The presentation describes "Resume Summary" project done using Hadoop. It talks about how to parse different types of resumes (.doc, .docx, .pdf) and how to filter out some resumes based on some keywords.
Apache Hive is a data warehousing system that allows users to query large datasets stored in Hadoop files using SQL. It addresses limitations of MapReduce by providing an SQL interface and generating MapReduce execution plans. Hive uses a SQL-like query language and stores metadata about tables, partitions, and clusters in a metastore. It provides performance optimizations for common operations like GROUP BY, JOIN, serialization/deserialization. While Hive provides a familiar interface, files in HDFS are immutable so appending data has limitations. Hive is used at companies like Facebook for large-scale log and report processing.
Reversim Summit 2014: re:dash a new way to query, visualize and collaborate o...Arik Fraimovich
re:dash is EverythingMe's take on freeing the data within our company in a way that will better fit our culture and usage patterns.
Prior to re:dash, we tried to use traditional BI suites and discovered a set of bloated, technically challenged and slow tools/flows. What we were looking for was a more hacker'ish way to look at data, so we built one.
re:dash was built to allow fast and easy access to billions of records, that we process and collect using Amazon Redshift ("petabyte scale data warehouse" that "speaks" PostgreSQL).
More information about re:dash and background: http://geeks.everything.me/2013/12/05/introducing_redash/
GitHub: https://github.com/everythingme/redash
Hadoop foundation for analytics,B Monica II M.sc computer science ,BON SECOUR...BMonica1
This document provides an overview of Hadoop, an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It discusses how Hadoop is scalable, economical, efficient, and reliable by distributing data across clusters and maintaining multiple copies for fault tolerance. Key Hadoop components like MapReduce, HDFS, and YARN are introduced. Example uses of Hadoop include log and data analysis at large companies. The history and evolution of Hadoop from its origins in 2004 are also summarized.
This document provides an overview of Hadoop, an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It discusses how Hadoop is scalable, economical, efficient, and reliable by distributing data across nodes and maintaining multiple copies for fault tolerance. Key components of Hadoop include MapReduce for distributed computing and HDFS for storage. The document also gives examples of how Hadoop is used by large organizations and describes some related tools.
This document discusses tools for large scale data analysis. It begins by defining business value as anything that makes people more likely to give money or saves costs. It then discusses how data has outgrown local storage and requires scaling out to clusters and distributed systems. The document lists various systems that can be used for data ingestion, storage, querying, processing and output. It covers batch systems like Hadoop and real-time systems like Storm. It emphasizes that to generate business value, one needs to start analyzing big data from various sources like web logs, sensors and parse noise to find signals.
Slides from the Big Data Gurus meetup at Samsung R&D, August 14, 2013
This presentation covers the high level architecture of the Netflix Data Platform with a deep dive into the architecture, implementation, use cases, and future of Lipstick (https://github.com/Netflix/Lipstick) - our open source tool for graphically analyzing and monitoring the execution of Apache Pig scripts.
Netflix uses Apache Pig to express many complex data manipulation and analytics workflows. While Pig provides a great level of abstraction between MapReduce and data flow logic, once scripts reach a sufficient level of complexity, it becomes very difficult to understand how data is being transformed and manipulated across MapReduce jobs. To address this problem, we created (and open sourced) a tool named Lipstick that visualizes and monitors the progress and performance of Pig scripts.
Hw09 Rethinking The Data Warehouse With Hadoop And HiveCloudera, Inc.
The document discusses Hive, a system for managing and querying large datasets stored in Hadoop. It describes how Hive provides a familiar SQL-like interface, simplifying Hadoop programming. The document also outlines how Facebook uses Hive and Hadoop for analytics, with over 4TB of new data added daily across a large cluster.
This document discusses the statistics program for electronic government publications (e-govpubs) at San Jose State University. It provides details on:
- How the in-house program works by collecting data from a text file and government publications database to generate statistics.
- The architecture of the program which uses ColdFusion and SQL Server and retrieves data through a front-end interface and stores it in a back-end database.
- The process for modifying bibliographic records by identifying the record number, adding tracking information to the URL, and using scripts to batch update a large number of records. It estimates the initial time required to update 37,000 records was at least 2 weeks.
Apache Hive is a data warehousing system that allows users to query large datasets stored in Hadoop files using SQL. It addresses limitations of MapReduce by providing an SQL interface and generating MapReduce execution plans. Hive uses a SQL-like query language and stores metadata about tables, partitions, and clusters in a metastore. It provides performance optimizations for common operations like GROUP BY, JOIN, serialization/deserialization. While Hive provides a familiar interface, files in HDFS are immutable so appending data has limitations. Hive is used at companies like Facebook for large-scale log and report processing.
Reversim Summit 2014: re:dash a new way to query, visualize and collaborate o...Arik Fraimovich
re:dash is EverythingMe's take on freeing the data within our company in a way that will better fit our culture and usage patterns.
Prior to re:dash, we tried to use traditional BI suites and discovered a set of bloated, technically challenged and slow tools/flows. What we were looking for was a more hacker'ish way to look at data, so we built one.
re:dash was built to allow fast and easy access to billions of records, that we process and collect using Amazon Redshift ("petabyte scale data warehouse" that "speaks" PostgreSQL).
More information about re:dash and background: http://geeks.everything.me/2013/12/05/introducing_redash/
GitHub: https://github.com/everythingme/redash
Hadoop foundation for analytics,B Monica II M.sc computer science ,BON SECOUR...BMonica1
This document provides an overview of Hadoop, an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It discusses how Hadoop is scalable, economical, efficient, and reliable by distributing data across clusters and maintaining multiple copies for fault tolerance. Key Hadoop components like MapReduce, HDFS, and YARN are introduced. Example uses of Hadoop include log and data analysis at large companies. The history and evolution of Hadoop from its origins in 2004 are also summarized.
This document provides an overview of Hadoop, an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It discusses how Hadoop is scalable, economical, efficient, and reliable by distributing data across nodes and maintaining multiple copies for fault tolerance. Key components of Hadoop include MapReduce for distributed computing and HDFS for storage. The document also gives examples of how Hadoop is used by large organizations and describes some related tools.
This document discusses tools for large scale data analysis. It begins by defining business value as anything that makes people more likely to give money or saves costs. It then discusses how data has outgrown local storage and requires scaling out to clusters and distributed systems. The document lists various systems that can be used for data ingestion, storage, querying, processing and output. It covers batch systems like Hadoop and real-time systems like Storm. It emphasizes that to generate business value, one needs to start analyzing big data from various sources like web logs, sensors and parse noise to find signals.
Slides from the Big Data Gurus meetup at Samsung R&D, August 14, 2013
This presentation covers the high level architecture of the Netflix Data Platform with a deep dive into the architecture, implementation, use cases, and future of Lipstick (https://github.com/Netflix/Lipstick) - our open source tool for graphically analyzing and monitoring the execution of Apache Pig scripts.
Netflix uses Apache Pig to express many complex data manipulation and analytics workflows. While Pig provides a great level of abstraction between MapReduce and data flow logic, once scripts reach a sufficient level of complexity, it becomes very difficult to understand how data is being transformed and manipulated across MapReduce jobs. To address this problem, we created (and open sourced) a tool named Lipstick that visualizes and monitors the progress and performance of Pig scripts.
Hw09 Rethinking The Data Warehouse With Hadoop And HiveCloudera, Inc.
The document discusses Hive, a system for managing and querying large datasets stored in Hadoop. It describes how Hive provides a familiar SQL-like interface, simplifying Hadoop programming. The document also outlines how Facebook uses Hive and Hadoop for analytics, with over 4TB of new data added daily across a large cluster.
This document discusses the statistics program for electronic government publications (e-govpubs) at San Jose State University. It provides details on:
- How the in-house program works by collecting data from a text file and government publications database to generate statistics.
- The architecture of the program which uses ColdFusion and SQL Server and retrieves data through a front-end interface and stores it in a back-end database.
- The process for modifying bibliographic records by identifying the record number, adding tracking information to the URL, and using scripts to batch update a large number of records. It estimates the initial time required to update 37,000 records was at least 2 weeks.
Data validation is the process of identifying errors in data sets that have been moved or transformed to ensure data is complete and accurate. Currently, most companies perform data validation testing manually through SQL scripts or Excel, which is time-consuming, error-prone, and cannot provide thorough testing. The Data Validation Option (DVO) provides automation, repeatability and auditability for data validation and reconciliation testing across various data sources. It has helped several companies reduce testing time by 80%, ensure data quality, and find errors caused by faulty data integration logic or processes.
Beeldkwaliteit in een DVO: Paul Verbakel (Atlant-groep) en Martijn van Duuren...CROW
De Atlant groep staat voor één van haar deelnemende gemeenten op het punt om een detacherings- en dienstverleningsovereenkomst (DDVO) af te sluiten. In dit DDVO is beeldgericht werken een belangrijk uitgangspunt. Atlant werkt conform beeld op basis van ruim 20 beeldmeetlatten en voert ook de beeldmetingen uit. Atlant gebruikt de beeldmeting om de bedrijfsvoering aan te scherpen. De gemeente stuurt voor op output en tevredenheid op 3 hoofdonderdelen: openbare ruimte, arbeidsparticipatie en dienstverlening. Echter er is geen sprake van een keiharde verplichting, de capaciteit van de ploeg kan beperkend zijn. Dit vraagt een andere manier van sturen van directie en aannemer. Het contract kent tevens vernieuwende uitgangspunten in de arbeidsparticipatie binnen dit contract, de rol van Atlant en de quotumwet.
This document is a resume for Jordan L. Janiak. It lists their education at Dunwoody College of Technology where they are studying Construction Project Management and expect to graduate in December 2017. It also lists previous education at North Dakota State University where they studied Architecture. The resume then outlines skills, work experience as a Project Manager/Foreman for OutdoorDesignGroup, GroundsKeeper for Vision of Glory Church, and Hotel Mystery Shopper.
Santhosh Kumar has over 2 years of experience as an Informatica PowerCenter developer and administrator. He is certified in Informatica PowerCenter 9.X and has experience developing mappings, managing environments, performing upgrades, and automating tasks. Some of his key skills include managing multiple Informatica domains, developing automation scripts, and setting up web service hubs. He has worked on various projects for clients like Aviva and AXA involving data migration, ETL, and Informatica upgrades.
Somappa Srinivasan of sparrowanalytics.com presents their goal of creating a scalable recommendation engine using Hadoop and real-time analytics. Their system will acquire data from various sources into a data lake stored on Hadoop. A real-time engine will then process user requests, select predictive models, score items, and recommend contextual offerings to users browsing movies. The system components include data acquisition, ingestion into a data hub of Hive and HBase tables, a real-time engine for validation, modeling, scoring and recommendations, and a UI dashboard.
Undraleu ETL Code Review Tool for Informatica PowerCenter, Data SheetAcctiva Ltd.
“The world of ETL has advanced technologically to the point that automated management of code is beginning to be a necessity. I recommend you look at Undraleu and their approach to managing the ETL environment.”
Bill Inmon
ETL Validator: Testing for Referential IntegrityDatagaps Inc
The document describes a referential integrity test conducted by a data testing company. The test aims to ensure that a company's data warehouse meets referential integrity requirements. It allows the user to select a foreign key, database connection, and entities or joins to test. Running the test will display results and show the underlying query used.
Political campaigns are shifting from text and images to mobile video messages. This requires optimizing audience targeting, message development for small screens, and stable mobile video delivery over low bandwidth networks. A new Digital Video Object (DVO) architecture significantly reduces file sizes and transfer loads compared to traditional video formats, allowing quality mobile video even on unstable networks. DVO was originally developed for the Defense Department and is now commercially available to support political campaigns with mobile video messaging resources and a test program.
Somappa Srinivasan of sparrowanalytics.com presents their goal of creating a scalable recommendation engine using Hadoop and real-time analytics. Their system will acquire data from various sources into a data lake stored on Hadoop. A real-time engine will then select models, score recommendations, and return personalized suggestions to users as they browse. The components outlined include data acquisition, ingestion into a data hub of Hive and HBase tables, model selection, scoring, recommendation generation, and a UI dashboard.
Informatica data quality online trainingDivya Shree
United Global Soft provides corporate trainings to help professionals groom themselves. They conduct training programs and workshops led by industry experts to share experiences using case studies and current technologies. The document outlines training courses for Informatica Analyst 9 and Developer 9, covering topics such as metadata, profiling, rules, transformations, and data quality. Features of the training include practical exercises, training materials, personal attention, resume preparation, and interview tips.
Email verification and email list cleaning has many benefits. Users who regularly use an email verifier or validation service experience lower bounce rates, increased conversion, increased email ROI, and more accurate campaign statistics. In this slideshare, we discuss the benefits of using an email list cleaning service as part of your overall email deliverability strategy. List cleaning does not replace permission best practices, so it's important to use double optin and ensure you have permission to email your contacts.
The document outlines the key steps in an online training program for Hadoop including setting up a virtual Hadoop cluster, loading and parsing payment data from XML files into databases incrementally using scheduling, building a migration flow from databases into Hadoop and Hive, running Hive queries and exporting data back to databases, and visualizing output data in reports. The training will be delivered online over 20 hours using tools like GoToMeeting.
Somappa Srinivasan of sparrowanalytics.com presents their goal of creating a scalable recommendation engine using Hadoop and real-time analytics. Their system will acquire data from various sources into a data lake stored on Hadoop. A real-time engine will then process user requests, select predictive models, score items, and recommend contextual options to users browsing movies. The system components include data acquisition, ingestion into a data hub of Hive and HBase tables, a real-time engine for validation, modeling, scoring and recommendations, and a UI dashboard.
This document discusses different techniques for validating data models, including verification versus validation, why and what to validate, the base of validation, and specific validation techniques. The key techniques discussed are team review, simulation, direct application, and testing. Team review involves both formal and informal peer review steps. Simulation validates the model by simulating real-world conditions. Direct application builds and tests a model in stages. Testing establishes a baseline and uses a test-driven approach to validate changes.
Bigdata Hadoop project payment gateway domainKamal A
Live Hadoop project in payment gateway domain for people seeking real time work experience in bigdata domain. Email: Onlinetraining2011@gmail.com ,
Skypeid: onlinetraining2011
My profile: www.linkedin.com/pub/kamal-a/65/2b2/2b5
The document discusses Informatica's data integration platform and its capabilities for big data and analytics projects. Some key points:
- Informatica is a leading data integration vendor with over 5,000 customers including over 70% of the Global 500.
- The Informatica platform provides capabilities across the entire data lifecycle from ingestion to delivery including data quality, master data management, integration, and analytics.
- It supports a variety of data sources including structured, unstructured, cloud, and big data and can run on-premises or in the cloud.
- Customers report the Informatica platform improves agility, scalability, and operational confidence for data integration projects compared to
Data Validation Option is an ETL testing tool that comes with Informatica PowerCenter. It reads table definitions from PowerCenter repositories and validates data by checking for inconsistencies. It can verify that data moved or transformed by PowerCenter workflows is complete, accurate, and unchanged. Data Validation Option defines validation rules, runs tests against those rules, and examines results to identify errors in the ETL process.
Using Query Store in Azure PostgreSQL to Understand Query PerformanceGrant Fritchey
Microsoft has added an excellent new extension in PostgreSQL on their Azure Platform. This session, presented at Posette 2024, covers what Query Store is and the types of information you can get out of it.
Graspan: A Big Data System for Big Code AnalysisAftab Hussain
We built a disk-based parallel graph system, Graspan, that uses a novel edge-pair centric computation model to compute dynamic transitive closures on very large program graphs.
We implement context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases such as Linux shows that their Graspan implementations scale to millions of lines of code and are much simpler than their original implementations.
These analyses were used to augment the existing checkers; these augmented checkers found 132 new NULL pointer bugs and 1308 unnecessary NULL tests in Linux 4.4.0-rc5, PostgreSQL 8.3.9, and Apache httpd 2.2.18.
- Accepted in ASPLOS ‘17, Xi’an, China.
- Featured in the tutorial, Systemized Program Analyses: A Big Data Perspective on Static Analysis Scalability, ASPLOS ‘17.
- Invited for presentation at SoCal PLS ‘16.
- Invited for poster presentation at PLDI SRC ‘16.
Data validation is the process of identifying errors in data sets that have been moved or transformed to ensure data is complete and accurate. Currently, most companies perform data validation testing manually through SQL scripts or Excel, which is time-consuming, error-prone, and cannot provide thorough testing. The Data Validation Option (DVO) provides automation, repeatability and auditability for data validation and reconciliation testing across various data sources. It has helped several companies reduce testing time by 80%, ensure data quality, and find errors caused by faulty data integration logic or processes.
Beeldkwaliteit in een DVO: Paul Verbakel (Atlant-groep) en Martijn van Duuren...CROW
De Atlant groep staat voor één van haar deelnemende gemeenten op het punt om een detacherings- en dienstverleningsovereenkomst (DDVO) af te sluiten. In dit DDVO is beeldgericht werken een belangrijk uitgangspunt. Atlant werkt conform beeld op basis van ruim 20 beeldmeetlatten en voert ook de beeldmetingen uit. Atlant gebruikt de beeldmeting om de bedrijfsvoering aan te scherpen. De gemeente stuurt voor op output en tevredenheid op 3 hoofdonderdelen: openbare ruimte, arbeidsparticipatie en dienstverlening. Echter er is geen sprake van een keiharde verplichting, de capaciteit van de ploeg kan beperkend zijn. Dit vraagt een andere manier van sturen van directie en aannemer. Het contract kent tevens vernieuwende uitgangspunten in de arbeidsparticipatie binnen dit contract, de rol van Atlant en de quotumwet.
This document is a resume for Jordan L. Janiak. It lists their education at Dunwoody College of Technology where they are studying Construction Project Management and expect to graduate in December 2017. It also lists previous education at North Dakota State University where they studied Architecture. The resume then outlines skills, work experience as a Project Manager/Foreman for OutdoorDesignGroup, GroundsKeeper for Vision of Glory Church, and Hotel Mystery Shopper.
Santhosh Kumar has over 2 years of experience as an Informatica PowerCenter developer and administrator. He is certified in Informatica PowerCenter 9.X and has experience developing mappings, managing environments, performing upgrades, and automating tasks. Some of his key skills include managing multiple Informatica domains, developing automation scripts, and setting up web service hubs. He has worked on various projects for clients like Aviva and AXA involving data migration, ETL, and Informatica upgrades.
Somappa Srinivasan of sparrowanalytics.com presents their goal of creating a scalable recommendation engine using Hadoop and real-time analytics. Their system will acquire data from various sources into a data lake stored on Hadoop. A real-time engine will then process user requests, select predictive models, score items, and recommend contextual offerings to users browsing movies. The system components include data acquisition, ingestion into a data hub of Hive and HBase tables, a real-time engine for validation, modeling, scoring and recommendations, and a UI dashboard.
Undraleu ETL Code Review Tool for Informatica PowerCenter, Data SheetAcctiva Ltd.
“The world of ETL has advanced technologically to the point that automated management of code is beginning to be a necessity. I recommend you look at Undraleu and their approach to managing the ETL environment.”
Bill Inmon
ETL Validator: Testing for Referential IntegrityDatagaps Inc
The document describes a referential integrity test conducted by a data testing company. The test aims to ensure that a company's data warehouse meets referential integrity requirements. It allows the user to select a foreign key, database connection, and entities or joins to test. Running the test will display results and show the underlying query used.
Political campaigns are shifting from text and images to mobile video messages. This requires optimizing audience targeting, message development for small screens, and stable mobile video delivery over low bandwidth networks. A new Digital Video Object (DVO) architecture significantly reduces file sizes and transfer loads compared to traditional video formats, allowing quality mobile video even on unstable networks. DVO was originally developed for the Defense Department and is now commercially available to support political campaigns with mobile video messaging resources and a test program.
Somappa Srinivasan of sparrowanalytics.com presents their goal of creating a scalable recommendation engine using Hadoop and real-time analytics. Their system will acquire data from various sources into a data lake stored on Hadoop. A real-time engine will then select models, score recommendations, and return personalized suggestions to users as they browse. The components outlined include data acquisition, ingestion into a data hub of Hive and HBase tables, model selection, scoring, recommendation generation, and a UI dashboard.
Informatica data quality online trainingDivya Shree
United Global Soft provides corporate trainings to help professionals groom themselves. They conduct training programs and workshops led by industry experts to share experiences using case studies and current technologies. The document outlines training courses for Informatica Analyst 9 and Developer 9, covering topics such as metadata, profiling, rules, transformations, and data quality. Features of the training include practical exercises, training materials, personal attention, resume preparation, and interview tips.
Email verification and email list cleaning has many benefits. Users who regularly use an email verifier or validation service experience lower bounce rates, increased conversion, increased email ROI, and more accurate campaign statistics. In this slideshare, we discuss the benefits of using an email list cleaning service as part of your overall email deliverability strategy. List cleaning does not replace permission best practices, so it's important to use double optin and ensure you have permission to email your contacts.
The document outlines the key steps in an online training program for Hadoop including setting up a virtual Hadoop cluster, loading and parsing payment data from XML files into databases incrementally using scheduling, building a migration flow from databases into Hadoop and Hive, running Hive queries and exporting data back to databases, and visualizing output data in reports. The training will be delivered online over 20 hours using tools like GoToMeeting.
Somappa Srinivasan of sparrowanalytics.com presents their goal of creating a scalable recommendation engine using Hadoop and real-time analytics. Their system will acquire data from various sources into a data lake stored on Hadoop. A real-time engine will then process user requests, select predictive models, score items, and recommend contextual options to users browsing movies. The system components include data acquisition, ingestion into a data hub of Hive and HBase tables, a real-time engine for validation, modeling, scoring and recommendations, and a UI dashboard.
This document discusses different techniques for validating data models, including verification versus validation, why and what to validate, the base of validation, and specific validation techniques. The key techniques discussed are team review, simulation, direct application, and testing. Team review involves both formal and informal peer review steps. Simulation validates the model by simulating real-world conditions. Direct application builds and tests a model in stages. Testing establishes a baseline and uses a test-driven approach to validate changes.
Bigdata Hadoop project payment gateway domainKamal A
Live Hadoop project in payment gateway domain for people seeking real time work experience in bigdata domain. Email: Onlinetraining2011@gmail.com ,
Skypeid: onlinetraining2011
My profile: www.linkedin.com/pub/kamal-a/65/2b2/2b5
The document discusses Informatica's data integration platform and its capabilities for big data and analytics projects. Some key points:
- Informatica is a leading data integration vendor with over 5,000 customers including over 70% of the Global 500.
- The Informatica platform provides capabilities across the entire data lifecycle from ingestion to delivery including data quality, master data management, integration, and analytics.
- It supports a variety of data sources including structured, unstructured, cloud, and big data and can run on-premises or in the cloud.
- Customers report the Informatica platform improves agility, scalability, and operational confidence for data integration projects compared to
Data Validation Option is an ETL testing tool that comes with Informatica PowerCenter. It reads table definitions from PowerCenter repositories and validates data by checking for inconsistencies. It can verify that data moved or transformed by PowerCenter workflows is complete, accurate, and unchanged. Data Validation Option defines validation rules, runs tests against those rules, and examines results to identify errors in the ETL process.
Using Query Store in Azure PostgreSQL to Understand Query PerformanceGrant Fritchey
Microsoft has added an excellent new extension in PostgreSQL on their Azure Platform. This session, presented at Posette 2024, covers what Query Store is and the types of information you can get out of it.
Graspan: A Big Data System for Big Code AnalysisAftab Hussain
We built a disk-based parallel graph system, Graspan, that uses a novel edge-pair centric computation model to compute dynamic transitive closures on very large program graphs.
We implement context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases such as Linux shows that their Graspan implementations scale to millions of lines of code and are much simpler than their original implementations.
These analyses were used to augment the existing checkers; these augmented checkers found 132 new NULL pointer bugs and 1308 unnecessary NULL tests in Linux 4.4.0-rc5, PostgreSQL 8.3.9, and Apache httpd 2.2.18.
- Accepted in ASPLOS ‘17, Xi’an, China.
- Featured in the tutorial, Systemized Program Analyses: A Big Data Perspective on Static Analysis Scalability, ASPLOS ‘17.
- Invited for presentation at SoCal PLS ‘16.
- Invited for poster presentation at PLDI SRC ‘16.
OpenMetadata Community Meeting - 5th June 2024OpenMetadata
The OpenMetadata Community Meeting was held on June 5th, 2024. In this meeting, we discussed about the data quality capabilities that are integrated with the Incident Manager, providing a complete solution to handle your data observability needs. Watch the end-to-end demo of the data quality features.
* How to run your own data quality framework
* What is the performance impact of running data quality frameworks
* How to run the test cases in your own ETL pipelines
* How the Incident Manager is integrated
* Get notified with alerts when test cases fail
Watch the meeting recording here - https://www.youtube.com/watch?v=UbNOje0kf6E
SOCRadar's Aviation Industry Q1 Incident Report is out now!
The aviation industry has always been a prime target for cybercriminals due to its critical infrastructure and high stakes. In the first quarter of 2024, the sector faced an alarming surge in cybersecurity threats, revealing its vulnerabilities and the relentless sophistication of cyber attackers.
SOCRadar’s Aviation Industry, Quarterly Incident Report, provides an in-depth analysis of these threats, detected and examined through our extensive monitoring of hacker forums, Telegram channels, and dark web platforms.
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI AppGoogle
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-fusion-buddy-review
AI Fusion Buddy Review: Key Features
✅Create Stunning AI App Suite Fully Powered By Google's Latest AI technology, Gemini
✅Use Gemini to Build high-converting Converting Sales Video Scripts, ad copies, Trending Articles, blogs, etc.100% unique!
✅Create Ultra-HD graphics with a single keyword or phrase that commands 10x eyeballs!
✅Fully automated AI articles bulk generation!
✅Auto-post or schedule stunning AI content across all your accounts at once—WordPress, Facebook, LinkedIn, Blogger, and more.
✅With one keyword or URL, generate complete websites, landing pages, and more…
✅Automatically create & sell AI content, graphics, websites, landing pages, & all that gets you paid non-stop 24*7.
✅Pre-built High-Converting 100+ website Templates and 2000+ graphic templates logos, banners, and thumbnail images in Trending Niches.
✅Say goodbye to wasting time logging into multiple Chat GPT & AI Apps once & for all!
✅Save over $5000 per year and kick out dependency on third parties completely!
✅Brand New App: Not available anywhere else!
✅ Beginner-friendly!
✅ZERO upfront cost or any extra expenses
✅Risk-Free: 30-Day Money-Back Guarantee!
✅Commercial License included!
See My Other Reviews Article:
(1) AI Genie Review: https://sumonreview.com/ai-genie-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
#AIFusionBuddyReview,
#AIFusionBuddyFeatures,
#AIFusionBuddyPricing,
#AIFusionBuddyProsandCons,
#AIFusionBuddyTutorial,
#AIFusionBuddyUserExperience
#AIFusionBuddyforBeginners,
#AIFusionBuddyBenefits,
#AIFusionBuddyComparison,
#AIFusionBuddyInstallation,
#AIFusionBuddyRefundPolicy,
#AIFusionBuddyDemo,
#AIFusionBuddyMaintenanceFees,
#AIFusionBuddyNewbieFriendly,
#WhatIsAIFusionBuddy?,
#HowDoesAIFusionBuddyWorks
DDS Security Version 1.2 was adopted in 2024. This revision strengthens support for long runnings systems adding new cryptographic algorithms, certificate revocation, and hardness against DoS attacks.
Odoo ERP software
Odoo ERP software, a leading open-source software for Enterprise Resource Planning (ERP) and business management, has recently launched its latest version, Odoo 17 Community Edition. This update introduces a range of new features and enhancements designed to streamline business operations and support growth.
The Odoo Community serves as a cost-free edition within the Odoo suite of ERP systems. Tailored to accommodate the standard needs of business operations, it provides a robust platform suitable for organisations of different sizes and business sectors. Within the Odoo Community Edition, users can access a variety of essential features and services essential for managing day-to-day tasks efficiently.
This blog presents a detailed overview of the features available within the Odoo 17 Community edition, and the differences between Odoo 17 community and enterprise editions, aiming to equip you with the necessary information to make an informed decision about its suitability for your business.
Flutter is a popular open source, cross-platform framework developed by Google. In this webinar we'll explore Flutter and its architecture, delve into the Flutter Embedder and Flutter’s Dart language, discover how to leverage Flutter for embedded device development, learn about Automotive Grade Linux (AGL) and its consortium and understand the rationale behind AGL's choice of Flutter for next-gen IVI systems. Don’t miss this opportunity to discover whether Flutter is right for your project.
Unveiling the Advantages of Agile Software Development.pdfbrainerhub1
Learn about Agile Software Development's advantages. Simplify your workflow to spur quicker innovation. Jump right in! We have also discussed the advantages.
GraphSummit Paris - The art of the possible with Graph TechnologyNeo4j
Sudhir Hasbe, Chief Product Officer, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Software Engineering, Software Consulting, Tech Lead, Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Transaction, Spring MVC, OpenShift Cloud Platform, Kafka, REST, SOAP, LLD & HLD.
E-commerce Development Services- Hornet DynamicsHornet Dynamics
For any business hoping to succeed in the digital age, having a strong online presence is crucial. We offer Ecommerce Development Services that are customized according to your business requirements and client preferences, enabling you to create a dynamic, safe, and user-friendly online store.
What is Augmented Reality Image Trackingpavan998932
Augmented Reality (AR) Image Tracking is a technology that enables AR applications to recognize and track images in the real world, overlaying digital content onto them. This enhances the user's interaction with their environment by providing additional information and interactive elements directly tied to physical images.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Revolutionizing Visual Effects Mastering AI Face Swaps.pdfUndress Baby
The quest for the best AI face swap solution is marked by an amalgamation of technological prowess and artistic finesse, where cutting-edge algorithms seamlessly replace faces in images or videos with striking realism. Leveraging advanced deep learning techniques, the best AI face swap tools meticulously analyze facial features, lighting conditions, and expressions to execute flawless transformations, ensuring natural-looking results that blur the line between reality and illusion, captivating users with their ingenuity and sophistication.
Web:- https://undressbaby.com/