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 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
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.
This document provides an overview of big data. It begins by defining big data and noting that it first emerged in the early 2000s among online companies like Google and Facebook. It then discusses the three key characteristics of big data: volume, velocity, and variety. The document outlines the large quantities of data generated daily by companies and sensors. It also discusses how big data is stored and processed using tools like Hadoop and MapReduce. Examples are given of how big data analytics can be applied across different industries. Finally, the document briefly discusses some risks and benefits of big data, as well as its impact on IT jobs.
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.
Data engineers build massive data storage systems and develop architectures like databases and data processing systems. They install continuous pipelines to move data between these large data "pools" and allow data scientists to access relevant data sets. Data engineers require technical skills in databases, SQL, data modeling, ETL, programming languages, data warehousing and newer technologies like NoSQL, Hadoop and machine learning. They are responsible for designing, implementing, testing and maintaining scalable data systems, ensuring business requirements are met, researching new data sources, cleaning and analyzing data, and collaborating with other teams. The role continues to evolve with new database and development technologies.
Independent of the source of data, the integration of event streams into an Enterprise Architecture gets more and more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analysed, often with many consumers or systems interested in all or part of the events. Storing such huge event streams into HDFS or a NoSQL datastore is feasible and not such a challenge anymore. But if you want to be able to react fast, with minimal latency, you can not afford to first store the data and doing the analysis/analytics later. You have to be able to include part of your analytics right after you consume the data streams. Products for doing event processing, such as Oracle Event Processing or Esper, are avaialble for quite a long time and used to be called Complex Event Processing (CEP). In the past few years, another family of products appeared, mostly out of the Big Data Technology space, called Stream Processing or Streaming Analytics. These are mostly open source products/frameworks such as Apache Storm, Spark Streaming, Flink, Kafka Streams as well as supporting infrastructures such as Apache Kafka. In this talk I will present the theoretical foundations for Stream Processing, discuss the core properties a Stream Processing platform should provide and highlight what differences you might find between the more traditional CEP and the more modern Stream Processing solutions.
This document introduces data science, big data, and data analytics. It discusses the roles of data scientists, big data professionals, and data analysts. Data scientists use machine learning and AI to find patterns in data from multiple sources to make predictions. Big data professionals build large-scale data processing systems and use big data tools. Data analysts acquire, analyze, and process data to find insights and create reports. The document also provides examples of how Netflix uses data analytics, data science, and big data professionals to optimize content caching, quality, and create personalized streaming experiences based on quality of experience and user behavior analysis.
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
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.
This document provides an overview of big data. It begins by defining big data and noting that it first emerged in the early 2000s among online companies like Google and Facebook. It then discusses the three key characteristics of big data: volume, velocity, and variety. The document outlines the large quantities of data generated daily by companies and sensors. It also discusses how big data is stored and processed using tools like Hadoop and MapReduce. Examples are given of how big data analytics can be applied across different industries. Finally, the document briefly discusses some risks and benefits of big data, as well as its impact on IT jobs.
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.
Data engineers build massive data storage systems and develop architectures like databases and data processing systems. They install continuous pipelines to move data between these large data "pools" and allow data scientists to access relevant data sets. Data engineers require technical skills in databases, SQL, data modeling, ETL, programming languages, data warehousing and newer technologies like NoSQL, Hadoop and machine learning. They are responsible for designing, implementing, testing and maintaining scalable data systems, ensuring business requirements are met, researching new data sources, cleaning and analyzing data, and collaborating with other teams. The role continues to evolve with new database and development technologies.
Independent of the source of data, the integration of event streams into an Enterprise Architecture gets more and more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analysed, often with many consumers or systems interested in all or part of the events. Storing such huge event streams into HDFS or a NoSQL datastore is feasible and not such a challenge anymore. But if you want to be able to react fast, with minimal latency, you can not afford to first store the data and doing the analysis/analytics later. You have to be able to include part of your analytics right after you consume the data streams. Products for doing event processing, such as Oracle Event Processing or Esper, are avaialble for quite a long time and used to be called Complex Event Processing (CEP). In the past few years, another family of products appeared, mostly out of the Big Data Technology space, called Stream Processing or Streaming Analytics. These are mostly open source products/frameworks such as Apache Storm, Spark Streaming, Flink, Kafka Streams as well as supporting infrastructures such as Apache Kafka. In this talk I will present the theoretical foundations for Stream Processing, discuss the core properties a Stream Processing platform should provide and highlight what differences you might find between the more traditional CEP and the more modern Stream Processing solutions.
This document introduces data science, big data, and data analytics. It discusses the roles of data scientists, big data professionals, and data analysts. Data scientists use machine learning and AI to find patterns in data from multiple sources to make predictions. Big data professionals build large-scale data processing systems and use big data tools. Data analysts acquire, analyze, and process data to find insights and create reports. The document also provides examples of how Netflix uses data analytics, data science, and big data professionals to optimize content caching, quality, and create personalized streaming experiences based on quality of experience and user behavior analysis.
Big Data Analytics (ML, DL, AI) hands-onDony Riyanto
Ini adalah slide tambahan dari materi pengenalan Big Data Analytics (di file berikutnya), yang mengajak kita mulai hands-on dengan beberapa hal terkait Machine/Deep Learning, Big Data (batch/streaming), dan AI menggunakan Tensor Flow
Data Architecture - The Foundation for Enterprise Architecture and GovernanceDATAVERSITY
Organizations are faced with an increasingly complex data landscape, finding themselves unable to cope with exponentially increasing data volumes, compounded by additional regulatory requirements with increased fines for non-compliance. Enterprise architecture and data governance are often discussed at length, but often with different stakeholder audiences. This can result in complementary and sometimes conflicting initiatives rather than a focused, integrated approach. Data governance requires a solid data architecture foundation in order to support the pillars of enterprise architecture. In this session, IDERA’s Ron Huizenga will discuss a practical, integrated approach to effectively understand, define and implement an cohesive enterprise architecture and data governance discipline with integrated modeling and metadata management.
Databricks + Snowflake: Catalyzing Data and AI InitiativesDatabricks
"Combining Databricks, the unified analytics platform with Snowflake, the data warehouse built for the cloud is a powerful combo.
Databricks offers the ability to process large amounts of data reliably, including developing scalable AI projects. Snowflake offers the elasticity of a cloud-based data warehouse that centralizes the access to data. Databricks brings the unparalleled utility of being based on a mature distributed big data processing and AI-enabled tool to the table, capable of integrating with nearly every technology, from message queues (e.g. Kafka) to databases (e.g. Snowflake) to object stores (e.g. S3) and AI tools (e.g. Tensorflow).
Key Takeaways:
How Databricks & Snowflake work;
Why they're so powerful;
How Databricks + Snowflake symbiotically catalyze analytics and AI initiatives"
The document provides an overview of the key aspects of the European Union's General Data Protection Regulation (GDPR). It discusses definitions like personal data, the rights of individuals as data subjects, and key principles of GDPR around consent, data breaches, international transfers, the right to be forgotten, and privacy by design. It outlines actors like controllers and processors, their obligations, and components of GDPR compliance like impact assessments, authorities, and fines for non-compliance.
JSON Data Modeling in Document DatabaseDATAVERSITY
Making the move to a document database can be intimidating. Yes, its flexible data model gives you a lot of choices, but it also raises questions: Which way is the right way? Is a document database even the right tool?
Join this live session on the basics of data modeling with JSON to learn:
- How a document database compares to a traditional RDBMS
- What JSON data modeling means for your application code
- Which tools might be helpful along the way
The data lake has become extremely popular, but there is still confusion on how it should be used. In this presentation I will cover common big data architectures that use the data lake, the characteristics and benefits of a data lake, and how it works in conjunction with a relational data warehouse. Then I’ll go into details on using Azure Data Lake Store Gen2 as your data lake, and various typical use cases of the data lake. As a bonus I’ll talk about how to organize a data lake and discuss the various products that can be used in a modern data warehouse.
This document provides an overview of big data and Hadoop. It defines big data as large volumes of diverse data that cannot be processed by traditional systems. Key characteristics are volume, velocity, variety, and veracity. Popular sources of big data include social media, emails, videos, and sensor data. Hadoop is presented as an open-source framework for distributed storage and processing of large datasets across clusters of computers. It uses HDFS for storage and MapReduce as a programming model. Major tech companies like Google, Facebook, and Amazon are discussed as big players in big data.
The document discusses the role of a full-stack data scientist. It begins with an introduction of the author, Alexey Grigorev, as a data scientist. It then outlines the plan to discuss the data science process, roles in a data science team, what defines a full-stack data scientist, and how to become a full-stack data scientist. It proceeds to explain the CRISP-DM process for data science projects. It describes the different roles in a data science team including product manager, data analyst, data engineer, data scientist, and ML engineer. It defines a full-stack data scientist as someone who can work across the entire data science lifecycle and discusses the breadth of skills required to become a
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
* Free Data Engineering Bootcamp - https://www.youtube.com/playlist?list=PLf0swTFhTI8pBe2Vr2neQV7shh9Rus8rl
* Join our Meetup group - https://www.meetup.com/itversityin/
* Enroll for our labs - https://labs.itversity.com/plans
* Subscribe to our YouTube Channel for Videos - http://youtube.com/itversityin/?sub_confirmation=1
* Access Content via our GitHub - https://github.com/dgadiraju/itversity-books
* Lab and Content Support using Slack
This document provides an overview of big data. It defines big data as large volumes of diverse data that are growing rapidly and require new techniques to capture, store, distribute, manage, and analyze. The key characteristics of big data are volume, velocity, and variety. Common sources of big data include sensors, mobile devices, social media, and business transactions. Tools like Hadoop and MapReduce are used to store and process big data across distributed systems. Applications of big data include smarter healthcare, traffic control, and personalized marketing. The future of big data is promising with the market expected to grow substantially in the coming years.
Data Warehouse - Incremental Migration to the CloudMichael Rainey
A data warehouse (DW) migration is no small undertaking, especially when moving from on-premises to the cloud. A typical data warehouse has numerous data sources connecting and loading data into the DW, ETL tools and data integration scripts performing transformations, and reporting, advanced analytics, or ad-hoc query tools accessing the data for insights and analysis. That’s a lot to coordinate and the data warehouse cannot be migrated all at once. Using a data replication technology such as Oracle GoldenGate, the data warehouse migration can be performed incrementally by keeping the data in-sync between the original DW and the new, cloud DW. This session will dive into the steps necessary for this incremental migration approach and walk through a customer use case scenario, leaving attendees with an understanding of how to perform a data warehouse migration to the cloud.
Presented at RMOUG Training Days 2019
The document discusses modern data architectures. It presents conceptual models for data ingestion, storage, processing, and insights/actions. It compares traditional vs modern architectures. The modern architecture uses a data lake for storage and allows for on-demand analysis. It provides an example of how this could be implemented on Microsoft Azure using services like Azure Data Lake Storage, Azure Data Bricks, and Azure Data Warehouse. It also outlines common data management functions such as data governance, architecture, development, operations, and security.
DataOps: Nine steps to transform your data science impact Strata London May 18Harvinder Atwal
According to Forrester Research, only 22% of companies are currently seeing a significant return from data science expenditures. Most data science implementations are high-cost IT projects, local applications that are not built to scale for production workflows, or laptop decision support projects that never impact customers. Despite this high failure rate, we keep hearing the same mantra and solutions over and over again. Everybody talks about how to create models, but not many people talk about getting them into production where they can impact customers.
Harvinder Atwal offers an entertaining and practical introduction to DataOps, a new and independent approach to delivering data science value at scale, used at companies like Facebook, Uber, LinkedIn, Twitter, and eBay. The key to adding value through DataOps is to adapt and borrow principles from Agile, Lean, and DevOps. However, DataOps is not just about shipping working machine learning models; it starts with better alignment of data science with the rest of the organization and its goals. Harvinder shares experience-based solutions for increasing your velocity of value creation, including Agile prioritization and collaboration, new operational processes for an end-to-end data lifecycle, developer principles for data scientists, cloud solution architectures to reduce data friction, self-service tools giving data scientists freedom from bottlenecks, and more. The DataOps methodology will enable you to eliminate daily barriers, putting your data scientists in control of delivering ever-faster cutting-edge innovation for your organization and customers.
Mind Map of Big Data Technologies and ConceptsAmir Hadad
My first try to capture #bigdata related concepts and technologies in a #mindmap!
#NoSQL vs #SQL https://db-engines.com/
#Streaming vs #Batchprocessing https://lnkd.in/gNEdsrC
#BigDataSecurity https://lnkd.in/gA8e7RC
#OLAP https://lnkd.in/gh3z6r9
This document discusses spatial computing and its potential applications for utility GIS. It begins by providing context on the evolution of spatial computing technologies like digital twins and sensor webs. It then discusses several emerging ideas for spatial computing in utilities, such as using digital twins to model urban energy systems, integrating predictive models across domains, and enabling geo-enabled edge computing. Finally, it considers the technology evolution required to realize these opportunities through standards, interoperability, and integrating emerging techniques like semantics and artificial intelligence.
DataOps is a methodology and culture shift that brings the successful combination of development and operations (DevOps) to data processing environments. It breaks down silos between developers, data scientists, and operators, resulting in lean data feature development processes with quick feedback. In this presentation, we will explain the methodology, and focus on practical aspects of DataOps.
This is the Complete Information about Data Replication you need, i am focused on these topics:
What is replication?
Who use it?
Types ?
Implementation Methods?
This document discusses big data principles including what data is, why big data is important, how it differs from traditional data, and its key characteristics. Big data is characterized by volume, variety, and velocity. It comes from many sources and in many formats. Tools like Hadoop enable storage and analysis at scale. Applications include search, customer analytics, business optimization, health, and security. Benefits are better decisions and flexibility to store now and analyze later. The future of big data is predicted to be a $100 billion industry growing at 10% annually.
This document discusses using real data in student projects and making institutional data more available to students. It provides examples of using a university's timetable data and personal event booking feeds in projects. It acknowledges that making data available is challenging within a large organization but provides suggestions like involving departments and management to prioritize it and using standards like JSON to share data openly with all students.
In Drazen talk, you will get a chance to listen to how Data Science Master 4.0 on Belgrade University was created, and what are the benefits of the program.
Big Data Analytics (ML, DL, AI) hands-onDony Riyanto
Ini adalah slide tambahan dari materi pengenalan Big Data Analytics (di file berikutnya), yang mengajak kita mulai hands-on dengan beberapa hal terkait Machine/Deep Learning, Big Data (batch/streaming), dan AI menggunakan Tensor Flow
Data Architecture - The Foundation for Enterprise Architecture and GovernanceDATAVERSITY
Organizations are faced with an increasingly complex data landscape, finding themselves unable to cope with exponentially increasing data volumes, compounded by additional regulatory requirements with increased fines for non-compliance. Enterprise architecture and data governance are often discussed at length, but often with different stakeholder audiences. This can result in complementary and sometimes conflicting initiatives rather than a focused, integrated approach. Data governance requires a solid data architecture foundation in order to support the pillars of enterprise architecture. In this session, IDERA’s Ron Huizenga will discuss a practical, integrated approach to effectively understand, define and implement an cohesive enterprise architecture and data governance discipline with integrated modeling and metadata management.
Databricks + Snowflake: Catalyzing Data and AI InitiativesDatabricks
"Combining Databricks, the unified analytics platform with Snowflake, the data warehouse built for the cloud is a powerful combo.
Databricks offers the ability to process large amounts of data reliably, including developing scalable AI projects. Snowflake offers the elasticity of a cloud-based data warehouse that centralizes the access to data. Databricks brings the unparalleled utility of being based on a mature distributed big data processing and AI-enabled tool to the table, capable of integrating with nearly every technology, from message queues (e.g. Kafka) to databases (e.g. Snowflake) to object stores (e.g. S3) and AI tools (e.g. Tensorflow).
Key Takeaways:
How Databricks & Snowflake work;
Why they're so powerful;
How Databricks + Snowflake symbiotically catalyze analytics and AI initiatives"
The document provides an overview of the key aspects of the European Union's General Data Protection Regulation (GDPR). It discusses definitions like personal data, the rights of individuals as data subjects, and key principles of GDPR around consent, data breaches, international transfers, the right to be forgotten, and privacy by design. It outlines actors like controllers and processors, their obligations, and components of GDPR compliance like impact assessments, authorities, and fines for non-compliance.
JSON Data Modeling in Document DatabaseDATAVERSITY
Making the move to a document database can be intimidating. Yes, its flexible data model gives you a lot of choices, but it also raises questions: Which way is the right way? Is a document database even the right tool?
Join this live session on the basics of data modeling with JSON to learn:
- How a document database compares to a traditional RDBMS
- What JSON data modeling means for your application code
- Which tools might be helpful along the way
The data lake has become extremely popular, but there is still confusion on how it should be used. In this presentation I will cover common big data architectures that use the data lake, the characteristics and benefits of a data lake, and how it works in conjunction with a relational data warehouse. Then I’ll go into details on using Azure Data Lake Store Gen2 as your data lake, and various typical use cases of the data lake. As a bonus I’ll talk about how to organize a data lake and discuss the various products that can be used in a modern data warehouse.
This document provides an overview of big data and Hadoop. It defines big data as large volumes of diverse data that cannot be processed by traditional systems. Key characteristics are volume, velocity, variety, and veracity. Popular sources of big data include social media, emails, videos, and sensor data. Hadoop is presented as an open-source framework for distributed storage and processing of large datasets across clusters of computers. It uses HDFS for storage and MapReduce as a programming model. Major tech companies like Google, Facebook, and Amazon are discussed as big players in big data.
The document discusses the role of a full-stack data scientist. It begins with an introduction of the author, Alexey Grigorev, as a data scientist. It then outlines the plan to discuss the data science process, roles in a data science team, what defines a full-stack data scientist, and how to become a full-stack data scientist. It proceeds to explain the CRISP-DM process for data science projects. It describes the different roles in a data science team including product manager, data analyst, data engineer, data scientist, and ML engineer. It defines a full-stack data scientist as someone who can work across the entire data science lifecycle and discusses the breadth of skills required to become a
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
* Free Data Engineering Bootcamp - https://www.youtube.com/playlist?list=PLf0swTFhTI8pBe2Vr2neQV7shh9Rus8rl
* Join our Meetup group - https://www.meetup.com/itversityin/
* Enroll for our labs - https://labs.itversity.com/plans
* Subscribe to our YouTube Channel for Videos - http://youtube.com/itversityin/?sub_confirmation=1
* Access Content via our GitHub - https://github.com/dgadiraju/itversity-books
* Lab and Content Support using Slack
This document provides an overview of big data. It defines big data as large volumes of diverse data that are growing rapidly and require new techniques to capture, store, distribute, manage, and analyze. The key characteristics of big data are volume, velocity, and variety. Common sources of big data include sensors, mobile devices, social media, and business transactions. Tools like Hadoop and MapReduce are used to store and process big data across distributed systems. Applications of big data include smarter healthcare, traffic control, and personalized marketing. The future of big data is promising with the market expected to grow substantially in the coming years.
Data Warehouse - Incremental Migration to the CloudMichael Rainey
A data warehouse (DW) migration is no small undertaking, especially when moving from on-premises to the cloud. A typical data warehouse has numerous data sources connecting and loading data into the DW, ETL tools and data integration scripts performing transformations, and reporting, advanced analytics, or ad-hoc query tools accessing the data for insights and analysis. That’s a lot to coordinate and the data warehouse cannot be migrated all at once. Using a data replication technology such as Oracle GoldenGate, the data warehouse migration can be performed incrementally by keeping the data in-sync between the original DW and the new, cloud DW. This session will dive into the steps necessary for this incremental migration approach and walk through a customer use case scenario, leaving attendees with an understanding of how to perform a data warehouse migration to the cloud.
Presented at RMOUG Training Days 2019
The document discusses modern data architectures. It presents conceptual models for data ingestion, storage, processing, and insights/actions. It compares traditional vs modern architectures. The modern architecture uses a data lake for storage and allows for on-demand analysis. It provides an example of how this could be implemented on Microsoft Azure using services like Azure Data Lake Storage, Azure Data Bricks, and Azure Data Warehouse. It also outlines common data management functions such as data governance, architecture, development, operations, and security.
DataOps: Nine steps to transform your data science impact Strata London May 18Harvinder Atwal
According to Forrester Research, only 22% of companies are currently seeing a significant return from data science expenditures. Most data science implementations are high-cost IT projects, local applications that are not built to scale for production workflows, or laptop decision support projects that never impact customers. Despite this high failure rate, we keep hearing the same mantra and solutions over and over again. Everybody talks about how to create models, but not many people talk about getting them into production where they can impact customers.
Harvinder Atwal offers an entertaining and practical introduction to DataOps, a new and independent approach to delivering data science value at scale, used at companies like Facebook, Uber, LinkedIn, Twitter, and eBay. The key to adding value through DataOps is to adapt and borrow principles from Agile, Lean, and DevOps. However, DataOps is not just about shipping working machine learning models; it starts with better alignment of data science with the rest of the organization and its goals. Harvinder shares experience-based solutions for increasing your velocity of value creation, including Agile prioritization and collaboration, new operational processes for an end-to-end data lifecycle, developer principles for data scientists, cloud solution architectures to reduce data friction, self-service tools giving data scientists freedom from bottlenecks, and more. The DataOps methodology will enable you to eliminate daily barriers, putting your data scientists in control of delivering ever-faster cutting-edge innovation for your organization and customers.
Mind Map of Big Data Technologies and ConceptsAmir Hadad
My first try to capture #bigdata related concepts and technologies in a #mindmap!
#NoSQL vs #SQL https://db-engines.com/
#Streaming vs #Batchprocessing https://lnkd.in/gNEdsrC
#BigDataSecurity https://lnkd.in/gA8e7RC
#OLAP https://lnkd.in/gh3z6r9
This document discusses spatial computing and its potential applications for utility GIS. It begins by providing context on the evolution of spatial computing technologies like digital twins and sensor webs. It then discusses several emerging ideas for spatial computing in utilities, such as using digital twins to model urban energy systems, integrating predictive models across domains, and enabling geo-enabled edge computing. Finally, it considers the technology evolution required to realize these opportunities through standards, interoperability, and integrating emerging techniques like semantics and artificial intelligence.
DataOps is a methodology and culture shift that brings the successful combination of development and operations (DevOps) to data processing environments. It breaks down silos between developers, data scientists, and operators, resulting in lean data feature development processes with quick feedback. In this presentation, we will explain the methodology, and focus on practical aspects of DataOps.
This is the Complete Information about Data Replication you need, i am focused on these topics:
What is replication?
Who use it?
Types ?
Implementation Methods?
This document discusses big data principles including what data is, why big data is important, how it differs from traditional data, and its key characteristics. Big data is characterized by volume, variety, and velocity. It comes from many sources and in many formats. Tools like Hadoop enable storage and analysis at scale. Applications include search, customer analytics, business optimization, health, and security. Benefits are better decisions and flexibility to store now and analyze later. The future of big data is predicted to be a $100 billion industry growing at 10% annually.
This document discusses using real data in student projects and making institutional data more available to students. It provides examples of using a university's timetable data and personal event booking feeds in projects. It acknowledges that making data available is challenging within a large organization but provides suggestions like involving departments and management to prioritize it and using standards like JSON to share data openly with all students.
In Drazen talk, you will get a chance to listen to how Data Science Master 4.0 on Belgrade University was created, and what are the benefits of the program.
The document provides an overview of big data analytics. It defines big data as high-volume, high-velocity, and high-variety information assets that require cost-effective and innovative forms of processing for insights and decision making. Big data is characterized by the 3Vs - volume, velocity, and variety. The emergence of big data is driven by the massive amount of data now being generated and stored, availability of open source tools, and commodity hardware. The course will cover Apache Hadoop, Apache Spark, streaming analytics, visualization, linked data analysis, and big data systems and AI solutions.
Elementary Data Analysis with MS excel_Day-1Redwan Ferdous
This document provides an overview of an elementary data analysis course using MS Excel. The 6-day course will introduce basic concepts like data, data types, and data analysis processes. It will cover collecting, cleaning, and analyzing data in Excel. Topics will include functions, formulas, charts, pivot tables, and more. The goal is to help professionals and students better understand and utilize data through hands-on Excel training and examples.
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.
The data economy is driving an incredible rate of innovation. New job roles are emerging, existing job roles are evolving. While much of the hype has focused on the data scientist role it's just one of many.
8 th International Conference on Data Mining (DaMi 2022)IJDKP
8
th International Conference on Data Mining (DaMi 2022) Conference provides a forum for
researchers who address this issue and to present their work in a peer-reviewed forum.
Authors are solicited to contribute to the conference by submitting articles that illustrate research
results, projects, surveying works and industrial experiences that describe significant advances in
the following areas, but are not limited to.
Data Scientist Certification in Kozhikode-JuneDataMites
Data science is an interdisciplinary field that combines statistical analysis, machine learning, data engineering, and domain expertise to extract meaningful insights and knowledge from structured and unstructured data.
For more info visit: https://datamites.com/data-science-course-training-kozhikode/
Alexander Y. Kyei Jr. is a computer science student at the University of Maryland expected to graduate in May 2016 with a 3.22 GPA. He has experience as an intern at Liberty Mutual Insurance where he contributed to software releases, created training documents, and worked on an API project. He also works as a system administrator for the University of Maryland Computer Science Department, providing support and services for various operating systems. In a hackathon, he created an application called Hitch! to allow users to carpool with others traveling similar routes.
This document summarizes a presentation on data visualization and school finance. It discusses using data to build trust and relationships, defines data and how it is used, and provides examples of quantitative and qualitative data tools. It also shows examples of viral school finance reports and the school budgeting process. The presentation aims to demonstrate how data can be presented to understand school funding issues.
Roadmaps, Roles and Re-engineering: Developing Data Informatics Capability in...LIBER Europe
A presentation by Dr. Liz Lyon of the United Kingdom Office for Library and Information Networking, as given at LIBER's 42nd annual conference in Munich, Germany.
Creating a resume for a fresher looking to start a career in Business Analytics or as a Business Consultant should focus on highlighting relevant education, skills, and any internship or project experiences that demonstrate your analytical and problem-solving abilities.
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
Launch of the Week: Eastern Washington UniversityLaura Faccone
Each week, Schneider Associates analyzes the most significant brand, product, campaign or idea launch of the week. Learn more about launch at www.schneiderpr.com or email launch@schneiderpr.com.
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 from IBM discusses the emerging roles in big data and analytics. It identifies several new roles including data scientist, data architect, data policy professional, data developer, business analyst, and chief data officer. For each role, the document outlines the core skills and disciplines involved, as well as example degree programs that could prepare individuals for each position. It also provides common job titles associated with each analytics role.
Whether you call it data munging, data cleansing, or data wrangling, everyone agrees that data preparation activities account for 80% of analysts’ time, leaving only 20% for analysis. Shifting this work to more specialized talent represents a major source of data analysis productivity improvements. This program “walks” through the major preparation categories including collection, evaluation, evolution, access design, and storage requirements. Understanding each in context also provides opportunities to develop complementary Data Governance/ethics frameworks. A generalized approach is presented.
Learning objectives:
- Appreciate the savings that can accrue from transforming data preparation from one-off to an improvable process
- Recognize what data preparation knowledge/skills your organization has and/or needs
- Better know the transformations that data can survive as it is prepared to be analyzed
Big Data and Clouds: Research and EducationGeoffrey Fox
Presentation September 9 2013 PPAM 2013 Warsaw
Economic Imperative: There are a lot of data and a lot of jobs
Computing Model: Industry adopted clouds which are attractive for data analytics. HPC also useful in some cases
Progress in scalable robust Algorithms: new data need different algorithms than before
Progress in Data Intensive Programming Models
Progress in Data Science Education: opportunities at universities
Data-centric design and the knowledge graphAlan Morrison
The #knowledgegraph--smart data that can describe your business and its domains--is now eating software. We won't be able to scale AI or other emerging tech without knowledge graphs, because those techs all require a transformed data foundation, large-scale integration, and shared data infrastructure.
Key to knowledge graphs are #semantics, #graphdatabase technology and a Tinker Toy-style approach to adding the missing verbs (which provide connections and context) back into your data. A knowledge graph foundation provides a means of contextualizing business domains, your content and other data, for #AI at scale.
This is from a talk I gave at the Data Centric Design for SMART DATA & CONTENT Enthusiasts meetup on July 31, 2019 at PwC Chicago. Thanks to Mary Yurkovic and Matt Turner for a very fun event!.
Similar to What makes it worth becoming a Data Engineer? (20)
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.
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Natural Language Processing (NLP), RAG and its applications .pptxfkyes25
1. In the realm of Natural Language Processing (NLP), knowledge-intensive tasks such as question answering, fact verification, and open-domain dialogue generation require the integration of vast and up-to-date information. Traditional neural models, though powerful, struggle with encoding all necessary knowledge within their parameters, leading to limitations in generalization and scalability. The paper "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" introduces RAG (Retrieval-Augmented Generation), a novel framework that synergizes retrieval mechanisms with generative models, enhancing performance by dynamically incorporating external knowledge during inference.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
3. Plan
• What is Data?
• What is Data Engineering?
• Data Engineer vs. Data Scientist vs. Data Analyst
• Data Engineer Jobs / Required Skills
• Universities teaching data engineering
• Helpful Tips
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• Lebanese University – Faculty of Sciences:
• Master in Information Systems and Risk Management
• Master in Information Systems and Data Intelligence
• Lebanese University – Faculty of Economics and Business
Administration:
• BS, Master in Business Computing
• Lebanese University – Faculty of Information:
• BS in Data Science
• Lebanese University – ISAE CNAM
• Master in Big Data Management
• Saint-Joseph University of Beirut – Faculty of Sciences:
• BS, Master, Ph.D. in Data Science
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Even if other universities doesn’t have data
specializations but recently most of the universities
added the data science, Big Data and other related
courses to the computer science major.
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Earning a data science / data engineering degree is not
the only way!
Practice… Practice… Practice…
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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