This document provides an introduction to big data and NoSQL databases. It begins with an introduction of the presenter. It then discusses how the era of big data came to be due to limitations of traditional relational databases and scaling approaches. The document introduces different NoSQL data models including document, key-value, graph and column-oriented databases. It provides examples of NoSQL databases that use each data model. The document discusses how NoSQL databases are better suited than relational databases for big data problems and provides a real-world example of Twitter's use of FlockDB. It concludes by discussing approaches for working with big data using MapReduce and provides examples of using MongoDB and Azure for big data.
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.
OLTP systems are used for operational tasks like processing transactions, while OLAP systems are used for analysis of historical data extracted from OLTP systems. OLAP systems allow for complex queries and reporting on aggregated and multidimensional views of the data. Both systems are complementary, with OLTP housing and processing the source transactional data and OLAP leveraging that data for planning, problem solving, and decision making.
Hadoop Tutorial For Beginners | Apache Hadoop Tutorial For Beginners | Hadoop...Simplilearn
This presentation about Hadoop for beginners will help you understand what is Hadoop, why Hadoop, what is Hadoop HDFS, Hadoop MapReduce, Hadoop YARN, a use case of Hadoop and finally a demo on HDFS (Hadoop Distributed File System), MapReduce and YARN. Big Data is a massive amount of data which cannot be stored, processed, and analyzed using traditional systems. To overcome this problem, we use Hadoop. Hadoop is a framework which stores and handles Big Data in a distributed and parallel fashion. Hadoop overcomes the challenges of Big Data. Hadoop has three components HDFS, MapReduce, and YARN. HDFS is the storage unit of Hadoop, MapReduce is its processing unit, and YARN is the resource management unit of Hadoop. In this video, we will look into these units individually and also see a demo on each of these units.
Below topics are explained in this Hadoop presentation:
1. What is Hadoop
2. Why Hadoop
3. Big Data generation
4. Hadoop HDFS
5. Hadoop MapReduce
6. Hadoop YARN
7. Use of Hadoop
8. Demo on HDFS, MapReduce and YARN
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
“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 document provides an overview of the 3-tier data warehouse architecture. It discusses the three tiers: the bottom tier contains the data warehouse server which fetches relevant data from various data sources and loads it into the data warehouse using backend tools for extraction, cleaning, transformation and loading. The bottom tier also contains the data marts and metadata repository. The middle tier contains the OLAP server which presents multidimensional data to users from the data warehouse and data marts. The top tier contains the front-end tools like query, reporting and analysis tools that allow users to access and analyze the data.
The document summarizes a meetup about NoSQL databases hosted by AWS in Sydney in 2012. It includes an agenda with presentations on Introduction to NoSQL and using EMR and DynamoDB. NoSQL is introduced as a class of databases that don't use SQL as the primary query language and are focused on scalability, availability and handling large volumes of data in real-time. Common NoSQL databases mentioned include DynamoDB, BigTable and document databases.
DDBMS, characteristics, Centralized vs. Distributed Database, Homogeneous DDBMS, Heterogeneous DDBMS, Advantages, Disadvantages, What is parallel database, Data fragmentation, Replication, Distribution Transaction
This document discusses the key components of a database system including applications, file systems, data views, query processors, users and administrators, data languages, transaction management, and storage managers. It provides examples of common database applications and describes how data is abstracted at the physical, logical, and view levels. It also explains the roles of DDL, DML, transactions, and storage managers in database design and management.
MongoDB is a document-oriented NoSQL database written in C++. It uses a document data model and stores data in BSON format, which is a binary form of JSON that is lightweight, traversable, and efficient. MongoDB is schema-less, supports replication and high availability, auto-sharding for scaling, and rich queries. It is suitable for big data, content management, mobile and social applications, and user data management.
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
This document provides an introduction to NoSQL databases. It discusses the history and limitations of relational databases that led to the development of NoSQL databases. The key motivations for NoSQL databases are that they can handle big data, provide better scalability and flexibility than relational databases. The document describes some core NoSQL concepts like the CAP theorem and different types of NoSQL databases like key-value, columnar, document and graph databases. It also outlines some remaining research challenges in the area of NoSQL databases.
There are three common data warehouse architectures: basic, with a staging area, and with a staging area and data marts. The basic architecture extracts data directly from source systems into the data warehouse for users. The staging area architecture uses a staging area to clean and process data before loading it into the warehouse. The third architecture adds data marts, which are subsets of the warehouse organized for specific business units like sales or purchasing.
Basic Concept Of Database Management System (DBMS) [Presentation Slide]Atik Israk
This document provides an overview of basic concepts in database management systems (DBMS). It defines key terms like database, DBMS, software examples, purposes of DBMS, applications, and terminology. Specifically, it outlines what a database is, the role of a DBMS in providing management and control of data access. It lists example DBMS software and how DBMS reduce data redundancy and ensure security. Applications of DBMS mentioned include libraries, banking, education and telecommunications. Terminology defined includes entity, attribute, record, key, and relationship.
There are three main points about data streams and stream processing:
1) A data stream is a continuous, ordered sequence of data items that arrives too rapidly to be stored fully. Common sources include sensors, web traffic, and social media.
2) Data stream management systems process continuous queries over streams in real-time using bounded memory. They provide summaries of historical data rather than storing entire streams.
3) Challenges of stream processing include limited memory, complex continuous queries, and unpredictable data rates and characteristics. Approximate query processing techniques like windows, sampling, and load shedding help address these challenges.
This document discusses transaction processing and concurrency control in database systems. It defines a transaction as a unit of program execution that accesses and possibly modifies data. It describes the key properties of transactions as atomicity, consistency, isolation, and durability. It discusses how concurrency control techniques like locking and two-phase locking protocols are used to ensure serializable execution of concurrent transactions.
Data Warehouse : Dimensional Model: Snowflake Schema In the snowflake schema, dimension are present in a normalized from in multiple related tables.
The snowflake structure materialized when the dimensions of a star schema are detailed and highly structured, having several levels of relationship, and the child tables have multiple parent table.
This document compares SQL and NoSQL databases. It defines databases, describes different types including relational and NoSQL, and explains key differences between SQL and NoSQL in areas like scaling, modeling, and query syntax. SQL databases are better suited for projects with logical related discrete data requirements and data integrity needs, while NoSQL is more ideal for projects with unrelated, evolving data where speed and scalability are important. MongoDB is provided as an example of a NoSQL database, and the CAP theorem is introduced to explain tradeoffs in distributed systems.
The document introduces MongoDB as an open source, high performance database that is a popular NoSQL option. It discusses how MongoDB stores data as JSON-like documents, supports dynamic schemas, and scales horizontally across commodity servers. MongoDB is seen as a good alternative to SQL databases for applications dealing with large volumes of diverse data that need to scale.
Spring is an open source framework for building Java applications. It provides features like dependency injection, aspect-oriented programming, and abstraction layers for web services, security, and data access. Spring aims to simplify enterprise application development by reducing boilerplate code and providing a flexible, configurable architecture. It consists of several core modules that can be used independently or together, including support for web applications, data access, transactions, and more. Spring promotes loose coupling between application layers and components through its lightweight container and declarative configuration.
This document is an introduction to NoSQL databases presented by Trisha Gee from MongoDB. It discusses relational databases and their advantages like ACID transactions. However, relational databases have disadvantages for large or unstructured datasets. NoSQL databases are non-relational and come in different flavors like key-value stores, document databases, graph databases, and column-oriented databases. NoSQL databases offer more flexibility in schemas and can be a better fit than relational databases depending on an application's needs around queries, scalability, or data structure.
The document discusses NoSQL databases and their different classes, including column stores, document stores, and key-value stores. It provides examples of column store databases BigTable and HBase, and notes that document stores like CouchDB allow data to be stored without a predefined schema. The document also discusses object databases and their advantages over relational databases in avoiding the object-relational impedance mismatch.
J2EE is a Java platform for developing distributed, transactional, multi-tier enterprise applications. It includes technologies like servlets, JSPs, EJBs, and services like JMS, JTA, and JNDI. The purpose of J2EE is to support developing applications that are distributed, transactional, and secure across multiple tiers. Common architectures include 2-tier client-server, 3-tier with separation of presentation, business and data layers, and n-tier with additional logical separations. MVC is a common design pattern that separates the application into model, view and controller components.
Extract business value by analyzing large volumes of multi-structured data from various sources such as databases, websites, blogs, social media, smart sensors...
Database recovery techniques restore the database to its most recent consistent state before a failure. There are three states: pre-failure consistency, failure occurrence, and post-recovery consistency. Recovery approaches include steal/no-steal and force/no-force, while update strategies are deferred or immediate. Shadow paging maintains current and shadow tables to recover pre-transaction states. The ARIES algorithm analyzes dirty pages, redoes committed transactions, and undoes uncommitted ones. Disk crash recovery uses log/database separation or backups.
The document discusses choosing between SQL and NoSQL databases. It covers the evolution of data architectures from traditional client-server models to newer distributed NoSQL solutions. It provides an overview of different data store types like SQL, NoSQL, key-value, document, column family, and graph databases. The document advises picking the right data model based on business needs, use cases, data storage requirements, and growth patterns then evaluating solutions based on pros and cons. It concludes that for large, growing data, both SQL and NoSQL solutions may be needed.
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 document provides an introduction to NoSQL and MongoDB. It discusses that NoSQL is a non-relational database management system that avoids joins and is easy to scale. It then summarizes the different flavors of NoSQL including key-value stores, graphs, BigTable, and document stores. The remainder of the document focuses on MongoDB, describing its structure, how to perform inserts and searches, features like map-reduce and replication. It concludes by encouraging the reader to try MongoDB themselves.
This document provides an overview of big data. It defines big data as large volumes of diverse data that are growing rapidly and require new techniques to capture, store, distribute, manage, and analyze. The key characteristics of big data are volume, velocity, and variety. Common sources of big data include sensors, mobile devices, social media, and business transactions. Tools like Hadoop and MapReduce are used to store and process big data across distributed systems. Applications of big data include smarter healthcare, traffic control, and personalized marketing. The future of big data is promising with the market expected to grow substantially in the coming years.
The relational database model was designed to solve the problems of yesterday’s data storage requirements. The massively connected world of today presents different problems and new challenges. We’ll explore the NoSQL philosophy, before comparing and contrasting the strengths and weaknesses of the relational model versus the NoSQL model. While stepping through real-world scenarios, we’ll discuss the reasons for choosing one solution over the other.
To complete this session, let’s demonstrate our findings with an application written with a NoSQL storage layer and explain the advantages that accrue from that decision. By taking a look at the new challenges we face with our data storage needs, we’ll examine why the principles behind NoSQL make it a better candidate as a solution, than yesterday’s relational model.
This document provides a summary of a presentation on Big Data and NoSQL databases. It introduces the presenters, Melissa Demsak and Don Demsak, and their backgrounds. It then discusses how data storage needs have changed with the rise of Big Data, including the problems created by large volumes of data. The presentation contrasts traditional relational database implementations with NoSQL data stores, identifying five categories of NoSQL data models: document, key-value, graph, and column family. It provides examples of databases that fall under each category. The presentation concludes with a comparison of real-world scenarios and which data storage solutions might be best suited to each scenario.
The document provides an overview of SQL vs NoSQL databases. It discusses how RDBMS systems focus on ACID properties to ensure consistency but sacrifice availability and scalability. NoSQL systems embrace the CAP theorem, prioritizing availability and partition tolerance over consistency to better support distributed and cloud-scale architectures. The document outlines different NoSQL database models and how they are suited for high volume operations through an asynchronous and eventually consistent approach.
A Survey of Advanced Non-relational Database Systems: Approaches and Applicat...Qian Lin
This document summarizes a survey of advanced non-relational database systems, their approaches, applications, and comparison to relational database management systems (RDBMS). It outlines the problem of scaling to meet new web-scale demands, describes how non-relational databases provide a solution by sacrificing consistency for availability and partition tolerance. Examples of non-relational databases are provided, including their data models, APIs, optimizations, and benefits compared to RDBMS such as improved scalability and fault tolerance.
"Navigating the Database Universe" by Dr. Michael Stonebraker and Scott Jarr,...lisapaglia
Webinar presentation delivered by Dr. Michael Stonebraker and Scott Jarr of VoltDB on December 11, 2012. www.voltdb.com
The design decisions you make today will have a huge performance impact down the line. Until recently, when it came to databases, the choice was easy. Essentially, you had one option: the RDBMS. Today, there's a new universe of databases being thrown into production — and not always with the greatest success. How do you make the right choice for your next application? Database pioneer Dr. Michael Stonebraker and VoltDB co-founder Scott Jarr have some thoughts.
This document discusses large scale computing with MapReduce. It provides background on the growth of digital data, noting that by 2020 there will be over 5,200 GB of data for every person on Earth. It introduces MapReduce as a programming model for processing large datasets in a distributed manner, describing the key aspects of Map and Reduce functions. Examples of MapReduce jobs are also provided, such as counting URL access frequencies and generating a reverse web link graph.
Big Data Cloud Meetup - Jan 29 2013 - Mike Stonebraker & Scott Jarr of VoltDBBigDataCloud
"Navigating the Database Universe" was the topic of the Big Data Cloud meetup held on Jan 24th 2013 in Santa Clara, CA. This is the presentation made by Mike Stonebraker & Scott Jarr of VoltDB.
This meetup was sponsored by VoltDb.
Jay Kreps on Project Voldemort Scaling Simple Storage At LinkedInLinkedIn
Jay Kreps on Project Voldemort Scaling Simple Storage At LinkedIn. This was a presentation made at QCon 2009 and is embedded on LinkedIn's blog - http://blog.linkedin.com/
Navigating Transactions: ACID Complexity in Modern DatabasesShivji Kumar Jha
Transactions are anything but straightforward, with each database vendor offering its unique interpretation of the term. By scrutinising the internal architectures of these databases, engineers can gain valuable insights, enabling them to write more stable applications.This talk explores the intricacies of transactions, focusing on modern databases. Delving into distributed transactions, we discuss network challenges and cloud deployments in the contemporary era. The session provides a concise examination of the internal architectures of cloud-scale, multi-tenant databases such as Spanner, DynamoDB, and Amazon Aurora.
Navigating Transactions: ACID Complexity in Modern Databases- Mydbops Open So...Mydbops
Navigating Transactions: ACID Complexity in Modern Databases- Mydbops Open Source Database Meetup 15
Shivji explores the evolution of transactions, implementation challenges, and insights into distributed database environments. Whether you're a database enthusiast or a tech enthusiast, this presentation offers valuable insights into the world of database management.
Contents:
• Historical perspective of transactions
• Implementing transactions
• Challenges and trade-offs in ACID properties
• Distributed transactions in modern databases like Amazon Aurora, DynamoDB, and Google Spanner
Key Takeaways:
• Understanding the evolution of transactions in databases
• Insights into the challenges of implementing ACID properties
• Exploration of distributed transaction models in leading database systems
Performance Management in ‘Big Data’ ApplicationsMichael Kopp
Do applications using NoSQL still require performance management? Is it always the best option to throw more hardware at a MapReduce job? In both cases, performance management is still about the application, but "Big Data" technologies have added a new wrinkle.
This document provides an introduction to relational databases, NoSQL databases, and data in general. It includes the following:
- An overview of relational databases and their ACID properties. Relational databases are best for structured, centralized data and scale vertically.
- A survey of several popular NoSQL databases like MongoDB, Cassandra, Redis, and HBase. NoSQL databases are best for unstructured, large quantities of data and scale horizontally.
- General advice that the data and query models, durability needs, scalability needs, and consistency requirements should determine the best database choice. Trying different options is recommended.
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.
Solr cloud the 'search first' nosql database extended deep divelucenerevolution
Presented by Mark Miller, Software Engineer, Cloudera
As the NoSQL ecosystem looks to integrate great search, great search is naturally beginning to expose many NoSQL features. Will these Goliath's collide? Or will they remain specialized while intermingling – two sides of the same coin.
Come learn about where SolrCloud fits into the NoSQL landscape. What can it do? What will it do? And how will the big data, NoSQL, Search ecosystem evolve. If you are interested in Big Data, NoSQL, distributed systems, CAP theorem and other hype filled terms, than this talk may be for you.
Ciel, mes données ne sont plus relationnellesXavier Gorse
Quand la gestion des données de nos applications web dépasse la simple persistance dans une base de données relationnelle (type SGBD), l’utilisation de technologies alternatives dites « NoSql » est nécessaire. Nous aborderons les 4 grandes familles de NoSql (Key/Value, Document, Column-oriented et Graph) ainsi que leur intégration dans des applications PHP.
NoSQL is not a buzzword anymore. The array of non- relational technologies have found wide-scale adoption even in non-Internet scale focus areas. With the advent of the Cloud...the churn has increased even more yet there is no crystal clear guidance on adoption techniques and architectural choices surrounding the plethora of options available. This session initiates you into the whys & wherefores, architectural patterns, caveats and techniques that will augment your decision making process & boost your perception of architecting scalable, fault-tolerant & distributed solutions.
Rakuten, Inc. developed ROMA, a user-customizable NoSQL database in Ruby. ROMA provides high scalability, availability, and fault tolerance using techniques like consistent hashing and virtual nodes. It has a plug-in architecture that allows users to extend ROMA's functionality through custom commands. The plug-ins use a domain specific language for easily defining commands to perform operations on structured data stored in ROMA, such as lists and maps.
The document provides an overview of NoSQL databases. It discusses how NoSQL databases were developed as an alternative to relational databases to address issues of scale, diversity of data types, and large data sizes. It describes some key aspects of NoSQL databases, including their use of eventual consistency, automatic partitioning of large amounts of data, and various data storage models like key-value, columnar, and document-based approaches. Examples of NoSQL databases discussed include DynamoDB, Bigtable, and CouchDB.
This PDF delves into the aspects of information security from a forensic perspective, focusing on privacy leaks. It provides insights into the methods and tools used in forensic investigations to uncover and mitigate privacy breaches in mobile and cloud environments.
TrustArc Webinar - Innovating with TRUSTe Responsible AI CertificationTrustArc
In a landmark year marked by significant AI advancements, it’s vital to prioritize transparency, accountability, and respect for privacy rights with your AI innovation.
Learn how to navigate the shifting AI landscape with our innovative solution TRUSTe Responsible AI Certification, the first AI certification designed for data protection and privacy. Crafted by a team with 10,000+ privacy certifications issued, this framework integrated industry standards and laws for responsible AI governance.
This webinar will review:
- How compliance can play a role in the development and deployment of AI systems
- How to model trust and transparency across products and services
- How to save time and work smarter in understanding regulatory obligations, including AI
- How to operationalize and deploy AI governance best practices in your organization
It's your unstructured data: How to get your GenAI app to production (and spe...Zilliz
So you've successfully built a GenAI app POC for your company -- now comes the hard part: bringing it to production. Aparavi addresses the challenges of AI projects while addressing data privacy and PII. Our Service for RAG helps AI developers and data scientists to scale their app to 1000s to millions of users using corporate unstructured data. Aparavi’s AI Data Loader cleans, prepares and then loads only the relevant unstructured data for each AI project/app, enabling you to operationalize the creation of GenAI apps easily and accurately while giving you the time to focus on what you really want to do - building a great AI application with useful and relevant context. All within your environment and never having to share private corporate data with anyone - not even Aparavi.
Keynote : Presentation on SASE TechnologyPriyanka Aash
Secure Access Service Edge (SASE) solutions are revolutionizing enterprise networks by integrating SD-WAN with comprehensive security services. Traditionally, enterprises managed multiple point solutions for network and security needs, leading to complexity and resource-intensive operations. SASE, as defined by Gartner, consolidates these functions into a unified cloud-based service, offering SD-WAN capabilities alongside advanced security features like secure web gateways, CASB, and remote browser isolation. This convergence not only simplifies management but also enhances security posture and application performance across global networks and cloud environments. Discover how adopting SASE can streamline operations and fortify your enterprise's digital transformation strategy.
Welcome to Cyberbiosecurity. Because regular cybersecurity wasn't complicated...Snarky Security
How wonderful it is that in our modern age, every bit of our biological data can be digitized, stored, and potentially pilfered by cyber thieves! Isn't it just splendid to think that while scientists are busy pushing the boundaries of biotechnology, hackers could be plotting the next big bio-data heist? This delightful scenario is brought to you by the ever-expanding digital landscape of biology and biotechnology, where the integration of computer science, engineering, and data science transforms our understanding and manipulation of biological systems.
While the fusion of technology and biology offers immense benefits, it also necessitates a careful consideration of the ethical, security, and associated social implications. But let's be honest, in the grand scheme of things, what's a little risk compared to potential scientific achievements? After all, progress in biotechnology waits for no one, and we're just along for the ride in this thrilling, slightly terrifying, adventure.
So, as we continue to navigate this complex landscape, let's not forget the importance of robust data protection measures and collaborative international efforts to safeguard sensitive biological information. After all, what could possibly go wrong?
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This document provides a comprehensive analysis of the security implications biological data use. The analysis explores various aspects of biological data security, including the vulnerabilities associated with data access, the potential for misuse by state and non-state actors, and the implications for national and transnational security. Key aspects considered include the impact of technological advancements on data security, the role of international policies in data governance, and the strategies for mitigating risks associated with unauthorized data access.
This view offers valuable insights for security professionals, policymakers, and industry leaders across various sectors, highlighting the importance of robust data protection measures and collaborative international efforts to safeguard sensitive biological information. The analysis serves as a crucial resource for understanding the complex dynamics at the intersection of biotechnology and security, providing actionable recommendations to enhance biosecurity in an digital and interconnected world.
The evolving landscape of biology and biotechnology, significantly influenced by advancements in computer science, engineering, and data science, is reshaping our understanding and manipulation of biological systems. The integration of these disciplines has led to the development of fields such as computational biology and synthetic biology, which utilize computational power and engineering principles to solve complex biological problems and innovate new biotechnological applications. This interdisciplinary approach has not only accelerated research and development but also introduced new capabilities such as gene editing and biomanufact
The Zaitechno Handheld Raman Spectrometer is a powerful and portable tool for rapid, non-destructive chemical analysis. It utilizes Raman spectroscopy, a technique that analyzes the vibrational fingerprint of molecules to identify their chemical composition. This handheld instrument allows for on-site analysis of materials, making it ideal for a variety of applications, including:
Material identification: Identify unknown materials, minerals, and contaminants.
Quality control: Ensure the quality and consistency of raw materials and finished products.
Pharmaceutical analysis: Verify the identity and purity of pharmaceutical compounds.
Food safety testing: Detect contaminants and adulterants in food products.
Field analysis: Analyze materials in the field, such as during environmental monitoring or forensic investigations.
The Zaitechno Handheld Raman Spectrometer is easy to use and features a user-friendly interface. It is compact and lightweight, making it ideal for field applications. With its rapid analysis capabilities, the Zaitechno Handheld Raman Spectrometer can help you improve efficiency and productivity in your research or quality control workflows.
Finetuning GenAI For Hacking and DefendingPriyanka Aash
Generative AI, particularly through the lens of large language models (LLMs), represents a transformative leap in artificial intelligence. With advancements that have fundamentally altered our approach to AI, understanding and leveraging these technologies is crucial for innovators and practitioners alike. This comprehensive exploration delves into the intricacies of GenAI, from its foundational principles and historical evolution to its practical applications in security and beyond.
"Building Future-Ready Apps with .NET 8 and Azure Serverless Ecosystem", Stan...Fwdays
.NET 8 brought a lot of improvements for developers and maturity to the Azure serverless container ecosystem. So, this talk will cover these changes and explain how you can apply them to your projects. Another reason for this talk is the re-invention of Serverless from a DevOps perspective as a Platform Engineering trend with Backstage and the recent Radius project from Microsoft. So now is the perfect time to look at developer productivity tooling and serverless apps from Microsoft's perspective.
DefCamp_2016_Chemerkin_Yury-publish.pdf - Presentation by Yury Chemerkin at DefCamp 2016 discussing mobile app vulnerabilities, data protection issues, and analysis of security levels across different types of mobile applications.
"Making .NET Application Even Faster", Sergey Teplyakov.pptxFwdays
In this talk we're going to explore performance improvement lifecycle, starting with setting the performance goals, using profilers to figure out the bottle necks, making a fix and validating that the fix works by benchmarking it. The talk will be useful for novice and seasoned .NET developers and architects interested in making their application fast and understanding how things work under the hood.
How UiPath Discovery Suite supports identification of Agentic Process Automat...DianaGray10
📚 Understand the basics of the newly persona-based LLM-powered Agentic Process Automation and discover how existing UiPath Discovery Suite products like Communication Mining, Process Mining, and Task Mining can be leveraged to identify APA candidates.
Topics Covered:
💡 Idea Behind APA: Explore the innovative concept of Agentic Process Automation and its significance in modern workflows.
🔄 How APA is Different from RPA: Learn the key differences between Agentic Process Automation and Robotic Process Automation.
🚀 Discover the Advantages of APA: Uncover the unique benefits of implementing APA in your organization.
🔍 Identifying APA Candidates with UiPath Discovery Products: See how UiPath's Communication Mining, Process Mining, and Task Mining tools can help pinpoint potential APA candidates.
🔮 Discussion on Expected Future Impacts: Engage in a discussion on the potential future impacts of APA on various industries and business processes.
Enhance your knowledge on the forefront of automation technology and stay ahead with Agentic Process Automation. 🧠💼✨
Speakers:
Arun Kumar Asokan, Delivery Director (US) @ qBotica and UiPath MVP
Naveen Chatlapalli, Solution Architect @ Ashling Partners and UiPath MVP
Redefining Cybersecurity with AI CapabilitiesPriyanka Aash
In this comprehensive overview of Cisco's latest innovations in cybersecurity, the focus is squarely on resilience and adaptation in the face of evolving threats. The discussion covers the imperative of tackling Mal information, the increasing sophistication of insider attacks, and the expanding attack surfaces in a hybrid work environment. Emphasizing a shift towards integrated platforms over fragmented tools, Cisco introduces its Security Cloud, designed to provide end-to-end visibility and robust protection across user interactions, cloud environments, and breaches. AI emerges as a pivotal tool, from enhancing user experiences to predicting and defending against cyber threats. The blog underscores Cisco's commitment to simplifying security stacks while ensuring efficacy and economic feasibility, making a compelling case for their platform approach in safeguarding digital landscapes.
4. How did we get here?
• Expensive • Monoculture
– Processors – Limit CPU cycles
– Disk space – Limit disk space
– Memory – Limit memory
– Operating Systems – Limited OS
– Software Development
– Programmers – Limited Software
– Programmers
• Mono-lingual
• Mono-persistence
4
5. Typical RDBMS Implementations
• Fixed table schemas
• Small but frequent reads/writes
• Large batch transactions
• Focus on ACID
– Atomicity
– Consistency
– Isolation
– Durability
5
15. Where Did NoSQL Originate?
• 1998 - Carlo Strozzi
– NoSQL project - lightweight open-source relational DB
with no SQL interface
• 2009 - Eric Evans & Johan Oskarsson of Last.fm
wanted to organize an event to discuss open-
source distributed databases
15
17. Atlanta 2009
• No:sql(east) conference
– select fun, profit from real_world where relational=false
• Billed as “conference of no-rel datastores”
17
19. 5 Groups of Data Models
Relational
Document
Key Value
Graph
Column Family
19
20. Document Store
• Apache Jackrabbit
• CouchDB
• MongoDB
• SimpleDB
• XML Databases
– MarkLogic Server
– eXist.
20
21. Document?
• Okay think of a web page...
– Relational model requires column/tag
– Lots of empty columns
– Wasted space
• Document model just stores the pages as is
– Saves on space
– Very flexible.
21
23. What‟s a graph?
• Graph consists of
– Node („stations‟ of the graph)
– Edges (lines between them)
• FlockDB
– Created by the Twitter folks
– Nodes = Users
– Edges = Nature of relationship between nodes.
23
24. Key/Value Stores
• On disk
• Cache in Ram
• Eventually Consistent
– Weak Definition
• “If no updates occur for a period, eventually all updates will
propagate through the system and all replicas will be consistent”
– Strong Definition
• “for a given update and a given replica eventually either the
update reaches the replica or the replica retires”
• Ordered
– Distributed Hash Table allows lexicographical processing
24
30. Big Data Definition
• Volumes & volumes of data
• Unstructured
• Semi-structured
• Not suited for Relational Databases
• Often utilizes MapReduce frameworks
30
32. Real World Example
• Twitter
– The challenges
• Needs to store many graphs
Who you are following
Who‟s following you
Who you receive phone
notifications from etc
• To deliver a tweet requires
rapid paging of followers
• Heavy write load as followers
are added and removed
• Set arithmetic for @mentions
(intersection of users).
32
33. What did they try?
• Started with Relational
Databases
• Tried Key-Value storage
of denormalized lists
• Did it work?
– Nope
• Either good at
Handling the write load
Or paging large
amounts of data
But not both
33
34. What did they need?
• Simplest possible thing that would work
• Allow for horizontal partitioning
• Allow write operations to
• Arrive out of order
– Or be processed more than once
– Failures should result in redundant work
• Not lost work!
34
35. The Result was FlockDB
• Stores graph data
• Not optimized for graph traversal operations
• Optimized for large adjacency lists
– List of all edges in a graph
• Key is the edge value a set of the node end points
• Optimized for fast read and write
• Optimized for page-able set arithmetic.
35
36. How Does it Work?
• Stores graphs as sets of edges between nodes
• Data is partitioned by node
– All queries can be answered by a single partition
• Write operations are idempotent
– Can be applied multiple times without changing the
result
• And commutative
– Changing the order of operands doesn‟t change the
result.
36
38. ACID
• Atomicity
– All or Nothing
• Consistency
– Valid according to all defined rules
• Isolation
– No transaction should be able to interfere with another
transaction
• Durability
– Once a transaction has been committed, it will remain
so, even in the event of power loss, crashes, or errors
38
39. BASE
• Basically Available
– High availability but not always consistent
• Soft state
– Background cleanup mechanism
• Eventual consistency
– Given a sufficiently long period of time over which no
changes are sent, all updates can be expected to
propagate eventually through the system and all the
replicas will be consistent.
39
41. Big Data Approach
• MapReduce Pattern/Framework
– an Input Reader
– Map Function – To transform to a common shape
(format)
– a partition function
– a compare function
– Reduce Function
– an Output Writer
41
42. MongoDB Example
> // map function > // reduce function
> m = function(){ > r = function( key , values ){
... this.tags.forEach( ... var total = 0;
... function(z){ ... for ( var i=0; i<values.length; i++ )
... emit( z , { count : 1 } ... total += values[i].count;
); ... return { count : total };
... } ...};
... );
...};
> // execute
> res = db.things.mapReduce(m, r, { out : "myoutput" } );
42
44. Big Data on Azure
• Azure Table Storage
– Azure Service Bus
• SQL Azure Federations
• MongoDB on Azure
– http://www.mongodb.org/display/DOCS/MongoDB+on+Azure
• Hadoop on Azure
– https://www.hadooponazure.com/
44
45. Using Azure for Computing
Data
Data Worker
Data
Client Master Worker
Job/Task Scheduler Worker
Data
45
46. Moving to Event Based Architecture
Web Role Worker Role
Web Role Worker Role
Web Role Worker Role
Req Req Req
Queue
Web Role Worker Role
Web Role Monitor queue Worker Role
length against
Web Role user‟s expectations Worker Role
46
51. Next Steps
• Learn a NoSQL product
– Great place to start – AppFabric Cache, Azure Table
Storage, MongoDB
• Pick a new programming language to learn
– Not Java or C#/VB
– Node.js, JavaScript, F#
51
t least four groups of data model: key-value, document, column-family, and graph. Looking at this list, there's a big similarity between the first three - all have a fundamental unit of storage which is a rich structure of closely related data: for key-value stores it's the value, for document stores it's the document, and for column-family stores it's the column family. In DDD terms, this group of data is an aggregate.A Graph Database stores data structured in the Nodes and Relationships of a graphColumn Family (BigTable-style) databases are an evolution of key-value, using "families" to allow grouping of rows. The rise of NoSQL databases has been driven primarily by the desire to store data effectively on large clusters - such as the setups used by Google and Amazon. Relational databases were not designed with clusters in mind, which is why people have cast around for an alternative. Storing aggregates as fundamental units makes a lot of sense for running on a cluster. Aggregates make natural units for distribution strategies such as sharding, since you have a large clump of data that you expect to be accessed together.The Relational ModelThe relational model provides for the storage of records that are made up of tuples. Records are stored in tables. Tables are defined by a schema, which determines what columns are in the table. Columns have a name and a type. All records within a table fit that table's definition. SQL is a query language designed to operate over tables. SQL provides syntax for finding records that meet criteria, as well as for relating records in one table to another via joins; a join finds a record in one table based on its relationship to a record in another table.Records can be created (inserted) or deleted. Fields within a record can be updated individually.Implementations of the relational model usually provide transactions, which provide a means to make modifications spanning multiple records atomically.In terms of what programming languages provide, tables are like arrays or lists of records or structures. For high performance access, tables can be indexed in various ways using b-trees or hash maps.Key-Value StoresKey-Value stores provide access to a value based on a key.The key-value pair can be created (inserted), or deleted. The value associated with a key may be updated.Key-value stores don't usually provide transactions.In terms of what programming languages provide, key-value stores resemble hash tables; these have many names: HashMap (Java), hash (Perl), dict (Python), associative array (PHP), boost::unordered_map<...> (C++).Key-value stores provide one implicit index on the key itself.A key-value store may not sound like the most useful thing, but a lot of information can be stored in the value. It is quite common for the value to be an XML document, a JSON object, or some other serialized form. The key point here is that the storage engine is not aware of the internal structure of the value. It is up to the client application to interpet the value andmanage its contents. The value can only be written as a whole; if the client is storing a JSON object, and only wants to update one field, the entire value must be fetched, the new value substituted, and then the entire value must be written back.The inability to fetch data by anything other than one key may appear limited, but there are workarounds. If the application requires a secondary index, the application can maintain one itself. To do this, the application manages a second collection of key-value pairs where the key is the value of another field in the first collection, and the value is the primary key in the first collection. Because there are no transactions that can be used to make sure that the secondary index is kept synchronized with the original collection, any application that does this would be wise to have a periodic syncing process to clean up after any partial changes that occur due to application crashes, bugs, or errors.Document StoresDocument stores provide access to structured data, but unlike the relational model, there may not be a schema that is enforced. In essence, the application stores bags of key-value pairs. In order to operate in this environment, the application adopts some conventions about how to deal with differing bags it may retrieve, or it may take advantage of the storage engine's ability to put different documents in different collections, which the application will use to manage its data.Unlike a relational store, document stores usually support nested structures. For example, for document stores that support XML or JSON documents, the value of a field may be something that looks like another document. Document stores can also support array or list-valued keys.Unlike a key-value store, document stores are aware of the internal structure of the document. This allows the storage engine to support secondary indexes directly, allowing for efficient queries on any field. The ability to support nested document storage leads to query languages that can be used to search for items nested inside others; XQuery is one example of this. MongoDB supports some similar functionality by allowing the specification of JSON field paths in queries.Column StoresColumn stores are like relational stores, except that they flip the data around. Instead of storing records, column stores store all the values for a column together in a stream. An index provides a means to get column values for any particular record.Map-reduce implementations such as Hadoop are most efficient if they can stream in their data. Column stores work particularly well for that. As a result, stores like HBase and Hypertable are often used as non-relational data warehouses to feed map-reduce for analytics.A relational-style column scalar may not be the most useful for analytics, so users often store more complex structures in columns. This manifests directly in Cassandra, which introduces the notion of "column families," which get treated as a "super-column."Column-oriented stores support retrieving records, but this requires fetching the column values from their individual columns and re-assembling the record.Graph DatabasesGraph databases store vertices and the edges between them. Some support adding annotations to the vertices and/or edges. This can be used to model things like social graphs (people are represented by vertices, and their relationships are the edges), or real-world objects (components are represented by vertices, and their connectedness is represented by edges). The content on IMDB is tied together by a graph: movies are related to to the actors in them, and actors are related to the movies they star in, forming a large complex graph.The access and query languages for graph databases are the most different of the set of those discussed here. Graph database query languages are generally about finding paths in the graph based on either endpoints, or constraints on attributes of the paths between endpoints; one example is SPARQL.