RDBMS gave us table schemas. A table schema, which is an essential metadata component, gave us the power to validate data types, and enforce constraints. In the age of varying data and schema-less data stores, how can we enforce these rules and how can we leverage metadata (even in RDBMS) to empower data validity, code checks, and automation.
This is a brief background into Big data (data lake) to put in context the importance of metadata from a governance perspective and more especially in todays heterogeneous big data platforms.
Manage tracability with Apache Atlas, a flexible metadata repositorySynaltic Group
Do you know where is your data ?
Do you know who is responsible of this specific datasets ?
Do you know from which application or task this entity was modified last friday ?
Apache Atlas helps you to manage all your metadata of your data. With Apache Atlas you can know all lineages between your datasets and process that use them.
Metadata & brokering - a modern approach #2Daniele Bailo
The second episode of metadata and brokering.
Topics covered:
1. additional definition (ontology, relational database and others)
2. the wide picture: data fabric elements from Research Data Alliance (RDA) and possible concrete implementations of those guidelines
The Big Data Analytics Ecosystem at LinkedInrajappaiyer
LinkedIn has several data driven products that improve the experience of its users -- whether they are professionals or enterprises. Supporting this is a large ecosystem of systems and processes that provide data and insights in a timely manner to the products that are driven by it.
This talk provides an overview of the various components of this ecosystem which are:
- Hadoop
- Teradata
- Kafka
- Databus
- Camus
- Lumos
etc.
An overview about several technologies which contribute to the landscape of Big Data.
An intro about the technology challenges of Big Data, follow by key open-source components which help out in dealing with various big data aspects such as OLAP, Real-Time Online
Analytics, Machine Learning on Map-Reduce. I conclude with an enumeration of the key areas where those technologies are most likely unleashing new opportunity for various businesses.
Denodo Platform 7.0: Redefine Analytics with In-Memory Parallel Processing an...Denodo
Watch Pablo's session from Fast Data Strategy on-demand here: https://goo.gl/1aEBo8
The tide is changing for analytics architectures. Traditional approaches, from the data warehouse to the data lake, implicitly assume that all relevant data can be stored in a single, centralized repository. But this approach is slow and expensive, and sometimes not even feasible, because some data sources are too big to be replicated, and data is often too distributed such as those found in cloud data sources to make a “full centralization” strategy successful.
Watch this session to learn more about:
• Modern data architectures
• Why logical architectures are the best option when integrating big data
• How Denodo’s parallel in-memory capabilities with dynamic query optimization redefine analytics architectures
RDBMS gave us table schemas. A table schema, which is an essential metadata component, gave us the power to validate data types, and enforce constraints. In the age of varying data and schema-less data stores, how can we enforce these rules and how can we leverage metadata (even in RDBMS) to empower data validity, code checks, and automation.
This is a brief background into Big data (data lake) to put in context the importance of metadata from a governance perspective and more especially in todays heterogeneous big data platforms.
Manage tracability with Apache Atlas, a flexible metadata repositorySynaltic Group
Do you know where is your data ?
Do you know who is responsible of this specific datasets ?
Do you know from which application or task this entity was modified last friday ?
Apache Atlas helps you to manage all your metadata of your data. With Apache Atlas you can know all lineages between your datasets and process that use them.
Metadata & brokering - a modern approach #2Daniele Bailo
The second episode of metadata and brokering.
Topics covered:
1. additional definition (ontology, relational database and others)
2. the wide picture: data fabric elements from Research Data Alliance (RDA) and possible concrete implementations of those guidelines
The Big Data Analytics Ecosystem at LinkedInrajappaiyer
LinkedIn has several data driven products that improve the experience of its users -- whether they are professionals or enterprises. Supporting this is a large ecosystem of systems and processes that provide data and insights in a timely manner to the products that are driven by it.
This talk provides an overview of the various components of this ecosystem which are:
- Hadoop
- Teradata
- Kafka
- Databus
- Camus
- Lumos
etc.
An overview about several technologies which contribute to the landscape of Big Data.
An intro about the technology challenges of Big Data, follow by key open-source components which help out in dealing with various big data aspects such as OLAP, Real-Time Online
Analytics, Machine Learning on Map-Reduce. I conclude with an enumeration of the key areas where those technologies are most likely unleashing new opportunity for various businesses.
Denodo Platform 7.0: Redefine Analytics with In-Memory Parallel Processing an...Denodo
Watch Pablo's session from Fast Data Strategy on-demand here: https://goo.gl/1aEBo8
The tide is changing for analytics architectures. Traditional approaches, from the data warehouse to the data lake, implicitly assume that all relevant data can be stored in a single, centralized repository. But this approach is slow and expensive, and sometimes not even feasible, because some data sources are too big to be replicated, and data is often too distributed such as those found in cloud data sources to make a “full centralization” strategy successful.
Watch this session to learn more about:
• Modern data architectures
• Why logical architectures are the best option when integrating big data
• How Denodo’s parallel in-memory capabilities with dynamic query optimization redefine analytics architectures
Dataverse data repository is great opportunity for research communities to make their data FAIR: Findable, Accessible, Interoperable, and Re-usable. It's developed to help open outcome of scientific research projects to the public.
this is the ppt this contains definition of data ware house , data , ware house, data modeling , data warehouse architecture and its type , data warehouse types, single tire, two tire, three tire .
All about Big Data components and the best tools to ingest, process, store and visualize the data.
This is a keynote from the series "by Developer for Developers" powered by eSolutionsGrup.
Big Data is the reality of modern business: from big companies to small ones, everybody is trying to find their own benefit. Big Data technologies are not meant to replace traditional ones, but to be complementary to them. In this presentation you will hear what is Big Data and Data Lake and what are the most popular technologies used in Big Data world. We will also speak about Hadoop and Spark, and how they integrate with traditional systems and their benefits.
This is a presentation by Peter Coppola, VP of Product and Marketing at Basho Technologies and Matthew Aslett, Research Director at 451 Research. Join them as they discuss whether multi-model databases and polyglot persistence have increased operational complexity. They'll discuss the benefits and importance of NoSQL databases and how the Basho Data Platform helps enterprises leverage Big Data applications.
The term "Data Lake" has become almost as overused and undescriptive as "Big Data". Many believe that centralizing datasets in HDFS makes a data lake, but then they struggle to realize any tangible value. This talk will redefine the "Data Lake" by describing four specific, key characteristics that we at Koverse have learned are crucial to successful enterprise data lake deployments. These characteristics are 1) indexing and search across all data sets, 2) interactive access for all users in the enterprise, 3) multi-level access control, and 4) integration with data science tools. These characteristics define a system that lets people realize value from their data versus getting lost in the hype. The talk will go on to provide a technical description of how we have integrated several projects, namely Apache Accumulo, Hadoop, and Spark, to implement an enterprise data lake with these key features.
Knowledge graphs - it’s what all businesses now are on the lookout for. But what exactly is a knowledge graph and, more importantly, how do you get one? Do you get it as an out-of-the-box solution or do you have to build it (or have someone else build it for you)? With the help of our knowledge graph technology experts, we have created a step-by-step list of how to build a knowledge graph. It will properly expose and enforce the semantics of the semantic data model via inference, consistency checking and validation and thus offer organizations many more opportunities to transform and interlink data into coherent knowledge.
Introduction of the bridge between DataverseNL data repository for ongoing research and EASY trusted digital repository on
workshop PID Information Types for the Social Sciences.
Denodo DataFest 2016: What’s New in Denodo Platform – Demo and RoadmapDenodo
Watch the full session: Denodo DataFest 2016 sessions: https://goo.gl/ptGwp7
Curious about product roadmap? In this session, we will review some of the new key features introduced this year in the Denodo Platform in areas such as performance, self-service, security and monitoring. We will also take a sneak peek at the most exciting features in the roadmap for Denodo 7.0.
In this session, you will learn:
• New performance-related features in big data scenarios
• New governance and self-service features
• New connectivity, data transformation, and enterprise-wide deployment features
This session is part of the Denodo DataFest 2016 event. You can also watch more Denodo DataFest sessions on demand here: https://goo.gl/VXb6M6
Data Integration Alternatives: When to use Data Virtualization, ETL, and ESBDenodo
Data integration is paramount, in this presentation you will find three different paradigms: using client-side tools, creating traditional data warehouses and the data virtualization solution - the logical data warehouse, comparing each other and positioning data virtualization as an integral part of any future-proof IT infrastructure.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/1q94Ka.
Role of Data Cleaning in Data WarehouseRamakant Soni
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.
Big data perspective solution & technologyPankaj Khattar
This presentation talks about the requirement, growth, usage of big data platform. Discuss in brief about the available tools for big data under various branch of Visualisation, Analytics & Storage
I have presented Hadoop, Hive, HBase, Mahout technologies research on Chungbuk National University "Big Data Infrastructure and Analytics Solution in FITAT2013"
Dataverse data repository is great opportunity for research communities to make their data FAIR: Findable, Accessible, Interoperable, and Re-usable. It's developed to help open outcome of scientific research projects to the public.
this is the ppt this contains definition of data ware house , data , ware house, data modeling , data warehouse architecture and its type , data warehouse types, single tire, two tire, three tire .
All about Big Data components and the best tools to ingest, process, store and visualize the data.
This is a keynote from the series "by Developer for Developers" powered by eSolutionsGrup.
Big Data is the reality of modern business: from big companies to small ones, everybody is trying to find their own benefit. Big Data technologies are not meant to replace traditional ones, but to be complementary to them. In this presentation you will hear what is Big Data and Data Lake and what are the most popular technologies used in Big Data world. We will also speak about Hadoop and Spark, and how they integrate with traditional systems and their benefits.
This is a presentation by Peter Coppola, VP of Product and Marketing at Basho Technologies and Matthew Aslett, Research Director at 451 Research. Join them as they discuss whether multi-model databases and polyglot persistence have increased operational complexity. They'll discuss the benefits and importance of NoSQL databases and how the Basho Data Platform helps enterprises leverage Big Data applications.
The term "Data Lake" has become almost as overused and undescriptive as "Big Data". Many believe that centralizing datasets in HDFS makes a data lake, but then they struggle to realize any tangible value. This talk will redefine the "Data Lake" by describing four specific, key characteristics that we at Koverse have learned are crucial to successful enterprise data lake deployments. These characteristics are 1) indexing and search across all data sets, 2) interactive access for all users in the enterprise, 3) multi-level access control, and 4) integration with data science tools. These characteristics define a system that lets people realize value from their data versus getting lost in the hype. The talk will go on to provide a technical description of how we have integrated several projects, namely Apache Accumulo, Hadoop, and Spark, to implement an enterprise data lake with these key features.
Knowledge graphs - it’s what all businesses now are on the lookout for. But what exactly is a knowledge graph and, more importantly, how do you get one? Do you get it as an out-of-the-box solution or do you have to build it (or have someone else build it for you)? With the help of our knowledge graph technology experts, we have created a step-by-step list of how to build a knowledge graph. It will properly expose and enforce the semantics of the semantic data model via inference, consistency checking and validation and thus offer organizations many more opportunities to transform and interlink data into coherent knowledge.
Introduction of the bridge between DataverseNL data repository for ongoing research and EASY trusted digital repository on
workshop PID Information Types for the Social Sciences.
Denodo DataFest 2016: What’s New in Denodo Platform – Demo and RoadmapDenodo
Watch the full session: Denodo DataFest 2016 sessions: https://goo.gl/ptGwp7
Curious about product roadmap? In this session, we will review some of the new key features introduced this year in the Denodo Platform in areas such as performance, self-service, security and monitoring. We will also take a sneak peek at the most exciting features in the roadmap for Denodo 7.0.
In this session, you will learn:
• New performance-related features in big data scenarios
• New governance and self-service features
• New connectivity, data transformation, and enterprise-wide deployment features
This session is part of the Denodo DataFest 2016 event. You can also watch more Denodo DataFest sessions on demand here: https://goo.gl/VXb6M6
Data Integration Alternatives: When to use Data Virtualization, ETL, and ESBDenodo
Data integration is paramount, in this presentation you will find three different paradigms: using client-side tools, creating traditional data warehouses and the data virtualization solution - the logical data warehouse, comparing each other and positioning data virtualization as an integral part of any future-proof IT infrastructure.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/1q94Ka.
Role of Data Cleaning in Data WarehouseRamakant Soni
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.
Big data perspective solution & technologyPankaj Khattar
This presentation talks about the requirement, growth, usage of big data platform. Discuss in brief about the available tools for big data under various branch of Visualisation, Analytics & Storage
I have presented Hadoop, Hive, HBase, Mahout technologies research on Chungbuk National University "Big Data Infrastructure and Analytics Solution in FITAT2013"
Understanding Metadata: Why it's essential to your big data solution and how ...Zaloni
In this O'Reilly webcast, Ben Sharma (cofounder and CEO of Zaloni) and Vikram Sreekanti (software engineer in the AMPLab at UC Berkeley) discuss the value of collecting and analyzing metadata, and its potential to impact your big data solution and your business.
Watch the replay here: http://oreil.ly/28LO7IW
Présentation commerciale Movico - Véhicules pour événements sportifs et grand...Movico
With its mobile sports facilities, Movico is a recognised player at international sporting events. Movico is perfectly at ease where rapid building and dismantling of many types of facilities are required. Movico has all the expertise in-house. Together with its enthusiastic team of employees and smoothly running organisation, Movico takes every obstacle in its stride.
Thanks to the comfort, speed and precision, Movico and its sports facilities are number one at athletic events, motor and motor-cycle race meetings, cycling events and other imagination-capturing sporting occasions.
Movico has an extensive selection of mobile facilities at your disposal. We have developed concepts especially for sports events. Our units for registration, production and montage of TV pictures and TV broadcasts are flexible, compact, quick and comfortable. Movico’s mobile studios can be used, among other things, as camera platforms, editing rooms, interviews, etc. Add to this the high level of service and the technical and catering facilities, and the reporting of sports events becomes even more attractive than ever.
Check http://www.movico.nl for more information about our products and services!
Wie kann ich mein Kind für die Schule digital fit machen?Alicia Bankhofer
Webinar #35 für digi4family.at am 22.09.2016
"Wie kann ich mein Kind für die Schule digital fit machen? http://www.digi4family.at/events/event/wie-kann-ich-mein-kind-fuer-die-schule-digital-fit-machen-webinar-35/
Digital fit und kompetent durch den Einsatz von Technik im UnterrichtAlicia Bankhofer
Präsentation anlässlich des Bildungskongresses an der Villa Wewersbusch "Lernen der Zukunft" am 03.03.2017
http://www.villawewersbusch.org/programm/
Alle Inhalte unter Creative Commons CC BY-SA 4.0.
MAKING SENSE OF IOT DATA W/ BIG DATA + DATA SCIENCE - CHARLES CAIBig Data Week
Charles Cai has more than two decades of experience and track records of global transformational programme deliveries – from vision, evangelism to end-to-end execution in global investment banks, and energy trading companies, where he excels at designing and building innovative, large scale, Big Data systems in high volume low latency trading, global Energy Trading & Risk Management, and advanced temporal and geospatial predictive analytics, as Chief Front Office Technical Architect and Head of Data Science. He’s also a frequent speaker at Google Campus, Big Data Innovation Summit, Cloud World Forum, Data Science London, QCon London and MoD CIO Symposium etc, to promote knowledge and best practice sharing, with audience ranging from developers, data scientists, to CXO level senior executives from both IT and business background. He has in-depth knowledge and experience Scala, Python, C# / F#, C++, Node.js, Java, R, Haskell programming languages in Mobile, Desktop, Hadoop/Spark, Cloud IoT/MCU and BlockChain etc, and TOGAF9, EMC-DS, AWS CNE4 etc. certifications.
Metadata can play a vital role in enabling the effective management, discovery, and re-usability of digital information. Digital preservation metadata provides provenance information, supports and documents preservation activity, identifies technical features, and aids in verifying the authenticity of a digital object. This presentation gives and introduction to Digital preservation matadata and preservation metada in practise. Presentation was delivered during the joint DPE/Planets/CAPAR/nestor training event, ‘The Preservation challenge: basic concepts and practical applications’ (Barcelona, March 2009)
Hadoop was born out of the need to process Big Data.Today data is being generated liked never before and it is becoming difficult to store and process this enormous volume and large variety of data, In order to cope this Big Data technology comes in.Today Hadoop software stack is go-to framework for large scale,data intensive storage and compute solution for Big Data Analytics Applications.The beauty of Hadoop is that it is designed to process large volume of data in clustered commodity computers work in parallel.Distributing the data that is too large across the nodes in clusters solves the problem of having too large data sets to be processed onto the single machine.
Solving the Really Big Tech Problems with IoTEric Kavanagh
The Briefing Room with Dr. Robin Bloor and HPE Security
The Internet of Things brings new technological problems: sensor communications are bi-directional, the scale of data generation points has no precedent and, in this new world, security, privacy and data protection need to go out to the edge. Likely, most of that data lands in Hadoop and Big Data platforms. With the need for rapid analytics never greater, companies try to seize opportunities in tighter time windows. Yet, cyber-threats are at an all-time high, targeting the most valuable of assets—the data.
Register for this episode of The Briefing Room to hear Analyst Dr. Robin Bloor explain the implications of today's divergent data forces. He’ll be briefed by Reiner Kappenberger of HPE, who will discuss how a recent innovation -- NiFi -- is revolutionizing the big data ecosystem. He’ll explain how this technology dramatically simplifies data flow design, enabling a new era of business-driven analysis, while also protecting sensitive data.
The presentation gives an overview of what metadata is and why it is important. It also addresses the benefits that metadata can bring and offers advice and tips on how to produce good quality metadata and, to close, how EUDAT uses metadata in the B2FIND service.
November 2016
How do data analysts work with big data and distributed computing frameworks.pdfSoumodeep Nanee Kundu
The era of big data has ushered in a new paradigm for data analysis, presenting unique challenges and opportunities. This article delves into the world of big data analytics and explores how data analysts work with distributed computing frameworks to handle large and complex datasets. We'll discuss the concept of big data, the challenges it poses, and the evolution of distributed computing frameworks. Furthermore, we'll dive into the role of data analysts, their skills and tools, and the practical applications of big data analytics. By the end of this article, readers will have a comprehensive understanding of how data analysts leverage distributed computing frameworks to extract valuable insights from vast datasets.
Debunking "Purpose-Built Data Systems:": Enter the Universal DatabaseStavros Papadopoulos
Purpose-built databases and platforms have actually created more complexity, effort, and unnecessary reinvention. The status quo is a big mess. TileDB took the opposite approach.
In this presentation, Stavros, the original creator of TileDB, shared the underlying principles of the TileDB universal database built on multi-dimensional arrays, making the case for it as a true first in the data management industry.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
10. Data: qualifies or quantifies a concept or a real-world
occurence, often in the form of a variable across
time. Used to measure and understand.
Metadata: classifies and describes data. Used to
understand, structure, track and manipulate data.
What is metadata?
12. What is metadata?
ID Time Dimension 1 Time Dimension 2 Value
112056 27-11-2006 23:00:00 28-11-2006 01:00:00 830
112056 27-11-2006 23:00:00 28-11-2006 02:00:00 12.7
Descriptive or
Semantic Metadata:
Commodity
Variable
Contract type
Facility type
Technology
Geography
Sector
Etc.
Structural or
Technical Metadata:
Creation date
Origin system
Set ID
Publication freq.
Value freq.
Variable type
Change date
Source
Source file
Etc.
13. Precisely!
We cannot afford not to use metadata:
- Structure, traceability and common standards save time
and resources. The more data – the greater the savings.
- Matadata removes the human bottleneck. Enables data
usage and reusage by both people and processes.
But that’s even more data! Don’t
we have enough/too much already?
14. No.
-Aggregation. Easier to process than the underlying data
even across sets and dimensions.
- Abstraction. Easier for people with different levels of
experience to understand.
- Tool. It has a bi-directional relationship with its subject and
can be used to manipulate it.
Just data about data?
15. - Julia’s File or
WeaCity.ECeENS_Europe.Precip;;WeaCity;PC;EC.Ens;F;H.12;UTC;SVK.SK01.BRATIS;Wea.Precip;mm;H.6;;03
;
How do we use it?
18. Application dictionary
Easy, powerful, and robust Matlab quieries.
Easy groupings of data in containers: charts, files, tables.
Reusable and pivotable code.
Efficient manipulation of groups of curves.
Powerful and scalable monitoring and debugging of large
amounts of heterogenous data.
20. Some cool stuff which would be
impossible without metaSmart homes and IoT
Machine learning
Natural language processing
Bitcoin operations and new uses for the blockchain
meta
Tergeted online content
Smart grids
Big data analysis
Modern video and audio libraries
iTunes
21. Future uses
Emergent algorithms – like those underpinning
swarm intelligence behavior and artificial neural
networks
Emergent technology – technology the effects of
which are greated than its building blocks
Singularity?
22. Summary
Humans are not optimised for raw data processing.
We think in abstractions, relationships and tool
manipulation.
If we want to keep up with data, we need to shape it
to the way our brains work.
That’s what metadata does.
23. “I've seen things you people wouldn't believe…” – Roy in Blade Runner
Questions?
