This document provides an overview of NoSQL databases by comparing them to SQL databases. It discusses how companies like Google and Facebook needed alternatives to SQL as their data grew tremendously. This led to the development of Google's BigTable database and Facebook's Cassandra. The document outlines some key benefits of NoSQL databases like elastic scaling, handling big data, reduced reliance on DBAs, and more flexible data models. It also compares NoSQL to SQL in areas like ACID properties, the CAP theorem, maturity, support, analytics capabilities, administration, and required expertise.
158ltd.com gives a rapid introduction to NoSQL databases: where they came from, the nature of the data models they use, and the different way you have to think about consistency.
NoSQL databases are currently used in several applications scenarios in contrast to Relations Databases. Several type of Databases there exist. In this presentation we compare Key Value, Column Oriented, Document Oriented and Graph Databases. Using a simple case study there are evaluated pros and cons of the NoSQL databases taken into account.
This presentation focuses on Apache Spark’s MLlib library for distributed ML, focusing on how we simplified elements of production-grade ML by building MLlib on top of Spark’s distributed DataFrame API.
158ltd.com gives a rapid introduction to NoSQL databases: where they came from, the nature of the data models they use, and the different way you have to think about consistency.
NoSQL databases are currently used in several applications scenarios in contrast to Relations Databases. Several type of Databases there exist. In this presentation we compare Key Value, Column Oriented, Document Oriented and Graph Databases. Using a simple case study there are evaluated pros and cons of the NoSQL databases taken into account.
This presentation focuses on Apache Spark’s MLlib library for distributed ML, focusing on how we simplified elements of production-grade ML by building MLlib on top of Spark’s distributed DataFrame API.
What to Expect for Big Data and Apache Spark in 2017 Databricks
Big data remains a rapidly evolving field with new applications and infrastructure appearing every year. In this talk, Matei Zaharia will cover new trends in 2016 / 2017 and how Apache Spark is moving to meet them. In particular, he will talk about work Databricks is doing to make Apache Spark interact better with native code (e.g. deep learning libraries), support heterogeneous hardware, and simplify production data pipelines in both streaming and batch settings through Structured Streaming.
Speaker: Matei Zaharia
Video: http://go.databricks.com/videos/spark-summit-east-2017/what-to-expect-big-data-apache-spark-2017
This talk was originally presented at Spark Summit East 2017.
Intoduction to sql 2012 Tabular ModelingKaran Gulati
New modeling option in SQL 2012 Analysis Services – Tabular Server
•BISM Vision
•Table-like modeling
•Finding Remote of James Bond Car
•ABCD – Anybody Can Dance
in this presentation we go through the differences and similarities between Redshift and BigQuery. It was presented during the Athens Big Data meetup May 2017.
GraphFrames: DataFrame-based graphs for Apache® Spark™Databricks
These slides support the GraphFrames: DataFrame-based graphs for Apache Spark webinar. In this webinar, the developers of the GraphFrames package will give an overview, a live demo, and a discussion of design decisions and future plans. This talk will be generally accessible, covering major improvements from GraphX and providing resources for getting started. A running example of analyzing flight delays will be used to explain the range of GraphFrame functionality: simple SQL and graph queries, motif finding, and powerful graph algorithms.
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
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.
What to Expect for Big Data and Apache Spark in 2017 Databricks
Big data remains a rapidly evolving field with new applications and infrastructure appearing every year. In this talk, Matei Zaharia will cover new trends in 2016 / 2017 and how Apache Spark is moving to meet them. In particular, he will talk about work Databricks is doing to make Apache Spark interact better with native code (e.g. deep learning libraries), support heterogeneous hardware, and simplify production data pipelines in both streaming and batch settings through Structured Streaming.
Speaker: Matei Zaharia
Video: http://go.databricks.com/videos/spark-summit-east-2017/what-to-expect-big-data-apache-spark-2017
This talk was originally presented at Spark Summit East 2017.
Intoduction to sql 2012 Tabular ModelingKaran Gulati
New modeling option in SQL 2012 Analysis Services – Tabular Server
•BISM Vision
•Table-like modeling
•Finding Remote of James Bond Car
•ABCD – Anybody Can Dance
in this presentation we go through the differences and similarities between Redshift and BigQuery. It was presented during the Athens Big Data meetup May 2017.
GraphFrames: DataFrame-based graphs for Apache® Spark™Databricks
These slides support the GraphFrames: DataFrame-based graphs for Apache Spark webinar. In this webinar, the developers of the GraphFrames package will give an overview, a live demo, and a discussion of design decisions and future plans. This talk will be generally accessible, covering major improvements from GraphX and providing resources for getting started. A running example of analyzing flight delays will be used to explain the range of GraphFrame functionality: simple SQL and graph queries, motif finding, and powerful graph algorithms.
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
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.
TOP NEWSQL DATABASES AND FEATURES CLASSIFICATIONijdms
Versatility of NewSQL databases is to achieve low latency constrains as well as to reduce cost commodity
nodes. Out work emphasize on how big data is addressed through top NewSQL databases considering their
features. This NewSQL databases paper conveys some of the top NewSQL databases [54] features collection
considering high demand and usage. First part, around 11 NewSQL databases have been investigated for
eliciting, comparing and examining their features so that they might assist to observe high hierarchy of
NewSQL databases and to reveal their similarities and their differences. Our taxonomy involves four types
categories in terms of how NewSQL databases handle, and process big data considering technologies are
offered or supported. Advantages and disadvantages are conveyed in this survey for each of NewSQL
databases. At second part, we register our findings based on several categories and aspects: first, by our
first taxonomy which sees features characteristics are either functional or non-functional. A second
taxonomy moved into another aspect regarding data integrity and data manipulation; we found data
features classified based on supervised, semi-supervised, or unsupervised. Third taxonomy was about how
diverse each single NewSQL database can deal with different types of databases. Surprisingly, Not only do
NewSQL databases process regular (raw) data, but also they are stringent enough to afford diverse type of
data such as historical and vertical distributed system, real-time, streaming, and timestamp databases.
Thereby we release NewSQL databases are significant enough to survive and associate with other
technologies to support other database types such as NoSQL, traditional, distributed system, and semirelationship
to be as our fourth taxonomy-based. We strive to visualize our results for the former categories
and the latter using chart graph. Eventually, NewSQL databases motivate us to analyze its big data
throughput and we could classify them into good data or bad data. We conclude this paper with couple
suggestions in how to manage big data using Predictable Analytics and other techniques.
Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.
NoSQL Database: Classification, Characteristics and ComparisonMayuree Srikulwong
My students' presentation of a paper "NoSQL Database: New Era of Databases for Big Data Analytics - Classification, Characteristics and Comparison" by Moniruzzaman, A.B.M. and Hossain, S.A. (2013).
The databases SQL and NoSQL have their own importance, and it profoundly depends on your business requirement and objectives. One of the biggest factors in understanding which database is the better choice depends on the type of data that needs to be stored. To Know more visit at https://www.zenesys.com/blog/sql-vs-nosql-top-10-comparisons
"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.
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.
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.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
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.
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.
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.
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.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
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
UiPath Test Automation using UiPath Test Suite series, part 4
NoSQL and SQL Databases
1. Seminar and Progress Report
A comparison between SQL (Conventional) & NOSQL (WebScale)
Databases using various scenarios
Gaurav Paliwal
0071641507
B.Tech (Information Technology)
8th Semester
8. The rise of Not only SQL - 1
1. Google invented for BigTable database.
9. The rise of Not only SQL - 1
1. Google invented for BigTable database.
2. BigTable maps two arbitrary string values (row key and column key)
and timestamp (hence three dimensional mapping) into associated
arbitrary byte array.
10. The rise of Not only SQL - 1
1. Google invented for BigTable database.
2. BigTable maps two arbitrary string values (row key and column key)
and timestamp (hence three dimensional mapping) into associated
arbitrary byte array.
3. It is not a relational database and can be better defined as a sparse,
distributed multi-dimensional sorted map.
11. The rise of Not only SQL - 1
1. Google invented for BigTable database.
2. BigTable maps two arbitrary string values (row key and column key)
and timestamp (hence three dimensional mapping) into associated
arbitrary byte array.
3. It is not a relational database and can be better defined as a sparse,
distributed multi-dimensional sorted map.
4. BigTable is designed to scale into the petabyte range across "hundreds
or thousands of machines, and to make it easy to add more machines to
the system and automatically start taking advantage of those resources
without any reconfiguration".
13. The rise of Not only SQL - 2
1. It is a NoSQL solution that was initially developed by Facebook and
powers their Inbox Search feature.
14. The rise of Not only SQL - 2
1. It is a NoSQL solution that was initially developed by Facebook and
powers their Inbox Search feature.
2. Jeff Hammerbacher, who led the Facebook Data team at the time, has
described Cassandra as a BigTable data model running on an Amazon
Dynamo-like infrastructure.
15. The rise of Not only SQL - 2
1. It is a NoSQL solution that was initially developed by Facebook and
powers their Inbox Search feature.
2. Jeff Hammerbacher, who led the Facebook Data team at the time, has
described Cassandra as a BigTable data model running on an Amazon
Dynamo-like infrastructure.
3. Cassandra is an open source distributed database management
system.
16. The rise of Not only SQL - 2
1. It is a NoSQL solution that was initially developed by Facebook and
powers their Inbox Search feature.
2. Jeff Hammerbacher, who led the Facebook Data team at the time, has
described Cassandra as a BigTable data model running on an Amazon
Dynamo-like infrastructure.
3. Cassandra is an open source distributed database management
system.
4. It is an Apache Software Foundation top-level project designed to
handle very large amounts of data spread out across many commodity
servers while providing a highly available service with no single point of
failure.
17. The rise of Not only SQL - Others
Hadoop / HBase
Hypertable
Amazon SimpleDB
MongoDB
Terrastore
CouchDB
MemcacheDB
And Many others {{The list is Endless}}.