Talend ETL Tutorial | Talend Tutorial For Beginners | Talend Online Training ...Edureka!
( Talend Training: https://www.edureka.co/talend-for-big... ) This Edureka PPT on Talend ETL Tutorial [Talend ETL Tutorial Blog: https://goo.gl/myMwuQ] will help you in understanding the basic concepts of ETL (Extract, Transform & Load) process and how Talend helps in simplifying the entire ETL process by integrating them into a single Job. This video helps you to learn following topics: Why ETL? What Is ETL? ETL Tools Talend As An ETL Tool Demo
Talend Open Studio Fundamentals #1: Workspaces, Jobs, Metadata and Trips & Tr...Gabriele Baldassarre
Introduction to Talend Open Studio for Data Integration, focusing on job architecture, metadata, workspaces, connection types and common use components. Rick Tips & Tricks sections
Talend Open Studio Introduction - OSSCamp 2014OSSCube
Talend Open Studio is the most open, innovative and
powerful data integration solution on the market today. Talend Open Studio for Data Integration allows you to
create ETL (extract, transform, load) jobs.
Intro to Talend Open Studio for Data IntegrationPhilip Yurchuk
An overview of Talend Open Studio for Data Integration, along with some tips learned from building production jobs and a list of resources. Feel free to contact me for more information.
Talend Data Integration Tutorial | Talend Tutorial For Beginners | Talend Onl...Edureka!
( Talend Training - https://www.edureka.co/talend-for-big... )
This Edureka PPT on Talend Data Integration Tutorial will help you in understanding the basic concepts of Talend and getting familiar with the Talend Open Studio which is an open source software provided by Talend to develop the ETL Jobs.
This video helps you to learn following topics:
1. What Is Talend?
2. Talend Open Studio
3. TOS Installation
4. TOS GUI
5. Talend Components & Connectors
6. Metadata & Context Variables
***** Talend Training: https://www.edureka.co/talend-for-big-data *****
This Edureka tutorial on Talend Components will demonstrate the usage of few of the majorly used components in Talend like tMap, tJoin, tFileInputDelimited, tMysqlRow etc.This video helps you to learn the following topics:
1. What is Talend?
2. Talend Component Families
3. Talend File Components
4. Talend Processing Components
5. Talend Database Components
Databricks is a Software-as-a-Service-like experience (or Spark-as-a-service) that is a tool for curating and processing massive amounts of data and developing, training and deploying models on that data, and managing the whole workflow process throughout the project. It is for those who are comfortable with Apache Spark as it is 100% based on Spark and is extensible with support for Scala, Java, R, and Python alongside Spark SQL, GraphX, Streaming and Machine Learning Library (Mllib). It has built-in integration with many data sources, has a workflow scheduler, allows for real-time workspace collaboration, and has performance improvements over traditional Apache Spark.
Talend ETL Tutorial | Talend Tutorial For Beginners | Talend Online Training ...Edureka!
( Talend Training: https://www.edureka.co/talend-for-big... ) This Edureka PPT on Talend ETL Tutorial [Talend ETL Tutorial Blog: https://goo.gl/myMwuQ] will help you in understanding the basic concepts of ETL (Extract, Transform & Load) process and how Talend helps in simplifying the entire ETL process by integrating them into a single Job. This video helps you to learn following topics: Why ETL? What Is ETL? ETL Tools Talend As An ETL Tool Demo
Talend Open Studio Fundamentals #1: Workspaces, Jobs, Metadata and Trips & Tr...Gabriele Baldassarre
Introduction to Talend Open Studio for Data Integration, focusing on job architecture, metadata, workspaces, connection types and common use components. Rick Tips & Tricks sections
Talend Open Studio Introduction - OSSCamp 2014OSSCube
Talend Open Studio is the most open, innovative and
powerful data integration solution on the market today. Talend Open Studio for Data Integration allows you to
create ETL (extract, transform, load) jobs.
Intro to Talend Open Studio for Data IntegrationPhilip Yurchuk
An overview of Talend Open Studio for Data Integration, along with some tips learned from building production jobs and a list of resources. Feel free to contact me for more information.
Talend Data Integration Tutorial | Talend Tutorial For Beginners | Talend Onl...Edureka!
( Talend Training - https://www.edureka.co/talend-for-big... )
This Edureka PPT on Talend Data Integration Tutorial will help you in understanding the basic concepts of Talend and getting familiar with the Talend Open Studio which is an open source software provided by Talend to develop the ETL Jobs.
This video helps you to learn following topics:
1. What Is Talend?
2. Talend Open Studio
3. TOS Installation
4. TOS GUI
5. Talend Components & Connectors
6. Metadata & Context Variables
***** Talend Training: https://www.edureka.co/talend-for-big-data *****
This Edureka tutorial on Talend Components will demonstrate the usage of few of the majorly used components in Talend like tMap, tJoin, tFileInputDelimited, tMysqlRow etc.This video helps you to learn the following topics:
1. What is Talend?
2. Talend Component Families
3. Talend File Components
4. Talend Processing Components
5. Talend Database Components
Databricks is a Software-as-a-Service-like experience (or Spark-as-a-service) that is a tool for curating and processing massive amounts of data and developing, training and deploying models on that data, and managing the whole workflow process throughout the project. It is for those who are comfortable with Apache Spark as it is 100% based on Spark and is extensible with support for Scala, Java, R, and Python alongside Spark SQL, GraphX, Streaming and Machine Learning Library (Mllib). It has built-in integration with many data sources, has a workflow scheduler, allows for real-time workspace collaboration, and has performance improvements over traditional Apache Spark.
All about Informatica PowerCenter features for both Business and Technical staff, it illustrates how Informatica PowerCenter solves core business challenges in Data Integration projects.
Change Data Feed is a new feature of Delta Lake on Databricks that is available as a public preview since DBR 8.2. This feature enables a new class of ETL workloads such as incremental table/view maintenance and change auditing that were not possible before. In short, users will now be able to query row level changes across different versions of a Delta table.
In this talk we will dive into how Change Data Feed works under the hood and how to use it with existing ETL jobs to make them more efficient and also go over some new workloads it can enable.
The data lake has become extremely popular, but there is still confusion on how it should be used. In this presentation I will cover common big data architectures that use the data lake, the characteristics and benefits of a data lake, and how it works in conjunction with a relational data warehouse. Then I’ll go into details on using Azure Data Lake Store Gen2 as your data lake, and various typical use cases of the data lake. As a bonus I’ll talk about how to organize a data lake and discuss the various products that can be used in a modern data warehouse.
Data Build Tool (DBT) is an open source technology to set up your data lake using best practices from software engineering. This SQL first technology is a great marriage between Databricks and Delta. This allows you to maintain high quality data and documentation during the entire datalake life-cycle. In this talk I’ll do an introduction into DBT, and show how we can leverage Databricks to do the actual heavy lifting. Next, I’ll present how DBT supports Delta to enable upserting using SQL. Finally, we show how we integrate DBT+Databricks into the Azure cloud. Finally we show how we emit the pipeline metrics to Azure monitor to make sure that you have observability over your pipeline.
A Work of Zhamak Dehghani
Principal consultant
ThoughtWorks
https://martinfowler.com/articles/data-monolith-to-mesh.html
https://fast.wistia.net/embed/iframe/vys2juvzc3?videoFoam
How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh
Many enterprises are investing in their next generation data lake, with the hope of democratizing data at scale to provide business insights and ultimately make automated intelligent decisions. Data platforms based on the data lake architecture have common failure modes that lead to unfulfilled promises at scale. To address these failure modes we need to shift from the centralized paradigm of a lake, or its predecessor data warehouse. We need to shift to a paradigm that draws from modern distributed architecture: considering domains as the first class concern, applying platform thinking to create self-serve data infrastructure, and treating data as a product.
Data Lakehouse Symposium | Day 1 | Part 1Databricks
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
The Modern Data Team for the Modern Data Stack: dbt and the Role of the Analy...Databricks
A traditional data team has roles including data engineer, data scientist, and data analyst. However, many organizations are finding success by integrating a new role – the analytics engineer. The analytics engineer develops a code-based data infrastructure that can serve both analytics and data science teams. He or she develops re-usable data models using the software engineering practices of version control and unit testing, and provides the critical domain expertise that ensures that data products are relevant and insightful. In this talk we’ll talk about the role and skill set of the analytics engineer, and discuss how dbt, an open source programming environment, empowers anyone with a SQL skillset to fulfill this new role on the data team. We’ll demonstrate how to use dbt to build version-controlled data models on top of Delta Lake, test both the code and our assumptions about the underlying data, and orchestrate complete data pipelines on Apache Spark™.
Talend Open Studio for Big Data | Talend Open Studio Tutorial | Talend Online...Edureka!
( Talend Training: https://www.edureka.co/https://www.edureka.co/talend-for-big-data)
This Edureka video on Talend Open Studio will guide you through the complete GUI of Talend Open Studio and build a strong foundation in Talend. This video helps you to learn following topics:
1. What is Talend Open Studio?
2. Advantages Of TOS
3. Downloading TOS
4. TOS GUI
5. Demo
Jeremy Engle's slides from Redshift / Big Data meetup on July 13, 2017AWS Chicago
"Strategies for supporting near real time analytics, OLAP, and interactive data exploration" - Dr. Jeremy Engle, Engineering Manager Data Team at Jellyvision
Building End-to-End Delta Pipelines on GCPDatabricks
Delta has been powering many production pipelines at scale in the Data and AI space since it has been introduced for the past few years.
Built on open standards, Delta provides data reliability, enhances storage and query performance to support big data use cases (both batch and streaming), fast interactive queries for BI and enabling machine learning. Delta has matured over the past couple of years in both AWS and AZURE and has become the de-facto standard for organizations building their Data and AI pipelines.
In today’s talk, we will explore building end-to-end pipelines on the Google Cloud Platform (GCP). Through presentation, code examples and notebooks, we will build the Delta Pipeline from ingest to consumption using our Delta Bronze-Silver-Gold architecture pattern and show examples of Consuming the delta files using the Big Query Connector.
Every business today wants to leverage data to drive strategic initiatives with machine learning, data science and analytics — but runs into challenges from siloed teams, proprietary technologies and unreliable data.
That’s why enterprises are turning to the lakehouse because it offers a single platform to unify all your data, analytics and AI workloads.
Join our How to Build a Lakehouse technical training, where we’ll explore how to use Apache SparkTM, Delta Lake, and other open source technologies to build a better lakehouse. This virtual session will include concepts, architectures and demos.
Here’s what you’ll learn in this 2-hour session:
How Delta Lake combines the best of data warehouses and data lakes for improved data reliability, performance and security
How to use Apache Spark and Delta Lake to perform ETL processing, manage late-arriving data, and repair corrupted data directly on your lakehouse
Building Lakehouses on Delta Lake with SQL Analytics PrimerDatabricks
You’ve heard the marketing buzz, maybe you have been to a workshop and worked with some Spark, Delta, SQL, Python, or R, but you still need some help putting all the pieces together? Join us as we review some common techniques to build a lakehouse using Delta Lake, use SQL Analytics to perform exploratory analysis, and build connectivity for BI applications.
Data Lakehouse Symposium | Day 1 | Part 2Databricks
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
All about Informatica PowerCenter features for both Business and Technical staff, it illustrates how Informatica PowerCenter solves core business challenges in Data Integration projects.
Change Data Feed is a new feature of Delta Lake on Databricks that is available as a public preview since DBR 8.2. This feature enables a new class of ETL workloads such as incremental table/view maintenance and change auditing that were not possible before. In short, users will now be able to query row level changes across different versions of a Delta table.
In this talk we will dive into how Change Data Feed works under the hood and how to use it with existing ETL jobs to make them more efficient and also go over some new workloads it can enable.
The data lake has become extremely popular, but there is still confusion on how it should be used. In this presentation I will cover common big data architectures that use the data lake, the characteristics and benefits of a data lake, and how it works in conjunction with a relational data warehouse. Then I’ll go into details on using Azure Data Lake Store Gen2 as your data lake, and various typical use cases of the data lake. As a bonus I’ll talk about how to organize a data lake and discuss the various products that can be used in a modern data warehouse.
Data Build Tool (DBT) is an open source technology to set up your data lake using best practices from software engineering. This SQL first technology is a great marriage between Databricks and Delta. This allows you to maintain high quality data and documentation during the entire datalake life-cycle. In this talk I’ll do an introduction into DBT, and show how we can leverage Databricks to do the actual heavy lifting. Next, I’ll present how DBT supports Delta to enable upserting using SQL. Finally, we show how we integrate DBT+Databricks into the Azure cloud. Finally we show how we emit the pipeline metrics to Azure monitor to make sure that you have observability over your pipeline.
A Work of Zhamak Dehghani
Principal consultant
ThoughtWorks
https://martinfowler.com/articles/data-monolith-to-mesh.html
https://fast.wistia.net/embed/iframe/vys2juvzc3?videoFoam
How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh
Many enterprises are investing in their next generation data lake, with the hope of democratizing data at scale to provide business insights and ultimately make automated intelligent decisions. Data platforms based on the data lake architecture have common failure modes that lead to unfulfilled promises at scale. To address these failure modes we need to shift from the centralized paradigm of a lake, or its predecessor data warehouse. We need to shift to a paradigm that draws from modern distributed architecture: considering domains as the first class concern, applying platform thinking to create self-serve data infrastructure, and treating data as a product.
Data Lakehouse Symposium | Day 1 | Part 1Databricks
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
The Modern Data Team for the Modern Data Stack: dbt and the Role of the Analy...Databricks
A traditional data team has roles including data engineer, data scientist, and data analyst. However, many organizations are finding success by integrating a new role – the analytics engineer. The analytics engineer develops a code-based data infrastructure that can serve both analytics and data science teams. He or she develops re-usable data models using the software engineering practices of version control and unit testing, and provides the critical domain expertise that ensures that data products are relevant and insightful. In this talk we’ll talk about the role and skill set of the analytics engineer, and discuss how dbt, an open source programming environment, empowers anyone with a SQL skillset to fulfill this new role on the data team. We’ll demonstrate how to use dbt to build version-controlled data models on top of Delta Lake, test both the code and our assumptions about the underlying data, and orchestrate complete data pipelines on Apache Spark™.
Talend Open Studio for Big Data | Talend Open Studio Tutorial | Talend Online...Edureka!
( Talend Training: https://www.edureka.co/https://www.edureka.co/talend-for-big-data)
This Edureka video on Talend Open Studio will guide you through the complete GUI of Talend Open Studio and build a strong foundation in Talend. This video helps you to learn following topics:
1. What is Talend Open Studio?
2. Advantages Of TOS
3. Downloading TOS
4. TOS GUI
5. Demo
Jeremy Engle's slides from Redshift / Big Data meetup on July 13, 2017AWS Chicago
"Strategies for supporting near real time analytics, OLAP, and interactive data exploration" - Dr. Jeremy Engle, Engineering Manager Data Team at Jellyvision
Building End-to-End Delta Pipelines on GCPDatabricks
Delta has been powering many production pipelines at scale in the Data and AI space since it has been introduced for the past few years.
Built on open standards, Delta provides data reliability, enhances storage and query performance to support big data use cases (both batch and streaming), fast interactive queries for BI and enabling machine learning. Delta has matured over the past couple of years in both AWS and AZURE and has become the de-facto standard for organizations building their Data and AI pipelines.
In today’s talk, we will explore building end-to-end pipelines on the Google Cloud Platform (GCP). Through presentation, code examples and notebooks, we will build the Delta Pipeline from ingest to consumption using our Delta Bronze-Silver-Gold architecture pattern and show examples of Consuming the delta files using the Big Query Connector.
Every business today wants to leverage data to drive strategic initiatives with machine learning, data science and analytics — but runs into challenges from siloed teams, proprietary technologies and unreliable data.
That’s why enterprises are turning to the lakehouse because it offers a single platform to unify all your data, analytics and AI workloads.
Join our How to Build a Lakehouse technical training, where we’ll explore how to use Apache SparkTM, Delta Lake, and other open source technologies to build a better lakehouse. This virtual session will include concepts, architectures and demos.
Here’s what you’ll learn in this 2-hour session:
How Delta Lake combines the best of data warehouses and data lakes for improved data reliability, performance and security
How to use Apache Spark and Delta Lake to perform ETL processing, manage late-arriving data, and repair corrupted data directly on your lakehouse
Building Lakehouses on Delta Lake with SQL Analytics PrimerDatabricks
You’ve heard the marketing buzz, maybe you have been to a workshop and worked with some Spark, Delta, SQL, Python, or R, but you still need some help putting all the pieces together? Join us as we review some common techniques to build a lakehouse using Delta Lake, use SQL Analytics to perform exploratory analysis, and build connectivity for BI applications.
Data Lakehouse Symposium | Day 1 | Part 2Databricks
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
Totango is an Analytics platform for Customer Success.
Our data pipeline converts usage information into actionable analytics. The pipeline is managed using Luigi workflow engine, and data transformations are done in Spark.
Dirty data? Clean it up! - Datapalooza Denver 2016Dan Lynn
Dan Lynn (AgilData) & Patrick Russell (Craftsy) present on how to do data science in the real world. We discuss data cleansing, ETL, pipelines, hosting, and share several tools used in the industry.
See what pitfalls companies are facing when running Liferay portal. In the previous year, our company has audited 5 real-life projects based on Liferay Portal which are now running in production mode and serving many users. The audits were focused on architecture, infrastructure, technical design and implementation. During the presentation, we will show you common anti-patterns we have found during the audits and their impacts and consequences on the portal.
How would you build the world’s largest, fastest, most complex Magento ecommerce store? Three COPIOUS engineers share their approaches to this problem, including best practices, code samples, and system configurations necessary to scale Magento up to 100,000 daily orders with a catalog of 100,000 products.
Fighting legacy with hexagonal architecture and frameworkless phpFabio Pellegrini
Molto spesso capita di venire a contatto con applicazioni legacy piuttosto datate, i classici monoliti che sono cresciuti a dismisura nel tempo accumulando debito tecnologico.
A causa delle priorità di business delle aziende, non sempre si riesce ad allocare il budget e il tempo necessario per iniziare subito il processo di ristrutturazione architetturale e di rimodellazione dei dati che servirebbe.
In questo talk presenterò una soluzione che mi è capitato di adottare recentemente per iniziare a ridefinire la struttura di un progetto legacy, utilizzando un approccio basato su Domain Driven Design, architettura esagonale e l’utilizzo di PHP senza framework.
Vedremo come è stato creato un nuovo servizio “satellite” da zero, come sono state implementate le componenti principali, come si è tenuto il codice legacy ai margini dell’applicazione, come si è approcciato il testing, il tutto nell’ottica di poter spacchettare il monolite in microservizi in un secondo momento.
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
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.
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.
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.
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.
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/
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
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.
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.
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.
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.
2. Data Integration
Data Integration involves combining data
residing in differente sources and providing the
user with a unified view of the data
Data Management combines different disciplines
to manage data as a valuable resource
www.robertomarchetto.com
3. Talend
● Talend is a company focused on Data
Integration and Data Management solutions
● Talend is a „Cool Vendor“ for Gartner (2010)
● Present in more than 12 locations around the
World
● Fast growing company
www.robertomarchetto.com
5. Talend Open Studio
● Open Source, professional tool
● Draw procedures linking components, each
component performs an operation
● DB vendor-specific optimized components
● Produces fully editable Java (or Perl) code
● Deployment with small and fast compiled Java
or as Web Service
● Eclipse based IDE, excellent flexibility
● BI Platform indipendent, DB Vendor indipendent
www.robertomarchetto.com
7. Extracion Transformation Loading
● ETL is a common process in Data Integration
● Extract, reading data from different datasources
(database, flat files, spreadsheet files, web
services, etc)
● Transfom, converting data in a form so that it can
be placed in another container (database, web
services, files, etc). Cleaning, computations and
verifications are also performed
● Load, write the data in the target format
www.robertomarchetto.com
10. Tutorial, Metadata
● Talend requires a preliminary definition of the
metadata
● Often a strong metadata definition means, as in
programming languages, fast, robust and
maintenable applications
● ..demo..
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11. Tutorial, Talend jobs basics
● Place components on the designer
● Link components to build a transformation
● Main type of link: Rows flow
● Schema metadata is propagated and must be
coherent
● ..demo..
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20. Extensibility, comunity plugins
● Many official
components
● Components for
every task released
by the comunity
● Geospatial
components, log
analysis, Google
analytics, data
encryption, etc
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22. And now.. reports, dashboards, OLAP,
Geoanalysis, KPIs..
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23. Do you trust your data?
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24. What about data quality?
● Customer A is present 5 times with different
names
● Null values can vary statistical indexes like
mean calculation
● Duplicated records
● Blank values
● Some records can contain errors (es -1 field
values)
● Some records can be garbage
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26. What abount data storage size?
● Some fields can be oversized for the data they
contain
● Sometimes fields are related and can be
calculated
● Some keys or values are never used
● When data grow garbage grow
● Data storage is not free (disks, electricity,
backups, DB licenses)
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27. Data is „the black gold“ that can produce
knowledge
● Data is a resource, you can extract knowledge
● A lot of Data produces concise informations
● Data storage is not free and a lot of data can
make system not fast
● Data cleansing is a central process in statistical
analysis and Data Mining
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