Apache HBase™ is the Hadoop database, a distributed, salable, big data store.Its a column-oriented database management system that runs on top of HDFS.
Apache HBase is an open source NoSQL database that provides real-time read/write access to those large data sets. ... HBase is natively integrated with Hadoop and works seamlessly alongside other data access engines through YARN.
4.1Introduction
- Potential Threats and Attacks on Computer System
- Confinement Problems
- Design Issues in Building Secure Distributed Systems
4.2 Cryptography
- Symmetric Cryptosystem Algorithm: DES
- Asymmetric Cryptosystem
4.3 Secure Channels
- Authentication
- Message Integrity and Confidentiality
- Secure Group Communication
4.4 Access Control
- General Issues
- Firewalls
- Secure Mobile Code
4.5 Security Management
- Key Management
- Issues in Key Distribution
- Secure Group Management
- Authorization Management
This presentation explains the major differences between SQL and NoSQL databases in terms of Scalability, Flexibility and Performance. It also talks about MongoDB which is a document-based NoSQL database and explains the database strutre for my mouse-human research classifier project.
Apache HBase™ is the Hadoop database, a distributed, salable, big data store.Its a column-oriented database management system that runs on top of HDFS.
Apache HBase is an open source NoSQL database that provides real-time read/write access to those large data sets. ... HBase is natively integrated with Hadoop and works seamlessly alongside other data access engines through YARN.
4.1Introduction
- Potential Threats and Attacks on Computer System
- Confinement Problems
- Design Issues in Building Secure Distributed Systems
4.2 Cryptography
- Symmetric Cryptosystem Algorithm: DES
- Asymmetric Cryptosystem
4.3 Secure Channels
- Authentication
- Message Integrity and Confidentiality
- Secure Group Communication
4.4 Access Control
- General Issues
- Firewalls
- Secure Mobile Code
4.5 Security Management
- Key Management
- Issues in Key Distribution
- Secure Group Management
- Authorization Management
This presentation explains the major differences between SQL and NoSQL databases in terms of Scalability, Flexibility and Performance. It also talks about MongoDB which is a document-based NoSQL database and explains the database strutre for my mouse-human research classifier project.
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
The Text Classification slides contains the research results about the possible natural language processing algorithms. Specifically, it contains the brief overview of the natural language processing steps, the common algorithms used to transform words into meaningful vectors/data, and the algorithms used to learn and classify the data.
To learn more about RAX Automation Suite, visit: www.raxsuite.com
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 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.
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
Project Voldermort é um banco de dados NoSQL utilizado e desenvolvido pela empresa LinkedIn. Escrito em Java, é utilizado para alta-escalabilidade de dados.
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
The Text Classification slides contains the research results about the possible natural language processing algorithms. Specifically, it contains the brief overview of the natural language processing steps, the common algorithms used to transform words into meaningful vectors/data, and the algorithms used to learn and classify the data.
To learn more about RAX Automation Suite, visit: www.raxsuite.com
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 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.
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
Project Voldermort é um banco de dados NoSQL utilizado e desenvolvido pela empresa LinkedIn. Escrito em Java, é utilizado para alta-escalabilidade de dados.
Cassandra - A Decentralized Structured Storage SystemVarad Meru
Slides created as a part of CS 295's week 4 on NoSQL Basics.
CS 295 (Cloud Computing and BigData) at UCI - https://sites.google.com/site/cs295cloudcomputing/
Amazon DynamoDB is a fast and flexible NoSQL database service for applications that need consistent, single-digit millisecond latency at any scale. It is a fully managed cloud database and supports both document and key-value store models. Its flexible data model and reliable performance make it a great fit for mobile, web, gaming, ad tech, IoT, and many other applications.
Learning Objectives:
Understand the differences between relational and non-relational databases
Learn about common use cases for DynamoDB across gaming, ad tech, IoT, and more
See how DynamoDB helps customers handle spikes in traffic and save development time for new feature launches
Who Should Attend:
Developers, IT Decision Makers, and Executives interested in learning more about Amazon Web Services’ serverless NoSQL service to scale mobile, web, IoT, ad tech, and gaming apps
Highlights of AWS ReInvent 2023 (Announcements and Best Practices)Emprovise
Highlights of AWS ReInvent 2023 in Las Vegas. Contains new announcements, deep dive into existing services and best practices, recommended design patterns.
Big Data in the Cloud with Azure Marketplace ImagesMark Kromer
Here are some of the trends that I'm seeing from customer looking to build Azure-based Cloud Big Data solutions using images from the Azure Marketplace
Apache Hive is a rapidly evolving project, many people are loved by the big data ecosystem. Hive continues to expand support for analytics, reporting, and bilateral queries, and the community is striving to improve support along with many other aspects and use cases. In this lecture, we introduce the latest and greatest features and optimization that appeared in this project last year. This includes benchmarks covering LLAP, Apache Druid's materialized views and integration, workload management, ACID improvements, using Hive in the cloud, and performance improvements. I will also tell you a little about what you can expect in the future.
Apache Hive is a rapidly evolving project, many people are loved by the big data ecosystem. Hive continues to expand support for analytics, reporting, and bilateral queries, and the community is striving to improve support along with many other aspects and use cases. In this lecture, we introduce the latest and greatest features and optimization that appeared in this project last year. This includes benchmarks covering LLAP, Apache Druid's materialized views and integration, workload management, ACID improvements, using Hive in the cloud, and performance improvements. I will also tell you a little about what you can expect in the future.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Which Database is Right for My Workload?: Database Week San FranciscoAmazon Web Services
Database Week at the San Francisco Loft: Which Database is Right for My Workload?
Monday, August 27th
Managed Relational Databases on the Cloud
9:30AM–10:00AM
Check In
10:00AM–10:15AM
Database Services at AWS
Short overview of AWS Database and Analytics offerings and an overview of the day's topics.
Speaker: Bill Baldwin - Global Enterprise Support Lead, AWS
10:15AM-11:15AM
Relational Database Services at AWS
Amazon RDS makes it easy to set up, operate, and scale a relational database in the cloud. We’ll look at what RDS does (and does not) do to manage the “muck” of database operations.
Speakers:
Vishwajit Tigadi - Manager, Strategic Accounts, AWS
Bill Baldwin - Global Enterprise Support Lead, AWS
11:15AM-12:15PM
Hands-On Lab: Managed Database Basics
Hands-on Lab to set up and use RDS and Aurora. You’ll need a laptop with a Firefox or Chrome browser.
Speakers:
Vishwajit Tigadi - Manager, Strategic Accounts, AWS
Chris Holmes - Technical Account Manager, AWS
12:15PM-1:15PM
Lunch
1:15PM-1:45PM
Open Source Databases on the Cloud
Speaker: Miguel Cervantes - Associate Solutions Architect, AWS
1:45PM-2:15PM
Oracle and SQL Server on the Cloud
Speaker: Joyjeet Banerjee - Enterprise Solutions Architect, AWS
Speakers:
Miguel Cervantes - Associate Solutions Architect, AWS
Joyjeet Banerjee - Enterprise Solutions Architect, AWS
Which Database is Right for My Workload: Database Week SFAmazon Web Services
Database Week at the San Francisco Loft
Which Database is Right for My Workload?
Picking the right database based on imperfect data is challenging. Decades of traditional app development have conditioned us to put everything in a big box. In this session we will look at selecting the right database for the right job.
Level: 200
Speakers:
Joyjeet Banerjee - Enterprise Solutions Architect, AWS
Vishwajit Tigadi - Manager, Strategic Accounts, AWS
Picking the right database based on imperfect data is challenging. Decades of traditional app development have conditioned us to put everything in a big box. In this session we will look at selecting the right database for the right job.
Speakers:
Steve Abraham - Principal Database Specialist Solutions Architect, AWS
Charles Hammell - Principal Enterprise Architect, AWS
SpringPeople - Introduction to Cloud ComputingSpringPeople
Cloud computing is no longer a fad that is going around. It is for real and is perhaps the most talked about subject. Various players in the cloud eco-system have provided a definition that is closely aligned to their sweet spot –let it be infrastructure, platforms or applications.
This presentation will provide an exposure of a variety of cloud computing techniques, architecture, technology options to the participants and in general will familiarize cloud fundamentals in a holistic manner spanning all dimensions such as cost, operations, technology etc
Migrate from Oracle to Aurora PostgreSQL: Best Practices, Design Patterns, & ...Amazon Web Services
In this session, we show you how to set the source Oracle database environment, the target PostgreSQL environment, and parameter group configuration. We also recommended database parameters to disable foreign keys and triggers. Finally, we discuss best practices for using AWS Database Migration Service (AWS DMS) and AWS Schema Conversion Tool (AWS SCT) and show you how to choose the instance type and configure AWS DMS.
Data Lakehouse, Data Mesh, and Data Fabric (r2)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a modern data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. They all may sound great in theory, but I'll dig into the concerns you need to be aware of before taking the plunge. I’ll also include use cases so you can see what approach will work best for your big data needs. And I'll discuss Microsoft version of the data mesh.
Maginatics @ SDC 2013: Architecting An Enterprise Storage Platform Using Obje...Maginatics
How did Maginatics build a strongly consistent and secure distributed file system? Niraj Tolia, Chief Architect at Maginatics, gave this presentation on the design of MagFS at the Storage Developer Conference on September 16, 2013.
For more information about MagFS—The File System for the Cloud, visit maginatics.com or contact us directly at info@maginatics.com.
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.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
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.
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/
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
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!
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
2. Voldemort
Voldemort is a distributed data store that is
designed as a key-value store used by
LinkedIn for high-scalability storage.
It is named after the fictional Harry Potter
villain Lord Voldemort.
3. there are job titles, job openings for people, Groups
and companies that offerings jobs.
Big
Data
Variety
velocity
volum
e
Need frequently read write
LinkedIn Big Data Problem
4. Voldemort Scale Both
• The amount of data we can store (write)
• The number of requests for that data (read)
5. Why Not Using Hadopp
Naturally the only way to do this is to spread both
the load and the data across many servers.
1. Need to find a way to split the data so that all
servers have different data
2. Need to find a way to handle server failures
without interrupting service
3. HBase still be write-heavy (due to horizontal
partitioning and use of SSTables, which are write
optimized)
6. Why voldemort
• Data is automatically replicated over multiple
servers.
• Data is automatically partitioned so each server
contains only a subset of the total data
• Provides tunable consistency (strict quorum or
eventual consistency)
• Server failure is handled transparently
• Pluggable Storage Engines -- BDB-JE, MySQL,
Read-Only
7. Why voldemort
• Pluggable serialization -- Protocol Buffers, Thrift,
Avro and Java Serialization
• Data items are versioned to maximize data
integrity in failure scenarios without
compromising availability of the system
• Each node is independent of other nodes with no
central point of failure or coordination
8. Why voldemort
• Good single node performance: you can expect 10-
20k operations per second depending on the
machines, the network, the disk system, and the
data replication factor
• Support for pluggable data placement strategies to
support things like distribution across data
centers that are geographically far apart.
9. Voldemort Storage Engines
Trivial to integrate new persistence mechanisms
with Voldemort
2 Classes:
Config(data) & Storage Engine(servers info)
3 Operations:
put(k, v), get(k), delete(k)
Complication:
k is Versioned<Key>
13. Key-Value Storage
• To enable high performance and availability it
allow only very simple key-value data access.
• Both keys and values can be complex compound
objects including lists or maps, but none-the-less
the only supported queries are effectively the
following:
value = store.get(key)
store.put(key, value)
store.delete(key)
14. Query execution
• Voldemort supports hashtable semantics, so a
single value can be modified at a time and
retrieval is by primary key.
• This makes distribution across machines
particularly easy since everything can be split by
the primary key.
15. Consistent Hashing Mechanism
• In order to effectively Scaling , the data in
Voldemort is split-up in such a way that each item
is stored on multiple Servers.
• For retrieving data first figure out which is the
correct server to use. This partitioning is done via
a consistent hashing mechanism that let’s any
server calculate the location of data without doing
any expensive look ups
16. Detecting Failure
• Voldemort set an SLA (service level agreement)
for the requests and ban servers who cannot meet
their SLA (this could be because they are down,
because requests are timing out).
• Servers that violate this SLA get banned for a short
period of time, after which they attempt to restore
them.
17. Dealing With Failure
Since each value is stored in multiple places it is
possible that one of these servers will not get
updated (say because it is crashed when the
update occurs).
To solve this problem Voldemort uses a data
versioning mechanism called Vector Clocks.
This data versioning allows the servers to detect
stale data when it is read and repair it.
18. Comparison to Hbase databases
Query
language
Architecture
Database
Model
Replication Issues
Voldemort API calls
Big Unordered
Map
Key-value
NoSQL
Distributed
data
structure
Topology
Aware
Routing
Strategies
Not
Satisfyin
g ACID
Properti
es.
Hbase
API calls
REST
XML
Thrift
Big Multi-
dimensional
Sorted Map
HDFS
Master-
slave/Master
-master
replication
Master
Slave
Which Is
Not
Highly
Availabl
e
21. Use Case
High-Performance Key-Value Store (Amazon
Dynamo clone)
treats the key‐value store as an API and adds an
in‐memory caching layer, which means that you
can plug into the back end that makes the most
sense for your particular needs.
22. Pros
• only efficient queries are possible, very predictable
performance.
• easy to distribute across a cluster.
• clean separation of storage and logic.
• The storage layer is completely mockable so
development and unit testing can be done against a
throw-away in-memory storage system without
needing a real cluster.
23. Cons
• no complex query filters
• no foreign key constraints
• no triggers
• No built-in support for “multiple data center”-
aware routing (there must be 1 copy of each key in
at least one data center)
24. Conclusion
• It is basically just a big, distributed, persistent,
fault-tolerant hash table.
• The redundancy of storage makes the system
more resilient to server failure. Since each value is
stored N times, you can tolerate as many as N –
1 machine failures without data loss.