Welcome to the world of NoSQL. NoSQL market is now expected to reach 4.2 billion dollar business in itself by 2020. If you are still confused by what does this term means then you are not ready for the Big Data world. However, just knowing the term is not enough.
Due to the enormous numbers of No SQL platforms out there, one of the key challenges is not how to use them but when to use what. In this webinar session, we will start with a small description of the NoSQL and try to understand why it was introduced after all. Then we will look into the four different types of NoSQL frameworks and some tips on how to choose what.
Key Takeaways:
1. Understanding NoSQL
2. SQL to NoSQL: Why the Need is There
3. The Four Main Types of NoSQL
4. How to Make the Best Choice
5. NoSQL User Stories & Deployment of Best Practices
SQL vs. NoSQL. It's always a hard choice.Denis Reznik
This will be an interesting and sometimes fun session with a small demo. This session will answer some of your questions and force you to think about new questions. It will not be very technical, so it's ok for choose another more technical session from the schedule :) But if will decide to come, I can assure you, that you will not be disappointed. We will do a thought experiment with one famous public high-loaded website, will look at advantages and disadvantages of SQL and NoSQL databases, and will choose the best database engine for it.
2007 iPres Beijing - MIXED: Preservation by migration to XMLDirk Roorda
File formats for tabular data are often proprietary. By creating conversions to and from XML we can preserve the tabular information over time, even when the proprietary formats become obsolete.
SQL vs. NoSQL. It's always a hard choice.Denis Reznik
This will be an interesting and sometimes fun session with a small demo. This session will answer some of your questions and force you to think about new questions. It will not be very technical, so it's ok for choose another more technical session from the schedule :) But if will decide to come, I can assure you, that you will not be disappointed. We will do a thought experiment with one famous public high-loaded website, will look at advantages and disadvantages of SQL and NoSQL databases, and will choose the best database engine for it.
2007 iPres Beijing - MIXED: Preservation by migration to XMLDirk Roorda
File formats for tabular data are often proprietary. By creating conversions to and from XML we can preserve the tabular information over time, even when the proprietary formats become obsolete.
This slides presented at monthly(May - 2015) meeting of the .NET forum in Sri Lanka.The NoSQL is about re-evaluating the constraints and scalability of data storage systems in the way modern web applications generate and consume data. NoSQL databases come in a variety of shapes and functionality. Arguably, the only feature that unifies them is that they are not relational. In this session will talk about function models of NoSQL and how to build modern web application using ASP.NET MVC and MongoDB, one of the most popular NoSQL database.
SQL vs NoSQL, an experiment with MongoDBMarco Segato
A simple experiment with MongoDB compared to Oracle classic RDBMS database: what are NoSQL databases, when to use them, why to choose MongoDB and how we can play with it.
Coming to cassandra from relational world (New)Nenad Bozic
Relational databases are something that we are familiar with, we have all started with them, we are using them for a while and we are taking common patterns for granted. When we need to choose, we go with vendor we are most familiar with, since all databases have similar functionality.
In NoSQL space however, story is completely different. Choice of type of database and even vendor is based solely on use case and non-functional requirements. You can try to force one vendor for various use cases, but soon you will see that you are having hard time modeling data, your queries are slow, your application layer is complex...
In this talk we will briefly touch the types of NoSQL databases out there. We will connect use cases with some databases which should give you good starting point when exploring solutions to your problem. Then we will switch to Cassandra, columnar key value store, and explain its architecture, specifics and give overview of common use cases. We will end up with things to avoid when using this database and our guidelines how to start with it.
This presentation explains the major differences between SQL and NoSQL databases in terms of Scalability, Flexibility and Performance. It also talks about MongoDB which is a document-based NoSQL database and explains the database strutre for my mouse-human research classifier project.
This slides presented at monthly(May - 2015) meeting of the .NET forum in Sri Lanka.The NoSQL is about re-evaluating the constraints and scalability of data storage systems in the way modern web applications generate and consume data. NoSQL databases come in a variety of shapes and functionality. Arguably, the only feature that unifies them is that they are not relational. In this session will talk about function models of NoSQL and how to build modern web application using ASP.NET MVC and MongoDB, one of the most popular NoSQL database.
SQL vs NoSQL, an experiment with MongoDBMarco Segato
A simple experiment with MongoDB compared to Oracle classic RDBMS database: what are NoSQL databases, when to use them, why to choose MongoDB and how we can play with it.
Coming to cassandra from relational world (New)Nenad Bozic
Relational databases are something that we are familiar with, we have all started with them, we are using them for a while and we are taking common patterns for granted. When we need to choose, we go with vendor we are most familiar with, since all databases have similar functionality.
In NoSQL space however, story is completely different. Choice of type of database and even vendor is based solely on use case and non-functional requirements. You can try to force one vendor for various use cases, but soon you will see that you are having hard time modeling data, your queries are slow, your application layer is complex...
In this talk we will briefly touch the types of NoSQL databases out there. We will connect use cases with some databases which should give you good starting point when exploring solutions to your problem. Then we will switch to Cassandra, columnar key value store, and explain its architecture, specifics and give overview of common use cases. We will end up with things to avoid when using this database and our guidelines how to start with it.
This presentation explains the major differences between SQL and NoSQL databases in terms of Scalability, Flexibility and Performance. It also talks about MongoDB which is a document-based NoSQL database and explains the database strutre for my mouse-human research classifier project.
In this document, we will present a very brief introduction to BigData (what is BigData?), Hadoop (how does Hadoop fits the picture?) and Cloudera Hadoop (what is the difference between Cloudera Hadoop and regular Hadoop?).
Please note that this document is for Hadoop beginners looking for a place to start.
Relational databases vs Non-relational databasesJames Serra
There is a lot of confusion about the place and purpose of the many recent non-relational database solutions ("NoSQL databases") compared to the relational database solutions that have been around for so many years. In this presentation I will first clarify what exactly these database solutions are, compare them, and discuss the best use cases for each. I'll discuss topics involving OLTP, scaling, data warehousing, polyglot persistence, and the CAP theorem. We will even touch on a new type of database solution called NewSQL. If you are building a new solution it is important to understand all your options so you take the right path to success.
Data Lake Acceleration vs. Data Virtualization - What’s the difference?Denodo
Watch full webinar here: https://bit.ly/3hgOSwm
Data Lake technologies have been in constant evolution in recent years, with each iteration primising to fix what previous ones failed to accomplish. Several data lake engines are hitting the market with better ingestion, governance, and acceleration capabilities that aim to create the ultimate data repository. But isn't that the promise of a logical architecture with data virtualization too? So, what’s the difference between the two technologies? Are they friends or foes? This session will explore the details.
Introducing NoSQL and MongoDB to complement Relational Databases (AMIS SIG 14...Lucas Jellema
This presentation gives an brief overview of the history of relational databases, ACID and SQL and presents some of the key strentgths and potential weaknesses. It introduces the rise of NoSQL - why it arose, what is entails, when to use it. The presentation focuses on MongoDB as prime example of NoSQL document store and it shows how to interact with MongoDB from JavaScript (NodeJS) and Java.
Introduction to Big Data and NoSQL.
This presentation was given to the Master DBA course at John Bryce Education in Israel.
Work is based on presentations by Michael Naumov, Baruch Osoveskiy, Bill Graham and Ronen Fidel.
A talk given by Ted Dunning on February 2013 on Apache Drill, an open-source community-driven project to provide easy, dependable, fast and flexible ad hoc query capabilities.
Emerging Trends in Marketing-Role of AI & Data ScienceDigital Vidya
Today marketing without the use of technology cannot exist. Both MarTech and AdTech are important cogs in the marketing wheel. However, the technological landscape is changing every rapidly and keeping track of the emerging trends in marketing is becoming very difficult and tiresome. This webinar will address the emerging technological landscape in marketing and what one should know about them.
Key Takeaways:
1. Ad Tech and Martech
2. AI in Marketing
3. Use of Videos
4. Hyperpersonilsation
5. Social Media Evolution
6. Change in the lead nurturing process
Digital Marketing Beyond Facebook & GoogleDigital Vidya
With Google & Facebook taking up a large percentage of the digital advertising investment in India, often marketers tend to focus only on these two platforms. But there are a lot more platforms out there today that can be leveraged to build a brand and to acquire more customers. Be it LinkedIn, Native, Platforms like Quora, Industry specific platforms like 99 Acres, CarDekho etc, multilingual platforms and publishers, audio apps and programmatic to name a few. Learn how these can be leveraged to achieve different marketing goals.
Key Takeaways:
1. Understand key platforms outside of the Google-FB ecosystem
2. Reasons why over-reliance on Google-FB ecosystem is not ideal
3. Best case practices for key platforms
4. Learn how Native & Content will play a key role in the future
5. See case studies of brands who have got ROI via these newer channels
In the recent past, we have learnt that data is the lifeline of any business and it is really important to collect data, more and more of it. But no one is telling us what to do with large volumes of data.
Shailendra has successfully delivered over One Billion Dollars in incremental value and will spend 30 minutes in showcasing how many large organisations are using data to their advantage by creating value through generating incremental revenue and optimising costs using analytics techniques.
Key Takeaways:
(i) Demystify the myths of analytics
(ii) Walkthrough a step-by-step approach to delivering successful projects that created an incremental value of hundreds and millions of dollars.
(iii) Three use cases where large organisations are using analytics to their advantage by creating value by generating incremental revenue and optimising costs.
Persuasion Strategies That Work Building Influence To Open Up Your Revenue St...Digital Vidya
Effective persuasion techniques not only help Marketing & Salespeople to generate more customers. Whether done internally or via Influencers, these persuasion techniques are very important to trigger sales.
This webinar session will discuss several persuasion techniques and as well as provide an understanding to learn more about human behaviour. It will also highlight what exactly triggers people to make purchase decisions.
Key Takeaways:
1) Understanding Why People Buy: Key motivation and Drivers
2) How to Develop the Art of Persuasion: Become a Key Influencer
3) Learn how Major Brands in India and Across the World have used these Principles
How To Set-up An SEO Agency From Scratch As A NewbieDigital Vidya
Know the process of 'How to set up an seo agency from scratch as a newbie'. Gain insights from the webinar led by Deepak Shukla, SEO Expert & Founder, Pearl Lemon.
7 B2B Marketing Trends for Driving GrowthDigital Vidya
Know about the top 7 B2B Marketing Trends for Driving Growth. Gain insights from the webinar led by Virginia Sharma,
Director, Marketing Solutions, LinkedIn India
Social Video Analytics: From Demography to Psychography of User BehaviourDigital Vidya
Know about Social Video Analytics: From Demography to Psychography of User Behaviour. Gain insights from the webinar led by Nishant Radia, Co-founder & CMO, Vidooly.
How to Use Marketing Automation to Convert More Leads to SalesDigital Vidya
Know about How to Use Marketing Automation to Convert More Leads to Sales. Gain insights from the webinar led by David Fallarme, Head of Marketing, SEA & India, HubSpot
Native Advertising: Changing Digital Advertising LandscapeDigital Vidya
Know about how Native Advertising is changing Digital Advertising landscape. Gain insights from the webinar led by Samir Tiwari, Co-Founder & CEO, Non Lineaar.
Apache Spark has been gaining steam, with rapidity, both in the headlines and in real-world adoption. Spark was developed in 2009, and open sourced in 2010. Since then, it has grown to become one of the largest open source communities in big data with over 200 contributors from more than 50 organizations. This open source analytics engine stands out for its ability to process large volumes of data significantly faster than contemporaries such as MapReduce, primarily owing to in-memory storage of data on its own processing framework. That being said, one of the top real-world industry use cases for Apache Spark is its ability to process ‘streaming data‘.
Community Development with Social MediaDigital Vidya
Know how to do 'Community Development with Social Media'. Gain insights from the webinar led by Saurabh Jain Head, Paytm - Build for India and Founder, Fun2Do Labs.
Framework of Digital Media Marketing in IndiaDigital Vidya
Know 'Framework of Digital Media Marketing in India'. Gain insights from the webinar led by Nishant Malsisaria, Associate Media Director. Dentsu Webchutney.
The Secret to Search Engine Marketing Success in 2018Digital Vidya
Know 'The Secret to Search Engine Marketing Success in 2018'. Gain insights from the webinar led by Prashant Nandan, Senior Director - Digital Trading & Buying, Amplifi India-Dentsu Aegis Network.
People Centric Marketing - Create Impact by Putting People First Digital Vidya
Know how to create impact by putting people first via 'People Centric Marketing'. Gain insights from the webinar led by Sakhee Dheer, Head of Digital, Global Business Marketing, Asia Pacific, Facebook.
Going Global? Key Steps to Expanding Your Business GloballyDigital Vidya
'Going Global? Key Steps to Expanding Your Business Globally'. Gain insights from the webinar led by Alexia Ohannessian, International Marketing Lead, Trello. Explore more webinars at www.digitalvidya.com/webinars/.
How to Optimize your Online Presence for 6X Growth in Sales?Digital Vidya
Explore 'How to Optimize Your Online Presence for 6x Growth in Sales?'. Gain insights from the webinar led by Advit Sahdev, Head of Marketing, Infibeam.com.
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.
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).
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
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.
8. Types Of NoSQL
Types Performance Scalability Flexibility Complexity Example
Key-Value high high high None Riak, redis,
Column Store high high Moderate Low Hbase, Google
big Table,
Cassandra
Document Store high Variable(high) high Low MongoDB,
Couchbase
Graph Database Variable Variable high high Neo4J
9. How To Decide??
• Step 1
Read about the various Type and how they work.
10. How To Decide??
• Step 2
Investigate all the use cases for your project. This will help to
identify which all type you need. Remember it could be a
mixed solution.
11. How To Decide??
• Step 3
Check the ranking and popularity of the selected type of
system. Analyze the pros and cons as per your requirement.
12. How To Decide??
Step 4 (as an expert)
•Can the database serve as the primary data source for the online application?
•Does the database have features that prevent the loss of critical data? Are writes
durable in nature by default so that the data is safe?
•Is the database fault-tolerant, and is it capable of providing continuous
availability?
•Can the database easily replicate data located in the same data center, across
multiple data centers, and across different cloud availability zones?
•Does the database offer read/write anywhere capabilities? (Can any node in the
cluster be written to and read from?)
•Does the database provide a robust set of security features?
Source: https://support.rackspace.com/how-to/choosing-between-rdbms-and-nosql/
13. How To Decide??
Step 4 (as an expert)
•Does the database support backup and recovery procedures that are easy to
create and manage?
•Does the database require special caching layers?
•Is the database capable of managing big data and delivering high performance
regardless of data size?
•Does the database offer linear scalability for adding new nodes?
•Can new nodes be added and removed online without impacting your business?
•Does the database support key platforms and developer languages?
•Does the database provide a query language that is similar to SQL?
Source: https://support.rackspace.com/how-to/choosing-between-rdbms-and-nosql/
14. How To Decide??
Step 4 (as an expert)
•Can the database run on commodity hardware with no special requirements?
•Is the database easy to implement and maintain for large deployments?
•Does the database offer data compression that results in significant storage savings?
•Is it easy to run analytic operations on the database?
•Can the database easily interface with and support modern data warehouses or data lakes
that use Hadoop?
•Is it easy to carry out search operations and functions directly on the NoSQL database?
•Can the database isolate the online, analytic, and search workloads within a single
application?
•Does the database have solid command-line and visual tools for development,
administration, and performance management?
Source: https://support.rackspace.com/how-to/choosing-between-rdbms-and-nosql/
15. How To Decide??
• Is Technical Consideration Enough?
Think about the business need. Such as Community support, Enterprise
fee, commercial Support, Documentation.