The document discusses 5 data trends that data engineers should know:
1. Rise of data pipelines to repeatedly move data around using code.
2. Use of compute engines to query cloud data without moving it by separating data and compute.
3. Data modeling to define metrics once for the entire organization.
4. Building of internal and external data products to extract insights from large amounts of data.
5. Ensuring data quality by developing tests and monitoring data flows to maintain data integrity.
Big Data Trends - WorldFuture 2015 ConferenceDavid Feinleib
David Feinleib's Big Data Trends presentation from the World Future Society's Annual Conference, WorldFuture 2015, held at the Hilton Union Square, San Francisco, California July 25, 2015.
Trends in Big Data & Business Challenges Experian_US
Join our #DataTalk on Thursdays at 5 p.m. ET. This week, we tweeted with Sushil Pramanick – who is the founder and president of the The Big Data Institute (TBDI).
You can learn about upcoming chats and see the archive of past big data tweetchats here
http://www.experian.com/blogs/news/about/datadriven
Explore our analysis of technology trends for 2019 and beyond: AI, IoT, Security, Big Data / Data Science, Mobile Apps Development, AR/VR, RPA (Robot Process Automation), Blockchain, Automotive Solutions, Business Intelligence, Cloud Computing, Service Desk, Autonomous Things, Augmented Analytics, AI-Driven Development, Digital Twins, Empowered Edge, Immersive Experience, Smart Spaces, Quantum Computing, and more.
Check our recommendations for businesses to stay current with the latest IT tendencies.
Includes a video by Gartner.
SmartData Webinar: Cognitive Computing in the Mobile App EconomyDATAVERSITY
Mobility is transforming work and life throughout the planet. Mobile apps--built for a growing range of handhelds, wearables, Internet of Things, and other platforms--are becoming the universal access paths to commerce, content, and community in the 21st century. The app economy refers to this new world where every decision, action, exploration, and experience is continuously enriched and optimized through the cloud-served apps that accompany you everywhere. In this webinar, James Kobielus, IBM's Big Data Evangelist, will discuss the potential of cognitive computing to super-power the emerging app economy. In addition to providing an overview of IBM's Watson strategy for cognitive computing, Kobielus will go in-depth on IBM's strategic partnership with Apple to draw on the strengths of each company to transform enterprise mobility through a new class of apps that leverage IBM’s Watson-based big data analytics cloud and add value to Apple's iPhone and iPad platforms in diverse industries.
Big Data and The Future of Insight - Future FoundationForesight Factory
As Big Data sweeps through consumer-facing businesses, we ask:
- If Big Data is truly a revolution, then what (and whom) will it eliminate or elevate?
- What value will still be derived from conventional market research and brand-building techniques?
- If every brand is backed by Big Data, can every brand prosper?
For more information please contact info@futurefoundation.net or visit www.futurefoundation.net
Big Data Trends - WorldFuture 2015 ConferenceDavid Feinleib
David Feinleib's Big Data Trends presentation from the World Future Society's Annual Conference, WorldFuture 2015, held at the Hilton Union Square, San Francisco, California July 25, 2015.
Trends in Big Data & Business Challenges Experian_US
Join our #DataTalk on Thursdays at 5 p.m. ET. This week, we tweeted with Sushil Pramanick – who is the founder and president of the The Big Data Institute (TBDI).
You can learn about upcoming chats and see the archive of past big data tweetchats here
http://www.experian.com/blogs/news/about/datadriven
Explore our analysis of technology trends for 2019 and beyond: AI, IoT, Security, Big Data / Data Science, Mobile Apps Development, AR/VR, RPA (Robot Process Automation), Blockchain, Automotive Solutions, Business Intelligence, Cloud Computing, Service Desk, Autonomous Things, Augmented Analytics, AI-Driven Development, Digital Twins, Empowered Edge, Immersive Experience, Smart Spaces, Quantum Computing, and more.
Check our recommendations for businesses to stay current with the latest IT tendencies.
Includes a video by Gartner.
SmartData Webinar: Cognitive Computing in the Mobile App EconomyDATAVERSITY
Mobility is transforming work and life throughout the planet. Mobile apps--built for a growing range of handhelds, wearables, Internet of Things, and other platforms--are becoming the universal access paths to commerce, content, and community in the 21st century. The app economy refers to this new world where every decision, action, exploration, and experience is continuously enriched and optimized through the cloud-served apps that accompany you everywhere. In this webinar, James Kobielus, IBM's Big Data Evangelist, will discuss the potential of cognitive computing to super-power the emerging app economy. In addition to providing an overview of IBM's Watson strategy for cognitive computing, Kobielus will go in-depth on IBM's strategic partnership with Apple to draw on the strengths of each company to transform enterprise mobility through a new class of apps that leverage IBM’s Watson-based big data analytics cloud and add value to Apple's iPhone and iPad platforms in diverse industries.
Big Data and The Future of Insight - Future FoundationForesight Factory
As Big Data sweeps through consumer-facing businesses, we ask:
- If Big Data is truly a revolution, then what (and whom) will it eliminate or elevate?
- What value will still be derived from conventional market research and brand-building techniques?
- If every brand is backed by Big Data, can every brand prosper?
For more information please contact info@futurefoundation.net or visit www.futurefoundation.net
Building an AI Startup: Realities & TacticsMatt Turck
AI is all the rage in tech circles, and the press is awash in tales of AI entrepreneurs striking it rich after being acquired by one of the giants. As always, the realities of building a startup are different, and the path to success requires not just technical prowess but also thoughtful market positioning and business excellence.
In a talk of interest to anyone building or implementing an AI product, Matt Turck and Peter Brodsky leverage hundreds of conversations with AI (and big data) founders and hard-learned lessons building companies from the ground up to highlight successful strategies and tactics.
Topics include:
Successful data acquisition strategies
Data network effects
Competing with the giants
A pragmatic approach to building an AI team
Why social engineering is just as important to success as groundbreaking AI technology
Big data introduction - Big Data from a Consulting perspective - SogetiEdzo Botjes
Big data introduction - Sogeti - Consulting Services - Business Technology - 20130628 v5
This is a small introduction to the topic Big Data and a small vision on how to enable a (big) company in using big data and embed it into the organisation.
Fundamentals of Big Data in 2 minutes!!Simplify360
In today’s world where information is increasing every second, BIG DATA takes up a major role in transforming any business.
Learn the fundamentals of big data in just 2 minutes!
Conversational Architecture, CAVE Language, Data StewardshipLoren Davie
These are the slides from the presentation I gave at the Semiotics Web meetup group on Nov 1st 2014. In this talk I discussed the emergency of the ubiquitous Internet, how to discuss the design of contextual apps, and presented an approach to privacy concerns that are inherently connected.
The Fog or Edge Computing model complements Cloud Computing with small, typically sensor-enabled and IOT connected devices that process distributed data at its source. As this model matures, we see an uptake on a 3-tier architecture with Intelligent Gateways to aggregate sensor input before communicating with data centers or a Cloud. Two forces will drive the practice of distributing Intelligence (Understanding/Reasoning/Learning) to the Gateway. The first is the presence of the Gateway itself, which enables a standards-based approach to distributing intelligence and moving it closer to the edge. The second is the trend for simplifying system requirements by processing training data or model validation with big data prior to deployment, and using small footprint devices for operational systems.
This webinar will present an overview of the relevant technologies and trends. Participants will learn about the state of the art today, and how to identify apps in their own environment that would be good candidates for Intelligent Edge solutions.
Digital Twin refers to a physical and functional description of a component, product or system together with all available operational data. This includes all information which could be useful in current and subsequent lifecycle phases. Benefit of it for mechatronic and cyber-physical systems is to provide the information created during design and engineering also at the operation of the system. The comprehensive networking of all information, shared between partners and connecting design, production and usage, forms the presented paradigm of next generation Digital Twin.
By definition, “big data” involves large volumes of diverse data sources.
Considering all the data that your activities generate and that 99% of this data is irrelevant “noise,” business users and stakeholders have to struggle to understand your company’s status.
See how a business perspective on your big, small or just complex data will generate business value.
AI-SDV 2020: AI, IoT, Blockchain & Co: How to keep track and take advantage o...Dr. Haxel Consult
The presentation first addresses 6 key questions: Where do we stand with the diversification of AI? Machines are getting better at understanding - where are we with writing? Has AI Research hit a wall in 2020? What's the progress made in AI ethics? Could synthetically created data make AI cheaper? And: Are deep fake deployments of AI becoming more insidious? The presentation then moves on to discuss the 4th generation of AI: Artificial Intuition. It discusses the lines of effort in AI of the US Government and concludes with three actualities on AI-based decision making, COVID-loneliness detection and extrapolation of AI into the long run.
The mountain of Big Data is growing, presenting immense opportunities for businesses ready to summit its peak, but the journey requires careful preparation. Integra helps businesses equip their network infrastructure to handle big requirements for Big Data—with fully-symmetrical Ethernet solutions designed to deliver low-latency, high-bandwidth connectivity between organizational peers, the cloud, and the servers where your data is stored. Our infographic, "Summiting the Mountain of Big Data" will help you understand how big "Big Data" really is; who's producing, consuming, managing and storing all that data; the business advantages you can capture by tapping into its power; and how you can prepare your organization to meet its demands—resulting in Big Gains from Big Data.
What is the impact of Big Data on Analytics from a Data Science perspective.
Presented at the Big Data and Analytics Summit 2014, Nasscom by Mamatha Upadhyaya.
Wanted to explore more details from the industry experts of big data hadoop training in Bangalore, then get the clear evolution & history of this technology from these big data hadoop training in Bangalore providers.
DataOps - The Foundation for Your Agile Data ArchitectureDATAVERSITY
Achieving agility in data and analytics is hard. It’s no secret that most data organizations struggle to deliver the on-demand data products that their business customers demand. Recently, there has been much hype around new design patterns that promise to deliver this much sought-after agility.
In this webinar, Chris Bergh, CEO and Head Chef of DataKitchen will cut through the noise and describe several elegant and effective data architecture design patterns that deliver low errors, rapid development, and high levels of collaboration. He’ll cover:
• DataOps, Data Mesh, Functional Design, and Hub & Spoke design patterns;
• Where Data Fabric fits into your architecture;
• How different patterns can work together to maximize agility; and
• How a DataOps platform serves as the foundational superstructure for your agile architecture.
How to Swiftly Operationalize the Data Lake for Advanced Analytics Using a Lo...Denodo
Watch full webinar here: https://bit.ly/3mfFJqb
Presented at Chief Data Officer Live Series 2021, ASEAN (August Edition)
While big data initiatives have become necessary for any business to generate actionable insights, big data fabric has become a necessity for any successful big data initiative. The best-of-breed big data fabrics should deliver actionable insights to the business users with minimal effort, provide end-to-end security to the entire enterprise data platform, and provide real-time data integration while delivering a self-service data platform to business users.
Watch this on-demand session to learn how big data fabric enabled by Data Virtualization:
- Provides lightning fast self-service data access to business users
- Centralizes data security, governance, and data privacy
- Fulfills the promise of data lakes to provide actionable insights
Building an AI Startup: Realities & TacticsMatt Turck
AI is all the rage in tech circles, and the press is awash in tales of AI entrepreneurs striking it rich after being acquired by one of the giants. As always, the realities of building a startup are different, and the path to success requires not just technical prowess but also thoughtful market positioning and business excellence.
In a talk of interest to anyone building or implementing an AI product, Matt Turck and Peter Brodsky leverage hundreds of conversations with AI (and big data) founders and hard-learned lessons building companies from the ground up to highlight successful strategies and tactics.
Topics include:
Successful data acquisition strategies
Data network effects
Competing with the giants
A pragmatic approach to building an AI team
Why social engineering is just as important to success as groundbreaking AI technology
Big data introduction - Big Data from a Consulting perspective - SogetiEdzo Botjes
Big data introduction - Sogeti - Consulting Services - Business Technology - 20130628 v5
This is a small introduction to the topic Big Data and a small vision on how to enable a (big) company in using big data and embed it into the organisation.
Fundamentals of Big Data in 2 minutes!!Simplify360
In today’s world where information is increasing every second, BIG DATA takes up a major role in transforming any business.
Learn the fundamentals of big data in just 2 minutes!
Conversational Architecture, CAVE Language, Data StewardshipLoren Davie
These are the slides from the presentation I gave at the Semiotics Web meetup group on Nov 1st 2014. In this talk I discussed the emergency of the ubiquitous Internet, how to discuss the design of contextual apps, and presented an approach to privacy concerns that are inherently connected.
The Fog or Edge Computing model complements Cloud Computing with small, typically sensor-enabled and IOT connected devices that process distributed data at its source. As this model matures, we see an uptake on a 3-tier architecture with Intelligent Gateways to aggregate sensor input before communicating with data centers or a Cloud. Two forces will drive the practice of distributing Intelligence (Understanding/Reasoning/Learning) to the Gateway. The first is the presence of the Gateway itself, which enables a standards-based approach to distributing intelligence and moving it closer to the edge. The second is the trend for simplifying system requirements by processing training data or model validation with big data prior to deployment, and using small footprint devices for operational systems.
This webinar will present an overview of the relevant technologies and trends. Participants will learn about the state of the art today, and how to identify apps in their own environment that would be good candidates for Intelligent Edge solutions.
Digital Twin refers to a physical and functional description of a component, product or system together with all available operational data. This includes all information which could be useful in current and subsequent lifecycle phases. Benefit of it for mechatronic and cyber-physical systems is to provide the information created during design and engineering also at the operation of the system. The comprehensive networking of all information, shared between partners and connecting design, production and usage, forms the presented paradigm of next generation Digital Twin.
By definition, “big data” involves large volumes of diverse data sources.
Considering all the data that your activities generate and that 99% of this data is irrelevant “noise,” business users and stakeholders have to struggle to understand your company’s status.
See how a business perspective on your big, small or just complex data will generate business value.
AI-SDV 2020: AI, IoT, Blockchain & Co: How to keep track and take advantage o...Dr. Haxel Consult
The presentation first addresses 6 key questions: Where do we stand with the diversification of AI? Machines are getting better at understanding - where are we with writing? Has AI Research hit a wall in 2020? What's the progress made in AI ethics? Could synthetically created data make AI cheaper? And: Are deep fake deployments of AI becoming more insidious? The presentation then moves on to discuss the 4th generation of AI: Artificial Intuition. It discusses the lines of effort in AI of the US Government and concludes with three actualities on AI-based decision making, COVID-loneliness detection and extrapolation of AI into the long run.
The mountain of Big Data is growing, presenting immense opportunities for businesses ready to summit its peak, but the journey requires careful preparation. Integra helps businesses equip their network infrastructure to handle big requirements for Big Data—with fully-symmetrical Ethernet solutions designed to deliver low-latency, high-bandwidth connectivity between organizational peers, the cloud, and the servers where your data is stored. Our infographic, "Summiting the Mountain of Big Data" will help you understand how big "Big Data" really is; who's producing, consuming, managing and storing all that data; the business advantages you can capture by tapping into its power; and how you can prepare your organization to meet its demands—resulting in Big Gains from Big Data.
What is the impact of Big Data on Analytics from a Data Science perspective.
Presented at the Big Data and Analytics Summit 2014, Nasscom by Mamatha Upadhyaya.
Wanted to explore more details from the industry experts of big data hadoop training in Bangalore, then get the clear evolution & history of this technology from these big data hadoop training in Bangalore providers.
DataOps - The Foundation for Your Agile Data ArchitectureDATAVERSITY
Achieving agility in data and analytics is hard. It’s no secret that most data organizations struggle to deliver the on-demand data products that their business customers demand. Recently, there has been much hype around new design patterns that promise to deliver this much sought-after agility.
In this webinar, Chris Bergh, CEO and Head Chef of DataKitchen will cut through the noise and describe several elegant and effective data architecture design patterns that deliver low errors, rapid development, and high levels of collaboration. He’ll cover:
• DataOps, Data Mesh, Functional Design, and Hub & Spoke design patterns;
• Where Data Fabric fits into your architecture;
• How different patterns can work together to maximize agility; and
• How a DataOps platform serves as the foundational superstructure for your agile architecture.
How to Swiftly Operationalize the Data Lake for Advanced Analytics Using a Lo...Denodo
Watch full webinar here: https://bit.ly/3mfFJqb
Presented at Chief Data Officer Live Series 2021, ASEAN (August Edition)
While big data initiatives have become necessary for any business to generate actionable insights, big data fabric has become a necessity for any successful big data initiative. The best-of-breed big data fabrics should deliver actionable insights to the business users with minimal effort, provide end-to-end security to the entire enterprise data platform, and provide real-time data integration while delivering a self-service data platform to business users.
Watch this on-demand session to learn how big data fabric enabled by Data Virtualization:
- Provides lightning fast self-service data access to business users
- Centralizes data security, governance, and data privacy
- Fulfills the promise of data lakes to provide actionable insights
Big Data kennen sehr viele IT-Experten, wenigstens haben Sie eine Vorstellung davon. In der Praxis arbeiten damit in Deutschland derzeit nur wenige. Dabei bringt Big Data ein ganz neues Momentum in moderne Softwarelösungen und ist im Kontext der Mobil-, Cloud- und Social-Veränderungen nicht wegzudenken. Big Data macht Software intelligent und damit auf eine ganz neue Art für die Benutzer erlebbar. Mit Big Data entstehen neue Softwarearchitekturen, weil Informationen völlig anders verarbeitet werden - nämlich schneller, differenzierter und oft mit dem Ziel, Schlüsse zu ziehen und Vorhersagen zu treffen.
In diesem Vortrag wird erläutert, wie moderne Softwarearchitekturen gestaltet werden, sodass Sie Big Data Paradigmen erfolgreich umsetzen und welche Vorteile sich für die zunehmend mobilen Softwarelösungen ergeben. Wir werfen zudem einen Blick auf die Potentiale und Optionen in Branchen wie Banken, Versicherung oder Handel.
Building a Data Platform Strata SF 2019mark madsen
Building a data lake involves more than installing Hadoop or putting data into AWS. The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This tutorial covers design assumptions, design principles, and how to approach the architecture and planning for multi-use data infrastructure in IT.
[This is a new, changed version of the presentations of the same title from last year's Strata]
Watch full webinar here: https://buff.ly/2mHGaLA
What started to evolve as the most agile and real-time enterprise data fabric, data virtualization is proving to go beyond its initial promise and is becoming one of the most important enterprise big data fabrics.
Attend this session to learn:
• What data virtualization really is
• How it differs from other enterprise data integration technologies
• Why data virtualization is finding enterprise-wide deployment inside some of the largest organizations
TAKE A LOOK AT THE TOP 7 SKILLS THAT A DATA ENGINEER CERTAINLY HAS TO HAVEEmilySmith271958
These days, everyone aspires to have a career in data science. What about those who work as data engineers? The reality of the matter is that a Data Scientist is only as good as the quality of the data they are given to work with.
Building a healthy data ecosystem around Kafka and Hadoop: Lessons learned at...Yael Garten
2017 StrataHadoop SJC conference talk. https://conferences.oreilly.com/strata/strata-ca/public/schedule/detail/56047
Description:
So, you finally have a data ecosystem with Kafka and Hadoop both deployed and operating correctly at scale. Congratulations. Are you done? Far from it.
As the birthplace of Kafka and an early adopter of Hadoop, LinkedIn has 13 years of combined experience using Kafka and Hadoop at scale to run a data-driven company. Both Kafka and Hadoop are flexible, scalable infrastructure pieces, but using these technologies without a clear idea of what the higher-level data ecosystem should be is perilous. Shirshanka Das and Yael Garten share best practices around data models and formats, choosing the right level of granularity of Kafka topics and Hadoop tables, and moving data efficiently and correctly between Kafka and Hadoop and explore a data abstraction layer, Dali, that can help you to process data seamlessly across Kafka and Hadoop.
Beyond pure technology, Shirshanka and Yael outline the three components of a great data culture and ecosystem and explain how to create maintainable data contracts between data producers and data consumers (like data scientists and data analysts) and how to standardize data effectively in a growing organization to enable (and not slow down) innovation and agility. They then look to the future, envisioning a world where you can successfully deploy a data abstraction of views on Hadoop data, like a data API as a protective and enabling shield. Along the way, Shirshanka and Yael discuss observations on how to enable teams to be good data citizens in producing, consuming, and owning datasets and offer an overview of LinkedIn’s governance model: the tools, process and teams that ensure that its data ecosystem can handle change and sustain #DataScienceHappiness.
Strata 2017 (San Jose): Building a healthy data ecosystem around Kafka and Ha...Shirshanka Das
So, you finally have a data ecosystem with Kafka and Hadoop both deployed and operating correctly at scale. Congratulations. Are you done? Far from it.
As the birthplace of Kafka and an early adopter of Hadoop, LinkedIn has 13 years of combined experience using Kafka and Hadoop at scale to run a data-driven company. Both Kafka and Hadoop are flexible, scalable infrastructure pieces, but using these technologies without a clear idea of what the higher-level data ecosystem should be is perilous. Shirshanka Das and Yael Garten share best practices around data models and formats, choosing the right level of granularity of Kafka topics and Hadoop tables, and moving data efficiently and correctly between Kafka and Hadoop and explore a data abstraction layer, Dali, that can help you to process data seamlessly across Kafka and Hadoop.
Beyond pure technology, Shirshanka and Yael outline the three components of a great data culture and ecosystem and explain how to create maintainable data contracts between data producers and data consumers (like data scientists and data analysts) and how to standardize data effectively in a growing organization to enable (and not slow down) innovation and agility. They then look to the future, envisioning a world where you can successfully deploy a data abstraction of views on Hadoop data, like a data API as a protective and enabling shield. Along the way, Shirshanka and Yael discuss observations on how to enable teams to be good data citizens in producing, consuming, and owning datasets and offer an overview of LinkedIn’s governance model: the tools, process and teams that ensure that its data ecosystem can handle change and sustain #datasciencehappiness.
The Evolving Role of the Data Engineer - Whitepaper | QuboleVasu S
A whitepaper about how the evolving data engineering profession helps data-driven companies work smarter and lower cloud costs with Qubole.
https://www.qubole.com/resources/white-papers/the-evolving-role-of-the-data-engineer
The Maturity Model: Taking the Growing Pains Out of HadoopInside Analysis
The Briefing Room with Rick van der Lans and Think Big, a Teradata Company
Live Webcast on June 16, 2015
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=197f8106531874cc5c14081ca214eaff
Hadoop is arguably one of the most disruptive technologies of the last decade. Once lauded solely for its ability to transform the speed of batch processing, it has marched steadily forward and promulgated an array of performance-enhancing accessories, notably Spark and YARN. Hadoop has evolved into much more than a file system and batch processor, and it now promises to stand as the data management and analytics backbone for enterprises.
Register for this episode of The Briefing Room to learn from veteran Analyst Rick van der Lans, as he discusses the emerging roles of Hadoop within the analytics ecosystem. He’ll be briefed by Ron Bodkin of Think Big, a Teradata Company, who will explore Hadoop’s maturity spectrum, from typical entry use cases all the way up the value chain. He’ll show how enterprises that already use Hadoop in production are finding new ways to exploit its power and build creative, dynamic analytics environments.
Visit InsideAnalysis.com for more information.
Data Con LA 2020
Description
Coming from a grand belief of data democratization, I believe that in order for any team to be successful collaborators, it has to be data centric and data should be accessible to all.
*To ensure that your non software or software engineering centric team has maximum efficiency, data should be visible, data lake should be accessible.
*Form a database for analytics summaries, talk about the different technologies(SQL, NoSQL) cost of deployment, need, team driven structure. Build an API for this database for external/inter team crosstalk.
*Build analytics and visual layer on top of it. Flask/Django/Node, etc.., to enable the team to have high visibility in their analysis, and to ensure a higher turnaround of data.
*Talk about an easy way of enabling the team to run code, could be local/cloud, JupyterHub is a great way of doing so, talk about the tremendous value added in that and the potential it enables
*Talk about the common tools user for version control/CICD/Coding technologies, etc..
*Finally summarize the value of the mixture of all these tools and technologies in order to ensure the maximum efficiency.
Speaker
Nawar Khabbaz, Rivian, Data Engineer
VoltDB and HPE Vertica Present: Building an IoT Architecture for Fast + Big DataVoltDB
This webinar with Chris Selland of HPE Vertica and Dennis Duckworth of VoltDB addresses the growing challenges with managing a complex IoT solution and how to enable real-time operational interaction with comprehensive data analytics.
Accelerate Self-Service Analytics with Data Virtualization and VisualizationDenodo
Watch full webinar here: https://bit.ly/3fpitC3
Enterprise organizations are shifting to self-service analytics as business users need real-time access to holistic and consistent views of data regardless of its location, source or type for arriving at critical decisions.
Data Virtualization and Data Visualization work together through a universal semantic layer. Learn how they enable self-service data discovery and improve performance of your reports and dashboards.
In this session, you will learn:
- Challenges faced by business users
- How data virtualization enables self-service analytics
- Use case and lessons from customer success
- Overview of the highlight features in Tableau
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...DATAVERSITY
A solid data architecture is critical to the success of any data initiative. But what is meant by “data architecture”? Throughout the industry, there are many different “flavors” of data architecture, each with its own unique value and use cases for describing key aspects of the data landscape. Join this webinar to demystify the various architecture styles and understand how they can add value to your organization.
Guest Speaker in the 2nd National level webinar titled "Big Data Driven Solutions to Combat Covid 19" on 4th July 2020, Ethiraj College for Women(Auto), Chennai.
Similar to 5 Major Trends in Data You Should Know (20)
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Quantitative Data AnalysisReliability Analysis (Cronbach Alpha) Common Method...2023240532
Quantitative data Analysis
Overview
Reliability Analysis (Cronbach Alpha)
Common Method Bias (Harman Single Factor Test)
Frequency Analysis (Demographic)
Descriptive Analysis
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
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.
6. Rise of Data Engineering as Craft
Why has
Data Become
So
Ubiquitous?
7. Rise of Data Engineering as Craft
Aggregated into
EDW
Output
Oracle SAP
Logs
TX
Actions
Cognos
Tableau
Data Produced
When a Single Monolithic Pipeline
Worked, It Looked Like This
8. Rise of Data Engineering as CraftBut Everyone Wanted One
Exec Team
Marketing Product Sales
9. Rise of Data Engineering as CraftAnd They Each Need Data from the
Others
Exec Team
Marketing Product Sales
10. Rise of Data Engineering as Craft
This is a Data Mesh:
A Network of Data Producers &
Consumers
16. Rise of Data Engineering as CraftWhat is a Data Engineer?
Data Engineers: the people who move, shape, and
transform data from where it is generated to
where it is needed, and do it
1.Consistently
2.Efficiently
3.Scalably
4.Accurately
5.Compliantly
17. Rise of Data Engineering as Craft
aka
Software Engineers Deep in Data
20. Rise of Data Engineering as Craft
What is the Data Engineering
Equivalent?
21. Rise of Data Engineering as Craft
The Data
Engineering
Lifecycle
22. Rise of Data Engineering as Craft
Each Step of the DELC Needs
New Tools
23. Rise of Data Engineering as CraftData Pipelines:
Watermains of Data
Code in a modern language to
repeatably move data around
Innovators
Airflow, Elementl, Prefect
25. Rise of Data Engineering as CraftCompute Engines:
Access Cloud Data
Query data in the cloud, without
moving it. Key insight: separation
of data and compute.
Innovators
Dremio, Databricks
26.
27. Rise of Data Engineering as CraftData Modeling:
Universal Metrics Library
Define metrics once for the entire
organization
Innovators
Transform Data, Looker
28.
29. Rise of Data Engineering as CraftData Products:
Stand on the Shoulders of Gigabytes
Build and deploy data products
internally and externally
Innovators
BI: Preset
ML: Streamlit, Tecton
30.
31.
32. Rise of Data Engineering as CraftData Quality:
Harness & Tame Error
Develop tests and monitor data
flows to ensure data integrity
Innovators
Monte Carlo, Great Expectations,
Soda Data, Data Gravity
33.
34.
35. 5 Data Trends You Should Know
1.Data Pipelines – move data with code
2.Compute Engines – query cloud data
3.Modeling – defines metrics once
4.Data Products – squeeze insight from data
5.Data Quality – keep data accurate
37. FiveDataTrends You Should Know
Tomasz Tunguz, Managing Director, Redpoint Ventures
@ttunguz & tomtunguz.com
Editor's Notes
Thank you for the warm introduction, Jason.
I’m thrilled to be here.
My name is Tomasz Tunguz. I’m a managing director at Redpoint Ventures and I write a blog at tomtunguz.com
It’s a data infused collection of posts about startups.
Let me tell you about Redpoint.
Redpoint is a venture firm based in Silicon Valley.
Invest anywhere from 1m to 50m in companies primarily in the US.
We’re a group of founders and operators who have founded startups, operated at hypergrowth companies, and helped startups scale to terrific heights.
We work or have worked with 26 Unicorns and some iconic companies with more than 25b in market cap.
Including Stripe, Hashicorp, Twilio, Duo Security and Zendesk.
We have deep domain experience in data. We were early investors in Looker, Snowflake and Dremio.
We evaluate about 7000 investment opportunities annually and this presentation is meant to distill some of the trends we see in market.
I’m passionate about data.
I was first exposed to the power of data studying machine learning at college. I studied control systems for satellites and saw how that technology could be used in the stock market.
Then went to Google. Google’s business is entirely predicated on data.
I saw first hand the impact and the leverage we could drive from great data if properly managed through the right systems and tools.
I have been deep in in data ever since
I co-authored a book on data called Winning with Data, that researched the challenges modern organizations face with data and how the best companies in the world mitigate those challenges and transform data into competitive advantage.
Like you all, I love data and the power & insight it can give businesses.
Today, I’ll share with you 5 trends we’re seeing in the data world. But you should know there is one megatrend, a huge wave, furthering these trends.
That trend is the rise of data engineering as a new craft. The word data engineer is new, and the idea is important.
Data engineers will define the next decade.
Ten years ago, the people working with data, moving it, shaping it, slicing it, came from many different backgrounds.
Some came from finance; others have statistics backgrounds; still others came from customer support (like me) and they all found themselves in data roles.
This convergence across disciplines occurred because data has become a critical part of every modern company’s technology stack.
Data has become essential. So companies invest in specialized people, processes and systems to maximize the benefit they can squeeze from data.
Data engineering has come about because data is everywhere. And every bit of a business’ data is valuable,.
The reason data has become so ubiquitous is it costs much less to store than it did 20 years ago.
20 years ago, we stored data in Oracle databases that were expensive and required new licenses as data scaled. So we filtered it aggressively.
Today, we store exabytes data in files on S3. Because we can afford it. For the price of two oat-milk macchiatos at BlueBottle, I can store half a terabyte of data on Amazon for a month. So, we store data because we can afford it. And we store buckets, reams, mountains of it.
Since we have all that data at hand, we decided to use it. 20 years ago, IT bought the systems to extract value from data. They procured them, installed them, and managed these systems. But ten years ago, forward thinking teams decided to do it for themselves. IT was too slow. A modern marketing team can’t wait 3 to 6 months to get the answers to their questions. They’ll be toast. So the marketing team bought their own system.
And, then the marketing team created data products. At first, these data products were dashboards. How many new clicks? How many leads? How many customers? How much ad spend? Then marketing operations teams became more sophisticated. They started to run scenarios to test different ideas, and experiment with new techniques. Today, marketing is a panoply of machine learning algorithms stuffed to the gills with first-party data, a quantitative hedge fund for buying online ads. All in 15 years.
Those predictive systems create data of their own, which is stored and processed. This is more than a process; it’s a flywheel that goes faster and faster and faster. A massive digital boulder of ones and zeros coming down the hill at top speed.
The problem is that this boulder isn’t just in marketing. It’s everywhere within a company.
Let me explain. 20 years ago this is how the data world worked at the highest level.
Systems produced data: system logs, transactions, customer actions on websites.
The data was filtered into an enterprise data warehouse and data cube because of cost.
And then pumped into a legacy output system like a Tableau or Cognos.
This worked for small data volumes. But it’s expensive, inflexible, closed and slow. Pop quiz: how long does it take to update a report in Cognos? Too long. Your business is dead.
But this was state of the art.
And everyone wanted one.
Each team manager saw success with data. The authority, the command of the business, the ideas that flowed from the data. It’s intoxicating when you can use data to see around corners, inspire confidence, and lead teams boldly into the future.
So each team developed their own data systems. IT couldn’t keep up. And consumerization of IT was born. For every $1 IT spent on technology, department heads spent an additional 47 cents to outfit their teams with the best kit.
At the outset, departments built small systems. But then each hired operations teams, doublespeak for data analysts and data engineers to help them understand the data, predict the future, and build data products on top.
A thousand digital flowers bloomed. And they grew and grew and grew.
And that garden quickly became overrun with complexity. Leaves and thorns everywhere.
The marketing team decided they needed data from other places; not just the central data store administered by IT. The marketing team needs access to the CRM data base to understand customer value. Oh, and customer support data to understand customer lifecycles. Plus, billing data from the finance team. And a bit of product data: those web analytics inform customer conversion.
It wasn’t just marketing that was sapping data from other teams. Each department needed data from the other to operate their businesses best. Which created a completely new concept.
This idea has a name. It is called the data mesh. It is a network of data producers and consumers within an organization.
Each team is responsible for producing its own data, publishing data via some API or common format. It’s responsible for documenting the data, explaining the lineage, keeping it up to date, so other teams can use it and rely on it to decide.
In exchange, other teams do the same. This creates a mesh, and enables the organization to send the data, use APIs, and develop increasingly sophisticated data products at scale.
And then, importantly, modern companies move this all to a cloud data lake. In the cloud, data is elastic, cheap, maintained by someone else, and accessible by everyone (with the right IAM permissions of course).
More importantly, teams stored data in these cloud data lakes in standard, open-source formats like Parquet and Arrow. These formats accelerate queries, create a single standard which makes it easier to work with tools that you have today and tools that have not been invented yet
That’s the vision. That’s where the industry is going. But we are all in different states of getting there. And the reality is more complicated than these beautiful diagrams.
In fact today, many companies don’t have a data mesh, they have a date mess.
Each team has their own tools, data storage depots and infrastructure. It’s a big bucket of Legos. Systems that don’t talk to each other. Confusion about three different definitions of revenue. Where is the customer support data table? Oh, that’s the old version. And that column that reads date_final_final is actually the wrong format. We moved it to a new column called dff..f. And to access that table you need to speak COBOL. But we lost the COBOL/NodeJS connector.
Data Messes have 4 consistent problems
data breadlines: I have a question about the business. Let me go and ask the engineer I met at lunch if she’ll do me the favor of pulling the data, again. Data breadlines are the invisible people people waiting around for answers to their data questions, who ask a question and go to the back of the line when they need a refinement.
Data obscurity or rogue databases: when I was at google, I operated a rogue database. I asked an engineer to run a MapReduce job to help me understand the competition and dump that table on a server underneath my desk. Then I bult reports on that table and we used it to prioritize customer acquisition techniques. No one knew it was there. No one validated the data.
Data fragmentation is the challenge of finding out where data is. You see the dashboard in front of you. You know the data is stored somewhere in the company, but where? Who owns it?
Data brawls: the fights between teams about the definition of payback period).
The vision, as it has always been with data systems is to put it all together and develop a breathtaking machine that enables a company to grow significantly faster. I can tell you from working with some of the leading companies in the data world, when you do achieve this vision, it’s a transformation. It enables teams to move faster, execute better, and outperform the competition.
I saw it at Google. I saw it at Looker and many of Looker’s customers. And we’re seeing it at Dremio too.
Companies that can migrate to data meshes suddenly unlock hidden productivity, It’s a big leap and challenge.
But, getting there and building a machine is not easy. So the question is when you put the bat signal who will come to save the day?
There’s a simple answer. It’s the data engineer. This is why this role has evolved. Because the complexity has gotten to a point that we need specialized people to manage this infrastructure and empower everyone within a company to use data effectively.
We believe that data engineering is the customer success of this decade. A new role, is critically important to a company, that will champion a discipline of the future. Although I can’t see you, I’m confident many of you in the audience are exactly the superhero, maybe minus the Batmobile.
What is a data engineer? They are the people who move, shape, and transform data from where it is generated to where it is needed and do it consistently, efficiently, scalability, accurately and compliantly.
Date engineers have many different skills. Some of them are infrastructure specialists. Others have focused on reporting and the tools associated with analytics. Still others develop and host and maintain machine learning infrastructure. It is a broad discipline of very smart people who are going to be key to business success in the next 10 years.
In other words, these people are software engineers who are deep data around
In researching this market, we had an insight. Software engineers have decades of experience writing software, building tooling and patterns of writing code.
The ost recent example of this is the cloud native computing foundations software development lifecycle.
This is a ourobouros, a snake eating its tail, an infinite cycle.
It is a consistent process for how to manage software releases in the most modern way. Vendors within that ecosystem use this diagram within their pitches to customers and describe exactly which part of the processes they address.
Managers use this process to talk about tooling at different steps of the engineering process. It has 8 steps.
Plan the software you want to build
Code it
Build it and package it to ship
Test it with a testing harness
Release the software by pushing into the production environment
Deploy the software across your cloud
Operate it
Monitor it
And repeat
If data engineering really is software engineers deep in data, what is the data engineering equivalent of the software development lifecycle? I haven’t been able to find one. But, in talking to hundreds of potential buyers of this kind of software, we have a hypothesis of what it should look like.
This is what we observe the market for the data engineering cycle. It has six steps
Ingesting data from whatever data producer is spewing data into storage system like Amazon S3
Planning: this is the phase of deciding what it is that you want to do with this data
Query: modern computer engines run over the data to filter and aggregate the data in a way that’s useful to a particular product.
Data Modeling: is the work of defining a metric once in a central place so that everyone within the company can benefit from it.
Developing Product: Is the work of actually building a product around the data and the insights contained within that data
Monitoring: the act and process of ensuring data is flowing normally and is accurate at all times
This cycle creates more data which is then ingested saved and pumped back into the rest of the cycle.
In each of the steps of the data engineering lifecycle, new tools are emerging to support the work of the data engineer. These are the five major trends within the data world.
First, data pipelines. These are the watermains of data moving data from where it’s produced to where it can be leveraged.
Data pipelines have been around forever. The main advance in these data pipelines are
Using modern computing languages
Creating higher levels of abstractions to enable engineers to reuse code across different data pipelines to improve productivity
Monitoring within these data pipelines
Visualization of the DAGs a directed acyclic graph, all the steps involved
Here are screenshots of Prefect’s products which ingests code and then creates a DAG visualization. You can see the different steps in the data process.
And on the right, there is a monitoring dashboard that shows the state of the data pipeline, the errors, and the activity.
The idea is to treat data pipelines as real code with true monitoring to ensure data is always accurate.
Some of these systems
Computer engines query the data within the cloud without moving it. This enables teams to get access to all the information they need from a single place in a cost-effective, compliant, and fast way.
These computer engines are the execution layer that sits on top all of the open format files. Compute engines accelerate queries. They make them faster not just for a single user vote for everybody. They reduce cost because you’re not having to move data around. They eliminate data lock-in because any tool can talk to them provided they use an open format like arrow.
We’ve been lucky enough work with Dremio from the beginning, and it’s our company that has seen this trend years ago and develop the infrastructure to enable you to achieve this vision
The next step in the process is data modeling. The idea is to define revenue once so that the sales team and the marketing team both have the same definition and don’t get into data disputes with each other. Make sure that the entire company is aligned on a single number.
I’m sure we’ve all lived through a meeting where we are arguing about a topic, and we’ve each got a different number for revenue or lead count or payback period. Modeling is all about creating an owner of a metric, explaining what that metric is, describing the lineage, so that everybody is on the same page and using the right number in the right column to make the best decision
The other important part of data modeling is to ensure you undersntand what your data is telling you. Variations inData definitions can have meaningful impact on how you interpret a number. So, companies like transform develop systems where dimensions and metrics of data are defined once, in a central place. This code is checked into Github.
Then, whenever you need data you query the data modeling interface, which ensures you know the revenue metric you are asking for is the revenue metric that everybody else is using and the one that has been approved by finance.
Data products are the insights, analytics, and software built using data within a company. There are two big buckets of these I’ll talk about today:
Their next generation data visualization companies like preset that enable teams to visualize trends within the data, share this insight with others, and publish them on an ongoing basis to key stakeholders. Preset is a company commercializing an open source software called superset which was created at AirBnB. In fact, the founder of the project and the company Max, spoke to you earlier today. Preset adopts many of the open principles that are consistent with the rest of this ecosystem and applies it to data visualization and data exploration
in addition, there is a parallel world within machine learning tooling. This world is huge and purity with the key players in it. Streamlet enables machine learning engineers to share their models with non-technical users either for direct consumption of those models like a recommendation system within a customer support tool for recommending email responses, or for help treating a model in a autonomous vehicle use case for example.
To give you a sense, this is a screenshot of preset’s mapping capability in San Francisco. This is entirely open-source software
This is an example of a StreamlitData product. On the left is the code written in Python. On the right, you see the web UI that is created. In this case, it is an example that allows an end user to tweak and tune data scientist’s machine learning model. And that user doesn’t have to be a technical user. It could be someone who operates autonomous vehicles helping data scientists to the object avoidance algorithm.
Last, data quality. Data quality was a wave in the late 90s. But it is disappeared for about 20 years, or at least hasn’t been adoptied within modern data stacks until now.
software engineering has many different systems to ensure new code operates well. There are a battery of performance tests, functional test, unit test, progression tests, concepts of test coverage, monitoring tools and anomaly detection tools. But we don’t have that today. And it manifests itself in the worst way.
Has your CEO ever looked at a report you showed him and said the numbers look way off? Has a customer ever called out incorrect data in your product’s dashboards? Data quality is meant to solve that issue and restore consistent credibility within people who use data.
There are two different approaches to data quality. The first is to write explicit tests. This is an expectation from Great Expectations.
It says the column room temperature should remain between 60 & 75 degrees for 95% of instances. This type of data integrity testing is like functional testing in software. If engineers know what to expect, this is an effective tool. It does require writing a huge battery of tests and having a test coverage metric similar to software.
There’s another approach using machine learning. Companies like Soda Data and Monte Carlo use ML to understand data patterns and then discover anomalies. These anomalies might be differences in data volumes. A data feed is broken. Or there’s a change in distribution of the data. Instead of a gaussian distribution in the data, now it’s a zipf and which has implications for analysis downstream.
The machine learning approach comes from anomaly detection in security systems. And the benefit is the system is automonous. The challenge is ensuring the signal to noise ratio is strong and meaningful, otherwise, users won’t pay attention to the results.
So, in summary, these are the five data trends you should know. These are the data trends that we have observed after meeting thousands of companies and talking to hundreds of prospective buyers. These are the technologies that we expect will define the data world over the next 10 years.
These five trends are not enough.
It’s really early in this decade of data engineering. We are 6 months into her 10 year long movement. The future depends on you. We need engineers to weave all these different technologies together into a beautiful data tapestry. These are not easy problems, and the landscape underneath you is changing all the time. There are new software tools, legacy applications, lots of demands from everybody around you to get them exactly what they need when they need it which is yesterday.
But at Redpoint, we believe this decade is the decade of the data engineer. An entirely new role that specializes in the critically important functions of getting data from the places it is generated to the places it creates insights and unlocks powerful decision-making ability within businesses
The future depends on you.