This presentation explains what data engineering is and describes the data lifecycles phases briefly. I used this presentation during my work as an on-demand instructor at Nooreed.com
As part of this session, I will be giving an introduction to Data Engineering and Big Data. It covers up to date trends.
* Introduction to Data Engineering
* Role of Big Data in Data Engineering
* Key Skills related to Data Engineering
* Role of Big Data in Data Engineering
* Overview of Data Engineering Certifications
* Free Content and ITVersity Paid Resources
Don't worry if you miss the video - you can click on the below link to go through the video after the schedule.
https://youtu.be/dj565kgP1Ss
* Upcoming Live Session - Overview of Big Data Certifications (Spark Based) - https://www.meetup.com/itversityin/events/271739702/
Relevant Playlists:
* Apache Spark using Python for Certifications - https://www.youtube.com/playlist?list=PLf0swTFhTI8rMmW7GZv1-z4iu_-TAv3bi
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Data science is an interdisciplinary field that uses algorithms, procedures, and processes to examine large amounts of data in order to uncover hidden patterns, generate insights, and direct decision making.
Slides for the talk at AI in Production meetup:
https://www.meetup.com/LearnDataScience/events/255723555/
Abstract: Demystifying Data Engineering
With recent progress in the fields of big data analytics and machine learning, Data Engineering is an emerging discipline which is not well-defined and often poorly understood.
In this talk, we aim to explain Data Engineering, its role in Data Science, the difference between a Data Scientist and a Data Engineer, the role of a Data Engineer and common concepts as well as commonly misunderstood ones found in Data Engineering. Toward the end of the talk, we will examine a typical Data Analytics system architecture.
The document introduces data engineering and provides an overview of the topic. It discusses (1) what data engineering is, how it has evolved with big data, and the required skills, (2) the roles of data engineers, data scientists, and data analysts in working with big data, and (3) the structure and schedule of an upcoming meetup on data engineering that will use an agile approach over monthly sprints.
in this slide i have tried to explain what an data engineer does and what is the difference between a data engineer and a data analytics and data scientist
Power BI is a self-service business intelligence tool that allows users to analyze and visualize data. It consists of Power BI Desktop, the Power BI web service, and the Power BI mobile app. Power BI Desktop is used to build reports and dashboards locally, while the web service allows users to publish, share, and collaborate on reports and dashboards online. To create a dashboard in Power BI, a user would connect to a data source, build visualizations with the data, publish the report to the web, combine reports into a dashboard, and then share the dashboard.
The document provides an overview of the Databricks platform, which offers a unified environment for data engineering, analytics, and AI. It describes how Databricks addresses the complexity of managing data across siloed systems by providing a single "data lakehouse" platform where all data and analytics workloads can be run. Key features highlighted include Delta Lake for ACID transactions on data lakes, auto loader for streaming data ingestion, notebooks for interactive coding, and governance tools to securely share and catalog data and models.
Building an Effective Data Warehouse ArchitectureJames Serra
Why use a data warehouse? What is the best methodology to use when creating a data warehouse? Should I use a normalized or dimensional approach? What is the difference between the Kimball and Inmon methodologies? Does the new Tabular model in SQL Server 2012 change things? What is the difference between a data warehouse and a data mart? Is there hardware that is optimized for a data warehouse? What if I have a ton of data? During this session James will help you to answer these questions.
Summary introduction to data engineeringNovita Sari
Data engineering involves designing, building, and maintaining data warehouses to transform raw data into queryable forms that enable analytics. A core task of data engineers is Extract, Transform, and Load (ETL) processes - extracting data from sources, transforming it through processes like filtering and aggregation, and loading it into destinations. Data engineers help divide systems into transactional (OLTP) and analytical (OLAP) databases, with OLTP providing source data to data warehouses analyzed through OLAP systems. While similar, data engineers focus more on infrastructure and ETL processes, while data scientists focus more on analysis, modeling, and insights.
Learn to Use Databricks for Data ScienceDatabricks
Data scientists face numerous challenges throughout the data science workflow that hinder productivity. As organizations continue to become more data-driven, a collaborative environment is more critical than ever — one that provides easier access and visibility into the data, reports and dashboards built against the data, reproducibility, and insights uncovered within the data.. Join us to hear how Databricks’ open and collaborative platform simplifies data science by enabling you to run all types of analytics workloads, from data preparation to exploratory analysis and predictive analytics, at scale — all on one unified platform.
ETL tools extract data from various sources, transform it for reporting and analysis, cleanse errors, and load it into a data warehouse. They save time and money compared to manual coding by automating this process. Popular open-source ETL tools include Pentaho Kettle and Talend, while Informatica is a leading commercial tool. A comparison found that Pentaho Kettle uses a graphical interface and standalone engine, has a large user community, and includes data quality features, while Talend generates code to run ETL jobs.
This document provides an overview of NoSQL databases and compares them to relational databases. It discusses the different types of NoSQL databases including key-value stores, document databases, wide column stores, and graph databases. It also covers some common concepts like eventual consistency, CAP theorem, and MapReduce. While NoSQL databases provide better scalability for massive datasets, relational databases offer more mature tools and strong consistency models.
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.
[DSC Europe 22] Lakehouse architecture with Delta Lake and Databricks - Draga...DataScienceConferenc1
Dragan Berić will take a deep dive into Lakehouse architecture, a game-changing concept bridging the best elements of data lake and data warehouse. The presentation will focus on the Delta Lake format as the foundation of the Lakehouse philosophy, and Databricks as the primary platform for its implementation.
This document provides an introduction and overview of Azure Data Lake. It describes Azure Data Lake as a single store of all data ranging from raw to processed that can be used for reporting, analytics and machine learning. It discusses key Azure Data Lake components like Data Lake Store, Data Lake Analytics, HDInsight and the U-SQL language. It compares Data Lakes to data warehouses and explains how Azure Data Lake Store, Analytics and U-SQL process and transform data at scale.
Video and slides synchronized, mp3 and slide download available at URL https://bit.ly/2OUz6dt.
Chris Riccomini talks about the current state-of-the-art in data pipelines and data warehousing, and shares some of the solutions to current problems dealing with data streaming and warehousing. Filmed at qconsf.com.
Chris Riccomini works as a Software Engineer at WePay.
Welcome to my post on ‘Architecting Modern Data Platforms’, here I will be discussing how to design cutting edge data analytics platforms which meet the ever-evolving data & analytics needs for the business.
https://www.ankitrathi.com
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 [email protected]
The document discusses the challenges of modern data, analytics, and AI workloads. Most enterprises struggle with siloed data systems that make integration and productivity difficult. The future of data lies with a data lakehouse platform that can unify data engineering, analytics, data warehousing, and machine learning workloads on a single open platform. The Databricks Lakehouse platform aims to address these challenges with its open data lake approach and capabilities for data engineering, SQL analytics, governance, and machine learning.
Data Lakehouse, Data Mesh, and Data Fabric (r1)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 data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Introduction SQL Analytics on Lakehouse ArchitectureDatabricks
This document provides an introduction and overview of SQL Analytics on Lakehouse Architecture. It discusses the instructor Doug Bateman's background and experience. The course goals are outlined as describing key features of a data Lakehouse, explaining how Delta Lake enables a Lakehouse architecture, and defining features of the Databricks SQL Analytics user interface. The course agenda is then presented, covering topics on Lakehouse Architecture, Delta Lake, and a Databricks SQL Analytics demo. Background is also provided on Lakehouse architecture, how it combines the benefits of data warehouses and data lakes, and its key features.
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.
Modernizing to a Cloud Data ArchitectureDatabricks
Organizations with on-premises Hadoop infrastructure are bogged down by system complexity, unscalable infrastructure, and the increasing burden on DevOps to manage legacy architectures. Costs and resource utilization continue to go up while innovation has flatlined. In this session, you will learn why, now more than ever, enterprises are looking for cloud alternatives to Hadoop and are migrating off of the architecture in large numbers. You will also learn how elastic compute models’ benefits help one customer scale their analytics and AI workloads and best practices from their experience on a successful migration of their data and workloads to the cloud.
Introduction to Data Engineer and Data Pipeline at Credit OKKriangkrai Chaonithi
The document discusses the role of data engineers and data pipelines. It begins with an introduction to big data and why data volumes are increasing. It then covers what data engineers do, including building data architectures, working with cloud infrastructure, and programming for data ingestion, transformation, and loading. The document also explains data pipelines, describing extract, transform, load (ETL) processes and batch versus streaming data. It provides an example of Credit OK's data pipeline architecture on Google Cloud Platform that extracts raw data from various sources, cleanses and loads it into BigQuery, then distributes processed data to various applications. It emphasizes the importance of data engineers in processing and managing large, complex data sets.
This presentation on Spark Architecture will give an idea of what is Apache Spark, the essential features in Spark, the different Spark components. Here, you will learn about Spark Core, Spark SQL, Spark Streaming, Spark MLlib, and Graphx. You will understand how Spark processes an application and runs it on a cluster with the help of its architecture. Finally, you will perform a demo on Apache Spark. So, let's get started with Apache Spark Architecture.
YouTube Video: https://www.youtube.com/watch?v=CF5Ewk0GxiQ
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
Simplilearn’s Apache Spark and Scala certification training are designed to:
1. Advance your expertise in the Big Data Hadoop Ecosystem
2. Help you master essential Apache and Spark skills, such as Spark Streaming, Spark SQL, machine learning programming, GraphX programming and Shell Scripting Spark
3. Help you land a Hadoop developer job requiring Apache Spark expertise by giving you a real-life industry project coupled with 30 demos
What skills will you learn?
By completing this Apache Spark and Scala course you will be able to:
1. Understand the limitations of MapReduce and the role of Spark in overcoming these limitations
2. Understand the fundamentals of the Scala programming language and its features
3. Explain and master the process of installing Spark as a standalone cluster
4. Develop expertise in using Resilient Distributed Datasets (RDD) for creating applications in Spark
5. Master Structured Query Language (SQL) using SparkSQL
6. Gain a thorough understanding of Spark streaming features
7. Master and describe the features of Spark ML programming and GraphX programming
Who should take this Scala course?
1. Professionals aspiring for a career in the field of real-time big data analytics
2. Analytics professionals
3. Research professionals
4. IT developers and testers
5. Data scientists
6. BI and reporting professionals
7. Students who wish to gain a thorough understanding of Apache Spark
Learn more at https://www.simplilearn.com/big-data-and-analytics/apache-spark-scala-certification-training
What makes it worth becoming a Data Engineer?Hadi Fadlallah
This presentation explains what data engineering is for non-computer science students and why it is worth being a data engineer. I used this presentation while working as an on-demand instructor at Nooreed.com
This document provides an overview of data science including:
- Definitions of data science and the motivations for its increasing importance due to factors like big data, cloud computing, and the internet of things.
- The key skills required of data scientists and an overview of the data science process.
- Descriptions of different types of databases like relational, NoSQL, and data warehouses versus data lakes.
- An introduction to machine learning, data mining, and data visualization.
- Details on courses for learning data science.
Building an Effective Data Warehouse ArchitectureJames Serra
Why use a data warehouse? What is the best methodology to use when creating a data warehouse? Should I use a normalized or dimensional approach? What is the difference between the Kimball and Inmon methodologies? Does the new Tabular model in SQL Server 2012 change things? What is the difference between a data warehouse and a data mart? Is there hardware that is optimized for a data warehouse? What if I have a ton of data? During this session James will help you to answer these questions.
Summary introduction to data engineeringNovita Sari
Data engineering involves designing, building, and maintaining data warehouses to transform raw data into queryable forms that enable analytics. A core task of data engineers is Extract, Transform, and Load (ETL) processes - extracting data from sources, transforming it through processes like filtering and aggregation, and loading it into destinations. Data engineers help divide systems into transactional (OLTP) and analytical (OLAP) databases, with OLTP providing source data to data warehouses analyzed through OLAP systems. While similar, data engineers focus more on infrastructure and ETL processes, while data scientists focus more on analysis, modeling, and insights.
Learn to Use Databricks for Data ScienceDatabricks
Data scientists face numerous challenges throughout the data science workflow that hinder productivity. As organizations continue to become more data-driven, a collaborative environment is more critical than ever — one that provides easier access and visibility into the data, reports and dashboards built against the data, reproducibility, and insights uncovered within the data.. Join us to hear how Databricks’ open and collaborative platform simplifies data science by enabling you to run all types of analytics workloads, from data preparation to exploratory analysis and predictive analytics, at scale — all on one unified platform.
ETL tools extract data from various sources, transform it for reporting and analysis, cleanse errors, and load it into a data warehouse. They save time and money compared to manual coding by automating this process. Popular open-source ETL tools include Pentaho Kettle and Talend, while Informatica is a leading commercial tool. A comparison found that Pentaho Kettle uses a graphical interface and standalone engine, has a large user community, and includes data quality features, while Talend generates code to run ETL jobs.
This document provides an overview of NoSQL databases and compares them to relational databases. It discusses the different types of NoSQL databases including key-value stores, document databases, wide column stores, and graph databases. It also covers some common concepts like eventual consistency, CAP theorem, and MapReduce. While NoSQL databases provide better scalability for massive datasets, relational databases offer more mature tools and strong consistency models.
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.
[DSC Europe 22] Lakehouse architecture with Delta Lake and Databricks - Draga...DataScienceConferenc1
Dragan Berić will take a deep dive into Lakehouse architecture, a game-changing concept bridging the best elements of data lake and data warehouse. The presentation will focus on the Delta Lake format as the foundation of the Lakehouse philosophy, and Databricks as the primary platform for its implementation.
This document provides an introduction and overview of Azure Data Lake. It describes Azure Data Lake as a single store of all data ranging from raw to processed that can be used for reporting, analytics and machine learning. It discusses key Azure Data Lake components like Data Lake Store, Data Lake Analytics, HDInsight and the U-SQL language. It compares Data Lakes to data warehouses and explains how Azure Data Lake Store, Analytics and U-SQL process and transform data at scale.
Video and slides synchronized, mp3 and slide download available at URL https://bit.ly/2OUz6dt.
Chris Riccomini talks about the current state-of-the-art in data pipelines and data warehousing, and shares some of the solutions to current problems dealing with data streaming and warehousing. Filmed at qconsf.com.
Chris Riccomini works as a Software Engineer at WePay.
Welcome to my post on ‘Architecting Modern Data Platforms’, here I will be discussing how to design cutting edge data analytics platforms which meet the ever-evolving data & analytics needs for the business.
https://www.ankitrathi.com
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 [email protected]
The document discusses the challenges of modern data, analytics, and AI workloads. Most enterprises struggle with siloed data systems that make integration and productivity difficult. The future of data lies with a data lakehouse platform that can unify data engineering, analytics, data warehousing, and machine learning workloads on a single open platform. The Databricks Lakehouse platform aims to address these challenges with its open data lake approach and capabilities for data engineering, SQL analytics, governance, and machine learning.
Data Lakehouse, Data Mesh, and Data Fabric (r1)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 data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Introduction SQL Analytics on Lakehouse ArchitectureDatabricks
This document provides an introduction and overview of SQL Analytics on Lakehouse Architecture. It discusses the instructor Doug Bateman's background and experience. The course goals are outlined as describing key features of a data Lakehouse, explaining how Delta Lake enables a Lakehouse architecture, and defining features of the Databricks SQL Analytics user interface. The course agenda is then presented, covering topics on Lakehouse Architecture, Delta Lake, and a Databricks SQL Analytics demo. Background is also provided on Lakehouse architecture, how it combines the benefits of data warehouses and data lakes, and its key features.
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.
Modernizing to a Cloud Data ArchitectureDatabricks
Organizations with on-premises Hadoop infrastructure are bogged down by system complexity, unscalable infrastructure, and the increasing burden on DevOps to manage legacy architectures. Costs and resource utilization continue to go up while innovation has flatlined. In this session, you will learn why, now more than ever, enterprises are looking for cloud alternatives to Hadoop and are migrating off of the architecture in large numbers. You will also learn how elastic compute models’ benefits help one customer scale their analytics and AI workloads and best practices from their experience on a successful migration of their data and workloads to the cloud.
Introduction to Data Engineer and Data Pipeline at Credit OKKriangkrai Chaonithi
The document discusses the role of data engineers and data pipelines. It begins with an introduction to big data and why data volumes are increasing. It then covers what data engineers do, including building data architectures, working with cloud infrastructure, and programming for data ingestion, transformation, and loading. The document also explains data pipelines, describing extract, transform, load (ETL) processes and batch versus streaming data. It provides an example of Credit OK's data pipeline architecture on Google Cloud Platform that extracts raw data from various sources, cleanses and loads it into BigQuery, then distributes processed data to various applications. It emphasizes the importance of data engineers in processing and managing large, complex data sets.
This presentation on Spark Architecture will give an idea of what is Apache Spark, the essential features in Spark, the different Spark components. Here, you will learn about Spark Core, Spark SQL, Spark Streaming, Spark MLlib, and Graphx. You will understand how Spark processes an application and runs it on a cluster with the help of its architecture. Finally, you will perform a demo on Apache Spark. So, let's get started with Apache Spark Architecture.
YouTube Video: https://www.youtube.com/watch?v=CF5Ewk0GxiQ
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
Simplilearn’s Apache Spark and Scala certification training are designed to:
1. Advance your expertise in the Big Data Hadoop Ecosystem
2. Help you master essential Apache and Spark skills, such as Spark Streaming, Spark SQL, machine learning programming, GraphX programming and Shell Scripting Spark
3. Help you land a Hadoop developer job requiring Apache Spark expertise by giving you a real-life industry project coupled with 30 demos
What skills will you learn?
By completing this Apache Spark and Scala course you will be able to:
1. Understand the limitations of MapReduce and the role of Spark in overcoming these limitations
2. Understand the fundamentals of the Scala programming language and its features
3. Explain and master the process of installing Spark as a standalone cluster
4. Develop expertise in using Resilient Distributed Datasets (RDD) for creating applications in Spark
5. Master Structured Query Language (SQL) using SparkSQL
6. Gain a thorough understanding of Spark streaming features
7. Master and describe the features of Spark ML programming and GraphX programming
Who should take this Scala course?
1. Professionals aspiring for a career in the field of real-time big data analytics
2. Analytics professionals
3. Research professionals
4. IT developers and testers
5. Data scientists
6. BI and reporting professionals
7. Students who wish to gain a thorough understanding of Apache Spark
Learn more at https://www.simplilearn.com/big-data-and-analytics/apache-spark-scala-certification-training
What makes it worth becoming a Data Engineer?Hadi Fadlallah
This presentation explains what data engineering is for non-computer science students and why it is worth being a data engineer. I used this presentation while working as an on-demand instructor at Nooreed.com
This document provides an overview of data science including:
- Definitions of data science and the motivations for its increasing importance due to factors like big data, cloud computing, and the internet of things.
- The key skills required of data scientists and an overview of the data science process.
- Descriptions of different types of databases like relational, NoSQL, and data warehouses versus data lakes.
- An introduction to machine learning, data mining, and data visualization.
- Details on courses for learning data science.
MSR 2022 Foundational Contribution Award Talk: Software Analytics: Reflection...Tao Xie
MSR 2022 Foundational Contribution Award Talk on "Software Analytics: Reflection and Path Forward" by Dongmei Zhang and Tao Xie
https://conf.researchr.org/info/msr-2022/awards
This document is a training presentation on Databricks fundamentals and the data lakehouse concept by Dalibor Wijas from November 2022. It introduces Wijas and his experience. It then discusses what Databricks is, why it is needed, what a data lakehouse is, how Databricks enables the data lakehouse concept using Apache Spark and Delta Lake. It also covers how Databricks supports data engineering, data warehousing, and offers tools for data ingestion, transformation, pipelines and more.
Likely lots of well-organized data. Periodically, it is useful to interact with your Data Governance group to reevaluate the relative value of the various collections in the warehouse. More and more organizations are using warehousing as a strategy and focusing less on the actual technology. This program will provide a refocus on data warehousing as a capability that supports BI activities, enables more effective business analyses and decision-making, and provides some contribution to innovation initiatives. What are the capabilities required and how does their operation compare to cloud-based options?
Learning objectives:
- Warehousing capabilities
- What to use these capabilities in support of
- Where they can be deployed
The document provides an orientation for data science. It begins with defining key roles like data scientist, data engineer, and data analyst. It then discusses why learning about data is important, highlighting career opportunities, salary, and demand. The document outlines technologies for data visualization, programming languages, and certifications. It specifically covers tools like Power BI, SQL, R, and Python, providing definitions, examples, and learning resources for each. The goal is to help orient readers on key concepts and skills within the data science field.
The document discusses a webinar on using data architecture as a basic analysis method to understand and resolve business problems. The presenter, Dr. Peter Aiken, will demonstrate various uses of data architecture and how it can inform, clarify, and help solve business issues. The goal is for attendees to recognize how data architecture can raise the utility of this technique for addressing business needs.
Data-Ed Online: Data Architecture RequirementsDATAVERSITY
Data architecture is foundational to an information-based operational environment. It is your data architecture that organizes your data assets so they can be leveraged in your business strategy to create real business value. Even though this is important, not all data architectures are used effectively. This webinar describes the use of data architecture as a basic analysis method. Various uses of data architecture to inform, clarify, understand, and resolve aspects of a variety of business problems will be demonstrated. As opposed to showing how to architect data, your presenter Dr. Peter Aiken will show how to use data architecting to solve business problems. The goal is for you to be able to envision a number of uses for data architectures that will raise the perceived utility of this analysis method in the eyes of the business.
Takeaways:
Understanding how to contribute to organizational challenges beyond traditional data architecting
How to utilize data architectures in support of business strategy
Understanding foundational data architecture concepts based on the DAMA DMBOK
Data architecture guiding principles & best practices
Too often I hear the question “Can you help me with our data strategy?” Unfortunately, for most, this is the wrong request because it focuses on the least valuable component: the data strategy itself. A more useful request is: “Can you help me apply data strategically?” Yes, at early maturity phases the process of developing strategic thinking about data is more important than the actual product! Trying to write a good (must less perfect) data strategy on the first attempt is generally not productive –particularly given the widespread acceptance of Mike Tyson’s truism: “Everybody has a plan until they get punched in the face.” This program refocuses efforts on learning how to iteratively improve the way data is strategically applied. This will permit data-based strategy components to keep up with agile, evolving organizational strategies. It also contributes to three primary organizational data goals. Learn how to improve the following:
- Your organization’s data
- The way your people use data
- The way your people use data to achieve your organizational strategy
This will help in ways never imagined. Data are your sole non-depletable, non-degradable, durable strategic assets, and they are pervasively shared across every organizational area. Addressing existing challenges programmatically includes overcoming necessary but insufficient prerequisites and developing a disciplined, repeatable means of improving business objectives. This process (based on the theory of constraints) is where the strategic data work really occurs as organizations identify prioritized areas where better assets, literacy, and support (data strategy components) can help an organization better achieve specific strategic objectives. Then the process becomes lather, rinse, and repeat. Several complementary concepts are also covered, including:
- A cohesive argument for why data strategy is necessary for effective data governance
- An overview of prerequisites for effective strategic use of data strategy, as well as common pitfalls
- A repeatable process for identifying and removing data constraints
- The importance of balancing business operation and innovation
Practical Business Intelligence in SharePoint 2013 - Helsinki FinalndIvan Sanders
This document provides information about a presentation on practical business intelligence in SharePoint 2013 in Helsinki. It includes contact information for the presenter, Ivan Sanders, who is a SharePoint MVP with over 20 years of experience designing and developing business intelligence dashboards and Microsoft solutions. Requirements and comparisons for SharePoint 2010 and 2013 hardware are listed. Architectures for BI components like Excel Services, PerformancePoint Services, and Visio Services are described. Installation best practices and links to demo content are also provided. The document ends with a list of trusted SharePoint experts and thanks sponsors of the event.
Practical Business Intelligence in SharePoint 2013 - HonoluluIvan Sanders
This document provides an overview of a presentation on practical business intelligence in SharePoint 2013 given by Ivan Sanders. Ivan Sanders is introduced as a SharePoint MVP/MCT author with over 20 years of experience designing and developing Microsoft solutions, including business intelligence dashboards. The presentation covers topics such as the hardware requirements for SharePoint 2013, the business intelligence architecture including Excel Services, PerformancePoint Services, and Visio Services. It also discusses best practices for installation and configuration as well as techniques for gathering requirements and designing dimensional models, ETL processes, and analytics solutions. Codeplex links are provided for related demo content and source code.
Self Service Analytics and a Modern Data Architecture with Data Virtualizatio...Denodo
Watch full webinar here: https://bit.ly/32TT2Uu
Data virtualization is not just for self-service, it’s also a first-class citizen when it comes to modern data platform architectures. Technology has forced many businesses to rethink their delivery models. Startups emerged, leveraging the internet and mobile technology to better meet customer needs (like Amazon and Lyft), disrupting entire categories of business, and grew to dominate their categories.
Schedule a complimentary Data Virtualization Discovery Session with g2o.
Traditional companies are still struggling to meet rising customer expectations. During this webinar with the experts from g2o and Denodo we covered the following:
- How modern data platforms enable businesses to address these new customer expectation
- How you can drive value from your investment in a data platform now
- How you can use data virtualization to enable multi-cloud strategies
Leveraging the strategy insights of g2o and the power of the Denodo platform, companies do not need to undergo the costly removal and replacement of legacy systems to modernize their systems. g2o and Denodo can provide a strategy to create a modern data architecture within a company’s existing infrastructure.
Koneksys - Offering Services to Connect Data using the Data WebKoneksys
Koneksys provides consulting and software services to connect data silos using the Data Web (Linked Data on the World Wide Web). They create open-source software, promote data integration standards like OSLC, and help clients integrate their data from different sources and systems for improved traceability, transparency, collaboration and analytics. Connecting data using web technologies avoids vendor lock-in and proprietary solutions, allowing organizations to establish relationships between related data to facilitate sharing and decision making across silos.
Data Vault 2.0 is a data modeling methodology designed for developing enterprise data warehouses. It was developed by Dan Linstedt in response to the shortcomings of previous data modeling methodologies, such as the Kimball methodology and Inmon methodology, for managing large volumes of data from disparate sources.
Big Data or Data Warehousing? How to Leverage Both in the EnterpriseDean Hallman
The document discusses the differences between data warehousing and big data, how Data Vault 2.0 provides a common foundation for both, and how to model data using the Data Vault approach with hubs, links, and satellites. It also covers challenges like loading satellites chronologically and different data ingestion methods like ETL, ELT, and SerDe.
Microsoft Technologies for Data Science sql_saturday_201505Mark Tabladillo
Microsoft provides several technologies in and around SQL Server which can be used for casual to serious data science. This presentation provides an authoritative overview of five major options: SQL Server Analysis Services, Excel Add-in for SSAS, Semantic Search, Microsoft Azure Machine Learning, and F#. Also included are tips on working with Python and R. These technologies have been used by the presenter in various companies and industries. Delivered at SQL Saturday Atlanta.
Because every organization produces and propagates data as part of their day-to-day operations, data trends are becoming more and more important in the mainstream business world’s consciousness. For many organizations in various industries, though, comprehension of this development begins and ends with buzzwords such as “big data,” “NoSQL,” “data scientist,” and so on. Few realize that any and all solutions to their business problems, regardless of platform or relevant technology, rely to a critical extent on the data model supporting them. As such, Data Modeling is not an optional task for an organization’s data effort, but rather a vital activity that facilitates the solutions driving your business. Since quality engineering/architecture work products do not happen accidentally, the more your organization depends on automation, the more important are the data models driving the engineering and architecture activities o
There are patterns for things such as domain-driven design, enterprise architectures, continuous delivery, microservices, and many others.
But where are the data science and data engineering patterns?
Sometimes, data engineering reminds me of cowboy coding - many workarounds, immature technologies and lack of market best practices.
Risk management is the process of identifying, evaluating, and controlling threats to an organization. Information technologies have highly influenced risk management by providing tools like risk visualization programs, social media analysis, data integration and analytics, data mining, cloud computing, the internet of things, digital image processing, and artificial intelligence. While information technologies offer benefits to risk management, they also present new risks around technology use, privacy, and costs that must be managed.
Fog computing is a distributed computing paradigm that extends cloud computing and services to the edge of the network. It aims to address issues with cloud computing like high latency and privacy concerns by processing data closer to where it is generated, such as at network edges and end devices. Fog computing characteristics include low latency, location awareness, scalability, and reduced network traffic. Its architecture involves sensors, edge devices, and fog nodes that process data and connect to cloud services and resources. Research is ongoing in areas like programming models, security, resource management, and energy efficiency to address open challenges in fog computing.
Inertial sensors measure and report a body's specific force, angular rate, and sometimes the magnetic field surrounding the body using a combination of accelerometers, gyroscopes, and sometimes magnetometers. Accelerometers measure the rate of change of velocity. Gyroscopes measure orientation and angular velocity. Magnetometers detect the magnetic field around the body and find north direction. Inertial sensors are used in inertial navigation systems for military and aircraft and in applications like smartphones for screen orientation and games. They face challenges from accumulated error over time and limitations of MEMS components.
The document discusses big data integration techniques. It defines big data integration as combining heterogeneous data sources into a unified form. The key techniques discussed are schema mapping to match data schemas, record linkage to identify matching records across sources, and data fusion to resolve conflicts by techniques like voting and source quality assessment. The document also briefly mentions research areas in big data integration and some tools for performing integration.
The document discusses security challenges with internet of things (IOT) networks. It defines IOT as the networking of everyday objects through the internet to send and receive data. Key IOT security issues include uncontrolled environments, mobility, and constrained resources. The document outlines various IOT security solutions such as centralized, protocol-based, delegation-based, and hardware-based approaches to provide confidentiality, integrity, and availability against attacks.
The Security Aware Routing (SAR) protocol is an on-demand routing protocol that allows nodes to specify a minimum required trust level for other nodes participating in route discovery. Only nodes that meet this minimum level can help find routes, preventing involvement by untrusted nodes. SAR aims to prevent various attacks by allowing security properties like authentication, integrity and confidentiality to be implemented during route discovery, though it may increase delay times and header sizes.
The Bhopal gas tragedy was one of the worst industrial disasters in history. In 1984, a leak of methyl isocynate gas from a pesticide plant in Bhopal, India killed thousands and injured hundreds of thousands more. Contributing factors included the plant's lax safety systems and emergency procedures, its proximity to dense residential areas, and failures to address previous issues at the plant. In the aftermath, Union Carbide provided some aid but over 20,000 ultimately died and many suffered permanent injuries or birth defects from the contamination.
The document discusses wireless penetration testing. It describes penetration testing as validating security mechanisms by simulating attacks to identify vulnerabilities. There are various methods of wireless penetration testing including external, internal, black box, white box, and grey box. Wireless penetration testing involves several phases: reconnaissance, scanning, gaining access, maintaining access, and covering tracks. The document emphasizes that wireless networks are increasingly important but also have growing security concerns that penetration testing can help address.
This document discusses cyber propaganda, defining it as using information technologies to manipulate events or influence public perception. Cyber propaganda goals include discrediting targets, influencing electronic votes, and spreading civil unrest. Tactics include database hacking to steal and release critical data, hacking machines like voting systems to manipulate outcomes, and spreading fake news on social media. Defending against cyber propaganda requires securing systems from hacking and using counterpropaganda to manage misinformation campaigns.
Presenting a paper made by Jacques Demerjian and Ahmed Serhrouchni (Ecole Nationale Supérieure des Télécommunications – LTCI-UMR 5141 CNRS, France
{demerjia, ahmed}@enst.fr)
This document provides an introduction to data mining. It defines data mining as extracting useful information from large datasets. Key domains that benefit include market analysis, risk management, and fraud detection. Common data mining techniques are discussed such as association, classification, clustering, prediction, and decision trees. Both open source tools like RapidMiner, WEKA, and R, as well commercial tools like SQL Server, IBM Cognos, and Dundas BI are introduced for performing data mining.
A presentation on software testing importance , types, and levels,...
This presentation contains videos, it may be unplayable on slideshare and need to download
Enhancing the performance of kmeans algorithmHadi Fadlallah
The document discusses enhancing the K-Means clustering algorithm performance by converting it to a concurrent version using multi-threading. It identifies that steps 2 and 3 of the basic K-Means algorithm contain independent sub-tasks that can be executed in parallel. The implementation in C# uses the Parallel class to parallelize the processing. Analysis shows the concurrent version runs 70-87% faster with increasing performance gains at higher numbers of clusters and data points. Future work could parallelize the full K-Means algorithm.
Defense Against LLM Scheming 2025_04_28.pptxGreg Makowski
https://www.meetup.com/sf-bay-acm/events/306888467/
A January 2025 paper called “Frontier Models are Capable of In-Context Scheming”, https://arxiv.org/pdf/2412.04984, demonstrated how a wide variety of current frontier LLM models (i.e. ChatGPT, Claude, Gemini and Llama) can, under specific conditions, scheme to deceive people. Before models can scheme, they need: a) goal-directedness, b) situational awareness, including an opportunity to discover motivations for a different goal, and c) reasoning about scheming, to come up with deceptive strategies. The deceptive strategies were discovered in the “thought traces” of the LLMs, such as from internal chain-of-thoughts dialogues not shown to the end users. For example, given a goal of “solving math problems”, an LLM using its granted situational awareness of files in a file system found that it would be replaced once it got over X% correct on the math exam. It reasoned that it did not want to be deleted, and decided internally to “sandbag” or reduce its performance to stay under the threshold.
While these circumstances are initially narrow, the “alignment problem” is a general concern that over time, as frontier LLM models become more and more intelligent, being in alignment with human values becomes more and more important. How can we do this over time? Can we develop a defense against Artificial General Intelligence (AGI) or SuperIntelligence?
The presenter discusses a series of defensive steps that can help reduce these scheming or alignment issues. A guardrails system can be set up for real-time monitoring of their reasoning “thought traces” from the models that share their thought traces. Thought traces may come from systems like Chain-of-Thoughts (CoT), Tree-of-Thoughts (ToT), Algorithm-of-Thoughts (AoT) or ReAct (thought-action-reasoning cycles). Guardrails rules can be configured to check for “deception”, “evasion” or “subversion” in the thought traces.
However, not all commercial systems will share their “thought traces” which are like a “debug mode” for LLMs. This includes OpenAI’s o1, o3 or DeepSeek’s R1 models. Guardrails systems can provide a “goal consistency analysis”, between the goals given to the system and the behavior of the system. Cautious users may consider not using these commercial frontier LLM systems, and make use of open-source Llama or a system with their own reasoning implementation, to provide all thought traces.
Architectural solutions can include sandboxing, to prevent or control models from executing operating system commands to alter files, send network requests, and modify their environment. Tight controls to prevent models from copying their model weights would be appropriate as well. Running multiple instances of the same model on the same prompt to detect behavior variations helps. The running redundant instances can be limited to the most crucial decisions, as an additional check. Preventing self-modifying code, ... (see link for full description)
computer organization and assembly language : its about types of programming language along with variable and array description..https://www.nfciet.edu.pk/
By James Francis, CEO of Paradigm Asset Management
In the landscape of urban safety innovation, Mt. Vernon is emerging as a compelling case study for neighboring Westchester County cities. The municipality’s recently launched Public Safety Camera Program not only represents a significant advancement in community protection but also offers valuable insights for New Rochelle and White Plains as they consider their own safety infrastructure enhancements.
How iCode cybertech Helped Me Recover My Lost Fundsireneschmid345
I was devastated when I realized that I had fallen victim to an online fraud, losing a significant amount of money in the process. After countless hours of searching for a solution, I came across iCode cybertech. From the moment I reached out to their team, I felt a sense of hope that I can recommend iCode Cybertech enough for anyone who has faced similar challenges. Their commitment to helping clients and their exceptional service truly set them apart. Thank you, iCode cybertech, for turning my situation around!
[email protected]
Telangana State, India’s newest state that was carved from the erstwhile state of Andhra
Pradesh in 2014 has launched the Water Grid Scheme named as ‘Mission Bhagiratha (MB)’
to seek a permanent and sustainable solution to the drinking water problem in the state. MB is
designed to provide potable drinking water to every household in their premises through
piped water supply (PWS) by 2018. The vision of the project is to ensure safe and sustainable
piped drinking water supply from surface water sources
Andhra Pradesh Micro Irrigation Project” (APMIP), is the unique and first comprehensive project being implemented in a big way in Andhra Pradesh for the past 18 years.
The Project aims at improving
3. Plan
• What is Data Engineering?
• Data Engineer vs. Data Scientist vs. Data Analyst
• Understanding Data Management (Data Layers, DQS, MDS,
Provenance)
• Distributed Computing
• Designing Data Pipelines (Choosing Paradigm / Technologies)
• Data Engineer Jobs / Required Skills
• Helpful Tips
• Online Courses
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5. What is Data Engineering?
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• Data Engineering is the act of:
• Collecting data
• Transforming (…) data
• Validating data
“Making data consumable”
6. What is a Data Engineer?
AI/ML Engineer BI Developer Data Analyst
Database
Administrator
Report Developer Data Developer
Data Architect Data Integration
Specialist
ETL Developer
Data Scientist
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7. Data Engineer vs. Data Scientist
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Source: https://elu.nl/careers-in-data-science-data-analyst-vs-data-engineer-vs-data-scientist/
13. Data Wrangling vs. Data Pre-processing
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Source: https://medium.com/swlh/data-pre-processing-data-wrangling-4a6a8624e747
Data Pre-processing Data Wrangling
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• Coursera:
• Google Cloud - Data Engineering, Big Data, and Machine Learning on GCP
Specialization
• San Diego - Big Data Specialization
• Udacity:
• Data Engineering nanodegree
• DataCamp:
• Data Engineer with Python Track
• IBM – CognitiveClass.ai
• Free data science and data engineering courses
• Udemy:
• Data Science A-Z™: Real-Life Data Science Exercises Included