This is a lecture given at the 2017 Reasoning Web Summer School
It has been clear from the beginning that the success of the Semantic Web hinges on integrating the vast amount of data stored in Relational Databases. In 2007, the W3C organized a workshop on RDF Access to Relational Databases. In 2012, two standards were ratified that map relational data to RDF: Direct Mapping and R2RML.
In this lecture, I will reflect on the last 10 years of research results and systems to integrate Relational Databases with the Semantic web. I will provide an answer to the following question: how and to what extent can Relational Databases be integrated with the Semantic Web? I will review how these standards and systems are being used in practice for data integration and discuss open challenges.
Observability for Data Pipelines With OpenLineageDatabricks
Data is increasingly becoming core to many products. Whether to provide recommendations for users, getting insights on how they use the product, or using machine learning to improve the experience. This creates a critical need for reliable data operations and understanding how data is flowing through our systems. Data pipelines must be auditable, reliable, and run on time. This proves particularly difficult in a constantly changing, fast-paced environment.
Collecting this lineage metadata as data pipelines are running provides an understanding of dependencies between many teams consuming and producing data and how constant changes impact them. It is the underlying foundation that enables the many use cases related to data operations. The OpenLineage project is an API standardizing this metadata across the ecosystem, reducing complexity and duplicate work in collecting lineage information. It enables many projects, consumers of lineage in the ecosystem whether they focus on operations, governance or security.
Marquez is an open source project part of the LF AI & Data foundation which instruments data pipelines to collect lineage and metadata and enable those use cases. It implements the OpenLineage API and provides context by making visible dependencies across organizations and technologies as they change over time.
Apresentação sobre como personalizamos a jornada do PicPay.
Explicamos sobre o uso de técnicas de big data e machine learning aplicado ao negócio.
Falamos sobre recomendação na home, problemas de NLP em busca e construção de plataformas.
Property graph vs. RDF Triplestore comparison in 2020Ontotext
This presentation goes all the way from intro "what graph databases are" to table comparing the RDF vs. PG plus two different diagrams presenting the market circa 2020
Observability for Data Pipelines With OpenLineageDatabricks
Data is increasingly becoming core to many products. Whether to provide recommendations for users, getting insights on how they use the product, or using machine learning to improve the experience. This creates a critical need for reliable data operations and understanding how data is flowing through our systems. Data pipelines must be auditable, reliable, and run on time. This proves particularly difficult in a constantly changing, fast-paced environment.
Collecting this lineage metadata as data pipelines are running provides an understanding of dependencies between many teams consuming and producing data and how constant changes impact them. It is the underlying foundation that enables the many use cases related to data operations. The OpenLineage project is an API standardizing this metadata across the ecosystem, reducing complexity and duplicate work in collecting lineage information. It enables many projects, consumers of lineage in the ecosystem whether they focus on operations, governance or security.
Marquez is an open source project part of the LF AI & Data foundation which instruments data pipelines to collect lineage and metadata and enable those use cases. It implements the OpenLineage API and provides context by making visible dependencies across organizations and technologies as they change over time.
Apresentação sobre como personalizamos a jornada do PicPay.
Explicamos sobre o uso de técnicas de big data e machine learning aplicado ao negócio.
Falamos sobre recomendação na home, problemas de NLP em busca e construção de plataformas.
Property graph vs. RDF Triplestore comparison in 2020Ontotext
This presentation goes all the way from intro "what graph databases are" to table comparing the RDF vs. PG plus two different diagrams presenting the market circa 2020
Effective Data Lakes: Challenges and Design Patterns (ANT316) - AWS re:Invent...Amazon Web Services
Data lakes are emerging as the most common architecture built in data-driven organizations today. A data lake enables you to store unstructured, semi-structured, or fully-structured raw data as well as processed data for different types of analytics—from dashboards and visualizations to big data processing, real-time analytics, and machine learning. Well-designed data lakes ensure that organizations get the most business value from their data assets. In this session, you learn about the common challenges and patterns for designing an effective data lake on the AWS Cloud, with wisdom distilled from various customer implementations. We walk through patterns to solve data lake challenges, like real-time ingestion, choosing a partitioning strategy, file compaction techniques, database replication to your data lake, handling mutable data, machine learning integration, security patterns, and more.
Understanding RDF: the Resource Description Framework in Context (1999)Dan Brickley
Dan Brickley, 3rd European Commission Metadata Workshop, Luxemburg, April 12th 1999
Understanding RDF: the Resource Description Framework in Context
http://ilrt.org/discovery/2001/01/understanding-rdf/
"SPARQL Cheat Sheet" is a short collection of slides intended to act as a guide to SPARQL developers. It includes the syntax and structure of SPARQL queries, common SPARQL prefixes and functions, and help with RDF datasets.
The "SPARQL Cheat Sheet" is intended to accompany the SPARQL By Example slides available at http://www.cambridgesemantics.com/2008/09/sparql-by-example/ .
Apache Spark continues to grow in popularity - due to advanced analytics/machine learning, high performance processing, real-time streaming and multiple language support. Big Data technology is adding more data processing options to an already long list of legacy databases and file systems. As a result, enterprises continue to look for effective and approachable ways to federate all these data sources to solve business information needs. One under-appreciated feature of Spark is its ability to help quickly and powerfully enable federated data access. This presentation will discuss and demonstrate using Spark to query/combine multiple disparate data sources. We will see how to access the various data sources from Spark, normalize to Spark RDDs and combine for processing. The demo will show combining sources such as HDFS, JSON files, HBase, Hive and PostgreSQL and write the result back to a Data Mart for analysis. Also we will show the use of SparkSQL to access federated data in Spark through the Spark Thrift Server using the the Tableau BI tool.
Securing data in hybrid environments using Apache RangerDataWorks Summit
Companies are increasingly moving to the cloud to store and process data. In this talk, we will talk through how companies can use tag-based policies in Apache Ranger to protect access to data both in on-premises environments as well in AWS-based cloud environments. We will go into details of how tag-based policies work and the integration with Apache Atlas and various services. We will also talk through how companies can leverage Ranger’s policies to anonymize or tokenize data while moving into the cloud and de-anonymize dynamically using Apache Kafka, Apache Hive, Apache Spark, or plain old ETL using MapReduce. We will also deep dive into Ranger’s proposed integration with S3 and other cloud-native systems. We will wrap it up with an end-to-end demo showing how tags and tag-based masking policies can be used to anonymize sensitive data and track how tags are propagated within the system and how sensitive data can be protected using tag-based policies
Speakers
Don Bosco Durai, Chief Security Architect, Privacera
Madhan Neethiraj, Sr. Director of Engineering, Hortonworks
Whoops, The Numbers Are Wrong! Scaling Data Quality @ NetflixDataWorks Summit
Netflix is a famously data-driven company. Data is used to make informed decisions on everything from content acquisition to content delivery, and everything in-between. As with any data-driven company, it’s critical that data used by the business is accurate. Or, at worst, that the business has visibility into potential quality issues as soon as they arise. But even in the most mature data warehouses, data quality can be hard. How can we ensure high quality in a cloud-based, internet-scale, modern big data warehouse employing a variety of data engineering technologies?
In this talk, Michelle Ufford will share how the Data Engineering & Analytics team at Netflix is doing exactly that. We’ll kick things off with a quick overview of Netflix’s analytics environment, then dig into details of our data quality solution. We’ll cover what worked, what didn’t work so well, and what we plan to work on next. We’ll conclude with some tips and lessons learned for ensuring data quality on big data.
Lambda architecture is a popular technique where records are processed by a batch system and streaming system in parallel. The results are then combined during query time to provide a complete answer. Strict latency requirements to process old and recently generated events made this architecture popular. The key downside to this architecture is the development and operational overhead of managing two different systems.
There have been attempts to unify batch and streaming into a single system in the past. Organizations have not been that successful though in those attempts. But, with the advent of Delta Lake, we are seeing lot of engineers adopting a simple continuous data flow model to process data as it arrives. We call this architecture, The Delta Architecture.
Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge ...Jeff Z. Pan
Tutorial on "Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge Graphs" presented at the 4th Joint International Conference on Semantic Technologies (JIST2014)
Building Data Quality pipelines with Apache Spark and Delta LakeDatabricks
Technical Leads and Databricks Champions Darren Fuller & Sandy May will give a fast paced view of how they have productionised Data Quality Pipelines across multiple enterprise customers. Their vision to empower business decisions on data remediation actions and self healing of Data Pipelines led them to build a library of Data Quality rule templates and accompanying reporting Data Model and PowerBI reports.
With the drive for more and more intelligence driven from the Lake and less from the Warehouse, also known as the Lakehouse pattern, Data Quality at the Lake layer becomes pivotal. Tools like Delta Lake become building blocks for Data Quality with Schema protection and simple column checking, however, for larger customers they often do not go far enough. Notebooks will be shown in quick fire demos how Spark can be leverage at point of Staging or Curation to apply rules over data.
Expect to see simple rules such as Net sales = Gross sales + Tax, or values existing with in a list. As well as complex rules such as validation of statistical distributions and complex pattern matching. Ending with a quick view into future work in the realm of Data Compliance for PII data with generations of rules using regex patterns and Machine Learning rules based on transfer learning.
Integrating Semantic Web in the Real World: A Journey between Two Cities Juan Sequeda
Keynote at The 9th International Conference on Knowledge Capture (KCAP2017), Austin, Texas, Dec 2017
An early vision in Computer Science has been to create intelligent systems capable of reasoning on large amounts of data. Today, this vision can be delivered by integrating Relational Databases with the Semantic Web using the W3C standards: a graph data model (RDF), ontology language (OWL), mapping language (R2RML) and query language (SPARQL). The research community has successfully been showing how intelligent systems can be created with Semantic Web technologies, dubbed now as Knowledge Graphs.
However, where is the mainstream industry adoption? What are the barriers to adoption? Are these engineering and social barriers or are they open scientific problems that need to be addressed?
This talk will chronicle our journey of deploying Semantic Web technologies with real world users to address Business Intelligence and Data Integration needs, describe technical and social obstacles that are present in large organizations, and scientific challenges that require attention.
Integrating Semantic Web with the Real World - A Journey between Two Cities ...Juan Sequeda
(The original version of this talk was a Keynote at KCAP2017. This is the final version of the slides after giving this talk 14 times in 2018)
An early vision in Computer Science has been to create intelligent systems capable of reasoning on large amounts of data. Today, this vision can be delivered by integrating Relational Databases with the Semantic Web using the W3C standards: a graph data model (RDF), ontology language (OWL), mapping language (R2RML) and query language (SPARQL). The research community has successfully been showing how intelligent systems can be created with Semantic Web technologies, dubbed now as Knowledge Graphs.
However, where is the mainstream industry adoption? What are the barriers to adoption? Are these engineering and social barriers or are they open scientific problems that need to be addressed?
This talk will chronicle our journey of deploying Semantic Web technologies with real world users to address Business Intelligence and Data Integration needs, describe technical and social obstacles that are present in large organizations, and scientific and engineering challenges that require attention.
Effective Data Lakes: Challenges and Design Patterns (ANT316) - AWS re:Invent...Amazon Web Services
Data lakes are emerging as the most common architecture built in data-driven organizations today. A data lake enables you to store unstructured, semi-structured, or fully-structured raw data as well as processed data for different types of analytics—from dashboards and visualizations to big data processing, real-time analytics, and machine learning. Well-designed data lakes ensure that organizations get the most business value from their data assets. In this session, you learn about the common challenges and patterns for designing an effective data lake on the AWS Cloud, with wisdom distilled from various customer implementations. We walk through patterns to solve data lake challenges, like real-time ingestion, choosing a partitioning strategy, file compaction techniques, database replication to your data lake, handling mutable data, machine learning integration, security patterns, and more.
Understanding RDF: the Resource Description Framework in Context (1999)Dan Brickley
Dan Brickley, 3rd European Commission Metadata Workshop, Luxemburg, April 12th 1999
Understanding RDF: the Resource Description Framework in Context
http://ilrt.org/discovery/2001/01/understanding-rdf/
"SPARQL Cheat Sheet" is a short collection of slides intended to act as a guide to SPARQL developers. It includes the syntax and structure of SPARQL queries, common SPARQL prefixes and functions, and help with RDF datasets.
The "SPARQL Cheat Sheet" is intended to accompany the SPARQL By Example slides available at http://www.cambridgesemantics.com/2008/09/sparql-by-example/ .
Apache Spark continues to grow in popularity - due to advanced analytics/machine learning, high performance processing, real-time streaming and multiple language support. Big Data technology is adding more data processing options to an already long list of legacy databases and file systems. As a result, enterprises continue to look for effective and approachable ways to federate all these data sources to solve business information needs. One under-appreciated feature of Spark is its ability to help quickly and powerfully enable federated data access. This presentation will discuss and demonstrate using Spark to query/combine multiple disparate data sources. We will see how to access the various data sources from Spark, normalize to Spark RDDs and combine for processing. The demo will show combining sources such as HDFS, JSON files, HBase, Hive and PostgreSQL and write the result back to a Data Mart for analysis. Also we will show the use of SparkSQL to access federated data in Spark through the Spark Thrift Server using the the Tableau BI tool.
Securing data in hybrid environments using Apache RangerDataWorks Summit
Companies are increasingly moving to the cloud to store and process data. In this talk, we will talk through how companies can use tag-based policies in Apache Ranger to protect access to data both in on-premises environments as well in AWS-based cloud environments. We will go into details of how tag-based policies work and the integration with Apache Atlas and various services. We will also talk through how companies can leverage Ranger’s policies to anonymize or tokenize data while moving into the cloud and de-anonymize dynamically using Apache Kafka, Apache Hive, Apache Spark, or plain old ETL using MapReduce. We will also deep dive into Ranger’s proposed integration with S3 and other cloud-native systems. We will wrap it up with an end-to-end demo showing how tags and tag-based masking policies can be used to anonymize sensitive data and track how tags are propagated within the system and how sensitive data can be protected using tag-based policies
Speakers
Don Bosco Durai, Chief Security Architect, Privacera
Madhan Neethiraj, Sr. Director of Engineering, Hortonworks
Whoops, The Numbers Are Wrong! Scaling Data Quality @ NetflixDataWorks Summit
Netflix is a famously data-driven company. Data is used to make informed decisions on everything from content acquisition to content delivery, and everything in-between. As with any data-driven company, it’s critical that data used by the business is accurate. Or, at worst, that the business has visibility into potential quality issues as soon as they arise. But even in the most mature data warehouses, data quality can be hard. How can we ensure high quality in a cloud-based, internet-scale, modern big data warehouse employing a variety of data engineering technologies?
In this talk, Michelle Ufford will share how the Data Engineering & Analytics team at Netflix is doing exactly that. We’ll kick things off with a quick overview of Netflix’s analytics environment, then dig into details of our data quality solution. We’ll cover what worked, what didn’t work so well, and what we plan to work on next. We’ll conclude with some tips and lessons learned for ensuring data quality on big data.
Lambda architecture is a popular technique where records are processed by a batch system and streaming system in parallel. The results are then combined during query time to provide a complete answer. Strict latency requirements to process old and recently generated events made this architecture popular. The key downside to this architecture is the development and operational overhead of managing two different systems.
There have been attempts to unify batch and streaming into a single system in the past. Organizations have not been that successful though in those attempts. But, with the advent of Delta Lake, we are seeing lot of engineers adopting a simple continuous data flow model to process data as it arrives. We call this architecture, The Delta Architecture.
Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge ...Jeff Z. Pan
Tutorial on "Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge Graphs" presented at the 4th Joint International Conference on Semantic Technologies (JIST2014)
Building Data Quality pipelines with Apache Spark and Delta LakeDatabricks
Technical Leads and Databricks Champions Darren Fuller & Sandy May will give a fast paced view of how they have productionised Data Quality Pipelines across multiple enterprise customers. Their vision to empower business decisions on data remediation actions and self healing of Data Pipelines led them to build a library of Data Quality rule templates and accompanying reporting Data Model and PowerBI reports.
With the drive for more and more intelligence driven from the Lake and less from the Warehouse, also known as the Lakehouse pattern, Data Quality at the Lake layer becomes pivotal. Tools like Delta Lake become building blocks for Data Quality with Schema protection and simple column checking, however, for larger customers they often do not go far enough. Notebooks will be shown in quick fire demos how Spark can be leverage at point of Staging or Curation to apply rules over data.
Expect to see simple rules such as Net sales = Gross sales + Tax, or values existing with in a list. As well as complex rules such as validation of statistical distributions and complex pattern matching. Ending with a quick view into future work in the realm of Data Compliance for PII data with generations of rules using regex patterns and Machine Learning rules based on transfer learning.
Integrating Semantic Web in the Real World: A Journey between Two Cities Juan Sequeda
Keynote at The 9th International Conference on Knowledge Capture (KCAP2017), Austin, Texas, Dec 2017
An early vision in Computer Science has been to create intelligent systems capable of reasoning on large amounts of data. Today, this vision can be delivered by integrating Relational Databases with the Semantic Web using the W3C standards: a graph data model (RDF), ontology language (OWL), mapping language (R2RML) and query language (SPARQL). The research community has successfully been showing how intelligent systems can be created with Semantic Web technologies, dubbed now as Knowledge Graphs.
However, where is the mainstream industry adoption? What are the barriers to adoption? Are these engineering and social barriers or are they open scientific problems that need to be addressed?
This talk will chronicle our journey of deploying Semantic Web technologies with real world users to address Business Intelligence and Data Integration needs, describe technical and social obstacles that are present in large organizations, and scientific challenges that require attention.
Integrating Semantic Web with the Real World - A Journey between Two Cities ...Juan Sequeda
(The original version of this talk was a Keynote at KCAP2017. This is the final version of the slides after giving this talk 14 times in 2018)
An early vision in Computer Science has been to create intelligent systems capable of reasoning on large amounts of data. Today, this vision can be delivered by integrating Relational Databases with the Semantic Web using the W3C standards: a graph data model (RDF), ontology language (OWL), mapping language (R2RML) and query language (SPARQL). The research community has successfully been showing how intelligent systems can be created with Semantic Web technologies, dubbed now as Knowledge Graphs.
However, where is the mainstream industry adoption? What are the barriers to adoption? Are these engineering and social barriers or are they open scientific problems that need to be addressed?
This talk will chronicle our journey of deploying Semantic Web technologies with real world users to address Business Intelligence and Data Integration needs, describe technical and social obstacles that are present in large organizations, and scientific and engineering challenges that require attention.
Virtualizing Relational Databases as Graphs: a multi-model approachJuan Sequeda
Talk given at Smart Data 2017
Relational Databases are inflexible due to the rigid constraints of the relational data model. If you have new data that doesn’t fit your schema, you will need to alter your schema (add a column or a new table). This is a task that is not always possible. IT departments don't have time, or they won't allow it - just more nulls that can lead to query performance degradation, etc.
A goal of graph databases is to address this problem with their schema-less graph data model. However, many businesses have large investments in commercial RDBMSs and their associated applications and can't expect to move all of their data to a graph database.
In this talk, I will present a multi-model graph/relational architecture solution. Keep your relational data where it is, virtualize it as a graph, and then connect it with additional data stored in a graph database. This way, both graph and relational technologies can seamlessly interact together.
Operational Analytics Using Spark and NoSQL Data StoresDATAVERSITY
NoSQL data stores have emerged for scalable capture and real-time analysis of data. Apache Spark and Hadoop provide additional scalable analytics processing. This session looks at these technologies and how they can be used to support operational analytics to improve operational effectiveness. It also looks at an example of how operational analytics can be implemented in NoSQL environments using the Basho Data Platform with Apache Spark:
•The emergence of NoSQL, Hadoop and Apache Spark
•NoSQL Use Cases
•The need for operational analytics
•Types of operational analysis
•Key requirements for operational analytics
•Operational analytics using the Basho Data Platform with Apache Spark.
Sap Leonardo - what is it, and why would I want one?Tom Raftery
A quick run through of the technologies running through SAP's Innovation portfolio of products, called SAP Leonardo, and use cases where it has been deployed successfully with customers
Data APIs as a Foundation for Systems of EngagementVictor Olex
APIs have finally crossed over to the world of enterprise software, data analytics and application integration. Spearheaded by Amazon, propagated by internet startups and now adopted by the largest of businesses including Wall Street top firm Goldman Sachs - the APIs are here to stay. In this presentation we are linking all the facts and examine the opportunities stemming from Resource Oriented Architecture - a holistic approach to API implementation in large organizations.
Achieving Business Value by Fusing Hadoop and Corporate DataInside Analysis
The Briefing Room with Richard Hackathorn and Teradata
Live Webcast March 25, 2015
Watch the Archive: https://bloorgroup.webex.com/bloorgroup/onstage/g.php?MTID=e7254708146d056339a0974f097f569b2
Hadoop data lakes are emerging as peers to corporate data warehouses. However, successful analytic solutions require a fusion of all relevant data, big and small, which has proven challenging for many companies. By allowing business analysts to quickly access data wherever it rests, success factors shift to focus on three key aspects: 1) business objectives, 2) organizational workflow, and 3) data placement.
Register for this Special Edition of The Briefing Room to hear veteran Analyst Richard Hackathorn as he provides details from his recent research report focused on success stories using Teradata QueryGrid. Examples of use cases described will include:
Joining sensor data in Hadoop with data warehouse labor schedules in seconds
How bridging corporate cultures and systems creates new business opportunities
The 360 view of customer journeys using weblogs in Hadoop via BI tools
How can you put the data where you want and query it however you want
Virtualizing Hadoop data with Teradata QueryGrid
Visit InsideAnalysis.com for more information.
Deploying Enterprise Scale Deep Learning in Actuarial Modeling at NationwideDatabricks
The traditional approach to insurance pricing involves fitting a generalized linear model (GLM) to data collected on historical claims payments and premiums received. The explosive growth in data availability and increasing competitiveness in the marketplace are challenging actuaries to find new insights in their data and make predictions with more granularity, improved speed and efficiency, and with tighter integration among business units to support strategic decisions.
In this session we will share our experience implementing deep hierarchical neural networks using TensorFlow and PySpark on Databricks. We will discuss the benefits of the ML Runtime, our experience using the goofys mount, our process for hyperparameter tuning, specific considerations for the large dataset size and extreme volatility present in insurance data, among other topics.
Authors: Bryn Clark, Krish Rajaram
Key Methodologies for Migrating from Oracle to PostgresEDB
This presentation reviews the key methodologies that all members of your team should consider, before planning a migration from Oracle to Postgres including:
• Prioritizing the right application or project for your first Oracle migration
• Planning a well-defined, phased migration process to minimize risk and increase time to value
• Handling common concerns and pitfalls related to a migration project
• Leveraging resources before, during, and after your migration
• Becoming independent from an Oracle database – without sacrificing performance
With EDB Postgres’ database compatibility for Oracle, it is easy to migrate from your existing Oracle databases. The compatibility feature set includes compatibility for PL/SQL, Oracle’s SQL syntax, and built in SQL functions. This means that many applications can be easily migrated over to EDB Postgres. It also allows you to continue using your existing Oracle skills.
For more information please contact us at sales@enterprisedb.com
Data Vault 2.0 is a data modeling methodology designed for developing enterprise data warehouses. It was developed by Dan Linstedt in response to the shortcomings of previous data modeling methodologies, such as the Kimball methodology and Inmon methodology, for managing large volumes of data from disparate sources.
Databricks + Snowflake: Catalyzing Data and AI InitiativesDatabricks
"Combining Databricks, the unified analytics platform with Snowflake, the data warehouse built for the cloud is a powerful combo.
Databricks offers the ability to process large amounts of data reliably, including developing scalable AI projects. Snowflake offers the elasticity of a cloud-based data warehouse that centralizes the access to data. Databricks brings the unparalleled utility of being based on a mature distributed big data processing and AI-enabled tool to the table, capable of integrating with nearly every technology, from message queues (e.g. Kafka) to databases (e.g. Snowflake) to object stores (e.g. S3) and AI tools (e.g. Tensorflow).
Key Takeaways:
How Databricks & Snowflake work;
Why they're so powerful;
How Databricks + Snowflake symbiotically catalyze analytics and AI initiatives"
Lambda Architecture in the Cloud with Azure Databricks with Andrei VaranovichDatabricks
The term “Lambda Architecture” stands for a generic, scalable and fault-tolerant data processing architecture. As the hyper-scale now offers a various PaaS services for data ingestion, storage and processing, the need for a revised, cloud-native implementation of the lambda architecture is arising.
In this talk we demonstrate the blueprint for such an implementation in Microsoft Azure, with Azure Databricks — a PaaS Spark offering – as a key component. We go back to some core principles of functional programming and link them to the capabilities of Apache Spark for various end-to-end big data analytics scenarios.
We also illustrate the “Lambda architecture in use” and the associated tread-offs using the real customer scenario – Rijksmuseum in Amsterdam – a terabyte-scale Azure-based data platform handles data from 2.500.000 visitors per year.
RWDG Slides: Data Governance and Three Levels of Metadata ManagementDATAVERSITY
There are three levels of metadata that every organization must govern well. These levels are the semantic level, the business level, and the technical level. All three levels are important components of Data Governance and must be stewarded to focus on the goals and scope of your Data Governance program.
In this month’s installment of the Real-World Data Governance webinar series, Bob Seiner will present a three-tiered approach to defining, producing, and using all levels of metadata to further the cause of Data Governance. Governing the processes associated with this metadata tends to be a central focus of successful Data Governance programs. Join Bob to learn how to simplify the metadata focus.
In this webinar, Bob will discuss:
• The three levels of metadata and how they differ
• Sources of the metadata at each level
• Metadata linkage between the levels
• Processes to govern all the levels of metadata
• Institutionalizing policy to assure quality metadata at all levels
The Briefing Room with Dr. Robin Bloor and RedPoint Global
Live Webcast on September 23, 2014
Watch the archive:
https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=6cd94ed2ed7cc7090f7d5db1bf343438
Ask anyone who knows, and they’ll tell you candidly: traditional Master Data Management programs require not just tools, technologies and people, but also a level of cooperation and collaboration in the business that can be very difficult to manage. Many of the consequent hurdles that appear stem from long cycle times and lack of transparency into the creation and management of the rules that govern such programs. But now, the power of Hadoop 2.0 has opened up a very different method of action.
Register for this episode of The Briefing Room to learn from veteran Analyst Dr. Robin Bloor who will explain how the Hadoop ecosystem, powered by YARN, can transform MDM from a segmented, often disjointed set of business processes, into a much tighter platform that can finally deliver on the original promise of the discipline. He’ll be briefed by George Corugedo of RedPoint Global, who will showcase his company’s unified data management platform, which weaves together the best practices of traditional MDM with the power and flexibility of Hadoop.
Visit InsideAnlaysis.com for more information.
Graph Query Languages: update from LDBCJuan Sequeda
The Linked Data Benchmark Council (LDBC) is a non-profit organization dedicated to establishing benchmarks, benchmark practices and benchmark results for graph data management software. The Graph Query Language task force of LDBC is studying query languages for graph data management systems, and specifically those systems storing so-called Property Graph data. The goals of the GraphQL task force are to:
Devise a list of desired features and functionalities of a graph query language.
Evaluate a number of existing languages (i.e. Cypher, Gremlin, PGQL, SPARQL, SQL), and identify possible issues.
Provide a better understanding of the design space and state-of-the-art.
Develop proposals for changes to existing query languages or even a new graph query language.
This query language should cover the needs of the most important use-cases for such systems, such as social network and Business Intelligence workloads.
This talk will present an update of the work accomplished by the LDBC GraphQL task force. We also look for input from the graph community.
Presentation at Data/Graph Day Texas Conference.
Austin, Texas
January 14, 2017
This talk grew out Juan Sequeda's office hours following the Seattle Graph Meetup. Some of the questions posed were: How do I recognize problem best solved with a graph solution? How do I determine the best type of graph to solve the problem? How do I manage the data where both graph and relational operations will be performed? Juan did such a great job of explaining the options, we asked him to develop his responses into a formal talk.
My Linked Data tutorial presentation that I presented at Semtech 2012.
http://semtechbizsf2012.semanticweb.com/sessionPop.cfm?confid=65&proposalid=4724
Consuming Linked Data by Humans - WWW2010Juan Sequeda
These are the Consuming Linked Data by Humans slides that we presented at the Consuming Linked Data tutorial at WWW2010 in Raleigh, NC on April 26, 2010
Consuming Linked Data by Machines - WWW2010Juan Sequeda
These are the Consuming Linked Data by Machines slides that we presented at the Consuming Linked Data tutorial at WWW2010 in Raleigh, NC on April 26, 2010. These slides are originally by Patrick Sinclair from BBC
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
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During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.