Introduction to Cloud and Cloud computing.
Architecture of Cloud Computing.
Cloud Deployment and Service Model.
Risks, Challenges, Issues and Applications of Cloud Computing
Introduction to Cloud
Cloud Types
Cloud Deployment Models
Cloud Service Model
Cloud architecture
Challenges and Risks in cloud Computing
Cloud Features, Characteristics and Applications
Introduction to Cloud and Cloud computing.
Architecture of Cloud Computing.
Cloud Deployment and Service Model.
Risks, Challenges, Issues and Applications of Cloud Computing
Introduction to Cloud
Cloud Types
Cloud Deployment Models
Cloud Service Model
Cloud architecture
Challenges and Risks in cloud Computing
Cloud Features, Characteristics and Applications
Cloud computing Definition, Types of cloud, Cloud services: Benefits and challenges of cloud computing, Evolution of Cloud Computing, Applications cloud computing, Business models around Cloud, Major Players in Cloud Computing, Issues in Cloud - Eucalyptus - Nimbus - Open Nebula, CloudSim.
Cloud Computing is the internet-based computing wherby shared resources, software, and information are provided to computers and other devices on demand, like the electrcity grid
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Cloud computing Definition, Types of cloud, Cloud services: Benefits and challenges of cloud computing, Evolution of Cloud Computing, Applications cloud computing, Business models around Cloud, Major Players in Cloud Computing, Issues in Cloud - Eucalyptus - Nimbus - Open Nebula, CloudSim.
Cloud Computing is the internet-based computing wherby shared resources, software, and information are provided to computers and other devices on demand, like the electrcity grid
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
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).
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.
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.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdfEnterprise Wired
In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
2. WHAT IS CLOUD COMPUTING ?
Cloud computing is shared pools of configurable
computer system resources and higher-level
services that can be rapidly provisioned with
minimal management effort, often over the
Internet. Cloud computing relies on sharing of
resources to achieve coherence and economies
of scale, similar to a public utility.
In simple words, cloud computing is using the
internet to access to someone else's software
running on someone else's hardware in someone
else's data centre .
4. Private cloud :
Private cloud is cloud infrastructure operated solely
for a single organization, whether managed internally
or by a third-party, and hosted either internally or
externally.
Undertaking a private cloud project requires
significant engagement to virtualize the business
environment, and requires the organization to
reevaluate decisions about existing resources.
It can improve business, but every step in the project
raises security issues that must be addressed to
prevent serious vulnerabilities.
5. Public cloud:
A cloud is called a "public cloud" when the services are
rendered over a network that is open for public use.
Public cloud services may be free.
Technically there may be little or no difference between
public and private cloud architecture, however, security
consideration may be substantially different for services
(applications, storage, and other resources) that are
made available by a service provider for a public
audience and when communication is effected over a
non-trusted network.
Generally, public cloud service providers like Amazon
Web Services (AWS), Oracle, Microsoft and Google own
and operate the infrastructure at their data center and
access is generally via the Internet.
6. Hybrid cloud :
Hybrid cloud is a composition of two or more clouds
(private, community or public) that remain distinct
entities but are bound together, offering the benefits
of multiple deployment models. Hybrid cloud can also
mean the ability to connect collocation, managed
and/or dedicated services with cloud resources
A hybrid cloud service crosses isolation and provider
boundaries so that it can't be simply put in one
category of private, public, or community cloud
service. It allows one to extend either the capacity or
the capability of a cloud service, by aggregation,
integration or customization with another cloud
service.
7. Uses :
Helps to use
application without
installations.
Access the personal
files at any computer.
It allows efficient
computation by
centralizing storage
memory, processing
and bandwidth.
8. Advantages of cloud computing :
Reduces spending
on technology
Globalize your
workforce on the
cheap
Reduces capital cost
Improve accessibility
, flexibility .
9. Disadvantges of cloud computing:
Its of no use if you
dont have internet.
Secuirty and privacy
Vulnerablity to attack
Limited control and
flexibility .
Costs
Incompability