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.
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.
An Intro to NoSQL Databases -- NoSQL databases will not become the new dominators. Relational will still be popular, and used in the majority of situations. They, however, will no longer be the automatic choice. (source : http://martinfowler.com/)
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
Introduction to NoSQL | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2kyP2Ct
This CloudxLab Introduction to NoSQL tutorial helps you to understand NoSQL in detail. Below are the topics covered in this slide:
1) Introduction to NoSQL
2) Scaling Out vs Scaling Up
3) ACID - Properties of DB Transactions
4) RDBMS - Story
5) What is NoSQL?
6) Types Of NoSQL Stores
7) CAP Theorem
8) Serialization
9) Column Oriented Database
10) Column Family Oriented DataStore
An Intro to NoSQL Databases -- NoSQL databases will not become the new dominators. Relational will still be popular, and used in the majority of situations. They, however, will no longer be the automatic choice. (source : http://martinfowler.com/)
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
Introduction to NoSQL | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2kyP2Ct
This CloudxLab Introduction to NoSQL tutorial helps you to understand NoSQL in detail. Below are the topics covered in this slide:
1) Introduction to NoSQL
2) Scaling Out vs Scaling Up
3) ACID - Properties of DB Transactions
4) RDBMS - Story
5) What is NoSQL?
6) Types Of NoSQL Stores
7) CAP Theorem
8) Serialization
9) Column Oriented Database
10) Column Family Oriented DataStore
JPoint'15 Mom, I so wish Hibernate for my NoSQL database...Alexey Zinoviev
Alexey Zinoviev presented this paper on the JPoint'15 conference javapoint.ru/talks/#zinoviev.
This paper covers next topics: Java, JPA, Morphia, Hibernate OGM, Spring Data, Hector, Kundera, NoSQL, Mongo, Cassandra, HBase, Riak
NoSQL Data Stores in Research and Practice - ICDE 2016 Tutorial - Extended Ve...Felix Gessert
The unprecedented scale at which data is consumed and generated today has shown a large demand for scalable data management and given rise to non-relational, distributed "NoSQL" database systems. Two central problems triggered this process: 1) vast amounts of user-generated content in modern applications and the resulting requests loads and data volumes 2) the desire of the developer community to employ problem-specific data models for storage and querying. To address these needs, various data stores have been developed by both industry and research, arguing that the era of one-size-fits-all database systems is over. The heterogeneity and sheer amount of these systems - now commonly referred to as NoSQL data stores - make it increasingly difficult to select the most appropriate system for a given application. Therefore, these systems are frequently combined in polyglot persistence architectures to leverage each system in its respective sweet spot. This tutorial gives an in-depth survey of the most relevant NoSQL databases to provide comparative classification and highlight open challenges. To this end, we analyze the approach of each system to derive its scalability, availability, consistency, data modeling and querying characteristics. We present how each system's design is governed by a central set of trade-offs over irreconcilable system properties. We then cover recent research results in distributed data management to illustrate that some shortcomings of NoSQL systems could already be solved in practice, whereas other NoSQL data management problems pose interesting and unsolved research challenges.
If you'd like to use these slides for e.g. teaching, contact us at gessert at informatik.uni-hamburg.de - we'll send you the PowerPoint.
comprehensive Introduction to NoSQL solutions inside the big data landscape. Graph store? Column store? key Value store? Document Store? redis or memcache? dynamo db? mongo db ? hbase? Cloud or open source?
1) Apache Cassandra in term of CAP Theorem
2) What makes Apache Cassandra "Available"?
3) How Apache Cassandra ensures data consistency?
4) Cassandra advantages and disadvantages
5) Frameworks/libraries to access Apache Cassandra + performance comparison
SQL? NoSQL? NewSQL?!? What’s a Java developer to do? - JDC2012 Cairo, EgyptChris Richardson
The database world is undergoing a major upheaval. NoSQL databases such as MongoDB and Cassandra are emerging as a compelling choice for many applications. They can simplify the persistence of complex data models and offering significantly better scalability and performance. But these databases have a very different and unfamiliar data model and APIs as well as a limited transaction model. Moreover, the relational world is fighting back with so-called NewSQL databases such as VoltDB, which by using a radically different architecture offers high scalability and performance as well as the familiar relational model and ACID transactions. Sounds great but unlike the traditional relational database you can’t use JDBC and must partition your data.
In this presentation you will learn about popular NoSQL databases – MongoDB, and Cassandra - as well at VoltDB. We will compare and contrast each database’s data model and Java API using NoSQL and NewSQL versions of a use case from the book POJOs in Action. We will learn about the benefits and drawbacks of using NoSQL and NewSQL databases.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
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.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
2. About Me
●
Social Computing + Database Systems
●
Easily Distracted: Wrote The NoSQL Ecosystem in
The Architecture of Open Source Applications 1
1
http://www.aosabook.org/en/nosql.html
10. The List, So You Don't Yell at Me
HBase CouchDB
Neo4j
Cassandra Riak InfoGrid
BerkeleyDB Voldemort HyperTable
Redis
MongoDB Sones
AllegroGraph HyperGraphDB
DEX FlockDB
VertexDB
Oracle NoSQL
Tokyo Cabinet MemcacheDB
11. The List, So You Don't Yell at Me
Marcus' Law of Databases:
HBase CouchDB
Neo4j
Cassandra Riak InfoGrid
The number of persistence
HyperTable
BerkeleyDB doubles every 1.5 years
Voldemort
options
Redis
MongoDB Sones
AllegroGraph HyperGraphDB
DEX FlockDB
VertexDB
Oracle NoSQL
Tokyo Cabinet MemcacheDB
12. The List, So You Don't Yell at Me
Marcus' Law of Databases:
HBase CouchDB
Neo4j
Cassandra Riak InfoGrid
The number of persistence
HyperTable
BerkeleyDB doubles every 1.5 years
Voldemort
options
Redis
MongoDB Sones
Corollary:
AllegroGraph HyperGraphDB
I'm sorry I missedDEX FlockDB
your
persistence optionOracle NoSQL
VertexDB
Tokyo Cabinet MemcacheDB
15. Conventional Wisdom on NoSQL
●
Key-based data model
●
Sloppy schema
●
Single-key transactions
●
In-app joins
●
Eventually consistent
16. Exceptions are More Interesting
●
Data Model
●
Query Model
●
Transactions
●
Consistency
17. Data Model
●
Usually key-based...
●
Column-families
●
Documents
●
Data structures
18. Data Model
●
Usually key-based...
●
Column-families
●
Documents
●
Data structures
●
...but not always
●
Graph stores
19. Query Model
●
Redis: data structure-specific operations
●
CouchDB, Riak: MapReduce
●
Cassandra, MongoDB: SQL-like languages, no
joins or transactions
20. Query Model
●
Redis: data structure-specific operations
●
CouchDB, Riak: MapReduce
●
Cassandra, MongoDB: SQL-like languages, no
joins or transactions
●
Third-party
●
High-level: PigLatin, HiveQL
●
Library: Cascading, Crunch
●
Streaming: Flume, Kafka, S4, Scribe
21. Transactions
●
Full ACID for single key
●
Redis: multi-key single-node transactions
22. Consistency
● Strong: Appears that all replicas see all writes
● Eventual: Replicas may have different, divergent versions
23. Consistency
● Strong: Appears that all replicas see all writes
● Eventual: Replicas may have different, divergent versions
● Dynamo: strong or eventual (quorum size)
24. Consistency
● Strong: Appears that all replicas see all writes
● Eventual: Replicas may have different, divergent versions
● Dynamo: strong or eventual (quorum size)
● PNUTs: Timeline consistency
● ...many consistency models!
See: http://www.allthingsdistributed.com/2007/12/eventually_consistent.html
27. Cassandra
●
BigTable data model: key column family
●
Dynamo sharding model: consistent hashing
●
Eventual or strong consistency
28. Cassandra at Netflix
●
Transitioned from Oracle
●
Store customer profiles, customer:movie watch
log, and detailed usage logging
Note: no multi-record locking (e.g., bank transfer)
In-datacenter: 3 replicas, per-app consistency
read/write quorum = 1, ~1ms latency
read/write quorum = 2, ~3ms latency
“Replicating Datacenter Oracle with Global Apache Cassandra on AWS” by Adrian Cockcroft
http://www.slideshare.net/adrianco/migrating-netflix-from-oracle-to-global-cassandra
29. Cassandra at Netflix
●
Transitioned from Oracle
●
Store customer profiles, customer:movie watch
log, and detailed usage logging
●
Note: no multi-record locking (e.g., bank transfer)
In-datacenter: 3 replicas, per-app consistency
read/write quorum = 1, ~1ms latency
read/write quorum = 2, ~3ms latency
“Replicating Datacenter Oracle with Global Apache Cassandra on AWS” by Adrian Cockcroft
http://www.slideshare.net/adrianco/migrating-netflix-from-oracle-to-global-cassandra
30. Cassandra at Netflix
●
Transitioned from Oracle
●
Store customer profiles, customer:movie watch
log, and detailed usage logging
●
Note: no multi-record locking (e.g., bank transfer)
●
In-datacenter: 3 replicas, per-app consistency
“Replicating Datacenter Oracle with Global Apache Cassandra on AWS” by Adrian Cockcroft
http://www.slideshare.net/adrianco/migrating-netflix-from-oracle-to-global-cassandra
31. Cassandra at Netflix (cont'd)
●
Benefit: async inter-datacenter replication
●
Benefit: no downtime for schema changes
●
Benefit: hooks for live backups
32. HBase
●
Data model: key column family
●
Sharding model: range partitioning
●
Strong consistency
Applications
Logging events/crawls, storing analytics
Twitter: replicate data from MySQL, Hadoop analytics
Facebook Messages
33. HBase
●
Data model: key column family
●
Sharding model: range partitioning
●
Strong consistency
●
Applications
●
Logging events/crawls, storing analytics
●
Twitter: replicate data from MySQL, Hadoop analytics
●
Facebook Messages
34. HBase for Facebook Messages
●
Cassandra/Dynamo eventual consistency was
difficult to program against
Benefit: simple consistency model
Benefit: flexible data model
Benefit: simple sharding, load balancing,
replication
35. HBase for Facebook Messages
●
Cassandra/Dynamo eventual consistency was
difficult to program against
●
Benefit: simple consistency model
●
Benefit: flexible data model
●
Benefit: simple sharding, load balancing,
replication
36. MongoDB
●
Document-based data model
●
Range-based partitioning
●
Consistency depends on how you use it
37. MongoDB: Two use-cases
●
Archiving at Craigslist
●
2.2B historical posts, semi-structured
●
Relatively large blobs: avg 2KB, max > 4 MB
38. MongoDB: Two use-cases
●
Archiving at Craigslist
●
2.2B historical posts, semi-structured
●
Relatively large blobs: avg 2KB, max > 4 MB
●
Checkins at Foursquare
●
Geospatial indexing
●
Small location-based updates, sharded on user
44. A Different Five-Minute Rule
What does a first-time user of your system
experience in their first five minutes?
(idea credit: Justin Sheehy, Basho)
49. Accessibility is Forever
Beyond five minutes:
●
changing schemas
●
scaling up
●
modifying topology
An accessible data store will ease
first-time users in, and support
experienced users as they expand
54. Spectrum of Reusability
Monolithic System System Kernel
MongoDB Cassandra ZooKeeper
Redis Voldemort LevelDB
Use as Pick between Reusable
provided components components
55. Spectrum of Reusability
Monolithic System System Kernel
MongoDB Cassandra ZooKeeper
Redis Voldemort LevelDB riak_core
Use as Pick between Reusable Build your
provided components components own system
58. Polyglot persistence: Where's the data?
“HBase at Mendeley,” Dan Harvey
http://www.slideshare.net/danharvey/hbase-at-mendeley
59. Polyglot persistence: Where's the data?
“HBase at Mendeley,” Dan Harvey
http://www.slideshare.net/danharvey/hbase-at-mendeley
60. Polyglot persistence: Where's the data?
Aid developers in reasoning about data consistency
across multiple storage engines
“HBase at Mendeley,” Dan Harvey
http://www.slideshare.net/danharvey/hbase-at-mendeley
