A Novel Use of Openflow and Its Applications in Connecting Docker and Dummify...DaoliCloud Ltd
Lift Version of this Deck:
When the lift is going up:
Deployment and operation maintenance of cloud operating systems, e.g., Openstack, from now on become plug-n-play easy for “dummies”
When the lift is going down:
What? Docker is not connected? Don’t you know Docker is for the cloud? Don’t you know cloud needs a controller? No? You’re finished! Yes? Read on!
OpenStack is an open source cloud project and community with broad commercial and developer support. OpenStack is currently developing two interrelated technologies: OpenStack Compute and OpenStack Object Storage. OpenStack Compute is the internal fabric of the cloud creating and managing large groups of virtual private servers and OpenStack Object Storage is software for creating redundant, scalable object storage using clusters of commodity servers to store terabytes or even petabytes of data. In this tutorial, Bret Piatt will explain how to deploy OpenStack Compute and Object Storage, including an overview of the architecture and technology requirements.
A Novel Use of Openflow and Its Applications in Connecting Docker and Dummify...DaoliCloud Ltd
Lift Version of this Deck:
When the lift is going up:
Deployment and operation maintenance of cloud operating systems, e.g., Openstack, from now on become plug-n-play easy for “dummies”
When the lift is going down:
What? Docker is not connected? Don’t you know Docker is for the cloud? Don’t you know cloud needs a controller? No? You’re finished! Yes? Read on!
OpenStack is an open source cloud project and community with broad commercial and developer support. OpenStack is currently developing two interrelated technologies: OpenStack Compute and OpenStack Object Storage. OpenStack Compute is the internal fabric of the cloud creating and managing large groups of virtual private servers and OpenStack Object Storage is software for creating redundant, scalable object storage using clusters of commodity servers to store terabytes or even petabytes of data. In this tutorial, Bret Piatt will explain how to deploy OpenStack Compute and Object Storage, including an overview of the architecture and technology requirements.
OpenStack and OpenDaylight Workshop: ONUG Spring 2014mestery
This was a presentation I gave at the Open Networking Users Group (ONUG), Spring 2014. This talk covers some background on OpenStack and OpenDaylight, walks through Group Based Policy and OpFlex, and ends with a tutorial walk through of installing and using OpenStack with OpenDaylight.
Unified Underlay and Overlay SDNs for OpenStack CloudsPLUMgrid
Slides from the SFBay OpenStack Meetup
TOPIC: Unified Underlay and Overlay SDNs for OpenStack Clouds
ABSTRACT: With unified underlay and overlay SDNs, IT and operators can leverage best of both technologies to build service-rich SDNs for OpenStack clouds. At this meet up, PLUMgrid will discuss an overlay SDN architecture for service rich SDNs with service function chaining for 3rd party VNFs and demonstrate how to build that using Cisco Nexus 9K as the underlay to leverage the power and throughput of the Nexus fabric.
Midokura OpenStack Day Korea Talk: MidoNet Open Source Network Virtualization...Dan Mihai Dumitriu
OpenStack deployments for public or private clouds require overlay networking. Due to the scale and rate of change of virtual resources, it isn't practical to rely on traditional network constructs and isolation mechanims. Today's deployments require performance, resilience, and high availability to be considered truly production-ready. In this session, we deep dive into the MidoNet architecture, and process of sending a data packet across an OpenStack environment through a network overlay. A distributed architecture implements logical constructs that are used to build networks without a single point of failure, all while adding network functionality in a highly-scalable manner. Network functions are applied in a single virtual hop. By applying network services right at the ingress host, the network is free from unnecessary clogging and bottlenecks by avoiding additional hops. Packets reach their destination more efficiently with the single virtual hop. After this session, the audience will understand how distributed architectures allow efficient networking with routing decisions and network services applied at the edge. Also, the audience will understand how it is easier to scale clouds when the network intelligence is distributed.
Project Onboarding gives attendees a chance to meet some of the project team and get to know the project. Attendees will learn about the project itself, the code structure/ overall architecture, etc, and places where contribution is needed. Attendees will also get to know some of the core contributors and other established community members.
SDN Scale-out Testing at OpenStack Innovation Center (OSIC)PLUMgrid
The OpenStack Innovation Center (OSIC), established by Intel and Rackspace, is created to accelerate adoption of open source cloud operating system while supporting open source principles. OSIC provides ready-to-use data center facilities to the OpenStack community for development and test. This case study presentation highlights a scale-out test performed within a 3 week period using OpenStack Ansible Community based on Liberty with an SDN overlay network connecting 131 nodes running over 1,000 VMs. Tempest and Rally tests were conducted to validate functions including high availability failure scenarios. Join this session to find out more about OSIC and the SDN scale-out test configuration, scenarios, and results.
Cloud Networking is not Virtual Networking - London VMUG 20130425Greg Ferro
Talking how and why virtual networking that we use today is not suitable for use in Cloud deployments. First I talk about the gap between "server" & "networks", then discuss the problems of virtual networking that we use today. Then into using software appliances instead of physical devices by highlighting the good & bad.
Then a brief overview of Software Defined Networking and how it will impact Cloud Networking in the next two years,
Eager to learn more about OpenStack? This presentation provides an overview of OpenStack basics and an introduction to the types of storage in OpenStack. Choosing the right storage for your cloud can be the hardest part of building out your environment – this is a great primer to picking the right storage for your OpenStack deployment.
[OpenStack Day in Korea 2015] Keynote 2 - Leveraging OpenStack to Realize the...OpenStack Korea Community
OpenStack Day in Korea 2015 - Keynote 2
Leveraging OpenStack to Realize the SKT Software-Defined Data Center
Jinsung Choi, Ph.D - CTO, Corporate R&D Center, SK Telecom
What's Next in OpenStack? A Glimpse At The RoadmapShamailXD
YouTube Recording: https://www.youtube.com/watch?v=cCdqOxD5G0M
Whether you are a newbie to OpenStack looking at building your first cloud or an experienced operator with years of OpenStack success behind you, you've probably spent some time wondering what to expect from the OpenStack project over the next several releases. Will it finally support that new capability you've been waiting for? Should you plan for an upgrade in the next 6 months? While the development community is always working and planning new features, its takes a lot of time on IRC to get a complete view across the different projects. The OpenStack Product WG spent time this cycle working with the project teams and PTLs to understand their priorities for the next several OpenStack releases. Where we have always had an understanding of what's to come in the next release, we're hoping to present a long-term view of the future landscape of OpenStack. In this session, we'll present our findings across the different projects in an effort to give users a glimpse into the OpenStack roadmap
Optimising nfv service chains on open stack using dockerAnanth Padmanabhan
Uploading slides presented in the OpenStack summit, at Austin in April, 2016. Here is the link to the video,
https://www.openstack.org/videos/video/optimising-nfv-service-chains-on-openstack-using-docker
OpenStack and OpenDaylight Workshop: ONUG Spring 2014mestery
This was a presentation I gave at the Open Networking Users Group (ONUG), Spring 2014. This talk covers some background on OpenStack and OpenDaylight, walks through Group Based Policy and OpFlex, and ends with a tutorial walk through of installing and using OpenStack with OpenDaylight.
Unified Underlay and Overlay SDNs for OpenStack CloudsPLUMgrid
Slides from the SFBay OpenStack Meetup
TOPIC: Unified Underlay and Overlay SDNs for OpenStack Clouds
ABSTRACT: With unified underlay and overlay SDNs, IT and operators can leverage best of both technologies to build service-rich SDNs for OpenStack clouds. At this meet up, PLUMgrid will discuss an overlay SDN architecture for service rich SDNs with service function chaining for 3rd party VNFs and demonstrate how to build that using Cisco Nexus 9K as the underlay to leverage the power and throughput of the Nexus fabric.
Midokura OpenStack Day Korea Talk: MidoNet Open Source Network Virtualization...Dan Mihai Dumitriu
OpenStack deployments for public or private clouds require overlay networking. Due to the scale and rate of change of virtual resources, it isn't practical to rely on traditional network constructs and isolation mechanims. Today's deployments require performance, resilience, and high availability to be considered truly production-ready. In this session, we deep dive into the MidoNet architecture, and process of sending a data packet across an OpenStack environment through a network overlay. A distributed architecture implements logical constructs that are used to build networks without a single point of failure, all while adding network functionality in a highly-scalable manner. Network functions are applied in a single virtual hop. By applying network services right at the ingress host, the network is free from unnecessary clogging and bottlenecks by avoiding additional hops. Packets reach their destination more efficiently with the single virtual hop. After this session, the audience will understand how distributed architectures allow efficient networking with routing decisions and network services applied at the edge. Also, the audience will understand how it is easier to scale clouds when the network intelligence is distributed.
Project Onboarding gives attendees a chance to meet some of the project team and get to know the project. Attendees will learn about the project itself, the code structure/ overall architecture, etc, and places where contribution is needed. Attendees will also get to know some of the core contributors and other established community members.
SDN Scale-out Testing at OpenStack Innovation Center (OSIC)PLUMgrid
The OpenStack Innovation Center (OSIC), established by Intel and Rackspace, is created to accelerate adoption of open source cloud operating system while supporting open source principles. OSIC provides ready-to-use data center facilities to the OpenStack community for development and test. This case study presentation highlights a scale-out test performed within a 3 week period using OpenStack Ansible Community based on Liberty with an SDN overlay network connecting 131 nodes running over 1,000 VMs. Tempest and Rally tests were conducted to validate functions including high availability failure scenarios. Join this session to find out more about OSIC and the SDN scale-out test configuration, scenarios, and results.
Cloud Networking is not Virtual Networking - London VMUG 20130425Greg Ferro
Talking how and why virtual networking that we use today is not suitable for use in Cloud deployments. First I talk about the gap between "server" & "networks", then discuss the problems of virtual networking that we use today. Then into using software appliances instead of physical devices by highlighting the good & bad.
Then a brief overview of Software Defined Networking and how it will impact Cloud Networking in the next two years,
Eager to learn more about OpenStack? This presentation provides an overview of OpenStack basics and an introduction to the types of storage in OpenStack. Choosing the right storage for your cloud can be the hardest part of building out your environment – this is a great primer to picking the right storage for your OpenStack deployment.
[OpenStack Day in Korea 2015] Keynote 2 - Leveraging OpenStack to Realize the...OpenStack Korea Community
OpenStack Day in Korea 2015 - Keynote 2
Leveraging OpenStack to Realize the SKT Software-Defined Data Center
Jinsung Choi, Ph.D - CTO, Corporate R&D Center, SK Telecom
What's Next in OpenStack? A Glimpse At The RoadmapShamailXD
YouTube Recording: https://www.youtube.com/watch?v=cCdqOxD5G0M
Whether you are a newbie to OpenStack looking at building your first cloud or an experienced operator with years of OpenStack success behind you, you've probably spent some time wondering what to expect from the OpenStack project over the next several releases. Will it finally support that new capability you've been waiting for? Should you plan for an upgrade in the next 6 months? While the development community is always working and planning new features, its takes a lot of time on IRC to get a complete view across the different projects. The OpenStack Product WG spent time this cycle working with the project teams and PTLs to understand their priorities for the next several OpenStack releases. Where we have always had an understanding of what's to come in the next release, we're hoping to present a long-term view of the future landscape of OpenStack. In this session, we'll present our findings across the different projects in an effort to give users a glimpse into the OpenStack roadmap
Optimising nfv service chains on open stack using dockerAnanth Padmanabhan
Uploading slides presented in the OpenStack summit, at Austin in April, 2016. Here is the link to the video,
https://www.openstack.org/videos/video/optimising-nfv-service-chains-on-openstack-using-docker
في الفيديو ده بيتم شرح ما هي المشاكل التي انتجت ظهور هذا النوع من قواعد البيانات
انواع المشاريع التي يمكن استخدامها بها
نبذة عن تاريخها و مزاياها و عيوبها
https://youtu.be/I9zgrdCf0fY
This deck talks about the basic overview of NoSQL technologies, implementation vendors/products, case studies, and some of the core implementation algorithms. The presentation also describes a quick overview of "Polyglot Persistency", "NewSQL" like emerging trends.
The deck is targeted to beginners who wants to get an overview of NoSQL databases.
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.
These slides gives an overview of NoSQL in the context of Big Data processing. We start by defining SQL vs NoSQL concepts, the CAP theorem, and why NoSQL technologies are needed. Then we discuss the various NoSQL technology breeds, including Key/Value stores, Document stores, Column Family (wide-column) stores, memory cache stores, and graph stores, along with related tools and examples. After that we present various solution architecture patterns, in which NoSQL data stores play viable roles. Next we delve into Microsoft Azure implementation of some of these NoSQL technologies, including Redis Cache, Azure Table Storage, HBase on HDInsight, and Azure DocumentDB. Finally, we conclude with some useful resource, before we give a sneak peek on how to use neo4j for Graph Processing.
In the past few years, the term "data lake" has leaked into our lexicon. But what exactly IS a data lake? Some IT managers confuse data lakes with data warehouses. Some people think data lakes replace data warehouses. Both of these conclusions are false. Their is room in your data architecture for both data lakes and data warehouses. They both have different use cases and those use cases can be complementary.
Todd Reichmuth, Solutions Engineer with Snowflake Computing, has spent the past 18 years in the world of Data Warehousing and Big Data. He spent that time at Netezza and then later at IBM Data. Earlier in 2018 making the jump to the cloud at Snowflake Computing.
Mike Myer, Sales Director with Snowflake Computing, has spent the past 6 years in the world of Security and looking to drive awareness to better Data Warehousing and Big Data solutions available! Was previously at local tech companies FireMon and Lockpath and decided to join Snowflake due to the disruptive technology that's truly helping folks in the Big Data world on a day to day basis.
A brief intro on the idea of what is Big Data and it's potential. This is primarily a basic study & I have quoted the source of infographics, stats & text at the end. If I have missed any reference due to human error & you recognize another source, please mention.
In this paper we describe NoSQL, a series of non-relational database
technologies and products developed to address the current problems the
RDMS system are facing: lack of true scalability, poor performance on high
data volumes and low availability. Some of these products have already been
involved in production and they perform very well: Amazon’s Dynamo,
Google’s Bigtable, Cassandra, etc. Also we provide a view on how these
systems influence the applications development in the social and semantic Web
sphere.
In this paper we describe NoSQL, a series of non-relational database technologies and products developed to address the current problems the RDMS system are facing: lack of true scalability, poor performance on high data volumes and low availability. Some of these products have already been involved in production and they perform very well: Amazon’s Dynamo, Google’s Bigtable, Cassandra, etc. Also we provide a view on how these systems influence the applications development in the social and semantic Web sphere.
Extracting value from Big Data is not easy. The field of technologies and vendors is fragmented and rapidly evolving. End-to-end, general purpose solutions that work out of the box don’t exist yet, and Hadoop is no exception. And most companies lack Big Data specialists. The key to unlocking real value lies with thinking smart and hard about the business requirements for a Big Data solution. There is a long list of crucial questions to think about. Is Hadoop really the best solution for all Big Data needs? Should companies run a Hadoop cluster on expensive enterprise-grade storage, or use cheap commodity servers? Should the chosen infrastructure be bare metal or virtualized? The picture becomes even more confusing at the analysis and visualization layer. The answer to Big Data ROI lies somewhere between the herd and nerd mentality. Thinking hard and being smart about each use case as early as possible avoids costly mistakes in choosing hardware and software. This talk will illustrate how Deutsche Telekom follows this segmentation approach to make sure every individual use case drives architecture design and the selection of technologies and vendors.
IoT (and M2M and WoT) From the Operators (CSP) perspectiveSamuel Dratwa
Short introduction to IoT for telecom operators, providers and vendors.
Including: value chain, working examples and more.
Also describe: Smart cities, Smart home, wearables, etc.
I'm not a prophet (but I'm evangelist) and therefore do not really know the future telecom. This lecture describes trends and developments in telecommunications worldwide and try to give recommendations for operators (PTTs) who not turn to infrastructure provide "dump pipe" only.
אני לא נביא (אבל כן מטיף) ולכן לא באמת יודע מה עתיד הטלקום.
הרצאה זו מתארת מגמות והתפתחויות בטלקומוניקציה (תקשורת) בעולם ומנסה לתת המלצות לפעילות שצרכות לבצע המפעילות כדי לא להפוך לספק תשתיות "טפשות" בלבד.
Web 2.0 contains 2 different but incorporated topics: User Generated Content and Long Tail. In this short lecture we will elaborate on both topics and how they influent the internet in general and even our "traditional life" outside the internet using the telecom industry.
רשתות חברתיות ומידע עסקי - או למה צריך להיות שםSamuel Dratwa
הרצאת מבוא לרשתות חברתיות ומידע עסקי.
בהרצאה נסקור בקצרה את (חלק) מהרשתות הקימות, ההבדלים בניהן והיתרונות והחסרונות של כל אחר.
נדבר על איזה רשת כדאי ל"השקיע" בה - ומה משמעות ההשקעה.
נראה דוגמאות למידע שניתן לאתר ברשתות ולשימוש נוספים בהן
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
7. Characteristics of Big Data:
1-Scale (Volume)
• Data Volume
• 44x increase from 2009 2020
• From 0.8 zettabytes to 35zb
• Data volume is increasing exponentially
7
Exponential increase in
collected/generated data
9. Characteristics of Big Data:
2-Complexity (Varity)
• Various formats, types, and
structures
• Text, numerical, images, audio,
video, sequences, time series, social
media data, multi-dim arrays, etc…
• Static data vs. streaming data
• A single application can be
generating/collecting many types of
data
9
11. Characteristics of Big Data:
3-Speed (Velocity)
• Data is begin generated fast and need to be processed
fast
• Online Data Analytics
• Late decisions missing opportunities
• Examples
• E-Promotions: Based on your current location, your purchase
history, what you like send promotions right now for store next to
you
• Healthcare monitoring: sensors monitoring your activities and body
any abnormal measurements require immediate reaction
11
14. Who’s Generating Big Data
Social media and networks
(all of us are generating data)
Scientific instruments
(collecting all sorts of data)
Mobile devices
(tracking all objects all the time)
Sensor technology and
networks
(measuring all kinds of data)
• The progress and innovation is no longer hindered by the ability to collect data
• But, by the ability to manage, analyze, summarize, visualize, and discover
knowledge from the collected data in a timely manner and in a scalable
fashion
14
15. The Model Has
Changed…
• The Model of Generating/Consuming Data has Changed
Old Model: Few companies are generating data, all others are consuming data
New Model: all of us are generating data, and all of us are consuming
data
15
46. BASE in Cassandra
Query
Closest replica
Cassandra Cluster
Replica A
Result
Replica B Replica C
Digest Query
Digest Response Digest Response
Result
Client
Read repair if
digests differ