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
Comparative study of no sql document, column store databases and evaluation o...ijdms
In the last decade, rapid growth in mobile applications, web technologies, social media generating
unstructured data has led to the advent of various nosql data stores. Demands of web scale are in
increasing trend everyday and nosql databases are evolving to meet up with stern big data requirements.
The purpose of this paper is to explore nosql technologies and present a comparative study of document
and column store nosql databases such as cassandra, MongoDB and Hbase in various attributes of
relational and distributed database system principles. Detailed study and analysis of architecture and
internal working cassandra, Mongo DB and HBase is done theoretically and core concepts are depicted.
This paper also presents evaluation of cassandra for an industry specific use case and results are
published.
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
Comparative study of no sql document, column store databases and evaluation o...ijdms
In the last decade, rapid growth in mobile applications, web technologies, social media generating
unstructured data has led to the advent of various nosql data stores. Demands of web scale are in
increasing trend everyday and nosql databases are evolving to meet up with stern big data requirements.
The purpose of this paper is to explore nosql technologies and present a comparative study of document
and column store nosql databases such as cassandra, MongoDB and Hbase in various attributes of
relational and distributed database system principles. Detailed study and analysis of architecture and
internal working cassandra, Mongo DB and HBase is done theoretically and core concepts are depicted.
This paper also presents evaluation of cassandra for an industry specific use case and results are
published.
What is NoSQL? How does it come to the picture? What are the types of NoSQL? Some basics of different NoSQL types? Differences between RDBMS and NoSQL. Pros and Cons of NoSQL.
What is MongoDB? What are the features of MongoDB? Nexus architecture of MongoDB. Data model and query model of MongoDB? Various MongoDB data management techniques. Indexing in MongoDB. A working example using MongoDB Java driver on Mac OSX.
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.
The growth of data and its effi cient handling is becoming more popular trend in recent years bringing
new challenges to explore new avenues. Data analytics can be done more effi ciently with the availability of
distributed architecture of “Not Only SQL” NoSQL databases.
The NoSQL movement has introduced four new database architectural patterns that complement, but not replace, traditional relational and analytical databases. This presentation will introduce these four patterns and discuss their relative strengths and weaknesses for solving a variety of business problems. These problems include Big Data (scalability), search, high availability and agility. For each type of problem we look at how NoSQL databases take different approaches to solving these problems and how you can use this knowledge to find the right database architecture for your business challenges.
Robin Bloor and Mark Madsen offer their theories on where the rapidly-changing database market stands today: What’s new? What’s standard? What is the trajectory of this evolving market? Each Analyst will present for 10-15 minutes, then will engage in a dialogue with the moderator and attendees.
The webcast audio and video archive can be found at https://bloorgroup.webex.com/bloorgroup/lsr.php?AT=pb&SP=EC&rID=4695777&rKey=4b284990a1db4ec0
Bioinformaticians constantly face challenges with data: from the large volumes of data to the need to integrate diverse data types. Relational databases have a long and successful history of managing data but have been unable to meet emerging needs of big data and highly integrated data stores. This talk discusses the limitations we face when using relational data models for bioinformatics applications. It describes the features, limitations and use cases of four alternative database models: key value databases, document databases, wide column data stores and graph databases. Use in bioinformatics applications is demonstrate with text mining and atherosclerosis research projects. The talk concludes with guidance on choosing an appropriate database model for varying bioinformatics requirements.
What is NoSQL? How does it come to the picture? What are the types of NoSQL? Some basics of different NoSQL types? Differences between RDBMS and NoSQL. Pros and Cons of NoSQL.
What is MongoDB? What are the features of MongoDB? Nexus architecture of MongoDB. Data model and query model of MongoDB? Various MongoDB data management techniques. Indexing in MongoDB. A working example using MongoDB Java driver on Mac OSX.
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.
The growth of data and its effi cient handling is becoming more popular trend in recent years bringing
new challenges to explore new avenues. Data analytics can be done more effi ciently with the availability of
distributed architecture of “Not Only SQL” NoSQL databases.
The NoSQL movement has introduced four new database architectural patterns that complement, but not replace, traditional relational and analytical databases. This presentation will introduce these four patterns and discuss their relative strengths and weaknesses for solving a variety of business problems. These problems include Big Data (scalability), search, high availability and agility. For each type of problem we look at how NoSQL databases take different approaches to solving these problems and how you can use this knowledge to find the right database architecture for your business challenges.
Robin Bloor and Mark Madsen offer their theories on where the rapidly-changing database market stands today: What’s new? What’s standard? What is the trajectory of this evolving market? Each Analyst will present for 10-15 minutes, then will engage in a dialogue with the moderator and attendees.
The webcast audio and video archive can be found at https://bloorgroup.webex.com/bloorgroup/lsr.php?AT=pb&SP=EC&rID=4695777&rKey=4b284990a1db4ec0
Bioinformaticians constantly face challenges with data: from the large volumes of data to the need to integrate diverse data types. Relational databases have a long and successful history of managing data but have been unable to meet emerging needs of big data and highly integrated data stores. This talk discusses the limitations we face when using relational data models for bioinformatics applications. It describes the features, limitations and use cases of four alternative database models: key value databases, document databases, wide column data stores and graph databases. Use in bioinformatics applications is demonstrate with text mining and atherosclerosis research projects. The talk concludes with guidance on choosing an appropriate database model for varying bioinformatics requirements.
The rising interest in NoSQL technology over the last few years resulted in an increasing number of evaluations and comparisons among competing NoSQL technologies From survey we create a concise and up-to-date comparison of NoSQL engines, identifying their most beneficial use from the software engineer point of view.
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.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
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.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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.
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/
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
1. What is Nosql?
NoSQL database, also called Not Only SQL, is an approach to data management and database
design that's useful for very large sets of distributed data. NoSQL is a whole new way of
thinking about a database. NoSQL is not a relational database. The reality is that a relational
database model may not be the best solution for all situations. The easiest way to think of
NoSQL, is that of a database which does not adhering to the traditional relational database
management system (RDMS) structure. Sometimes you will also see it revered to as 'not only
SQL'.the most popular NoSQL database is Apache Cassandra. Cassandra, which was once
Facebook’s proprietary database, was released as open source in 2008. Other NoSQL
implementations include SimpleDB, Google BigTable, Apache Hadoop, MapReduce,
MemcacheDB, and Voldemort. Companies that use NoSQL include NetFlix, LinkedIn and
Twitter.
Types of Nosql Databases?
There are four general types of NoSQL databases, each with their own specific attributes:
● Key-Value store – we start with this type of database because these are some of the least
complex NoSQL options. These databases are designed for storing data in a schema-less
way. In a key-value store, all of the data within consists of an indexed key and a value,
hence the name. Examples of this type of database include: Cassandra, DyanmoDB,
Azure Table Storage (ATS), Riak, BerkeleyDB.
● Column store – (also known as wide-column stores) instead of storing data in rows,
these databases are designed for storing data tables as sections of columns of data, rather
than as rows of data. While this simple description sounds like the inverse of a standard
database, wide-column stores offer very high performance and a highly scalable
architecture. Examples include: HBase, BigTable and HyperTable.
● Document database – expands on the basic idea of key-value stores where “documents”
contain more complex in that they contain data and each document is assigned a unique
key, which is used to retrieve the document. These are designed for storing, retrieving,
and managing document-oriented information, also known as semi-structured data.
Examples include: MongoDB and CouchDB.
● Graph database – Based on graph theory, these databases are designed for data whose
relations are well represented as a graph and has elements which are interconnected,
with an undetermined number of relations between them. Examples include: Neo4J and
Polyglot.
Why we should use Nosql or when to use Nosql?
Usecases:-
2. Bigness. NoSQL is seen as a key part of a new data stack supporting: big data, big
numbers of users, big numbers of computers, big supply chains, big science, and so on.
When something becomes so massive that it must become massively distributed,
NoSQL is there, though not all NoSQL systems are targeting big. Bigness can be across
many different dimensions, not just using a lot of disk space.
Massive write performance. This is probably the canonical usage based on Google's
influence. High volume. Facebook needs to store 135 billion messages a month. Twitter,
for example, has the problem of storing 7 TB/data per day with the prospect of this
requirement doubling multiple times per year. This is the data is too big to fit on one
node problem. At 80 MB/s it takes a day to store 7TB so writes need to be distributed
over a cluster, which implies key-value access, MapReduce, replication, fault tolerance,
consistency issues, and all the rest. For faster writes in-memory systems can be used.
Fast key-value access. This is probably the second most cited virtue of NoSQL in the
general mind set. When latency is important it's hard to beat hashing on a key and
reading the value directly from memory or in as little as one disk seek. Not every NoSQL
product is about fast access, some are more about reliability, for example. but what
people have wanted for a long time was a better memcached and many NoSQL systems
offer that.
Flexible schema and flexible datatypes. NoSQL products support a whole range of
new data types, and this is a major area of innovation in NoSQL. We have: column-
oriented, graph, advanced data structures, document-oriented, and key-value. Complex
objects can be easily stored without a lot of mapping. Developers love avoiding complex
schemas and ORM frameworks. Lack of structure allows for much more flexibility. We
also have program and programmer friendly compatible datatypes likes JSON.
Schema migration. Schemalessness makes it easier to deal with schema migrations
without so much worrying. Schemas are in a sense dynamic, because they are imposed
by the application at run-time, so different parts of an application can have a different
view of the schema.
Write availability. Do your writes need to succeed no mater what? Then we can get into
partitioning, CAP, eventual consistency and all that jazz.
Easier maintainability, administration and operations. This is very product specific,
but many NoSQL vendors are trying to gain adoption by making it easy for developers to
adopt them. They are spending a lot of effort on ease of use, minimal administration, and
automated operations. This can lead to lower operations costs as special code doesn't
have to be written to scale a system that was never intended to be used that way.
No single point of failure. Not every product is delivering on this, but we are seeing a
definite convergence on relatively easy to configure and manage high availability with
automatic load balancing and cluster sizing. A perfect cloud partner.
Generally available parallel computing. We are seeing MapReduce baked into
products, which makes parallel computing something that will be a normal part of
development in the future.
Programmer ease of use. Accessing your data should be easy. While the relational
model is intuitive for end users, like accountants, it's not very intuitive for developers.
Programmers grok keys, values, JSON, Javascript stored procedures, HTTP, and so on.
3. NoSQL is for programmers. This is a developer led coup. The response to a database
problem can't always be to hire a really knowledgeable DBA, get your schema right,
denormalize a little, etc., programmers would prefer a system that they can make work
for themselves. It shouldn't be so hard to make a product perform. Money is part of the
issue. If it costs a lot to scale a product then won't you go with the cheaper product, that
you control, that's easier to use, and that's easier to scale?
Use the right data model for the right problem. Different data models are used to
solve different problems. Much effort has been put into, for example, wedging graph
operations into a relational model, but it doesn't work. Isn't it better to solve a graph
problem in a graph database? We are now seeing a general strategy of trying find the
best fit between a problem and solution.
Avoid hitting the wall. Many projects hit some type of wall in their project. They've
exhausted all options to make their system scale or perform properly and are wondering
what next? It's comforting to select a product and an approach that can jump over the
wall by linearly scaling using incrementally added resources. At one time this wasn't
possible. It took custom built everything, but that's changed. We are now seeing usable
out-of-the-box products that a project can readily adopt.
Distributed systems support. Not everyone is worried about scale or performance over
and above that which can be achieved by non-NoSQL systems. What they need is a
distributed system that can span datacenters while handling failure scenarios without a
hiccup. NoSQL systems, because they have focussed on scale, tend to exploit partitions,
tend not use heavy strict consistency protocols, and so are well positioned to operate in
distributed scenarios.
Tunable CAP tradeoffs. NoSQL systems are generally the only products with a "slider"
for choosing where they want to land on the CAP spectrum. Relational databases pick
strong consistency which means they can't tolerate a partition failure. In the end this is a
business decision and should be decided on a case by case basis. Does your app even
care about consistency? Are a few drops OK? Does your app need strong or weak
consistency? Is availability more important or is consistency? Will being down be more
costly than being wrong? It's nice to have products that give you a choice.
Difference betwwen Sql and Nosql
SQL databases are primarily called as Relational Databases (RDBMS); whereas NoSQL
database are primarily called as non-relational or distributed database.
SQL databases are table based databases whereas NoSQL databases are document
based, key-value pairs, graph databases or wide-column stores. This means that SQL
databases represent data in form of tables which consists of n number of rows of data
whereas NoSQL databases are the collection of key-value pair, documents, graph
databases or wide-column stores which do not have standard schema definitions which
it needs to adhered to.
SQL databases have predefined schema whereas NoSQL databases have dynamic
schema for unstructured data.
4. SQL databases are vertically scalable whereas the NoSQL databases are horizontally
scalable. SQL databases are scaled by increasing the horse-power of the hardware.
NoSQL databases are scaled by increasing the databases servers in the pool of
resources to reduce the load.
SQL databases uses SQL ( structured query language ) for defining and manipulating
the data, which is very powerful. In NoSQL database, queries are focused on collection
of documents. Sometimes it is also called as UnQL (Unstructured Query Language). The
syntax of using UnQL varies from database to database.
SQL database examples: MySql, Oracle, Sqlite, Postgres and MS-SQL. NoSQL
database examples: MongoDB, BigTable, Redis, RavenDb, Cassandra, Hbase, Neo4j
and CouchDb
For complex queries: SQL databases are good fit for the complex query intensive
environment whereas NoSQL databases are not good fit for complex queries. On a high-
level, NoSQL don’t have standard interfaces to perform complex queries, and the
queries themselves in NoSQL are not as powerful as SQL query language.
For the type of data to be stored: SQL databases are not best fit for hierarchical data
storage. But, NoSQL database fits better for the hierarchical data storage as it follows
the key-value pair way of storing data similar to JSON data. NoSQL database are highly
preferred for large data set (i.e for big data). Hbase is an example for this purpose.
For scalability: In most typical situations, SQL databases are vertically scalable. You can
manage increasing load by increasing the CPU, RAM, SSD, etc, on a single server. On
the other hand, NoSQL databases are horizontally scalable. You can just add few more
servers easily in your NoSQL database infrastructure to handle the large traffic.
For high transactional based application: SQL databases are best fit for heavy duty
transactional type applications, as it is more stable and promises the atomicity as well as
integrity of the data. While you can use NoSQL for transactions purpose, it is still not
comparable and sable enough in high load and for complex transactional applications.
For support: Excellent support are available for all SQL database from their vendors.
There are also lot of independent consultations who can help you with SQL database for
a very large scale deployments. For some NoSQL database you still have to rely on
community support, and only limited outside experts are available for you to setup and
deploy your large scale NoSQL deployments.
For properties: SQL databases emphasizes on ACID properties ( Atomicity, Consistency,
Isolation and Durability) whereas the NoSQL database follows the Brewers CAP
theorem ( Consistency, Availability and Partition tolerance )
For DB types: On a high-level, we can classify SQL databases as either open-source or
close-sourced from commercial vendors. NoSQL databases can be classified on the
basis of way of storing data as graph databases, key-value store databases, document
store databases, column store database and XML databases.