This Presentation is about NoSQL which means Not Only SQL. This presentation covers the aspects of using NoSQL for Big Data and the differences from RDBMS.
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.
“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.
This Presentation is about NoSQL which means Not Only SQL. This presentation covers the aspects of using NoSQL for Big Data and the differences from RDBMS.
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.
“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 Information Technology have led us into an era where the production, sharing and use of information are now part of everyday life and of which we are often unaware actors almost: it is now almost inevitable not leave a digital trail of many of the actions we do every day; for example, by digital content such as photos, videos, blog posts and everything that revolves around the social networks (Facebook and Twitter in particular). Added to this is that with the "internet of things", we see an increase in devices such as watches, bracelets, thermostats and many other items that are able to connect to the network and therefore generate large data streams. This explosion of data justifies the birth, in the world of the term Big Data: it indicates the data produced in large quantities, with remarkable speed and in different formats, which requires processing technologies and resources that go far beyond the conventional systems management and storage of data. It is immediately clear that, 1) models of data storage based on the relational model, and 2) processing systems based on stored procedures and computations on grids are not applicable in these contexts. As regards the point 1, the RDBMS, widely used for a great variety of applications, have some problems when the amount of data grows beyond certain limits. The scalability and cost of implementation are only a part of the disadvantages: very often, in fact, when there is opposite to the management of big data, also the variability, or the lack of a fixed structure, represents a significant problem. This has given a boost to the development of the NoSQL database. The website NoSQL Databases defines NoSQL databases such as "Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open source and horizontally scalable." These databases are: distributed, open source, scalable horizontally, without a predetermined pattern (key-value, column-oriented, document-based and graph-based), easily replicable, devoid of the ACID and can handle large amounts of data. These databases are integrated or integrated with processing tools based on the MapReduce paradigm proposed by Google in 2009. MapReduce with the open source Hadoop framework represent the new model for distributed processing of large amounts of data that goes to supplant techniques based on stored procedures and computational grids (step 2). The relational model taught courses in basic database design, has many limitations compared to the demands posed by new applications based on Big Data and NoSQL databases that use to store data and MapReduce to process large amounts of data.
Course Website http://pbdmng.datatoknowledge.it/
Contact me for other informations and to download
The Information Technology have led us into an era where the production, sharing and use of information are now part of everyday life and of which we are often unaware actors almost: it is now almost inevitable not leave a digital trail of many of the actions we do every day; for example, by digital content such as photos, videos, blog posts and everything that revolves around the social networks (Facebook and Twitter in particular). Added to this is that with the "internet of things", we see an increase in devices such as watches, bracelets, thermostats and many other items that are able to connect to the network and therefore generate large data streams. This explosion of data justifies the birth, in the world of the term Big Data: it indicates the data produced in large quantities, with remarkable speed and in different formats, which requires processing technologies and resources that go far beyond the conventional systems management and storage of data. It is immediately clear that, 1) models of data storage based on the relational model, and 2) processing systems based on stored procedures and computations on grids are not applicable in these contexts. As regards the point 1, the RDBMS, widely used for a great variety of applications, have some problems when the amount of data grows beyond certain limits. The scalability and cost of implementation are only a part of the disadvantages: very often, in fact, when there is opposite to the management of big data, also the variability, or the lack of a fixed structure, represents a significant problem. This has given a boost to the development of the NoSQL database. The website NoSQL Databases defines NoSQL databases such as "Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open source and horizontally scalable." These databases are: distributed, open source, scalable horizontally, without a predetermined pattern (key-value, column-oriented, document-based and graph-based), easily replicable, devoid of the ACID and can handle large amounts of data. These databases are integrated or integrated with processing tools based on the MapReduce paradigm proposed by Google in 2009. MapReduce with the open source Hadoop framework represent the new model for distributed processing of large amounts of data that goes to supplant techniques based on stored procedures and computational grids (step 2). The relational model taught courses in basic database design, has many limitations compared to the demands posed by new applications based on Big Data and NoSQL databases that use to store data and MapReduce to process large amounts of data.
Course Website http://pbdmng.datatoknowledge.it/
Contact me for other informations and to download the slides
This presentation contains the introduction to NOSQL databases, it's types with examples, differentiation with 40 year old relational database management system, it's usage, why and we should use it.
Here is my seminar presentation on No-SQL Databases. it includes all the types of nosql databases, merits & demerits of nosql databases, examples of nosql databases etc.
For seminar report of NoSQL Databases please contact me: ndc@live.in
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.
In this lecture we analyze document oriented databases. In particular we consider why there are the first approach to nosql and what are the main features. Then, we analyze as example MongoDB. We consider the data model, CRUD operations, write concerns, scaling (replication and sharding).
Finally we presents other document oriented database and when to use or not document oriented databases.
This presentation discusses the follow topics
What is Hadoop?
Need for Hadoop
History of Hadoop
Hadoop Overview
Advantages and Disadvantages of Hadoop
Hadoop Distributed File System
Comparing: RDBMS vs. Hadoop
Advantages and Disadvantages of HDFS
Hadoop frameworks
Modules of Hadoop frameworks
Features of 'Hadoop‘
Hadoop Analytics Tools
Apache Sqoop efficiently transfers bulk data between Apache Hadoop and structured datastores such as relational databases. Sqoop helps offload certain tasks (such as ETL processing) from the EDW to Hadoop for efficient execution at a much lower cost. Sqoop can also be used to extract data from Hadoop and export it into external structured datastores. Sqoop works with relational databases such as Teradata, Netezza, Oracle, MySQL, Postgres, and HSQLDB
An Introduction to Architecture of Object Oriented Database Management System and how it differs from RDBMS means Relational Database Management System
NoSQL databases get a lot of press coverage, but there seems to be a lot of confusion surrounding them, as in which situations they work better than a Relational Database, and how to choose one over another. This talk will give an overview of the NoSQL landscape and a classification for the different architectural categories, clarifying the base concepts and the terminology, and will provide a comparison of the features, the strengths and the drawbacks of the most popular projects (CouchDB, MongoDB, Riak, Redis, Membase, Neo4j, Cassandra, HBase, Hypertable).
NoSQL databases are currently used in several applications scenarios in contrast to Relations Databases. Several type of Databases there exist. In this presentation we compare Key Value, Column Oriented, Document Oriented and Graph Databases. Using a simple case study there are evaluated pros and cons of the NoSQL databases taken into account.
The Information Technology have led us into an era where the production, sharing and use of information are now part of everyday life and of which we are often unaware actors almost: it is now almost inevitable not leave a digital trail of many of the actions we do every day; for example, by digital content such as photos, videos, blog posts and everything that revolves around the social networks (Facebook and Twitter in particular). Added to this is that with the "internet of things", we see an increase in devices such as watches, bracelets, thermostats and many other items that are able to connect to the network and therefore generate large data streams. This explosion of data justifies the birth, in the world of the term Big Data: it indicates the data produced in large quantities, with remarkable speed and in different formats, which requires processing technologies and resources that go far beyond the conventional systems management and storage of data. It is immediately clear that, 1) models of data storage based on the relational model, and 2) processing systems based on stored procedures and computations on grids are not applicable in these contexts. As regards the point 1, the RDBMS, widely used for a great variety of applications, have some problems when the amount of data grows beyond certain limits. The scalability and cost of implementation are only a part of the disadvantages: very often, in fact, when there is opposite to the management of big data, also the variability, or the lack of a fixed structure, represents a significant problem. This has given a boost to the development of the NoSQL database. The website NoSQL Databases defines NoSQL databases such as "Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open source and horizontally scalable." These databases are: distributed, open source, scalable horizontally, without a predetermined pattern (key-value, column-oriented, document-based and graph-based), easily replicable, devoid of the ACID and can handle large amounts of data. These databases are integrated or integrated with processing tools based on the MapReduce paradigm proposed by Google in 2009. MapReduce with the open source Hadoop framework represent the new model for distributed processing of large amounts of data that goes to supplant techniques based on stored procedures and computational grids (step 2). The relational model taught courses in basic database design, has many limitations compared to the demands posed by new applications based on Big Data and NoSQL databases that use to store data and MapReduce to process large amounts of data.
Course Website http://pbdmng.datatoknowledge.it/
Contact me for other informations and to download
The Information Technology have led us into an era where the production, sharing and use of information are now part of everyday life and of which we are often unaware actors almost: it is now almost inevitable not leave a digital trail of many of the actions we do every day; for example, by digital content such as photos, videos, blog posts and everything that revolves around the social networks (Facebook and Twitter in particular). Added to this is that with the "internet of things", we see an increase in devices such as watches, bracelets, thermostats and many other items that are able to connect to the network and therefore generate large data streams. This explosion of data justifies the birth, in the world of the term Big Data: it indicates the data produced in large quantities, with remarkable speed and in different formats, which requires processing technologies and resources that go far beyond the conventional systems management and storage of data. It is immediately clear that, 1) models of data storage based on the relational model, and 2) processing systems based on stored procedures and computations on grids are not applicable in these contexts. As regards the point 1, the RDBMS, widely used for a great variety of applications, have some problems when the amount of data grows beyond certain limits. The scalability and cost of implementation are only a part of the disadvantages: very often, in fact, when there is opposite to the management of big data, also the variability, or the lack of a fixed structure, represents a significant problem. This has given a boost to the development of the NoSQL database. The website NoSQL Databases defines NoSQL databases such as "Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open source and horizontally scalable." These databases are: distributed, open source, scalable horizontally, without a predetermined pattern (key-value, column-oriented, document-based and graph-based), easily replicable, devoid of the ACID and can handle large amounts of data. These databases are integrated or integrated with processing tools based on the MapReduce paradigm proposed by Google in 2009. MapReduce with the open source Hadoop framework represent the new model for distributed processing of large amounts of data that goes to supplant techniques based on stored procedures and computational grids (step 2). The relational model taught courses in basic database design, has many limitations compared to the demands posed by new applications based on Big Data and NoSQL databases that use to store data and MapReduce to process large amounts of data.
Course Website http://pbdmng.datatoknowledge.it/
Contact me for other informations and to download the slides
This presentation contains the introduction to NOSQL databases, it's types with examples, differentiation with 40 year old relational database management system, it's usage, why and we should use it.
Here is my seminar presentation on No-SQL Databases. it includes all the types of nosql databases, merits & demerits of nosql databases, examples of nosql databases etc.
For seminar report of NoSQL Databases please contact me: ndc@live.in
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.
In this lecture we analyze document oriented databases. In particular we consider why there are the first approach to nosql and what are the main features. Then, we analyze as example MongoDB. We consider the data model, CRUD operations, write concerns, scaling (replication and sharding).
Finally we presents other document oriented database and when to use or not document oriented databases.
This presentation discusses the follow topics
What is Hadoop?
Need for Hadoop
History of Hadoop
Hadoop Overview
Advantages and Disadvantages of Hadoop
Hadoop Distributed File System
Comparing: RDBMS vs. Hadoop
Advantages and Disadvantages of HDFS
Hadoop frameworks
Modules of Hadoop frameworks
Features of 'Hadoop‘
Hadoop Analytics Tools
Apache Sqoop efficiently transfers bulk data between Apache Hadoop and structured datastores such as relational databases. Sqoop helps offload certain tasks (such as ETL processing) from the EDW to Hadoop for efficient execution at a much lower cost. Sqoop can also be used to extract data from Hadoop and export it into external structured datastores. Sqoop works with relational databases such as Teradata, Netezza, Oracle, MySQL, Postgres, and HSQLDB
An Introduction to Architecture of Object Oriented Database Management System and how it differs from RDBMS means Relational Database Management System
NoSQL databases get a lot of press coverage, but there seems to be a lot of confusion surrounding them, as in which situations they work better than a Relational Database, and how to choose one over another. This talk will give an overview of the NoSQL landscape and a classification for the different architectural categories, clarifying the base concepts and the terminology, and will provide a comparison of the features, the strengths and the drawbacks of the most popular projects (CouchDB, MongoDB, Riak, Redis, Membase, Neo4j, Cassandra, HBase, Hypertable).
NoSQL databases are currently used in several applications scenarios in contrast to Relations Databases. Several type of Databases there exist. In this presentation we compare Key Value, Column Oriented, Document Oriented and Graph Databases. Using a simple case study there are evaluated pros and cons of the NoSQL databases taken into account.
This talk was given at DEF CON 2010 by Kuon Ding and Wayne Huang
https://www.defcon.org/html/defcon-18/dc-18-speakers.html#Huang
NOSQL == NO SQL INJECTIONS?
This is a short talk on NoSQL technologies and their impacts on traditional injection threats such as SQL injection. This talk surveys existing NoSQL technologies, and then demos proof-of-concept threats found with CouchDB. We then discuss impacts of NoSQL technologies to existing security technologies such as blackbox scanning, static analysis, and web application firewalls.
Startups: Attracting and Retaining Talent (updated 3/6/13)Patrick Seaman
White Paper on attracting and retaining talent for your startup. Based on my own experiences in many startups and early stage companies. Topics include: Introduction 3
Insanity & Genius 4
Founders & a Whiteboard 5
Wearing Many Hats 7
First Hires 9
Prototype 10
Beta 11
Pre-Launch 12
Launch / A-Round 13
State of the Team 14
Growing and Growing 15
Startups are Nimble 16
Startups –vs- Corporate Culture 17
Networking 20
Referral Incentives 21
Events 22
Interns & College/Universities 24
Compelling? 26
Who works for a Startup? 27
Early Employees 28
Poaching? 29
Location & Recruiting 31
Flex 32
Compensation 33
Options Value 34
Compensation Plans 35
Retention 36
The Simple Things 39
Family 41
Perks & Bennies 44
Change of Control 47
Flush with Cash 50
Or not 51
About the Author 52
About Pepperwood Partners 53
Most business leaders believe that some portion of employee pay should be in the form of incentives, but are left struggling to find answers to key questions: How much of someone’s pay should be variable? And who should have incentive pay as part of their mix? How much of the incentive should be short-term and how much should be based on long-term performance? What type of incentive(s) should it be? What if I don’t pay incentives and just pay higher salaries than my competitors? Will that work just as well?
If these are questions you are facing, don’t miss this presentation!
This presentation was given at "Hands-on Workshop for Negotiation Prowess" and geared towards women consultants and solopreneurs. We discussed ways to get over the fear of "No", negotiation frameworks, and experts scripts for making concessions and for raising your rate as a consultant.
How to Divide the Pie? Dynamic Equity Share by Mike Moyer Ed Kuiters
This is presentation held at the Tokyo Business Meetup on June 27th. Topic of the presentation; how to make sure that all particpants in a start-up get their fair share. Method by Mike Moyer - Slicing Pie
Raising Your Seed Round Financing: Should You Use Convertible Notes or Prefe...Bart Greenberg
This slide show outlines and discusses the basic differences between preferred stock and convertible notes and the pros and cons to the issuer and the investor in using one over the other.
This ppt explain about choosing your NoSQL database. This also contains factors which needs to be consider while choosing NoSQL database. Thanks Arun Chandrasekaran(https://www.linkedin.com/profile/view?id=AAMAAAQKxWsB9tkk7s2ll2T2BvLvR9QDv_OdJXs&trk=hp-identity-name) for helping me.
Selecting the right database type for your knowledge management needs.Synaptica, LLC
This presentation looks at relational vs. graph databases and their advantages and disadvantages in storing semantic data for taxonomies and ontologies.
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.
Cloud Strategies for Financial Firms : Migrating one step at a timeSuvradeep Rudra
A very few financial firms are currently using cloud computing for their core applications, different hosting architectures provided by IaaS cloud providers and new avenues in the community and hybrid cloud space, will drive more firms to move their core applications to the cloud. In fact, core solutions, such as batch processes running throughout the day, analytics and reporting applications, are perfect candidates.
The idea behind a design patterns is to learn about it's strengths and weaknesses. And more importantly, understand where and how to use a particular design correctly, so as to use its strengths properly and overcome its weaknesses.
In today's competitive market, many organizations are unaware of the quantity of poor-quality data in their systems. Some organizations assume that their data is of adequate quality, although they have conducted no metrical or statistical analysis to support the assumption. Others know that their performance is hampered by poor-quality data, but they cannot measure the problem.
A column-oriented DBMS is a database management system (DBMS) that stores its content by column rather than by row. This has advantages for data warehouses and library catalogues where aggregates are computed over large numbers of similar data items.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
2. Agenda
• The four categories of NoSQL databases
• When to Use NoSQL
• When NOT to use NoSQL
• Use cases NoSQL (Each Category)
3. Executive Summary
• A NoSQL database provides a mechanism for
storage and retrieval of data that is modeled in
means other than the tabular relations used in
relational databases. Motivations for this
approach include simplicity of design, horizontal
scaling and finer control over availability. The
data structure (e.g., tree, graph, key-value)
differs from the RDBMS, and therefore some
operations are faster in NoSQL and some in
RDBMS.
4. 4 categories of NoSQL DB
• Key-values Stores
• Column Family Stores
• Document Databases
• Graph Databases
5. Key-values Stores
Key valued stores are those types of NoSQL database that are scheme free, and also your
values stored as key i.e in one column you will be having a key “Name” and the value
would be “Zack” and in the second column it’s not necessary mean that you must have
the value of Name again you could store different kind of data in the same column in
different row, and also you could have more column in one row than previous or vice
versa, this is the most common kinds of NoSQL database that are currently in the market
and other kinds of NoSQL database are built upon the principle of this kinds of NoSQL
database and added some features on that.
The Key-Value database is a very simple structure based on Amazon’s Dynamo DB. Data
is indexed and queried based on it’s key. Key-value stores provide consistent hashing so
they can scale incrementally as your data scales. They communicate node structure
through a gossip-based membership protocol to keep all the nodes synchronized. If you
are looking to scale very large sets of low complexity data, key-value stores are the best
option.
Examples: Tokyo Cabinet/Tyrant, Redis, Voldemort, Oracle BDB, Amazon
SimpleDB, Riak
Strengths: Fast lookups
Weaknesses: Stored data has no schema
6. Column Family Stores
These were created to store and process very large amounts of data distributed over many
machines. There are still keys but they point to multiple columns. The columns are
arranged by column family.
These data stores are based on Google’s BigTable implementation. They may look
similar to relational databases on the surface but under the hood a lot has changed. A
column family database can have different columns on each row so is not relational and
doesn’t have what qualifies in an RDBMS as a table. The only key concepts in a column
family database are columns, column families and super columns. All you really need to
start with is a column family. Column families define how the data is structured on disk.
A column by itself is just a key-value pair that exists in a column family. A super column
is like a catalogue or a collection of other columns except for other super columns.
Column family databases are still extremely scalable but less-so than key-value stores.
However, they work better with more complex data sets.
Examples: Cassandra, HBase
7. Document Databases
These were inspired by Lotus Notes and are similar to key-value stores. The model is
basically versioned documents that are collections of other key-value collections. The
semi-structured documents are stored in formats like JSON.
A document database is not a new idea. It was used to power one of the more
prominent communication platforms of the 90’s and still in service today, Lotus Notes
now called Lotus Domino. APIs for document DBs use Restful web services and JSON
for message structure making them easy to move data in and out.
A document database has a fairly simple data model based on collections of key-value
pairs. A typical record in a document database would look like this:
• { “Subject”: “I like Plankton”
• “Author”: “Rusty”
• “PostedDate”: “5/23/2006″
• “Tags”: ["plankton", "baseball", "decisions"]
• “Body”: “I decided today that I don’t like baseball. I like plankton.” }
Examples: CouchDB, MongoDb
Strengths: Tolerant of incomplete data
Weaknesses: Query performance, no standard query syntax
8. Graph Databases
Instead of tables of rows and columns and the rigid structure of SQL, a flexible graph
model is used which, again, can scale across multiple machines. NoSQL databases do not
provide a high-level declarative query language like SQL to avoid overtime in
processing. Rather, querying these databases is data-model specific. Many of the NoSQL
platforms allow for RESTful interfaces to the data, while other offer query APIs.
Graph databases take document databases to the extreme by introducing the concept of
type relationships between documents or nodes. The most common example is the
relationship between people on a social network such as Facebook.
A graph database is a big dense network structure. While it could take an RDBMS hours
to sift through a huge linked list of people, a graph database uses sophisticated shortest
path algorithms to make data queries more efficient. Although slower than its other
NoSQL counterparts, a graph database can have the most complex structure of them all
and still traverse billions of nodes and relationships with light speed.
Examples: Neo4J, InfoGrid, Infinite Graph
Strengths: Graph algorithms e.g. shortest path,n degree relationships, etc.
Weaknesses: Traverse the entire graph to achieve a definitive answer. Not easy to cluster
9. When is NoSQL a poor choice?
After spending so long extolling the benefits of the various NoSQL solutions, I would like to
point out at least one scenario where I haven’t seen a good NosQL solution for the RDBMS:
Reporting. One of the great things about RDBMS is that given the information that it already
have, it is very easy to massage the data into a lot of interesting forms. That is especially
important when you are trying to do things like give the user the ability to analyze the data
on their own, such as by providing the user with a report tool that allows them to query,
aggregate and manipulate the data to their heart’s content. While it is certainly possible to
produce reports on top of a NoSQL store, you wouldn’t be able to come close to the level of
flexibility that a RDMBS will offer. That is one of the major benefits of the RDBMS, its
flexibility. The NoSQL solutions will tend to outperform the RDBMS solution (as long as you
stay in the appropriate niche for each NoSQL solution) and they certainly have better
scalability story than the RDBMS, but for user driven reports, the RDBMS is still my tool of
choice
10. Suvradeep Rudra is a Sr. Data Architect and has more than 10
years of experience in Data Management. He held a number
of roles at Caritor Inc. (now NTT DATA), Oracle, Deloitte
Consulting. Experienced in building overall data strategy,
tapping value from data assets and capabilities and driving
value to the business. He has worked in various projects,
establishing and building data management solutions for
customers in the industries such as High Tech, Health
Insurance, Oil and Gas, Payments services and Banking. His
experience ranges from Data strategy, Product Strategy,
MDM, Business Intelligence and Analytics, Data Architecture
(Data Warehouse), Data Governance.
Suvradeep writes and speaks about Monetizing Company’s
Data and Technology trends.
He holds Masters in Computer Applications from University
of Madras, Chennai, India.
He can be reached via LinkedIn profile