Disadvantages
Data redundancy after writing
Weakness in writing complex query
Weakness in supporting relational concepts
Transitions are atomic in document scale
9. Documental Model
Document collection concept is similar with
table
Document concept is similar with record
Documental model is similar with key-value
stores
Clusterpoint, Couchbase, DocumentDB, HyperDex, Lotus Notes,
MarkLogic, MongoDB, OrientDB, Qizx, RethinkDB
13. Data Security
basically available soft state eventually
consistent
Weak consistency because it does not contain
unique schema
Transitions are atomic in document level
Not supporting cascade effect
Programmer is responsible for data authenticity
so
22. Secondary Policy
Prevent a Secondary to become Primary
Hide a Secondary
Prohibit reading from a Secondary
Assign Secondary as preserver of database
previous condition
Prevent a Secondary to vote in elections
25. Primary Election
It is assign through an election
The Secondary which is in same server
with Primary could not be elected
Hidden and preserver Secondary could not
be candidate but it could vote
Secondary which does not possess vote
right could also be candidate
29. Security
Could grant permission for several user
All authentication data are saved in Admin
database
At the beginning, connection protocols are not
secure
30. Data security
Default encrypting system is AES256-CBC
with OpenSSL
Also support AES256-GCM and FIPS
EncryptionSteps
Generate master key
Generate key for each database
Encrypt each database with its key
Encrypt key with master key