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
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.
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.
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.
This document provides an overview of a data catalog called Amundsen that was created to improve the productivity of data users. Amundsen indexes data resources and powers search based on usage patterns to help users discover, understand, and analyze data. It aims to reduce the time data scientists spend on data discovery activities from one-third to increase their productivity. The tool provides search of metadata from various data sources and displays table details, column metadata stats, and people profiles to help users find and understand corporate data.
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.
Data Warehouse or Data Lake, Which Do I Choose?DATAVERSITY
Today’s data-driven companies have a choice to make – where do we store our data? As the move to the cloud continues to be a driving factor, the choice becomes either the data warehouse (Snowflake et al) or the data lake (AWS S3 et al). There are pro’s and con’s for each approach. While the data warehouse will give you strong data management with analytics, they don’t do well with semi-structured and unstructured data with tightly coupled storage and compute, not to mention expensive vendor lock-in. On the other hand, data lakes allow you to store all kinds of data and are extremely affordable, but they’re only meant for storage and by themselves provide no direct value to an organization.
Enter the Open Data Lakehouse, the next evolution of the data stack that gives you the openness and flexibility of the data lake with the key aspects of the data warehouse like management and transaction support.
In this webinar, you’ll hear from Ali LeClerc who will discuss the data landscape and why many companies are moving to an open data lakehouse. Ali will share more perspective on how you should think about what fits best based on your use case and workloads, and how some real world customers are using Presto, a SQL query engine, to bring analytics to the data lakehouse.
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
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.
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.
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.
This document provides an overview of a data catalog called Amundsen that was created to improve the productivity of data users. Amundsen indexes data resources and powers search based on usage patterns to help users discover, understand, and analyze data. It aims to reduce the time data scientists spend on data discovery activities from one-third to increase their productivity. The tool provides search of metadata from various data sources and displays table details, column metadata stats, and people profiles to help users find and understand corporate data.
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.
Data Warehouse or Data Lake, Which Do I Choose?DATAVERSITY
Today’s data-driven companies have a choice to make – where do we store our data? As the move to the cloud continues to be a driving factor, the choice becomes either the data warehouse (Snowflake et al) or the data lake (AWS S3 et al). There are pro’s and con’s for each approach. While the data warehouse will give you strong data management with analytics, they don’t do well with semi-structured and unstructured data with tightly coupled storage and compute, not to mention expensive vendor lock-in. On the other hand, data lakes allow you to store all kinds of data and are extremely affordable, but they’re only meant for storage and by themselves provide no direct value to an organization.
Enter the Open Data Lakehouse, the next evolution of the data stack that gives you the openness and flexibility of the data lake with the key aspects of the data warehouse like management and transaction support.
In this webinar, you’ll hear from Ali LeClerc who will discuss the data landscape and why many companies are moving to an open data lakehouse. Ali will share more perspective on how you should think about what fits best based on your use case and workloads, and how some real world customers are using Presto, a SQL query engine, to bring analytics to the data lakehouse.
Architecting Agile Data Applications for ScaleDatabricks
Data analytics and reporting platforms historically have been rigid, monolithic, hard to change and have limited ability to scale up or scale down. I can’t tell you how many times I have heard a business user ask for something as simple as an additional column in a report and IT says it will take 6 months to add that column because it doesn’t exist in the datawarehouse. As a former DBA, I can tell you the countless hours I have spent “tuning” SQL queries to hit pre-established SLAs. This talk will talk about how to architect modern data and analytics platforms in the cloud to support agility and scalability. We will include topics like end to end data pipeline flow, data mesh and data catalogs, live data and streaming, performing advanced analytics, applying agile software development practices like CI/CD and testability to data applications and finally taking advantage of the cloud for infinite scalability both up and down.
Using a Semantic and Graph-based Data Catalog in a Modern Data FabricCambridge Semantics
Watch this webinar to learn about the benefits of using semantic and graph database technology to create a Data Catalog of all of an enterprise's data, regardless of source or format, as part of a modern IT or data management stack and an important step toward building an Enterprise Data Fabric.
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
BI Consultancy - Data, Analytics and StrategyShivam Dhawan
The presentation describes my views around the data we encounter in digital businesses like:
- Looking at common Data collection methodologies,
-What are the common issues within the decision support system and optimiztion lifecycle,
- Where are most of failing?
and most importantly, "How to connect the dots and move from Data to Strategy?"
I work with all facets of Web Analytics and Business Strategy and see the structures and governance models of various domains to establish and analyze the key performance indicators that allow you to have a 360º overview of online and offline multi-channel environment.
Apart from my experience with the leading analytic tools in the market like Google Analytics, Omniture and BI tools for Big Data, I am developing new solutions to solve complex digital / business problems.
As a resourceful consultant, I can connect with your team in any modality or in any form that meets your needs and solves any data/strategy problem.
Henry Peyret Presentation - Data Governance 2.0.
Based on the analysis of Digital Transformation and Values Transformation, Forrester gives its insight and orientations in terms of Data Governance 2.0 and Data Citizenship.
Data Lakehouse Symposium | Day 1 | Part 1Databricks
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
In this session, Sergio covered the Lakehouse concept and how companies implement it, from data ingestion to insight. He showed how you could use Azure Data Services to speed up your Analytics project from ingesting, modelling and delivering insights to end users.
[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.
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.
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.
Making Data Timelier and More Reliable with Lakehouse TechnologyMatei Zaharia
Enterprise data architectures usually contain many systems—data lakes, message queues, and data warehouses—that data must pass through before it can be analyzed. Each transfer step between systems adds a delay and a potential source of errors. What if we could remove all these steps? In recent years, cloud storage and new open source systems have enabled a radically new architecture: the lakehouse, an ACID transactional layer over cloud storage that can provide streaming, management features, indexing, and high-performance access similar to a data warehouse. Thousands of organizations including the largest Internet companies are now using lakehouses to replace separate data lake, warehouse and streaming systems and deliver high-quality data faster internally. I’ll discuss the key trends and recent advances in this area based on Delta Lake, the most widely used open source lakehouse platform, which was developed at Databricks.
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.
Business Intelligence & Data Analytics– An Architected ApproachDATAVERSITY
Business intelligence (BI) and data analytics are increasing in popularity as more organizations are looking to become more data-driven. Many tools have powerful visualization techniques that can create dynamic displays of critical information. To ensure that the data displayed on these visualizations is accurate and timely, a strong Data Architecture is needed. Join this webinar to understand how to create a robust Data Architecture for BI and data analytics that takes both business and technology needs into consideration.
Data Architecture Strategies: Data Architecture for Digital TransformationDATAVERSITY
MDM, data quality, data architecture, and more. At the same time, combining these foundational data management approaches with other innovative techniques can help drive organizational change as well as technological transformation. This webinar will provide practical steps for creating a data foundation for effective digital transformation.
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.
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.
Building Lakehouses on Delta Lake with SQL Analytics PrimerDatabricks
You’ve heard the marketing buzz, maybe you have been to a workshop and worked with some Spark, Delta, SQL, Python, or R, but you still need some help putting all the pieces together? Join us as we review some common techniques to build a lakehouse using Delta Lake, use SQL Analytics to perform exploratory analysis, and build connectivity for BI applications.
Data Lakehouse Symposium | Day 1 | Part 2Databricks
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
Delta Lake brings reliability, performance, and security to data lakes. It provides ACID transactions, schema enforcement, and unified handling of batch and streaming data to make data lakes more reliable. Delta Lake also features lightning fast query performance through its optimized Delta Engine. It enables security and compliance at scale through access controls and versioning of data. Delta Lake further offers an open approach and avoids vendor lock-in by using open formats like Parquet that can integrate with various ecosystems.
This document discusses big data analytics. It provides links to resources on big data from different views, the roles in big data, and the data analytics lifecycle. It also gives tips for optimizing the use of big data, including moving big data out of IT silos, separating dirty and clean data, focusing on predictive analytics, and developing skills. Additionally, it lists 8 trends in big data analytics such as big data in the cloud, Hadoop as the new data operating system, big data lakes without prior database design, more predictive analytics, SQL on Hadoop, more and better NoSQL, deep learning, and in-memory analytics. The document concludes with an invitation for questions.
Data engineering is the practice of designing and building systems for collecting, storing, and analyzing data at scale. It allows organizations to collect massive amounts of data and ensure the data is highly usable by data scientists and analysts. As data volumes continue to grow exponentially, data engineers are needed to process and channel data to enable fields like machine learning and deep learning.
Architecting Agile Data Applications for ScaleDatabricks
Data analytics and reporting platforms historically have been rigid, monolithic, hard to change and have limited ability to scale up or scale down. I can’t tell you how many times I have heard a business user ask for something as simple as an additional column in a report and IT says it will take 6 months to add that column because it doesn’t exist in the datawarehouse. As a former DBA, I can tell you the countless hours I have spent “tuning” SQL queries to hit pre-established SLAs. This talk will talk about how to architect modern data and analytics platforms in the cloud to support agility and scalability. We will include topics like end to end data pipeline flow, data mesh and data catalogs, live data and streaming, performing advanced analytics, applying agile software development practices like CI/CD and testability to data applications and finally taking advantage of the cloud for infinite scalability both up and down.
Using a Semantic and Graph-based Data Catalog in a Modern Data FabricCambridge Semantics
Watch this webinar to learn about the benefits of using semantic and graph database technology to create a Data Catalog of all of an enterprise's data, regardless of source or format, as part of a modern IT or data management stack and an important step toward building an Enterprise Data Fabric.
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
BI Consultancy - Data, Analytics and StrategyShivam Dhawan
The presentation describes my views around the data we encounter in digital businesses like:
- Looking at common Data collection methodologies,
-What are the common issues within the decision support system and optimiztion lifecycle,
- Where are most of failing?
and most importantly, "How to connect the dots and move from Data to Strategy?"
I work with all facets of Web Analytics and Business Strategy and see the structures and governance models of various domains to establish and analyze the key performance indicators that allow you to have a 360º overview of online and offline multi-channel environment.
Apart from my experience with the leading analytic tools in the market like Google Analytics, Omniture and BI tools for Big Data, I am developing new solutions to solve complex digital / business problems.
As a resourceful consultant, I can connect with your team in any modality or in any form that meets your needs and solves any data/strategy problem.
Henry Peyret Presentation - Data Governance 2.0.
Based on the analysis of Digital Transformation and Values Transformation, Forrester gives its insight and orientations in terms of Data Governance 2.0 and Data Citizenship.
Data Lakehouse Symposium | Day 1 | Part 1Databricks
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
In this session, Sergio covered the Lakehouse concept and how companies implement it, from data ingestion to insight. He showed how you could use Azure Data Services to speed up your Analytics project from ingesting, modelling and delivering insights to end users.
[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.
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.
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.
Making Data Timelier and More Reliable with Lakehouse TechnologyMatei Zaharia
Enterprise data architectures usually contain many systems—data lakes, message queues, and data warehouses—that data must pass through before it can be analyzed. Each transfer step between systems adds a delay and a potential source of errors. What if we could remove all these steps? In recent years, cloud storage and new open source systems have enabled a radically new architecture: the lakehouse, an ACID transactional layer over cloud storage that can provide streaming, management features, indexing, and high-performance access similar to a data warehouse. Thousands of organizations including the largest Internet companies are now using lakehouses to replace separate data lake, warehouse and streaming systems and deliver high-quality data faster internally. I’ll discuss the key trends and recent advances in this area based on Delta Lake, the most widely used open source lakehouse platform, which was developed at Databricks.
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.
Business Intelligence & Data Analytics– An Architected ApproachDATAVERSITY
Business intelligence (BI) and data analytics are increasing in popularity as more organizations are looking to become more data-driven. Many tools have powerful visualization techniques that can create dynamic displays of critical information. To ensure that the data displayed on these visualizations is accurate and timely, a strong Data Architecture is needed. Join this webinar to understand how to create a robust Data Architecture for BI and data analytics that takes both business and technology needs into consideration.
Data Architecture Strategies: Data Architecture for Digital TransformationDATAVERSITY
MDM, data quality, data architecture, and more. At the same time, combining these foundational data management approaches with other innovative techniques can help drive organizational change as well as technological transformation. This webinar will provide practical steps for creating a data foundation for effective digital transformation.
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.
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.
Building Lakehouses on Delta Lake with SQL Analytics PrimerDatabricks
You’ve heard the marketing buzz, maybe you have been to a workshop and worked with some Spark, Delta, SQL, Python, or R, but you still need some help putting all the pieces together? Join us as we review some common techniques to build a lakehouse using Delta Lake, use SQL Analytics to perform exploratory analysis, and build connectivity for BI applications.
Data Lakehouse Symposium | Day 1 | Part 2Databricks
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
Delta Lake brings reliability, performance, and security to data lakes. It provides ACID transactions, schema enforcement, and unified handling of batch and streaming data to make data lakes more reliable. Delta Lake also features lightning fast query performance through its optimized Delta Engine. It enables security and compliance at scale through access controls and versioning of data. Delta Lake further offers an open approach and avoids vendor lock-in by using open formats like Parquet that can integrate with various ecosystems.
This document discusses big data analytics. It provides links to resources on big data from different views, the roles in big data, and the data analytics lifecycle. It also gives tips for optimizing the use of big data, including moving big data out of IT silos, separating dirty and clean data, focusing on predictive analytics, and developing skills. Additionally, it lists 8 trends in big data analytics such as big data in the cloud, Hadoop as the new data operating system, big data lakes without prior database design, more predictive analytics, SQL on Hadoop, more and better NoSQL, deep learning, and in-memory analytics. The document concludes with an invitation for questions.
Data engineering is the practice of designing and building systems for collecting, storing, and analyzing data at scale. It allows organizations to collect massive amounts of data and ensure the data is highly usable by data scientists and analysts. As data volumes continue to grow exponentially, data engineers are needed to process and channel data to enable fields like machine learning and deep learning.
A presentation delivered by Mohammed Barakat on the 2nd Jordanian Continuous Improvement Open Day in Amman. The presentation is about Data Science and was delivered on 3rd October 2015.
This document outlines the syllabus for an Advance Operations Management course taught in 2013/2014 at Universitas Bengkulu in Indonesia. The course will be held on Thursdays from 2-4:30pm in room A-7 and will be taught by Dr. Willy Abdillah and Berto Usman. The course will cover topics such as product design, quality management, forecasting, supply chain management, and inventory management. Students will learn about operations management functions and how goods and services are produced. Grading will be based on a midterm exam, final exam, class participation, and a 10-15 page individual paper on an operations management topic.
get more idea about data science & how its works
http://techwaala.in/whats-data-science/about
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This is a presentation on data science in this presentation machine learning algorithems are explained with a brief description of artificial intellignece
According to recent research report by Wall Street Journal, AI project failure rates near 50%, more than 53% terminates at proof of concept level and does not make it to production. Gartner report says that nearly 80% of the analytics projects are not delivering any business value. That means for every 10 projects, only 2 projects are useful to the organization. Let us pause here a moment, rather than looking at what makes AI projects to fail, let’s look at the challenges involved in AI projects and find a solution to overcome these challenges.
AI projects are different from traditional software projects. Typical software projects, as shown in Figure 1, consist of well-defined software requirements, high level design, coding, unit testing, system testing, and deployment along with beta testing or field testing. Now, organizations are adopting Agile process instead of traditional V or waterfall model, but still steps mentioned are valid.
However, AI and Machine Learning projects’ methodology is different from the above. Our experience working on many AI/ML projects has given us insights on some of the challenges of executing AI projects. Also, we are in regular touch with senior executives and thought leaders from different industries who understand the success formula. The following discussion is based on our practical experience and knowledge gained in the field.
Successful execution of AI projects depends on the following factors:
1. Clearly aligned Business Expectations
2. Clarity on Terminologies
3. Meeting Data Requirements
4. Tools and Technology
5. Right Resources
6. Understanding Output Results
7. Project Planning and the Process
This document contains the resume of Pativada R Santosh Naidu. It summarizes his objective, work experience, projects, areas of expertise, education, strengths, and personal details. According to the resume, Santosh has over 2 years of experience as a web developer working with PHP, CMS, and WordPress. He has also worked on security projects related to vulnerability detection in PHP applications. Santosh holds an M.Tech in Computer Networking and Information Security and has published several papers in conferences and journals. He is seeking a position where he can apply his skills and passion for technology.
IRJET - Student Future Prediction System under Filtering MechanismIRJET Journal
This document describes a student future prediction system that uses data from student ID card usage to monitor student activities and interests in order to predict their future and provide notifications and suggestions. The system collects data on student usage of campus facilities like the fitness center and analyzes usage patterns over time using models like seasonal naive, ARIMA, and random forest. It finds the random forest model best fits the dataset. The system aims to demonstrate how this untapped data source can provide insights into student behavior and be used to improve student services. It discusses the hardware, software, and technologies used as well as data flow, modules, and design/implementation constraints.
Pativada R Santosh Naidu is a web developer and information security consultant seeking new opportunities. He has over 2 years of experience in web development using PHP, CMS tools like Joomla, and WordPress. He is proficient in HTML, PHP, Java Script, web design and Linux administration. Santosh holds an M.Tech in Computer Networking and Information Security and has published several papers in security and networking. He is passionate about technology and excels at understanding requirements and providing appropriate solutions. His strengths include self-confidence, teamwork, and flexibility in maintaining relationships.
Boost Your Data Career with Predictive Analytics! Learn How ? Edureka!
Advanced Predictive Modelling in R will allow one to gain an edge over other Data analysts and present the data in a much better and insightful manner.
This would help the learner to immediately implement these technique and create analysis and support decision making in the most scientific manner.
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
1. The document describes a search engine scraper that extracts data from websites, summarizes the extracted information, and converts it into a relevant result for users.
2. The search engine scraper works in three stages: extraction of data from website content, summarization of the extracted data using natural language processing techniques, and conversion of the summarized data into a meaningful format for users.
3. The summarization stage uses natural language toolkit processing libraries to determine sentence similarity, assign weights to sentences, and select sentences with higher ranks to include in the summary.
Intro to big data and applications - day 2Parviz Vakili
The document provides an introduction and references for a presentation on big data and applications. It includes sections on data architecture, data governance, data modeling and design, and reference architectures for big data analytics. The presentation template was created by Slidesgo and credits are provided.
Sohan Mittal is seeking a challenging career in a progressive organization where he can enhance his knowledge and skills in computing and research. He has a M.Tech in computer science from MDU University Rohtak with an 8.3 CGPA through 3 semesters. He obtained his B.Tech in information technology from Advanced Institute of Tech. and Mgmt. with 91.5%. His areas of interest include ad hoc networking, information retrieval, and network security. He has 2 years of work experience as a system administrator where he managed networks, user accounts, and troubleshooting issues. He is proficient in programming languages like C, C++, Java and tools like NS-2, OPNET and has completed
1) Data scientists are curious individuals who possess both technical skills like programming and mathematics/statistics abilities as well as business acumen. They make sense of large amounts of data to uncover trends and patterns that can help organizations.
2) Data scientists' responsibilities vary from developing machine learning algorithms to data mining and predictive modeling. Their daily tasks involve problem-solving through meetings and brainstorming.
3) Key skills for data scientists include programming, statistics, machine learning, and strong communication abilities. A background in these fields is common but not required, as passion and skills can qualify one for a career in data science.
Shumon Khan is seeking a position as a technical manager or project manager where he can utilize 11 years of experience in project management, Linux and Windows administration, Oracle database, and networking. He has managed numerous government and private projects in Bangladesh and abroad involving technologies like Oracle RAC, Data Guard, fusion middleware, and Linux. His career includes roles as a senior technology officer, country manager, and network administrator, and he has qualifications in fields like Oracle, project management, and networking.
Demystifying Data Science Webinar - February 14, 2018Analytics8
In this webinar, we talked about data science and machine learning. It is not as hard as you may think to get started; and once you do, you’ll see immediate business value.
This curriculum vitae is for Anuj Singh Thakur. He has an objective of working in software testing and quality assurance to improve his skills and help his organization. He has experience preparing test plans and writing test scripts. He has skills in C, C++, Python, JavaScript, Oracle11g, MySQL, and the software development life cycle. He completed a B.E. in 2013. His projects include extracting data from HTML pages using multiple technologies and storing it in a MySQL database. He is a quick learner and has a positive attitude.
The document discusses the rise of big data and data science. It notes that as data has grown exponentially from various sources like social media and IoT devices, new tools like Hadoop and data lakes have been developed to manage large, complex data. It also discusses the roles of data scientists and data engineers on data science teams and provides examples of how companies in various industries like retail, transportation and manufacturing are using big data and analytics for applications like fraud detection, recommendation systems, and predictive maintenance. The document advocates for organizations to build cross-functional data teams and develop a data-driven culture focused on training employees to extract insights from data.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
"Financial Odyssey: Navigating Past Performance Through Diverse Analytical Lens"sameer shah
Embark on a captivating financial journey with 'Financial Odyssey,' our hackathon project. Delve deep into the past performance of two companies as we employ an array of financial statement analysis techniques. From ratio analysis to trend analysis, uncover insights crucial for informed decision-making in the dynamic world of finance."
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
1. What is “Data Engineering?”
Data Engineering Lab.
Kim Yong Dam
DataPub 12/3
Data Engineering Lab. in Sogang Univ.
2. <Contents>
1. Introduction
2. What is Data Engineering?
3. Role of Data Engineer
4. What I’m doing..?
5. Future Work
DataPub 12/3
Data Engineering Lab. in Sogang Univ.
4. DataPub 12/3
Data Engineering Lab. in Sogang Univ.
Introduction
https://blog.hackerrank.com/the-biggest-misconception-about-data-scientists/
5. DataPub 12/3
Data Engineering Lab. in Sogang Univ.
Introduction
https://blog.hackerrank.com/the-biggest-misconception-about-data-scientists/
Features / value
6. DataPub 12/3
Data Engineering Lab. in Sogang Univ.
Introduction
https://blog.hackerrank.com/the-biggest-misconception-about-data-scientists/
Features / value
Price / Analysis
7. DataPub 12/3
Data Engineering Lab. in Sogang Univ.
Introduction
https://blog.hackerrank.com/the-biggest-misconception-about-data-scientists/
Features / value
???? Price / Analysis
8. DataPub 12/3
Data Engineering Lab. in Sogang Univ.
Introduction
https://blog.hackerrank.com/the-biggest-misconception-about-data-scientists/
Features / value
Optimization Price / Analysis
9. DataPub 12/3
Data Engineering Lab. in Sogang Univ.
Introduction
https://blog.hackerrank.com/the-biggest-misconception-about-data-scientists/
Features / value
Optimization Price / Analysis
How?
11. DataPub 12/3
Data Engineering Lab. in Sogang Univ.
Data Engineering
https://blog.hackerrank.com/the-biggest-misconception-about-data-scientists/
Tons of to do..
Tons of to do..
Tons of to do..
Tons of to do..
12. DataPub 12/3
Data Engineering Lab. in Sogang Univ.
https://blog.hackerrank.com/the-biggest-misconception-about-data-scientists/
Tons of to do..
Tons of to do..
Tons of to do..
Tons of to do..
Build Systems
with respect to
each data domain
Data Engineering
13. DataPub 12/3
Data Engineering Lab. in Sogang Univ.
https://blog.hackerrank.com/the-biggest-misconception-about-data-scientists/
Tons of to do..
Tons of to do..
Tons of to do..
Tons of to do..
“On Computer Architecture”
Data Engineering
15. DataPub 12/3
Data Engineering Lab. in Sogang Univ.
Role of Data Engineer
https://jobs.apple.com/us/search?job=86260820&openJobId=86260820#&ss=Data%20Engineer&t=0&so=&pN=0&openJobId=99607161
16. DataPub 12/3
Data Engineering Lab. in Sogang Univ.
Role of Data Engineer
https://cloud.google.com/certification/data-engineer
17. DataPub 12/3
Data Engineering Lab. in Sogang Univ.
Role of Data Engineer
https://cloud.google.com/certification/data-engineer
18. DataPub 12/3
Data Engineering Lab. in Sogang Univ.
Role of Data Engineer
https://cloud.google.com/certification/data-engineer
19. DataPub 12/3
Data Engineering Lab. in Sogang Univ.
Role of Data Engineer
1. Designing data processing systems
2. Building and maintaining data structures and databases
3. Analyzing data and enabling machine learning
4. Modeling business processes for analysis and optimization
5. Ensuring reliability
6. Visualizing data and advocating policy
7. Designing for security and compliance
20. DataPub 12/3
Data Engineering Lab. in Sogang Univ.
Role of Data Engineer
1. Designing data processing systems
2. Building and maintaining data structures and databases
3. Analyzing data and enabling machine learning
4. Modeling business processes for analysis and optimization
5. Ensuring reliability
6. Visualizing data and advocating policy
7. Designing for security and compliance
“Should focus on something”
22. DataPub 12/3
Data Engineering Lab. in Sogang Univ.
My Voyage
1. Designing data processing systems
2. Building and maintaining data structures and databases
3. Analyzing data and enabling machine learning
4. Modeling business processes for analysis and optimization
5. Ensuring reliability
6. Visualizing data and advocating policy
7. Designing for security and compliance
23. DataPub 12/3
Data Engineering Lab. in Sogang Univ.
My Voyage
1. Designing data processing systems
2. Building and maintaining data structures and databases
3. Analyzing data and enabling machine learning
4. Modeling business processes for analysis and optimization
5. Ensuring reliability
6. Visualizing data and advocating policy
7. Designing for security and compliance
For what?
For what?
For what?
24. DataPub 12/3
Data Engineering Lab. in Sogang Univ.
My Voyage
http://www.ibmbigdatahub.com/infographic/four-vs-big-data
25. DataPub 12/3
Data Engineering Lab. in Sogang Univ.
My Voyage
http://www.jobs.ac.uk/enhanced/industry/lifesciences-london/
26. DataPub 12/3
Data Engineering Lab. in Sogang Univ.
My Voyage
http://www.jobs.ac.uk/enhanced/industry/lifesciences-london/
Make a implemented connection
27. DataPub 12/3
Data Engineering Lab. in Sogang Univ.
My Voyage
http://www.jobs.ac.uk/enhanced/industry/lifesciences-london/
As a TEAM!
29. DataPub 12/3
Data Engineering Lab. in Sogang Univ.
Future Work
1. Tree Optimization for Spatial data in Non-Volatile Memory
2. Keyword Clustering for SNS data analysis
3. Clustering technique as unsupervised learning
4. Spatial Web Querying using Spatial Database
30. DataPub 12/3
Data Engineering Lab. in Sogang Univ.
Future Work
1. PB+ tree, R-tree for PCM
2. Ontology-based Keyword Clustering, Review on Sematic
Document Clustering
3. An efficient K-Means Algorithm integrated with Jaccard Distance
Measure for Document Clustering, A New Mallows Distance
Based Metric for Comparing Clusterings, Measuring Similarity
between Sets of Overlapping Clusters
4. Efficient Processing of Spatial Group Keyword Queries, Keyword
Search in Spatial Databases: Toward Searching by Document