The role of data engineering in data science and analytics practice. Presented in the Philippine Software Industry Association (PSIA) 40th Enablement Seminar.
Slides template by Slides Carnival (https://www.slidescarnival.com/)
Creating a clearly articulated data strategy—a roadmap of technology-driven capability investments prioritized to deliver value—helps ensure from the get-go that you are focusing on the right things, so that your work with data has a business impact. In this presentation, the experts at Silicon Valley Data Science share their approach for crafting an actionable and flexible data strategy to maximize business value.
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.
Organizations across most industries make some attempt to utilize Data Management and Data Strategies. While most organizations have both concepts implemented, they must fully understand the difference to fully achieve their goals.
This webinar will cover three lessons, each illustrated with examples, that will help you distinguish the difference between Data Strategy and Data Management processes and communicate their value to both internal and external decision-makers:
Understanding the difference between Data Strategy and Data Management
Prioritizing organizational Data Management needs vs. Data Strategy needs
Discuss foundational Data Management and Data Strategy concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)
Data Modelling 101 half day workshop presented by Chris Bradley at the Enterprise Data and Business Intelligence conference London on November 3rd 2014.
Chris Bradley is a leading independent information strategist.
Contact chris.bradley@dmadvisors.co.uk
Becoming a Data-Driven Organization - Aligning Business & Data StrategyDATAVERSITY
More organizations are aspiring to become ‘data driven businesses’. But all too often this aim fails, as business goals and IT & data realities are misaligned, with IT lagging behind rapidly changing business needs. So how do you get the perfect fit where data strategy is driven by and underpins business strategy? This webinar will show you how by de-mystifying the building blocks of a global data strategy and highlighting a number of real world success stories. Topics include:
•How to align data strategy with business motivation and drivers
•Why business & data strategies often become misaligned & the impact
•Defining the core building blocks of a successful data strategy
•The role of business and IT
•Success stories in implementing global data strategies
This describes a conceptual model approach to designing an enterprise data fabric. This is the set of hardware and software infrastructure, tools and facilities to implement, administer, manage and operate data operations across the entire span of the data within the enterprise across all data activities including data acquisition, transformation, storage, distribution, integration, replication, availability, security, protection, disaster recovery, presentation, analytics, preservation, retention, backup, retrieval, archival, recall, deletion, monitoring, capacity planning across all data storage platforms enabling use by applications to meet the data needs of the enterprise.
The conceptual data fabric model represents a rich picture of the enterprise’s data context. It embodies an idealised and target data view.
Designing a data fabric enables the enterprise respond to and take advantage of key related data trends:
• Internal and External Digital Expectations
• Cloud Offerings and Services
• Data Regulations
• Analytics Capabilities
It enables the IT function demonstrate positive data leadership. It shows the IT function is able and willing to respond to business data needs. It allows the enterprise to meet data challenges
• More and more data of many different types
• Increasingly distributed platform landscape
• Compliance and regulation
• Newer data technologies
• Shadow IT where the IT function cannot deliver IT change and new data facilities quickly
It is concerned with the design an open and flexible data fabric that improves the responsiveness of the IT function and reduces shadow IT.
Creating a clearly articulated data strategy—a roadmap of technology-driven capability investments prioritized to deliver value—helps ensure from the get-go that you are focusing on the right things, so that your work with data has a business impact. In this presentation, the experts at Silicon Valley Data Science share their approach for crafting an actionable and flexible data strategy to maximize business value.
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.
Organizations across most industries make some attempt to utilize Data Management and Data Strategies. While most organizations have both concepts implemented, they must fully understand the difference to fully achieve their goals.
This webinar will cover three lessons, each illustrated with examples, that will help you distinguish the difference between Data Strategy and Data Management processes and communicate their value to both internal and external decision-makers:
Understanding the difference between Data Strategy and Data Management
Prioritizing organizational Data Management needs vs. Data Strategy needs
Discuss foundational Data Management and Data Strategy concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)
Data Modelling 101 half day workshop presented by Chris Bradley at the Enterprise Data and Business Intelligence conference London on November 3rd 2014.
Chris Bradley is a leading independent information strategist.
Contact chris.bradley@dmadvisors.co.uk
Becoming a Data-Driven Organization - Aligning Business & Data StrategyDATAVERSITY
More organizations are aspiring to become ‘data driven businesses’. But all too often this aim fails, as business goals and IT & data realities are misaligned, with IT lagging behind rapidly changing business needs. So how do you get the perfect fit where data strategy is driven by and underpins business strategy? This webinar will show you how by de-mystifying the building blocks of a global data strategy and highlighting a number of real world success stories. Topics include:
•How to align data strategy with business motivation and drivers
•Why business & data strategies often become misaligned & the impact
•Defining the core building blocks of a successful data strategy
•The role of business and IT
•Success stories in implementing global data strategies
This describes a conceptual model approach to designing an enterprise data fabric. This is the set of hardware and software infrastructure, tools and facilities to implement, administer, manage and operate data operations across the entire span of the data within the enterprise across all data activities including data acquisition, transformation, storage, distribution, integration, replication, availability, security, protection, disaster recovery, presentation, analytics, preservation, retention, backup, retrieval, archival, recall, deletion, monitoring, capacity planning across all data storage platforms enabling use by applications to meet the data needs of the enterprise.
The conceptual data fabric model represents a rich picture of the enterprise’s data context. It embodies an idealised and target data view.
Designing a data fabric enables the enterprise respond to and take advantage of key related data trends:
• Internal and External Digital Expectations
• Cloud Offerings and Services
• Data Regulations
• Analytics Capabilities
It enables the IT function demonstrate positive data leadership. It shows the IT function is able and willing to respond to business data needs. It allows the enterprise to meet data challenges
• More and more data of many different types
• Increasingly distributed platform landscape
• Compliance and regulation
• Newer data technologies
• Shadow IT where the IT function cannot deliver IT change and new data facilities quickly
It is concerned with the design an open and flexible data fabric that improves the responsiveness of the IT function and reduces shadow IT.
Data Architecture Best Practices for Advanced AnalyticsDATAVERSITY
Many organizations are immature when it comes to data and analytics use. The answer lies in delivering a greater level of insight from data, straight to the point of need.
There are so many Data Architecture best practices today, accumulated from years of practice. In this webinar, William will look at some Data Architecture best practices that he believes have emerged in the past two years and are not worked into many enterprise data programs yet. These are keepers and will be required to move towards, by one means or another, so it’s best to mindfully work them into the environment.
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.
How to Build & Sustain a Data Governance Operating Model DATUM LLC
Learn how to execute a data governance strategy through creation of a successful business case and operating model.
Originally presented to an audience of 400+ at the Master Data Management & Data Governance Summit.
Visit www.datumstrategy.com for more!
Enabling a Data Mesh Architecture with Data VirtualizationDenodo
Watch full webinar here: https://bit.ly/3rwWhyv
The Data Mesh architectural design was first proposed in 2019 by Zhamak Dehghani, principal technology consultant at Thoughtworks, a technology company that is closely associated with the development of distributed agile methodology. A data mesh is a distributed, de-centralized data infrastructure in which multiple autonomous domains manage and expose their own data, called “data products,” to the rest of the organization.
Organizations leverage data mesh architecture when they experience shortcomings in highly centralized architectures, such as the lack domain-specific expertise in data teams, the inflexibility of centralized data repositories in meeting the specific needs of different departments within large organizations, and the slow nature of centralized data infrastructures in provisioning data and responding to changes.
In this session, Pablo Alvarez, Global Director of Product Management at Denodo, explains how data virtualization is your best bet for implementing an effective data mesh architecture.
You will learn:
- How data mesh architecture not only enables better performance and agility, but also self-service data access
- The requirements for “data products” in the data mesh world, and how data virtualization supports them
- How data virtualization enables domains in a data mesh to be truly autonomous
- Why a data lake is not automatically a data mesh
- How to implement a simple, functional data mesh architecture using data virtualization
The Importance of MDM - Eternal Management of the Data MindDATAVERSITY
Despite its immaterial nature, data has a tendency to pile up as time goes on, and can quickly be rendered unusable or obsolete without careful maintenance and streamlining of processes for its management. This presentation will provide you with an understanding of reference and master data management (MDM), one such method for keeping mass amounts of business data organized and functional towards achieving business goals.
MDM’s guiding principles include the establishment and implementation of authoritative data sources and effective means of delivering data to various business processes, as well as increases to the quality of information used in organizational analytical functions (such as BI).
To that end, attendees of this webinar will learn how to:
- Structure their data management processes around these principles
- Incorporate data quality engineering into the planning of reference and MDM
- Understand why MDM is so critical to their organization’s overall data strategy
Top 8 Data Science Tools | Open Source Tools for Data Scientists | EdurekaEdureka!
** Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training **
This Edureka Session on Data Science Tools will help you understand the best tools to get you started with Data Science. Here’s a list of topics that are covered in this session:
Introduction To Data Science
Data Science Tools
Data Science Tools For Data Storage
Data Science Tools For Data Manipulation
Data Science Tools For EDA
Data Science Tools For Data Visualization
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Building the Data Lake with Azure Data Factory and Data Lake AnalyticsKhalid Salama
In essence, a data lake is commodity distributed file system that acts as a repository to hold raw data file extracts of all the enterprise source systems, so that it can serve the data management and analytics needs of the business. A data lake system provides means to ingest data, perform scalable big data processing, and serve information, in addition to manage, monitor and secure the it environment. In these slide, we discuss building data lakes using Azure Data Factory and Data Lake Analytics. We delve into the architecture if the data lake and explore its various components. We also describe the various data ingestion scenarios and considerations. We introduce the Azure Data Lake Store, then we discuss how to build Azure Data Factory pipeline to ingest the data lake. After that, we move into big data processing using Data Lake Analytics, and we delve into U-SQL.
LDM Slides: How Data Modeling Fits into an Overall Enterprise ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as it relates to data and its business impact across the organization.
Join this webinar for a discussion on how a data model can be combined with an overall enterprise architecture for enhanced business value and success.
Introduction to Data Management Maturity ModelsKingland
Jeff Gorball, the only individual accredited in the EDM Council Data Management Capability Model and the CMMI Institute Data Management Maturity Model, introduces audiences to both models and shares how you can choose which one is best for your needs.
Wonder what this data mesh stuff is all about? What are the principles of data mesh? Can you or should you consider data mesh as the approach for your analytics platform? And most important - how can Snowflake help?
Given in Montreal on 14-Dec-2021
This framework helps organizations align Data Strategy with Business Strategy to prioritize goals around the most pressing operational needs. It introduces Data Management & Data Ability Maturity Matrix to visualize the core path of business digital transformation, which is easy to understand and follow. And it provides the standard template for implementation, which can share the flexibility to engage applications of different industries.
Data Lake Architecture – Modern Strategies & ApproachesDATAVERSITY
Data Lake or Data Swamp? By now, we’ve likely all heard the comparison. Data Lake architectures have the opportunity to provide the ability to integrate vast amounts of disparate data across the organization for strategic business analytic value. But without a proper architecture and metadata management strategy in place, a Data Lake can quickly devolve into a swamp of information that is difficult to understand. This webinar will offer practical strategies to architect and manage your Data Lake in a way that optimizes its success.
Active Governance Across the Delta Lake with AlationDatabricks
Alation provides a single interface to provide users and stewards to provide active and agile data governance across Databricks Delta Lake and Databricks SQL Analytics Service. Understand how Alation can expand adoption in the data lake while providing safe and responsible data consumption.
Adopting a Process-Driven Approach to Master Data ManagementSoftware AG
What is a lasting solution to the sea of errors, headaches, and losses caused by inconsistent and inaccurate master data such as customer and product records? This is the data that your business counts on to operate business processes and make decisions. But this data is often incomplete or in conflict because it resides in multiple IT systems. Master Data Management (MDM)'s programs are the solution to this problem, but these programs can fail without the investment and involvement of business managers.
Listen to Rob Karel, Forrester analyst, and Jignesh Shah from Software AG to learn about a new, process-driven approach to MDM and why it is a win-win for both business and IT managers.
Visit us at http://www.softwareag.com Become part of our growing community: Facebook: http://www.facebook.com/softwareag Twitter: http://www.twitter.com/softwareag LinkedIn: http://www.linkedin.com/company/software-ag YouTube: http://www.youtube.com/softwareag
Data Architecture Best Practices for Advanced AnalyticsDATAVERSITY
Many organizations are immature when it comes to data and analytics use. The answer lies in delivering a greater level of insight from data, straight to the point of need.
There are so many Data Architecture best practices today, accumulated from years of practice. In this webinar, William will look at some Data Architecture best practices that he believes have emerged in the past two years and are not worked into many enterprise data programs yet. These are keepers and will be required to move towards, by one means or another, so it’s best to mindfully work them into the environment.
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.
How to Build & Sustain a Data Governance Operating Model DATUM LLC
Learn how to execute a data governance strategy through creation of a successful business case and operating model.
Originally presented to an audience of 400+ at the Master Data Management & Data Governance Summit.
Visit www.datumstrategy.com for more!
Enabling a Data Mesh Architecture with Data VirtualizationDenodo
Watch full webinar here: https://bit.ly/3rwWhyv
The Data Mesh architectural design was first proposed in 2019 by Zhamak Dehghani, principal technology consultant at Thoughtworks, a technology company that is closely associated with the development of distributed agile methodology. A data mesh is a distributed, de-centralized data infrastructure in which multiple autonomous domains manage and expose their own data, called “data products,” to the rest of the organization.
Organizations leverage data mesh architecture when they experience shortcomings in highly centralized architectures, such as the lack domain-specific expertise in data teams, the inflexibility of centralized data repositories in meeting the specific needs of different departments within large organizations, and the slow nature of centralized data infrastructures in provisioning data and responding to changes.
In this session, Pablo Alvarez, Global Director of Product Management at Denodo, explains how data virtualization is your best bet for implementing an effective data mesh architecture.
You will learn:
- How data mesh architecture not only enables better performance and agility, but also self-service data access
- The requirements for “data products” in the data mesh world, and how data virtualization supports them
- How data virtualization enables domains in a data mesh to be truly autonomous
- Why a data lake is not automatically a data mesh
- How to implement a simple, functional data mesh architecture using data virtualization
The Importance of MDM - Eternal Management of the Data MindDATAVERSITY
Despite its immaterial nature, data has a tendency to pile up as time goes on, and can quickly be rendered unusable or obsolete without careful maintenance and streamlining of processes for its management. This presentation will provide you with an understanding of reference and master data management (MDM), one such method for keeping mass amounts of business data organized and functional towards achieving business goals.
MDM’s guiding principles include the establishment and implementation of authoritative data sources and effective means of delivering data to various business processes, as well as increases to the quality of information used in organizational analytical functions (such as BI).
To that end, attendees of this webinar will learn how to:
- Structure their data management processes around these principles
- Incorporate data quality engineering into the planning of reference and MDM
- Understand why MDM is so critical to their organization’s overall data strategy
Top 8 Data Science Tools | Open Source Tools for Data Scientists | EdurekaEdureka!
** Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training **
This Edureka Session on Data Science Tools will help you understand the best tools to get you started with Data Science. Here’s a list of topics that are covered in this session:
Introduction To Data Science
Data Science Tools
Data Science Tools For Data Storage
Data Science Tools For Data Manipulation
Data Science Tools For EDA
Data Science Tools For Data Visualization
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Building the Data Lake with Azure Data Factory and Data Lake AnalyticsKhalid Salama
In essence, a data lake is commodity distributed file system that acts as a repository to hold raw data file extracts of all the enterprise source systems, so that it can serve the data management and analytics needs of the business. A data lake system provides means to ingest data, perform scalable big data processing, and serve information, in addition to manage, monitor and secure the it environment. In these slide, we discuss building data lakes using Azure Data Factory and Data Lake Analytics. We delve into the architecture if the data lake and explore its various components. We also describe the various data ingestion scenarios and considerations. We introduce the Azure Data Lake Store, then we discuss how to build Azure Data Factory pipeline to ingest the data lake. After that, we move into big data processing using Data Lake Analytics, and we delve into U-SQL.
LDM Slides: How Data Modeling Fits into an Overall Enterprise ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as it relates to data and its business impact across the organization.
Join this webinar for a discussion on how a data model can be combined with an overall enterprise architecture for enhanced business value and success.
Introduction to Data Management Maturity ModelsKingland
Jeff Gorball, the only individual accredited in the EDM Council Data Management Capability Model and the CMMI Institute Data Management Maturity Model, introduces audiences to both models and shares how you can choose which one is best for your needs.
Wonder what this data mesh stuff is all about? What are the principles of data mesh? Can you or should you consider data mesh as the approach for your analytics platform? And most important - how can Snowflake help?
Given in Montreal on 14-Dec-2021
This framework helps organizations align Data Strategy with Business Strategy to prioritize goals around the most pressing operational needs. It introduces Data Management & Data Ability Maturity Matrix to visualize the core path of business digital transformation, which is easy to understand and follow. And it provides the standard template for implementation, which can share the flexibility to engage applications of different industries.
Data Lake Architecture – Modern Strategies & ApproachesDATAVERSITY
Data Lake or Data Swamp? By now, we’ve likely all heard the comparison. Data Lake architectures have the opportunity to provide the ability to integrate vast amounts of disparate data across the organization for strategic business analytic value. But without a proper architecture and metadata management strategy in place, a Data Lake can quickly devolve into a swamp of information that is difficult to understand. This webinar will offer practical strategies to architect and manage your Data Lake in a way that optimizes its success.
Active Governance Across the Delta Lake with AlationDatabricks
Alation provides a single interface to provide users and stewards to provide active and agile data governance across Databricks Delta Lake and Databricks SQL Analytics Service. Understand how Alation can expand adoption in the data lake while providing safe and responsible data consumption.
Adopting a Process-Driven Approach to Master Data ManagementSoftware AG
What is a lasting solution to the sea of errors, headaches, and losses caused by inconsistent and inaccurate master data such as customer and product records? This is the data that your business counts on to operate business processes and make decisions. But this data is often incomplete or in conflict because it resides in multiple IT systems. Master Data Management (MDM)'s programs are the solution to this problem, but these programs can fail without the investment and involvement of business managers.
Listen to Rob Karel, Forrester analyst, and Jignesh Shah from Software AG to learn about a new, process-driven approach to MDM and why it is a win-win for both business and IT managers.
Visit us at http://www.softwareag.com Become part of our growing community: Facebook: http://www.facebook.com/softwareag Twitter: http://www.twitter.com/softwareag LinkedIn: http://www.linkedin.com/company/software-ag YouTube: http://www.youtube.com/softwareag
Come diventare data scientist - Si ringrazie per le slide Paolo Pellegrini, Senior Consultant presso P4I (Partners4Innovation) e referente di tutte le progettualità relative alle tematiche Data Science e Big Data Analytics. Owner del primo gruppo in Italia dedicato dai Data Scientist.
Smart Data Webinar: Choosing the Right Data Management Architecture for Cogni...DATAVERSITY
Once developers have a knowledge management model - covered in our August webinar - they still have to deal with real world implementation constraints. Big data is a fact of life for most modern AI/cognitive computing apps, which usually means ingesting, sampling, or analyzing large data sets from disparate sources, ranging from IOT sensors to social media streams to news feeds and weather forecasts. Frequently, historical data in legacy systems will also be required to generate new insights.
This webinar will present a framework to help participants evaluate streaming data management tools, IOT technology stacks, and graph databases as support tools for their modern AI/cognitive computing projects. They will also learn about emerging open source projects and ecosystems that can help kick start their projects today.
Python's Role in the Future of Data AnalysisPeter Wang
Why is "big data" a challenge, and what roles do high-level languages like Python have to play in this space?
The video of this talk is at: https://vimeo.com/79826022
Accelerate Digital Transformation with an Enterprise Big Data FabricCambridge Semantics
In this webinar by Cambridge Semantics' VP of Solution Engineering, Ben Szekely, you will learn more about how the Enterprise Data Fabric prevails as the bedrock of enterprise digital strategy. Connected and highly available data is the new normal - powering analytics and AI. The data lake itself is commoditized, like raw compute or disk, and becomes an unseen part of the stack. Semantic graph technology is central to Data Fabric initiatives that meaningfully contribute to digital transformation.
We share our vision for digital innovation - a shift to something powerful, expedient and future-proof. The Data Fabric connects enterprise data for unprecedented access in an overlay fashion that does not disrupt current investments. Interconnected and reliable data drives business outcomes by automating scalable AI and ML efforts. Graph technology is the way forward to realize this future.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
6. A data engineering Team is NOT a
collection of Data Engineers
A data engineering team isn’t made up of a
single type of person or title
A data engineering team is
multidisciplinary
6
Data Engineering Team
(Anderson, 2017)
7. Creates data pipelines
Brings together 10-30 different big data technologies
Understands and chooses the right tools for the job
Understands the various technologies and
frameworks in-depth
Combines them to create solutions to enable a
company’s business processes with data pipelines
7
Data Engineering Team
(Anderson, 2018)
8. Data science is an interdisciplinary field aiming to turn data into real value.
Data may be structured or unstructured, big or small, static or streaming.
Value may be provided in the form of predictions, automated decisions, models
learned from data, or any type of data visualization delivering insights.
Data science includes data extraction, data preparation, data exploration, data
transformation, storage and retrieval, computing infrastructures, various types of
mining and learning, presentation of explanations and predictions, and the
exploitation of results taking into account ethical, social, legal, and business aspects.
9
Where does data engineering fit in the
context of data analytics?
(Van Der Aalst, 2016)
9. Where does data engineering fit in the
context of data analytics?
10
The ingredients contributing to data science (Van Der Aalst, 2016)
10. Where does data engineering fit in the
context of data analytics?
11
The Internet of Events (Van Der Aalst, 2016)
11. 12
Alluvial diagram of Big Data job families vs. Big Data skill sets
(De Mauro, Greco, Grimaldi, & Ritala, 2018)
12. 13
Word cloud showing the top 50 words recurring in the Job Title of posts related to Big Data.
The font size of each word is proportional to the number of occurrences of each word.
(De Mauro, Greco, Grimaldi, & Ritala, 2018).
15. 2.
How do I get
started in Data
Engineering?
Process, Skills, Tools
16. “A Data Engineer is
someone who has
specialized their skills
in creating software
solutions around data.
17
(Anderson, 2017)
Jesse Anderson
Data Engineer
Managing Director, Big Data Institute
17. “I started getting into Data about 5
years ago when everyone started
talking about it and was becoming
the new buzz word. People were
actually starting to realize how much
you can do with data. Everyone
wanted to learn Data Science and all
the companies wanted to get into
Machine Learning or Artificial
Intelligence, but there was still a
missing piece - that's when I learned
about Data Engineering.
18
Miles Ong
Data Engineer
Kumu
18. “There was always the problem of
collecting the data, processing the
analysis and actually implementing
the insights. It was overwhelming at
first because I had no clue on where
to begin. What helped is to focus on
things one at a time and actually
trying things out. For Data
Engineering, the only way to learn is
by doing.
19
Miles Ong
Data Engineer
Kumu
19. “The more I learned, the more I
realized how powerful Data
Engineering is. It would give me the
capability to simply come up with an
idea and actually implement it. It's a
very underrated field, but I love the
challenge of conceptualizing,
building and implementing concrete
solutions that make a difference.
20
Miles Ong
Data Engineer
Kumu
20. Steps to Data Engineering
PRE-PROJECT
(Anderson, 2017)
21. Steps to Data Engineering
FORM TEAM
(Anderson, 2017)
30. Big Data (Working Definition)
Big data is a field that treats ways to
analyze, systematically extract information
from, or otherwise deal with data sets that
are too large or complex to be dealt with
by traditional data-processing
application software.
31
31. Big Data (Working Definition)
Big data is a field that treats ways to
analyze, systematically extract information
from, or otherwise deal with data sets that
are too large or complex to be dealt with
by traditional data-processing
application software.
32
VOLUME
VARIETY
VELOCITY
…
(more on this in the
next slide...)
32. Author's reinterpretation of The 5Vs of Big Data
(Ishwarappa & Anuradha, 2015; Yin & Kaynak, 2015)
33
VOLUME
VARIETY VERACITY
VELOCITY
VALUE
THE 5Vs
OF BIG
DATA
33. Author's reinterpretation of The 5Vs of Big Data
(Ishwarappa & Anuradha, 2015; Yin & Kaynak, 2015)
34
VOLUME
VARIETY VERACITY
VELOCITY
VALUE
THE 5Vs
OF BIG
DATAStructured
Unstructured
Text, Image,
video, social
relations
Multi-factor
Probabilistic
34. Author's reinterpretation of The 5Vs of Big Data
(Ishwarappa & Anuradha, 2015; Yin & Kaynak, 2015)
35
VOLUME
VARIETY VERACITY
VELOCITY
VALUE
THE 5Vs
OF BIG
DATA
Terabytes
Records
Architecture
Transactions
Tables, Files
35. Author's reinterpretation of The 5Vs of Big Data
(Ishwarappa & Anuradha, 2015; Yin & Kaynak, 2015)
36
VOLUME
VARIETY VERACITY
VELOCITY
VALUE
THE 5Vs
OF BIG
DATA
Statistical
Events
Correlations
Hypothetical
Fresh? Old?
36. Author's reinterpretation of The 5Vs of Big Data
(Ishwarappa & Anuradha, 2015; Yin & Kaynak, 2015)
37
VOLUME
VARIETY VERACITY
VELOCITY
VALUE
THE 5Vs
OF BIG
DATA
Batch
Real/near-time
Processes
Streams
37. Author's reinterpretation of The 5Vs of Big Data
(Ishwarappa & Anuradha, 2015; Yin & Kaynak, 2015)
38
VOLUME
VARIETY VERACITY
VELOCITY
VALUE
THE 5Vs
OF BIG
DATA
Trustworthiness
Authenticity
Origin, Reputation
Availability
Accountability
53. “If you think of a datamart as a
store of bottled water – cleansed
and packaged and structured for
easy consumption – the data lake
is a large body of water in a more
natural state. The contents of the
data lake stream in from a source
to fill the lake, and various users
of the lake can come to examine,
dive in, or take samples.
54
(Dixon, 2010; Miloslavskaya & Tolstoy, 2016)
James Dixon
Chief Technology Officer
Pentaho
54. 55
A data lake refers to a massively scalable storage
repository that holds a vast amount of raw data in its
native format («as is») until it is needed plus processing
systems (engine) that can ingest data without
compromising the data structure
(Laskowski, 2016, as cited by Miloslavskaya & Tolstoy, 2016)
55. Three types of big data
processing
Batch Processing Stream Processing
(Kappa Architecture)
Hybrid Processing
(Lambda Architecture)
56
(Marz & Warren, 2015)
(Miloslavskaya & Tolstoy, 2016; Samizadeh, 2018, March 15)
59. 60
What is Fast Data?
Fast data corresponds to the application of
big data analytics to smaller data sets in
near-real or real-time in order to solve a
particular problem.
(Laskowski, 2016, as cited by Miloslavskaya & Tolstoy, 2016)
60. 61
What is Fast Data?
The combination of in-memory databases and data
grid on top of flash devices will allow an increase in
the capacity of stream processing.
Fast data is a complementary approach to big data
for managing large quantities of «in-flight» data
(Laskowski, 2016, as cited by Miloslavskaya & Tolstoy, 2016)
61. 62
Fast Data requires two
technologies
(Laskowski, 2016, as cited by Miloslavskaya & Tolstoy, 2016)
Streaming system capable of of delivering
events as fast as they come in
Data store capable of processing each
item as fast as it arrives
64. 65
Data Cleaning
Data cleaning, also called data cleansing or scrubbing, deals with
detecting and removing errors and inconsistencies from data in order
to improve the quality of data.
Sample inconsistencies:
● misspellings during data entry
● missing information
● other invalid data
(Wang, Kon, & Madnick, 1993)
65. Data Cleaning Approaches
66
Data Analysis Definition of
Transformation
Workflow
Data
Verification
Data
Transformation
Backflow of
Cleaned Data
Special domain cleaning
Specialized cleaning tools
ETL Tools
71. 72
Event streaming is the digital equivalent of the human
body's central nervous system. It is the technological
foundation for the 'always-on' world where businesses
are increasingly software-defined and automated, and
where the user of software is more software.
(Apache Software Foundation, 2017)
73. 74
(Apache Software Foundation, 2017)
Capture data in
real-time from event
sources.
storing these event
streams durably for later
retrieval and manipulation
74. 75
(Apache Software Foundation, 2017)
Capture data in
real-time from event
sources.
storing these event
streams durably for later
retrieval and manipulation
routing the event streams
to different destination
technologies as needed
75. Data Streams (examples)
Time Series Data Network Traffic Telecommunications
76
Video Surveillance Website Clickstreams Sensor Networks
(Miloslavskaya & Tolstoy, 2016)
81. “When a system processes
trillions and trillions of
requests, events that normally
have a low probability of
occurrence are now
guaranteed to happen and
must be accounted for upfront
in the design and architecture
of the system.
82
(Vogels, 2009)
Werner Vogels
Vice President &
Chief Technology Officer
Amazon.com,
Source: Wikipedia
86. 87
No official NoSQL Taxonomy exists:
Soft NoSQL Example
Object Databases
db4o, Versant, Objectivity,
Gemstone, Progress,
Starcounter, Perst, ZODB,
NEO, PicoLisp, Sterling,
StupidDB, KiokuDB, Durus
Grid and Cloud Database
Solutions
GigaSpaces, Queplix,
Hazelcast, Joafip, GridGain,
Infinispan, Coherence,
eXtremeScale
XML Databases
Mark Logic Server, EMC
Documentum xDB, Tamino,
eXist, Sedna, BaseX, Xindice,
Qizx, Berkeley DB XML
Multivalue Databases
U2, OpenInsight, OpenQM,
Globals
Other NoSQL related
databases
IBM Lotus/Domino,
Intersystems Cache,
eXtremeDB, ISIS Family,
Prevayler, Yserial
(Tudorica & Bucur, 2011)
87. Key Value
88
Simplest form of database management
systems
Can only store pairs of keys and values,
as well as retrieve values when a key is
known
Normally not adequate for complex
applications
Simplicity makes these attractive in
certain circumstances
(Khazaei, 2016)
88. Column-Oriented
89
Stores data in records with an ability to
hold very large numbers of dynamic
columns
Can be seen as two-dimensional
key-value stores
Schema-free like document stores,
however the implementation is
significantly different
(Khazaei, 2016)
89. Document Stores
90
Also known as document-oriented
database systems
Schema-free organization
Records (or "documents") do not need
to have a uniform structure
The types of the values of individual
columns can be different
Columns can have more than one value
(arrays); records can have a nested
structure
Document stores often use internal
notations, usually JSON.
(Khazaei, 2016)
90. Graph Oriented
91
Represent data in graph structures as
nodes and edges
Edges represent relationships between
nodes
Allow easy processing of data in that
form (graphs)
Simple calculation of specific properties
of the graph, such as the number of
steps needed to get from one node to
another node
(Khazaei, 2016)
92. Interrelation between Big Data, Fast Data,
and Data Lake Concepts
(Laskowski, 2016, as cited by Miloslavskaya & Tolstoy, 2016)
93. 94
Takeaways
Upskilling not impossible
Understand workload management and
trade-offs when making architecture
decisions
Don't be afraid to work with other people
Experiment, experiment, experiment…
Be a wide reader and hungry learner
95. Credits
Special thanks to all the people who made
and released these awesome resources for
free:
⬡ Presentation template by SlidesCarnival
⬡ Photographs by Unsplash
96
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99