This document provides an overview and examples of projects completed as part of a Business Intelligence Master's program, including:
1) An SSIS project to extract data from Excel spreadsheets and load it into SQL Server tables. 11 packages were created to perform ETL.
2) An SSAS project to create a cube with dimensions, hierarchies, calculations and KPIs from fact tables. 19 MDX queries were written.
3) A project using SSRS, PerformancePoint and Excel Services to develop reports, charts, and dashboards. Reports were published to SharePoint.
4) A final team project to deliver a BI solution with database design, ETL, a cube, and 15 reports
Strategic imperative the enterprise data modelDATAVERSITY
With today's increasingly complex data ecosystems, the Enterprise Data Model (EDM) is a strategic imperative that every organization should adopt. An Enterprise Data Model provides context and consistency for all organizational data assets, as well as a classification framework for data governance. Enterprise modeling is also totally consistent with agile workflows, evolving incrementally to keep pace with changing organizational factors. In this session, IDERA’s Ron Huizenga will discuss the increasing importance of the EDM, how it serves as a framework for all enterprise data assets, and provides a foundation for data governance.
The Parquet Format and Performance Optimization OpportunitiesDatabricks
The Parquet format is one of the most widely used columnar storage formats in the Spark ecosystem. Given that I/O is expensive and that the storage layer is the entry point for any query execution, understanding the intricacies of your storage format is important for optimizing your workloads.
As an introduction, we will provide context around the format, covering the basics of structured data formats and the underlying physical data storage model alternatives (row-wise, columnar and hybrid). Given this context, we will dive deeper into specifics of the Parquet format: representation on disk, physical data organization (row-groups, column-chunks and pages) and encoding schemes. Now equipped with sufficient background knowledge, we will discuss several performance optimization opportunities with respect to the format: dictionary encoding, page compression, predicate pushdown (min/max skipping), dictionary filtering and partitioning schemes. We will learn how to combat the evil that is ‘many small files’, and will discuss the open-source Delta Lake format in relation to this and Parquet in general.
This talk serves both as an approachable refresher on columnar storage as well as a guide on how to leverage the Parquet format for speeding up analytical workloads in Spark using tangible tips and tricks.
End-to-End Spark/TensorFlow/PyTorch Pipelines with Databricks DeltaDatabricks
Hopsworks is an open-source data platform that can be used to both develop and operate horizontally scalable machine learning pipelines. A key part of our pipelines is the world’s first open-source Feature Store, based on Apache Hive, that acts as a data warehouse for features, providing a natural API between data engineers – who write feature engineering code in Spark (in Scala or Python) – and Data Scientists, who select features from the feature store to generate training/test data for models. In this talk, we will discuss how Databricks Delta solves several of the key challenges in building both feature engineering pipelines that feed our Feature Store and in managing the feature data itself.
Firstly, we will show how expectations and schema enforcement in Databricks Delta can be used to provide data validation, ensuring that feature data does not have missing or invalid values that could negatively affect model training. Secondly, time-travel in Databricks Delta can be used to provide version management and experiment reproducability for training/test datasets. That is, given a model, you can re-run the training experiment for that model using the same version of the data that was used to train the model.
We will also discuss the next steps needed to take this work to the next level. Finally, we will perform a live demo, showing how Delta can be used in end-to-end ML pipelines using Spark on Hopsworks.
DAS Slides: Building a Future-State Data Architecture Plan - Where to Begin?DATAVERSITY
With technology changing at an ever more rapid pace and business requirements ever-evolving to meet the needs of the market, building a future-state Data Architecture plan can be a challenge. Join this webinar to learn practical ways to balance technology and business needs as you develop your future-state architecture for the coming years.
Data Modelling 101 half day workshop presented by Chris Bradley at the Enterprise Data and Business Intelligence conference London on November 3rd 2014.
Chris Bradley is a leading independent information strategist.
Contact chris.bradley@dmadvisors.co.uk
I will begin with a brief overview of SQL. Then the five major topics a data scientist should understand when working with relational databases: basic statistics in SQL, data preparation in SQL, advanced filtering and data aggregation, window functions, and preparing data for use with analytics tools.
Organizations need to perform increasingly complex analysis on data — streaming analytics, ad-hoc querying, and predictive analytics — in order to get better customer insights and actionable business intelligence. Apache Spark has recently emerged as the framework of choice to address many of these challenges. In this session, we show you how to use Apache Spark on AWS to implement and scale common big data use cases such as real-time data processing, interactive data science, predictive analytics, and more. We will talk about common architectures, best practices to quickly create Spark clusters using Amazon EMR, and ways to integrate Spark with other big data services in AWS.
Learning Objectives:
• Learn why Spark is great for ad-hoc interactive analysis and real-time stream processing.
• How to deploy and tune scalable clusters running Spark on Amazon EMR.
• How to use EMR File System (EMRFS) with Spark to query data directly in Amazon S3.
• Common architectures to leverage Spark with Amazon DynamoDB, Amazon Redshift, Amazon Kinesis, and more.
Agile Data Warehouse Design for Big Data PresentationVishal Kumar
Synopsis:
[Video link: http://www.youtube.com/watch?v=ZNrTxSU5IQ0 ]
Jim Stagnitto and John DiPietro of consulting firm a2c) will discuss Agile Data Warehouse Design - a step-by-step method for data warehousing / business intelligence (DW/BI) professionals to better collect and translate business intelligence requirements into successful dimensional data warehouse designs.
The method utilizes BEAM✲ (Business Event Analysis and Modeling) - an agile approach to dimensional data modeling that can be used throughout analysis and design to improve productivity and communication between DW designers and BI stakeholders. BEAM✲ builds upon the body of mature "best practice" dimensional DW design techniques, and collects "just enough" non-technical business process information from BI stakeholders to allow the modeler to slot their business needs directly and simply into proven DW design patterns.
BEAM✲ encourages DW/BI designers to move away from the keyboard and their entity relationship modeling tools and begin "white board" modeling interactively with BI stakeholders. With the right guidance, BI stakeholders can and should model their own BI data requirements, so that they can fully understand and govern what they will be able to report on and analyze.
The BEAM✲ method is fully described in
Agile Data Warehouse Design - a text co-written by Lawrence Corr and Jim Stagnitto.
About the speaker:
Jim Stagnitto Director of a2c Data Services Practice
Data Warehouse Architect: specializing in powerful designs that extract the maximum business benefit from Intelligence and Insight investments.
Master Data Management (MDM) and Customer Data Integration (CDI) strategist and architect.
Data Warehousing, Data Quality, and Data Integration thought-leader: co-author with Lawrence Corr of "Agile Data Warehouse Design", guest author of Ralph Kimball’s “Data Warehouse Designer” column, and contributing author to Ralph and Joe Caserta's latest book: “The DW ETL Toolkit”.
John DiPietro Chief Technology Officer at A2C IT Consulting
John DiPietro is the Chief Technology Officer for a2c. Mr. DiPietro is responsible
for setting the vision, strategy, delivery, and methodologies for a2c’s Solution
Practice Offerings for all national accounts. The a2c CTO brings with him an
expansive depth and breadth of specialized skills in his field.
Sponsor Note:
Thanks to:
Microsoft NERD for providing awesome venue for the event.
http://A2C.com IT Consulting for providing the food/drinks.
http://Cognizeus.com for providing book to give away as raffle.
Strategic imperative the enterprise data modelDATAVERSITY
With today's increasingly complex data ecosystems, the Enterprise Data Model (EDM) is a strategic imperative that every organization should adopt. An Enterprise Data Model provides context and consistency for all organizational data assets, as well as a classification framework for data governance. Enterprise modeling is also totally consistent with agile workflows, evolving incrementally to keep pace with changing organizational factors. In this session, IDERA’s Ron Huizenga will discuss the increasing importance of the EDM, how it serves as a framework for all enterprise data assets, and provides a foundation for data governance.
The Parquet Format and Performance Optimization OpportunitiesDatabricks
The Parquet format is one of the most widely used columnar storage formats in the Spark ecosystem. Given that I/O is expensive and that the storage layer is the entry point for any query execution, understanding the intricacies of your storage format is important for optimizing your workloads.
As an introduction, we will provide context around the format, covering the basics of structured data formats and the underlying physical data storage model alternatives (row-wise, columnar and hybrid). Given this context, we will dive deeper into specifics of the Parquet format: representation on disk, physical data organization (row-groups, column-chunks and pages) and encoding schemes. Now equipped with sufficient background knowledge, we will discuss several performance optimization opportunities with respect to the format: dictionary encoding, page compression, predicate pushdown (min/max skipping), dictionary filtering and partitioning schemes. We will learn how to combat the evil that is ‘many small files’, and will discuss the open-source Delta Lake format in relation to this and Parquet in general.
This talk serves both as an approachable refresher on columnar storage as well as a guide on how to leverage the Parquet format for speeding up analytical workloads in Spark using tangible tips and tricks.
End-to-End Spark/TensorFlow/PyTorch Pipelines with Databricks DeltaDatabricks
Hopsworks is an open-source data platform that can be used to both develop and operate horizontally scalable machine learning pipelines. A key part of our pipelines is the world’s first open-source Feature Store, based on Apache Hive, that acts as a data warehouse for features, providing a natural API between data engineers – who write feature engineering code in Spark (in Scala or Python) – and Data Scientists, who select features from the feature store to generate training/test data for models. In this talk, we will discuss how Databricks Delta solves several of the key challenges in building both feature engineering pipelines that feed our Feature Store and in managing the feature data itself.
Firstly, we will show how expectations and schema enforcement in Databricks Delta can be used to provide data validation, ensuring that feature data does not have missing or invalid values that could negatively affect model training. Secondly, time-travel in Databricks Delta can be used to provide version management and experiment reproducability for training/test datasets. That is, given a model, you can re-run the training experiment for that model using the same version of the data that was used to train the model.
We will also discuss the next steps needed to take this work to the next level. Finally, we will perform a live demo, showing how Delta can be used in end-to-end ML pipelines using Spark on Hopsworks.
DAS Slides: Building a Future-State Data Architecture Plan - Where to Begin?DATAVERSITY
With technology changing at an ever more rapid pace and business requirements ever-evolving to meet the needs of the market, building a future-state Data Architecture plan can be a challenge. Join this webinar to learn practical ways to balance technology and business needs as you develop your future-state architecture for the coming years.
Data Modelling 101 half day workshop presented by Chris Bradley at the Enterprise Data and Business Intelligence conference London on November 3rd 2014.
Chris Bradley is a leading independent information strategist.
Contact chris.bradley@dmadvisors.co.uk
I will begin with a brief overview of SQL. Then the five major topics a data scientist should understand when working with relational databases: basic statistics in SQL, data preparation in SQL, advanced filtering and data aggregation, window functions, and preparing data for use with analytics tools.
Organizations need to perform increasingly complex analysis on data — streaming analytics, ad-hoc querying, and predictive analytics — in order to get better customer insights and actionable business intelligence. Apache Spark has recently emerged as the framework of choice to address many of these challenges. In this session, we show you how to use Apache Spark on AWS to implement and scale common big data use cases such as real-time data processing, interactive data science, predictive analytics, and more. We will talk about common architectures, best practices to quickly create Spark clusters using Amazon EMR, and ways to integrate Spark with other big data services in AWS.
Learning Objectives:
• Learn why Spark is great for ad-hoc interactive analysis and real-time stream processing.
• How to deploy and tune scalable clusters running Spark on Amazon EMR.
• How to use EMR File System (EMRFS) with Spark to query data directly in Amazon S3.
• Common architectures to leverage Spark with Amazon DynamoDB, Amazon Redshift, Amazon Kinesis, and more.
Agile Data Warehouse Design for Big Data PresentationVishal Kumar
Synopsis:
[Video link: http://www.youtube.com/watch?v=ZNrTxSU5IQ0 ]
Jim Stagnitto and John DiPietro of consulting firm a2c) will discuss Agile Data Warehouse Design - a step-by-step method for data warehousing / business intelligence (DW/BI) professionals to better collect and translate business intelligence requirements into successful dimensional data warehouse designs.
The method utilizes BEAM✲ (Business Event Analysis and Modeling) - an agile approach to dimensional data modeling that can be used throughout analysis and design to improve productivity and communication between DW designers and BI stakeholders. BEAM✲ builds upon the body of mature "best practice" dimensional DW design techniques, and collects "just enough" non-technical business process information from BI stakeholders to allow the modeler to slot their business needs directly and simply into proven DW design patterns.
BEAM✲ encourages DW/BI designers to move away from the keyboard and their entity relationship modeling tools and begin "white board" modeling interactively with BI stakeholders. With the right guidance, BI stakeholders can and should model their own BI data requirements, so that they can fully understand and govern what they will be able to report on and analyze.
The BEAM✲ method is fully described in
Agile Data Warehouse Design - a text co-written by Lawrence Corr and Jim Stagnitto.
About the speaker:
Jim Stagnitto Director of a2c Data Services Practice
Data Warehouse Architect: specializing in powerful designs that extract the maximum business benefit from Intelligence and Insight investments.
Master Data Management (MDM) and Customer Data Integration (CDI) strategist and architect.
Data Warehousing, Data Quality, and Data Integration thought-leader: co-author with Lawrence Corr of "Agile Data Warehouse Design", guest author of Ralph Kimball’s “Data Warehouse Designer” column, and contributing author to Ralph and Joe Caserta's latest book: “The DW ETL Toolkit”.
John DiPietro Chief Technology Officer at A2C IT Consulting
John DiPietro is the Chief Technology Officer for a2c. Mr. DiPietro is responsible
for setting the vision, strategy, delivery, and methodologies for a2c’s Solution
Practice Offerings for all national accounts. The a2c CTO brings with him an
expansive depth and breadth of specialized skills in his field.
Sponsor Note:
Thanks to:
Microsoft NERD for providing awesome venue for the event.
http://A2C.com IT Consulting for providing the food/drinks.
http://Cognizeus.com for providing book to give away as raffle.
Data products derive their value from data and generate new data in return; as a result, machine learning techniques must be applied to their architecture and their development. Machine learning fits models to make predictions on unknown inputs and must be generalizable and adaptable. As such, fitted models cannot exist in isolation; they must be operationalized and user facing so that applications can benefit from the new data, respond to it, and feed it back into the data product. Data product architectures are therefore life cycles and understanding the data product lifecycle will enable architects to develop robust, failure free workflows and applications. In this talk we will discuss the data product life cycle, explore how to engage a model build, evaluation, and selection phase with an operation and interaction phase. Following the lambda architecture, we will investigate wrapping a central computational store for speed and querying, as well as incorporating a discussion of monitoring, management, and data exploration for hypothesis driven development. From web applications to big data appliances; this architecture serves as a blueprint for handling data services of all sizes!
SAS DATAFLUX DATA MANAGEMENT STUDIO TRAININGbidwhm
SAS DataFlux Management studio training,Technical support ,Outsourcing ,DataFlux Data Management Platform
Overview of DataFlux Data Management Studio
DataFlux Methodology: Plan, Act, and Monitor
Managing Repositories
Different types of Data Connections
Creating and Managing Data Collections
Creating , Setting , Working with Data Explorations
Introduction ,Creating Business Rules and Custom Metrics
Overview, Creating , Preparing of Data Profiles
Best practices of data modeling with InfoSphere Data Architect. Covers best practices of logical data models, physical data models, model comparison, working in a team and more.
Parts of the presentation are from the InfoSphere Data Architect course
http://www.scispike.com/training/infosphere_data_architect_training.html
Property graph vs. RDF Triplestore comparison in 2020Ontotext
This presentation goes all the way from intro "what graph databases are" to table comparing the RDF vs. PG plus two different diagrams presenting the market circa 2020
DAX and Power BI Training - 004 Power QueryWill Harvey
I this session we are introducing Power Query for Excel, the data sources you can connect to, and the transformations you can apply. We also introduce more advanced topics of writing your own M functions.
Modern Data Warehousing with the Microsoft Analytics Platform SystemJames Serra
The traditional data warehouse has served us well for many years, but new trends are causing it to break in four different ways: data growth, fast query expectations from users, non-relational/unstructured data, and cloud-born data. How can you prevent this from happening? Enter the modern data warehouse, which is able to handle and excel with these new trends. It handles all types of data (Hadoop), provides a way to easily interface with all these types of data (PolyBase), and can handle “big data” and provide fast queries. Is there one appliance that can support this modern data warehouse? Yes! It is the Analytics Platform System (APS) from Microsoft (formally called Parallel Data Warehouse or PDW) , which is a Massively Parallel Processing (MPP) appliance that has been recently updated (v2 AU1). In this session I will dig into the details of the modern data warehouse and APS. I will give an overview of the APS hardware and software architecture, identify what makes APS different, and demonstrate the increased performance. In addition I will discuss how Hadoop, HDInsight, and PolyBase fit into this new modern data warehouse.
Power BI Desktop | Power BI Tutorial | Power BI Training | EdurekaEdureka!
This Edureka "Power BI Desktop" tutorial will help you to understand what is Power BI Desktop with examples and demo. Below are the topics covered in this tutorial:
1. Why Power BI?
2. What Power BI?
3. Who use Power BI?
4. Flow of Work
5. Power BI Trends
APEX Interactive Grids: Essentials and Then Some, Part 1Karen Cannell
APEX 18 Essentials for developers ready to take a closer look and build interactive grids for production requirements. Background, architecture, building, configuring for business needs. Alternated edit processing, Upgrading tabular forms to interactive grids.
NZOUG APAC Groundbreakers Tour 2018
Data Lakehouse, Data Mesh, and Data Fabric (r2)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a modern data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. They all may sound great in theory, but I'll dig into the concerns you need to be aware of before taking the plunge. I’ll also include use cases so you can see what approach will work best for your big data needs. And I'll discuss Microsoft version of the data mesh.
Data modelling for the business half day workshop presented at the Enterprise Data & Business Intelligence conference in London on November 3rd 2014
chris.bradley@dmadvisors.co.uk
Tame the small files problem and optimize data layout for streaming ingestion...Flink Forward
Flink Forward San Francisco 2022.
In modern data platform architectures, stream processing engines such as Apache Flink are used to ingest continuous streams of data into data lakes such as Apache Iceberg. Streaming ingestion to iceberg tables can suffer by two problems (1) small files problem that can hurt read performance (2) poor data clustering that can make file pruning less effective. To address those two problems, we propose adding a shuffling stage to the Flink Iceberg streaming writer. The shuffling stage can intelligently group data via bin packing or range partition. This can reduce the number of concurrent files that every task writes. It can also improve data clustering. In this talk, we will explain the motivations in details and dive into the design of the shuffling stage. We will also share the evaluation results that demonstrate the effectiveness of smart shuffling.
by
Gang Ye & Steven Wu
Power BI Interview Questions and Answers | Power BI Certification | Power BI ...Edureka!
( Power BI Training - https://www.edureka.co/power-bi-training )
This Edureka "PowerBI Interview Questions and Answers" tutorial will help you unravel concepts of Power BI and touch those topics that are very vital for succeeding in Power BI Interviews.
This video helps you to learn the following topics:
1. General Power BI Questions
2. DAX
3. Power Pivot
4. Power Query
5. Power Map
6. Additional Questions
Check out our Power BI Playlist: https://goo.gl/97sJv1
Power BI (Business Intelligence) is a new and emerging self-service business intelligence and business analytics framework that brings together and enhances a few key Microsoft technologies. Fundamentally, Power BI is considered a premium Office option, because Microsoft licenses it that way. We will answer the following questions:
◾What makes Power BI different from the Microsoft Office I have known for years?
◾What are the major Power BI features in Excel 2013 and 2010?
◾What are the major features for Power BI for Office 365?
◾How can our team license Power BI?
Methoden zur Integration und Verwaltung von Portaldaten
Motivation
Verwaltung von Daten
Datenbankzugriff
Objektrelationales Mapping
Leichtgewichtiges Mapping: Beispiel mittels Spring
Schwergewichtiges Mapping: Beispiel mittels Hibernate
Integration von Daten
Pull and Push Verfahren
ETL Verfahren : Extract – Transform - Load
Data products derive their value from data and generate new data in return; as a result, machine learning techniques must be applied to their architecture and their development. Machine learning fits models to make predictions on unknown inputs and must be generalizable and adaptable. As such, fitted models cannot exist in isolation; they must be operationalized and user facing so that applications can benefit from the new data, respond to it, and feed it back into the data product. Data product architectures are therefore life cycles and understanding the data product lifecycle will enable architects to develop robust, failure free workflows and applications. In this talk we will discuss the data product life cycle, explore how to engage a model build, evaluation, and selection phase with an operation and interaction phase. Following the lambda architecture, we will investigate wrapping a central computational store for speed and querying, as well as incorporating a discussion of monitoring, management, and data exploration for hypothesis driven development. From web applications to big data appliances; this architecture serves as a blueprint for handling data services of all sizes!
SAS DATAFLUX DATA MANAGEMENT STUDIO TRAININGbidwhm
SAS DataFlux Management studio training,Technical support ,Outsourcing ,DataFlux Data Management Platform
Overview of DataFlux Data Management Studio
DataFlux Methodology: Plan, Act, and Monitor
Managing Repositories
Different types of Data Connections
Creating and Managing Data Collections
Creating , Setting , Working with Data Explorations
Introduction ,Creating Business Rules and Custom Metrics
Overview, Creating , Preparing of Data Profiles
Best practices of data modeling with InfoSphere Data Architect. Covers best practices of logical data models, physical data models, model comparison, working in a team and more.
Parts of the presentation are from the InfoSphere Data Architect course
http://www.scispike.com/training/infosphere_data_architect_training.html
Property graph vs. RDF Triplestore comparison in 2020Ontotext
This presentation goes all the way from intro "what graph databases are" to table comparing the RDF vs. PG plus two different diagrams presenting the market circa 2020
DAX and Power BI Training - 004 Power QueryWill Harvey
I this session we are introducing Power Query for Excel, the data sources you can connect to, and the transformations you can apply. We also introduce more advanced topics of writing your own M functions.
Modern Data Warehousing with the Microsoft Analytics Platform SystemJames Serra
The traditional data warehouse has served us well for many years, but new trends are causing it to break in four different ways: data growth, fast query expectations from users, non-relational/unstructured data, and cloud-born data. How can you prevent this from happening? Enter the modern data warehouse, which is able to handle and excel with these new trends. It handles all types of data (Hadoop), provides a way to easily interface with all these types of data (PolyBase), and can handle “big data” and provide fast queries. Is there one appliance that can support this modern data warehouse? Yes! It is the Analytics Platform System (APS) from Microsoft (formally called Parallel Data Warehouse or PDW) , which is a Massively Parallel Processing (MPP) appliance that has been recently updated (v2 AU1). In this session I will dig into the details of the modern data warehouse and APS. I will give an overview of the APS hardware and software architecture, identify what makes APS different, and demonstrate the increased performance. In addition I will discuss how Hadoop, HDInsight, and PolyBase fit into this new modern data warehouse.
Power BI Desktop | Power BI Tutorial | Power BI Training | EdurekaEdureka!
This Edureka "Power BI Desktop" tutorial will help you to understand what is Power BI Desktop with examples and demo. Below are the topics covered in this tutorial:
1. Why Power BI?
2. What Power BI?
3. Who use Power BI?
4. Flow of Work
5. Power BI Trends
APEX Interactive Grids: Essentials and Then Some, Part 1Karen Cannell
APEX 18 Essentials for developers ready to take a closer look and build interactive grids for production requirements. Background, architecture, building, configuring for business needs. Alternated edit processing, Upgrading tabular forms to interactive grids.
NZOUG APAC Groundbreakers Tour 2018
Data Lakehouse, Data Mesh, and Data Fabric (r2)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a modern data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. They all may sound great in theory, but I'll dig into the concerns you need to be aware of before taking the plunge. I’ll also include use cases so you can see what approach will work best for your big data needs. And I'll discuss Microsoft version of the data mesh.
Data modelling for the business half day workshop presented at the Enterprise Data & Business Intelligence conference in London on November 3rd 2014
chris.bradley@dmadvisors.co.uk
Tame the small files problem and optimize data layout for streaming ingestion...Flink Forward
Flink Forward San Francisco 2022.
In modern data platform architectures, stream processing engines such as Apache Flink are used to ingest continuous streams of data into data lakes such as Apache Iceberg. Streaming ingestion to iceberg tables can suffer by two problems (1) small files problem that can hurt read performance (2) poor data clustering that can make file pruning less effective. To address those two problems, we propose adding a shuffling stage to the Flink Iceberg streaming writer. The shuffling stage can intelligently group data via bin packing or range partition. This can reduce the number of concurrent files that every task writes. It can also improve data clustering. In this talk, we will explain the motivations in details and dive into the design of the shuffling stage. We will also share the evaluation results that demonstrate the effectiveness of smart shuffling.
by
Gang Ye & Steven Wu
Power BI Interview Questions and Answers | Power BI Certification | Power BI ...Edureka!
( Power BI Training - https://www.edureka.co/power-bi-training )
This Edureka "PowerBI Interview Questions and Answers" tutorial will help you unravel concepts of Power BI and touch those topics that are very vital for succeeding in Power BI Interviews.
This video helps you to learn the following topics:
1. General Power BI Questions
2. DAX
3. Power Pivot
4. Power Query
5. Power Map
6. Additional Questions
Check out our Power BI Playlist: https://goo.gl/97sJv1
Power BI (Business Intelligence) is a new and emerging self-service business intelligence and business analytics framework that brings together and enhances a few key Microsoft technologies. Fundamentally, Power BI is considered a premium Office option, because Microsoft licenses it that way. We will answer the following questions:
◾What makes Power BI different from the Microsoft Office I have known for years?
◾What are the major Power BI features in Excel 2013 and 2010?
◾What are the major features for Power BI for Office 365?
◾How can our team license Power BI?
Methoden zur Integration und Verwaltung von Portaldaten
Motivation
Verwaltung von Daten
Datenbankzugriff
Objektrelationales Mapping
Leichtgewichtiges Mapping: Beispiel mittels Spring
Schwergewichtiges Mapping: Beispiel mittels Hibernate
Integration von Daten
Pull and Push Verfahren
ETL Verfahren : Extract – Transform - Load
Standardisierung von ETL Prozessen und ausgelagerte Geschäftsregelnuwe geercken
Vortrag 16. Februar 2017 - Frankfurt/Sachsenhausen.
Vereinfachung der IT Prozesse durch standardisierte ETL Prozesse unter Nutzung einer Rule Engine.
Flexible Geschäftsregeln, Separierung der Verantwortlichkeiten, übersichtlichere Prozesse.
Ergebnis: höhere Agilität und Qualität.
Der SQL Server bietet mit seinen verschiedenen BI-Modulen eine nahezu vollständige BI-Suite. Mit den Integration Services kann der klassische ETL-Prozess umgesetzt werden, die Analysis Services dienen der Cube und KPI-Erstellung und mit den Reporting Services lassen sich schließlich die resultierenden Informationen darstellen. Zusätzlich steht Excel als OLAP-Client zur Verfügung.
Doch was gibt es noch? SharePoint!
Dieser Vortrag von Florian Willmes (Consultant bei OPITZ CONSULTING) soll einen Überblick über die verschiedenen BI-Funktionen von SharePoint geben und die Möglichkeiten der Daten- und Funktionsintegration zwischen SQL Server, Office und SharePoint aufzeigen.
1\9.SSIS 2008R2_Training - Introduction to SSISPramod Singla
It's a SQL server 2008R2 SSIS introduction session.These slides will give you brief introduction of SSIS. You can skip the session if you are already know the basics and history of SSIS.
Neues in SQL Server 2016 – Evaluierung SQL Server 2016 CTP 3 für den BI Stackinovex GmbH
PASS Treffen der Regionalgruppe Karlsruhe, Februar 2016
Speaker: Sascha Götz, inovex GmbH
Weitere Tech-Vorträge: https://www.inovex.de/de/content-pool/vortraege
SEO Campixx 2015 | ETL & BI für SEO Analysen und Reportings von Johannes Kunzetakevalue Consulting GmbH
Microsoft Self-Service-BI Werkzeuge für standardisierte Reportings und Analysen im Bereich SEO.
Inhalt:
- Laden der Google Webmastertools Suchanfragen
- Verknüpfen mit anderen Datenquellen
- Analysen und Reportings mit Power Query & Powerpivot
Warum lohnt es sich, auf SAP HANA zu migrieren? Fallbeispiel - Sokołów SA BCC - Partner Expert SAP HANA.
SAP HANA ist eine innovative, auf der Verarbeitung im Speicher (In-Memory) basierende Technologie.
Der revolutionäre Charakter von SAP HANA besteht in der Übertragung der Verwaltung großer Datensätze aus dem Speicherplatz in den Arbeitsspeicher, die ganz neue Möglichkeiten der Berichterstattung bietet.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
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/
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.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
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.
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.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
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
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.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
AI for Every Business: Unlocking Your Product's Universal Potential by VP of ...
Business Intelligence Portfolio
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5. Total of 11 packages were developed for this project. Above is screen shot of one of the packages. It reads contents of the spreadsheet that contains employee data from a file share and add or update data to Employees table in the SQL Server AllWorksDBStudent database.
6. A Script Task is added to keep an accumulative count of number of files and rows being processed. It will return the total number of files and rows being processed. Total of 11 packages were developed for this project. Above is screen shot of one of the packages. It has a Foreach loop to loop through timesheet files in a file share. A Data Flow task reads the contents of the spreadsheet that contains timesheet data and add or update data to Employees JobTimeSheet table in the SQL Server AllWorksDBStudent database.
7. The data flow task is within the Foreach Loop task which will loop through the CSV files in the file share location. One CSV file will be processed. Content of each file will be read. Data conversion and validation will be performed. It will then insert or update row in the JobTimeSheets table based on the Job ID, Employee ID and Work Date.
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