Oracle was first released in 1977 and has since become a powerful database used by large enterprises, while MySQL was first released in 1995 and is a popular open-source database often used for web applications. The document compares the two databases, noting Oracle's strength in large applications while MySQL excels at web uses due to its low price and ease of setup. It outlines key differences in their features, functionality, and typical use cases.
(BDT303) Construct Your ETL Pipeline with AWS Data Pipeline, Amazon EMR, and ...Amazon Web Services
An advantage to leveraging Amazon Web Services for your data processing and warehousing use cases is the number of services available to construct complex, automated architectures easily. Using AWS Data Pipeline, Amazon EMR, and Amazon Redshift, we show you how to build a fault-tolerant, highly available, and highly scalable ETL pipeline and data warehouse. Coursera will show how they built their pipeline, and share best practices from their architecture.
Apache Sqoop efficiently transfers bulk data between Apache Hadoop and structured datastores such as relational databases. Sqoop helps offload certain tasks (such as ETL processing) from the EDW to Hadoop for efficient execution at a much lower cost. Sqoop can also be used to extract data from Hadoop and export it into external structured datastores. Sqoop works with relational databases such as Teradata, Netezza, Oracle, MySQL, Postgres, and HSQLDB
(BDT401) Amazon Redshift Deep Dive: Tuning and Best PracticesAmazon Web Services
Get a look under the covers: Learn tuning best practices for taking advantage of Amazon Redshift's columnar technology and parallel processing capabilities to improve your delivery of queries and improve overall database performance. This session explains how to migrate from existing data warehouses, create an optimized schema, efficiently load data, use work load management, tune your queries, and use Amazon Redshift's interleaved sorting features. Finally, learn how TripAdvisor uses these best practices to give their entire organization access to analytic insights at scale.
Serverlesss Big Data Analytics with Amazon Athena and QuicksightAmazon Web Services
Check out how you can easily query raw data in various formats in Amazon S3, transform it into a canonical form, analyze it, and build dashboards to get more insights from your data.
(BDT303) Construct Your ETL Pipeline with AWS Data Pipeline, Amazon EMR, and ...Amazon Web Services
An advantage to leveraging Amazon Web Services for your data processing and warehousing use cases is the number of services available to construct complex, automated architectures easily. Using AWS Data Pipeline, Amazon EMR, and Amazon Redshift, we show you how to build a fault-tolerant, highly available, and highly scalable ETL pipeline and data warehouse. Coursera will show how they built their pipeline, and share best practices from their architecture.
Apache Sqoop efficiently transfers bulk data between Apache Hadoop and structured datastores such as relational databases. Sqoop helps offload certain tasks (such as ETL processing) from the EDW to Hadoop for efficient execution at a much lower cost. Sqoop can also be used to extract data from Hadoop and export it into external structured datastores. Sqoop works with relational databases such as Teradata, Netezza, Oracle, MySQL, Postgres, and HSQLDB
(BDT401) Amazon Redshift Deep Dive: Tuning and Best PracticesAmazon Web Services
Get a look under the covers: Learn tuning best practices for taking advantage of Amazon Redshift's columnar technology and parallel processing capabilities to improve your delivery of queries and improve overall database performance. This session explains how to migrate from existing data warehouses, create an optimized schema, efficiently load data, use work load management, tune your queries, and use Amazon Redshift's interleaved sorting features. Finally, learn how TripAdvisor uses these best practices to give their entire organization access to analytic insights at scale.
Serverlesss Big Data Analytics with Amazon Athena and QuicksightAmazon Web Services
Check out how you can easily query raw data in various formats in Amazon S3, transform it into a canonical form, analyze it, and build dashboards to get more insights from your data.
For those contemplating re-architecting or greenfields data lakes/data hubs/data warehouses in a cloud environment, talk to our Altis AWS Practice Lead - Guillaume Jaudouin about why you should be considering the "tour de force" combination of AWS and Snowflake.
Wide Column Store NoSQL vs SQL Data ModelingScyllaDB
NoSQL schemas are designed with very different goals in mind than SQL schemas. Where SQL normalizes data, NoSQL denormalizes. Where SQL joins ad-hoc, NoSQL pre-joins. And where SQL tries to push performance to the runtime, NoSQL bakes performance into the schema. Join us for an exploration of the core concepts of NoSQL schema design, using Scylla as an example to demonstrate the tradeoffs and rationale.
This session will begin with an introduction to non-relational (NoSQL) databases and compare them with relational (SQL) databases. Learn the fundamentals of Amazon DynamoDB, a fully managed NoSQL database service, and see the DynamoDB console first-hand. See a walk-through demo of building a serverless web application using this high-performance key-value and JSON document store.
This presenation explains basics of ETL (Extract-Transform-Load) concept in relation to such data solutions as data warehousing, data migration, or data integration. CloverETL is presented closely as an example of enterprise ETL tool. It also covers typical phases of data integration projects.
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.
SDM (Standardized Data Management) - A Dynamic Adaptive Ingestion Frameworks ...DataWorks Summit
SDM is a distributed, reliable and highly available data lake ingestion framework that handles data processing, archival and reconciliation capabilities with an effective change based history management capabilities for batch and streaming data. It is meta-driven and provides automated schema evolution. The SDM platform is built completely on open source software/platforms, making it both extensible and robust. The data management, schema evolution and archival is achieved through Apache NiFi’s in-built capabilities and extensions via custom processors and controller services. The end-of-day construct is generated through an Apache Spark job.Types of Data :
Types of Data :
1. Batch
a. Full dump
b. Incremental
c. Hybrid (Daily incremental + Weekly/Monthly full dump)
2. Near Real time
a. CDC-Kafka
b. JMS-Kafka
3. Extractions
a. Incremental based on Change Data Capture tool (IBM Infosphere CDC)
b. Sqoop
c. JDBC/ODBC
4. Manual File Upload
a. Excel
Types of Process:
1. File validation
a. File integrity (header, trailer, data checksum)
b. File de-duplication
c. New line and non-printable control characters handling
2. Structural validation (Row validation)
a. Fixed width
b. Delimited
c. XML
d. JSON
e. Excel (Single/Multi tab)
f. Datatype validation
g. Constraint validation – Null, primary key and full row de-duplication
3. Defaulting
a. Condition based
b. Special data-type handling (mainframe systems)
4. Operational assurance
a. Row count logging
b. Reconciliation with source
c. File/Record rejections with reasons
5. Lineage tracking
a. Row-id for every single record is generated and referenced against the source file until the processed layer.
Storage formats :
1. Raw - Archival
2. Avro – Staged
3. ORC – Processed
Benefits
1. Metadata driven
2. Extensible
3. Scalable
4. Flexible
Plans
Current State :
1. Custom built ingestion framework leveraging upon standard open source software from Apache.
2. Data of 100+ source systems are ingested into the Hadoop data lake using the ingestion framework.
Plan :
1. Open sourcing the framework for general consumption.
2. Metadata management UI/API which would serve as a glossary of data available in the data lake with search capabilities.
3. Operational and Exception reporting.
4. Centralized data retention within the framework.
5. Health monitoring and alerting.
6. Provenance data maintenance in Atlas.
Speaker
Arun Manivannan, Senior Data Engineer, Standard Chartered Bank
In this session we will introduce key ETL features of AWS Glue and cover common use cases ranging from scheduled nightly data warehouse loads to near real-time, event-driven ETL flows for your data lake. We will also discuss how to build scalable, efficient, and serverless ETL pipelines.
This presentation explains why NoSQL databases came over SQL databases although SQL databases has been successfully technology for more than twenty years. Moreover, This presentation discuses the characteristics and classifications of NoSQL databases. Finally, These slides cover four NoSQL databases briefly.
In this webinar you'll learn about the best practices for Google BigQuery—and how Matillion ETL makes loading your data faster and easier. Find out from our experts how to leverage one of the largest, fastest, and most capable cloud data warehouses to improve your business and save money.
In this webinar:
- Discover how to work fast and efficiently with Google BigQuery
- Find out the best ways to monitor and control costs
- Learn to leverage Matillion ETL and optimize Google BigQuery
- Get tips and tricks for better performance
Integrating Blackboard Collaborate 12 and MoodleNetSpot Pty Ltd
Integration Capabilities
Increase the capacity of your Learning Management System (LMS) to connect and engage
For Educators
Schedule web conferencing sessions
List live sessions and recordings as content objects in course information and assignments
Pre-load content
Integrated grading
For Students
Single login for LMS and Collaborate
Attend sessions
View recordings
For those contemplating re-architecting or greenfields data lakes/data hubs/data warehouses in a cloud environment, talk to our Altis AWS Practice Lead - Guillaume Jaudouin about why you should be considering the "tour de force" combination of AWS and Snowflake.
Wide Column Store NoSQL vs SQL Data ModelingScyllaDB
NoSQL schemas are designed with very different goals in mind than SQL schemas. Where SQL normalizes data, NoSQL denormalizes. Where SQL joins ad-hoc, NoSQL pre-joins. And where SQL tries to push performance to the runtime, NoSQL bakes performance into the schema. Join us for an exploration of the core concepts of NoSQL schema design, using Scylla as an example to demonstrate the tradeoffs and rationale.
This session will begin with an introduction to non-relational (NoSQL) databases and compare them with relational (SQL) databases. Learn the fundamentals of Amazon DynamoDB, a fully managed NoSQL database service, and see the DynamoDB console first-hand. See a walk-through demo of building a serverless web application using this high-performance key-value and JSON document store.
This presenation explains basics of ETL (Extract-Transform-Load) concept in relation to such data solutions as data warehousing, data migration, or data integration. CloverETL is presented closely as an example of enterprise ETL tool. It also covers typical phases of data integration projects.
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.
SDM (Standardized Data Management) - A Dynamic Adaptive Ingestion Frameworks ...DataWorks Summit
SDM is a distributed, reliable and highly available data lake ingestion framework that handles data processing, archival and reconciliation capabilities with an effective change based history management capabilities for batch and streaming data. It is meta-driven and provides automated schema evolution. The SDM platform is built completely on open source software/platforms, making it both extensible and robust. The data management, schema evolution and archival is achieved through Apache NiFi’s in-built capabilities and extensions via custom processors and controller services. The end-of-day construct is generated through an Apache Spark job.Types of Data :
Types of Data :
1. Batch
a. Full dump
b. Incremental
c. Hybrid (Daily incremental + Weekly/Monthly full dump)
2. Near Real time
a. CDC-Kafka
b. JMS-Kafka
3. Extractions
a. Incremental based on Change Data Capture tool (IBM Infosphere CDC)
b. Sqoop
c. JDBC/ODBC
4. Manual File Upload
a. Excel
Types of Process:
1. File validation
a. File integrity (header, trailer, data checksum)
b. File de-duplication
c. New line and non-printable control characters handling
2. Structural validation (Row validation)
a. Fixed width
b. Delimited
c. XML
d. JSON
e. Excel (Single/Multi tab)
f. Datatype validation
g. Constraint validation – Null, primary key and full row de-duplication
3. Defaulting
a. Condition based
b. Special data-type handling (mainframe systems)
4. Operational assurance
a. Row count logging
b. Reconciliation with source
c. File/Record rejections with reasons
5. Lineage tracking
a. Row-id for every single record is generated and referenced against the source file until the processed layer.
Storage formats :
1. Raw - Archival
2. Avro – Staged
3. ORC – Processed
Benefits
1. Metadata driven
2. Extensible
3. Scalable
4. Flexible
Plans
Current State :
1. Custom built ingestion framework leveraging upon standard open source software from Apache.
2. Data of 100+ source systems are ingested into the Hadoop data lake using the ingestion framework.
Plan :
1. Open sourcing the framework for general consumption.
2. Metadata management UI/API which would serve as a glossary of data available in the data lake with search capabilities.
3. Operational and Exception reporting.
4. Centralized data retention within the framework.
5. Health monitoring and alerting.
6. Provenance data maintenance in Atlas.
Speaker
Arun Manivannan, Senior Data Engineer, Standard Chartered Bank
In this session we will introduce key ETL features of AWS Glue and cover common use cases ranging from scheduled nightly data warehouse loads to near real-time, event-driven ETL flows for your data lake. We will also discuss how to build scalable, efficient, and serverless ETL pipelines.
This presentation explains why NoSQL databases came over SQL databases although SQL databases has been successfully technology for more than twenty years. Moreover, This presentation discuses the characteristics and classifications of NoSQL databases. Finally, These slides cover four NoSQL databases briefly.
In this webinar you'll learn about the best practices for Google BigQuery—and how Matillion ETL makes loading your data faster and easier. Find out from our experts how to leverage one of the largest, fastest, and most capable cloud data warehouses to improve your business and save money.
In this webinar:
- Discover how to work fast and efficiently with Google BigQuery
- Find out the best ways to monitor and control costs
- Learn to leverage Matillion ETL and optimize Google BigQuery
- Get tips and tricks for better performance
Integrating Blackboard Collaborate 12 and MoodleNetSpot Pty Ltd
Integration Capabilities
Increase the capacity of your Learning Management System (LMS) to connect and engage
For Educators
Schedule web conferencing sessions
List live sessions and recordings as content objects in course information and assignments
Pre-load content
Integrated grading
For Students
Single login for LMS and Collaborate
Attend sessions
View recordings
Integrating the Student Information System and Blackboard - you just press a ...Blackboard APAC
In 2014, Victoria University of Wellington began a project to replace the existing data integration system between the Student Information System (Banner) and Blackboard.A project that touches an institution's Student Information System and Learning Management System will always have its own special challenges. Victoria University of Wellington made use of Blackboard's SIS Integration Mentoring service to help tackle some of the thorny issues and assist with the implementation. As well as challenges, the project provided important opportunities to engage with stakeholders, refine existing processes, and improve the quality of the integration between the systems with a view to future developments. This presentation will cover the goals and outcomes, the different stages of the project, and how we overcame some of the more difficult implementation and organisational issues, without delving too deeply into the specific technical details.
Delivered at Innovate and Educate: Teaching and Learning Conference by Blackboard. 24 -27 August 2015 in Adelaide, Australia.
Embedding Library Services in Blackboard LearnBlackboardEMEA
An overview of how Leeds Beckett University has embedded Library services within Blackboard Learn. Including:
1) Creating and personalising a Library tab within Blackboard Communities, using HTML Modules, Institutional Roles and custom Building Blocks.
2) Developing a personalised Library Account Building Block to pull information from the Sirsi Dynix Library Management System.
3) Embedding relevant Reading Lists in Blackboard Courses using a custom Resource List Building Block that integrates Blackboard with the REBUS:list Reading List system.
4) Using Google Analytics usage data was to inform further content development.
Presentation given January 26th, 2011 for Blackboard's Distinguished Lecture Series Titled: Getting Campus-Wide Adoption - Happy Faculty - via Blackboard Collaborate
Blackboard Learn integration overview: 9.1, SaaS, and Ultra - Scott Hurrey, M...Blackboard APAC
Blackboard has a long history of supporting developers who wish to integrate with or extend the capabilities of Learn. We are expanding that support with the release of a new Blackboard Developer Platform and support of REST APIs. In addition, with new support for Java 8, a shared code base for Learn 9.1 and Learn SaaS, and requirements for developing for the Learn Ultra experience, there are implications for how organizations will need to approach their development efforts. This session will provide an executive overview of how institutions can integrate with and extend Learn. It will cover the similarities and differences across Learn 9.1 and Learn SaaS, both the Original and Ultra experiences.
Supporting Blackboard today and tomorrow with integrated solutions, Pearson E...Blackboard APAC
Everyday millions of users log in to Blackboard with an aim to meet the specialised needs of evolving learners. An efficient and seamless system for course delivery is a key requirement for many educational institutions, particularly those targeting a unique approach to the online learning experience.
How are educators responding to the challenges associated with effective content accessibility, assessment and student engagement?
Our technology solutions support the Blackboard 9 series users each day with centrally stored course content, LTI integration, QTI assessment, as well as Kaltura and Echo360 integrations. Additionally, we focus on models of teaching in new physical and digital spaces and will share examples of how we have partnered with universities to cater for diverse learners in these new realities. There is a strong need for reporting and analytics on learning outcomes as part of a stable eLearning ecosystem and in response to these, we will share the core capabilities for our integrated solutions that interact with, and compliment Blackboard; EQUELLA, Learning Catalytics, and MyLab & Mastering, while providing an update on the latest integrated services relating to these solutions.
What does each solution deliver?
EQUELLA: Our digital repository provides one platform to house your teaching and learning, research, media and library
Delivered at Innovate and Educate: Teaching and Learning Conference by Blackboard. 24 -27 August 2015 in Adelaide, Australia.
Mysql User Camp : 20-June-14 : Mysql New features and NoSQL SupportMysql User Camp
This slide was presented at Mysql User Camp Event on 20-June-14 at Oracle bangalore. This presentation gives a good insight about New Features in Mysql 5.7 DMR 4 and Nosql Support in Mysql.
Nesta apresentação venceremos os obstáculos comuns para iniciar o desenvolvimento em Java usando corretamente o MySQL como banco de dados. O MySQL é o banco de dados open source mais popular do mundo, usado em grandes sites como Facebook, Youtube, Twitter, Yahoo, Globo.com etc. Abordaremos tópicos como: baixar e instalar um servidor MySQL para desenvolvimento, preparar o banco de dados para uso com JDBC, escrever e testar seus primeiros programas Java com MySQL e funcionalidades específicas do driver Connector/J para um desenvolvimento e implantação mais eficiente.
Embracing Database Diversity: The New Oracle / MySQL DBA - UKOUGKeith Hollman
Classic Oracle DBAs are somewhat starved for the "big overview" knowledge that will make them better decision makers and less hesitant to use MySQL.
The aim is to allow an existing Oracle DBA to get to grips with a MySQL environment, concentrating on the real focus points, and highlighting the similarities of both RDBMS'.
And both worlds provide the necessary tools to avoid a sleepless night.
Playing in the Same Sandbox: MySQL and Oraclelynnferrante
SCaLE Linux presentation January2012 "Playing in the Same Sandbox: MySQL and Oracle" describes current and upcoming integrations between MySQL and other Oracle products like Oracle Database firewall, Audit Vault, Secure Backup, Goldengate, My Oracle Support and MySQL Enterprise Monitor
Compare the capabilities of the Microsoft Access, Microsoft SQL Serv.pdfarihantplastictanksh
Compare the capabilities of the Microsoft Access, Microsoft SQL Server, Oracle’s MySQL, and
Oracle relational database management systems (RDBMSs). Your paper should discuss the
processing speeds, data storage capabilities, maximum users supported, platforms supported,
user interfaces, development tools, vendor support, and cost. Discuss and cite at least two
references in addition to our textbook. Your paper should be 3-5 pages in length (excluding title
and References pages)
Solution
Microsoft Access
Overview:
Microsoft Access is a part of Microsoft Office,
it is commercially available database in the market
Inexpensive/standard on most computers
users can create complex databases
database professionalas can use construct a database
customers of MS-Access:
It is mainly used in small corporate companies or IT Sectors with 1-80 endusers.
Features of MS-Access:
1.It is having GUI Interface for creating databases
2. A databae contains tables, forms, reports, queries, macros.
3. It facilitates autocontent wizards to build tables or forms or reports.
4. It acts as an interface to other DBMS using ODBC
5. It is used for small business companies
6. Provides security like password protection
7. Provides a Data dictionary
8. We can repair the database
9. We can create different views
10. External data can be imported into Access
11. We can create web pages based using the database
12. It has as built in Macro functions
13. It uses Structurered Query Language
14. We can create forms, reports etc by using Visual Basic Application programming
15. Provides Add in controls like calendars
16. It can merged into word and analysed with Excel etc.
Issues:
Security:
User level security is very difficult
Tuning:
It does not have the ability to split over multiple Hard Drives, multiple CPUs or to place tables
into memory.
Locking:
Basic handling of concurrent users Backup and recovery at basic level.
ANSI SQL standard often doesn\'t work,MS-Access has it\'s own modified version of ANSI
SQL.
MySQL
Overview
MySQL is a database engine. It has a command line interface that allows the creation of
database. It Requires Front-end applications to access it for end users. EX:- C#, PHP, Microsoft
ASP.Net.
Typical users
Small companies or workgroups, through to very large Internet databases with large numbers of
users
Ex:wikipedia,Moodle.
Features
1. Speed:One of the fastest databases available
2. Ease of use: when compared to larger databases such as Oracle Uses standard SQL
3. Capability: A multi-threaded server allowing many clients to connect at the same time Fully
networked for the Internet with built in security
4.Portability: Runs on a many operating systems and different hardware
5. Small size: when compared to other large databases e.g. Oracle
6. Availabliity and Cost: Open Source ,Free in most situations to use
7. Open distribution and source code: You can check how it works – if you have the knowledge.
8. interface to other DBMS’s using Open Database Connectivit.
An outline on why the MySQL 8 release is viewed as a gamechanger with a look at some of the new features like CTEs, Window Functions, MySQL InnoDB Cluster, Enterprise Data Masking, and more
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
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
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.
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/
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.
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.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
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.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
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.
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.
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.
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3. Oracle
(Since 1977)
Produced and marketed by Oracle Corporation
Larry Ellison and his friends started the consultancy Software Development
Laboratories (SDL) in 1977
1979 - Oracle release 2
1983 - Oracle release 3
1984 - Oracle release 4
1985 - Oracle release 5
1988 - Oracle release 6
1992 - Oracle release 7
1997 - Oracle release 8
1998 - Oracle release 8i
2001 - Oracle release 9i
2003 - Oracle release 10g
2007 - Oracle release 11g
4. Oracle
(Now)
Oracle Corporation is an American multinational computer
technology corporation headquartered in Redwood City, California,
The United States.
The company specializes in developing and marketing computer
hardware systems and enterprise software products –
particularly its own brands of database management systems.
Oracle is the third-largest software maker by revenue,
after Microsoft and IBM
5. MySQL is a powerful and the most popular Open Source Software
relational database management system (RDBMS) that uses SQL
(Structured Query Language).
MySQL is named after co-founder Monty Widenius's daughter, My .
MySQL is officially pronounced "My esquel".
It is popular for web applications.
Previously, MySQL was developed, distributed, and supported by
MySQL AB then acquired by Sun Microsystems. And now since
January 2010 accquired by Oracle Corporation.
6. MySQL Development History
- MySQL was first released internally on 23 May 1995
- Windows version was released on January 8, 1998 for Windows 95 and NT
- Version 3.23 release January 2001
- Version 4.0 release March 2003
- Version 4.1 release October 2004
- Version 5.0 release October 2005
- Sun Microsystems acquires MySQL AB on 26 February 2008
- Version 5.1 release 27 November 2008
- Oracle acquired Sun Microsystems on 27 January 2010
- Version 5.5 release December 2010
- Current Generally Available Release: 5.6.10, 5 February 2013
7. ORACLE: Different Types of DB
Enterprise Edition:
Most Powerful, with a vast array of tools and features for the large corporation
Standard Edition:
Oracle SE contains the basic database management functions for small- and
medium-sized shops at a far lower cost than the EE.
Standard Edition one:
Oracle SEO is specially-priced for single CPU servers used by small businesses.
Express Edition:
Entry-level, small-footprint database to develop, deploy, and distribute.
8. Different Types of DB
MySQL Community Server
MySQL Community Edition is a freely downloadable version of the world's most popular open
source database that is supported by an active community of open source developers and
enthusiasts.
MySQL Enterprise Edition
Commercial customers have the flexibility of choosing from multiple MySQL Editions to meet
specific business and technical requirements.
Difference between the community edition and the enterprise edition is
added support and tools. The server itself is the same, but the enterprise
edition gets updated more frequently and it is stable with quick bug fix
support.
MySQL is used in many high-profile, large-scale World Wide Web
products, including Wikipedia,Google(though not for searches),
Facebook,Twitter,Flickr,Nokia.com and YouTube.
9. Oracle versus MySQL Features/Functionality
Sr. Features/ Oracle MySQL
No. Functionality
1 Strengths Aircraft carrier database Price/Performance Great
capable of running large performance when
OLTP and VLDBs. applications leverage
architecture.
2 Database Enterprise ($$$$) Enterprise ($) – supported,
Products Standard ($$) more stable.
Standard One ($) Community (free)
Express (Free) - up to 4GB
3 Application More you do in the database Web applications often don’t
Perspective the more you will love Oracle leverage database server
with compiled PL/SQL, XML, functionality. Web apps more
APEX, Java, etc. concerned with fast reads.
4 Administration Requires lots of in-depth Can be trivial to get it setup
knowledge and skill to and running. Large and
manage large advanced configurations can
environments. Can get get complex.
extremely complex but also
very powerful.
10. Oracle versus MySQL Features/Functionality
Sr. No. Features/ Oracle MySQL
Functionality
5 Popularity Extremely popular in Fortune 100, Extremely popular with web
medium/large enterprise business companies, startups,
applications and medium/large data small/medium businesses,
warehouses. small/medium projects.
6 Application Medium/Large OLTP and enterprise Web (MySQL excels)
Domains applications. Oracle excels in large Data Warehouse
business applications. Gaming
Medium/Large data warehouse Small/media OLTP
environmnets
7 Development 1) Java 1) PHP
Environments 2) .NET 2) JAVA
(most common) 3) APEX 3) Ruby on Rails
4) Ruby on Rails 4) .NET
5) PHP 5) Perl
8 Database Database instance has numerous Database Instance stores
Server background processes dependent on global memory in mysqld
(Instance) configuration. System Global Area is background process.
shared memory for SMON, PMON, User sessions are managed
DBWR, LGWR, ARCH, RECO, through threads.
etc.Sessions are managed through
server processes.
11. Oracle versus MySQL Features/Functionality
Sr. No. Features/ Oracle MySQL
Functionality
9 Database Server Uses tablespaces for system Made up of database
metadata, user data and schemas.
indexes. Common tablespaces
include:
10 Partitioning $$$ with lots of options Free, basic features
11 Replication $$$, lots of features and Free, relatively easy to
options. Much higher setup and
complexity with a lot of manage. Basic features
features. Allows a lot of data but works great. Great
filtering and manipulation. horizontal scalability.
12 Transactions Regular and Index only tables InnoDB and upcoming
support transactions. Falcon and Maria
storage engines
13 Backup/Recovery Recovery Manager (RMAN) No online backup built-
supports hot backups and runs in.
as a separate central repository
for multiple Oracle database
servers.
12. Oracle versus MySQL Features/Functionality
Sr. No. Features/ Oracle MySQL
Functionality
14 Export/Import More features. Easy, very basic.
15 Data Dictionary Data dictionary offers lots of detailed Information_schema and
(catalog) information for tuning. Oracle mysql database schemas
starting to charge for use of new offer basic metadata.
metadata structures.
16 Management/Monit $$$$, Grid Control offers lots of $, MySQL Enterprise
oring functionality. Lots of 3rd party Monitor offers basic
options such as Quest. functionality. Additional open
source solutions. May also
use admin scripts.
17 Storage Tables managed in Each storage engine uses
tablespaces. ASM offers striping different storage. Varies from
and mirroring using cheap fast disks. individual files to
tablespaces.
18 Stored Procedures Advanced features, runs interpreted Very basic features, runs
or compiled. Lots of built in interpreted in session
packages add significant threads. Limited scalability.
functionality. Extremely scalable.
13. Summary
ORACLE
Very Powerful DB Good / ok Performance
For OLTP, VLDBs For Web, Small-Mid Appl.
Price: $$$$ Price: 0 to $
Ease of Use: Many Features Ease of Use: Limited Features
Performance: Very High Performance: Good / Ok