2nd in the AskTOM Office Hours series on graph database technologies. https://devgym.oracle.com/pls/apex/dg/office_hours/3084
With property graphs in Oracle Database, you can perform powerful analysis on big data such as social networks, financial transactions, sensor networks, and more.
To use property graphs, first, you’ll need a graph model. For a new user, modeling and generating a suitable graph for an application domain can be a challenge. This month, we’ll describe key steps required to construct a meaningful graph, and offer a few tips on validating the generated graph.
Albert Godfrind (EMEA Solutions Architect), Zhe Wu (Architect), and Jean Ihm (Product Manager) walk you through, and take your questions.
Build Knowledge Graphs with Oracle RDF to Extract More Value from Your DataJean Ihm
AnD Summit '19 slides - Souri Das, Matthew Perry, Melli Annamalai. This presentation covers knowledge graphs built using the RDF capabilities of Oracle Spatial and Graph. We will illustrate how to define a knowledge graph, create virtual or materialized graphs from existing data (relational tables, CSV files, etc.), derive new knowledge through logical inference, navigate and query graphs using W3C standards, analyze knowledge graphs with graph algorithms, and more. Real-world use cases from various industries will also be shared.
Introduction to Property Graph Features (AskTOM Office Hours part 1) Jean Ihm
1st in the AskTOM Office Hours series on graph database technologies. https://devgym.oracle.com/pls/apex/dg/office_hours/3084
Xavier Lopez (PM Senior Director) and Zhe Wu (Graph Architect) will share a brief intro to what property graphs can do for you, and take your questions - on property graphs or any other aspect of Oracle Database Spatial and Graph features. With property graphs, you can analyze relationships in Big Data like social networks, financial transactions, or IoT sensor networks; identify influencers; discover patterns of fraudulent behavior; recommend products, and much more -- right inside Oracle Database.
4th in the AskTOM Office Hours series on graph database technologies. https://devgym.oracle.com/pls/apex/dg/office_hours/3084
Learn how to visualize graphs – a powerful, intuitive way to interact with data. Using open source tools like Cytoscape or third party tools, you have several choices on how to visualize and interact with graphs from Oracle Database and big data platforms. Albert Godfrind (EMEA Solutions Architect) and Gabriela Montiel-Moreno (Software Development Manager) share all you need to get started, with detailed demos using a banking customer data set.
3rd in the AskTOM Office Hours series on graph database technologies. https://devgym.oracle.com/pls/apex/dg/office_hours/3084
See the magic of graphs in this session. Graph analysis can answer questions like detecting patterns of fraud or identifying influential customers - and do it quickly and efficiently. We’ll show you the APIs for accessing graphs and running analytics such as finding influencers, communities, anomalies, and how to use them from various languages including Groovy, Python, and Javascript, with Jupiter and Zeppelin notebooks.
Albert Godfrind (EMEA Solutions Architect), Zhe Wu (Architect), and Jean Ihm (Product Manager) walk you through, and take your questions.
5th in the AskTOM Office Hours series on graph database technologies. https://devgym.oracle.com/pls/apex/dg/office_hours/3084
PGQL: A Query Language for Graphs
Learn how to query graphs using PGQL, an expressive and intuitive graph query language that's a lot like SQL. With PGQL, it's easy to get going writing graph analysis queries to the database in a very short time. Albert and Oskar show what you can do with PGQL, and how to write and execute PGQL code.
Oracle Spatial Studio: Fast and Easy Spatial Analytics and MapsJean Ihm
Learn about a new tool, Spatial Studio, that lets you quickly and easily do spatial analytics and create maps, even if you don't have GIS or Spatial knowledge. Now business users and non-GIS developers have a simple user interface to access the spatial features in Oracle Database.
Spatial Studio lets you prepare your data for spatial analysis, perform spatial analysis operations, publish, and share the results – as well access spatial analyses results via REST and incorporate in applications and workflows. Presented by Carol Palmer, Sr. Principal Product Manager, and David Lapp, Sr. Principal Product Manager, Oracle Spatial and Graph.
Presentation video including demo and resources available here: https://devgym.oracle.com/pls/apex/dg/office_hours/3084 .
Learn how graph technologies can be applied to real-world use cases, using medical, network security, and financial data. By combining graph models and machine learning techniques, we can discover relationships, classify information, and identify patterns and anomalies in data. We can answer questions such as “How did other investigators approach similar cases?” and “Do these symptoms seem similar to ones we’ve seen in other diseases?” Presented by Sungpack Hong, Research Director, Oracle Labs.
An Introduction to Graph: Database, Analytics, and Cloud ServicesJean Ihm
Graph analysis employs powerful algorithms to explore and discover relationships in social network, IoT, big data, and complex transaction data. Learn how graph technologies are used in applications such as fraud detection for banking, customer 360, public safety, and manufacturing. This session will provide an overview and demos of graph technologies for Oracle Cloud Services, Oracle Database, NoSQL, Spark and Hadoop, including PGX analytics and PGQL property graph query language.
Presented at Analytics and Data Summit, March 20, 2018
Build Knowledge Graphs with Oracle RDF to Extract More Value from Your DataJean Ihm
AnD Summit '19 slides - Souri Das, Matthew Perry, Melli Annamalai. This presentation covers knowledge graphs built using the RDF capabilities of Oracle Spatial and Graph. We will illustrate how to define a knowledge graph, create virtual or materialized graphs from existing data (relational tables, CSV files, etc.), derive new knowledge through logical inference, navigate and query graphs using W3C standards, analyze knowledge graphs with graph algorithms, and more. Real-world use cases from various industries will also be shared.
Introduction to Property Graph Features (AskTOM Office Hours part 1) Jean Ihm
1st in the AskTOM Office Hours series on graph database technologies. https://devgym.oracle.com/pls/apex/dg/office_hours/3084
Xavier Lopez (PM Senior Director) and Zhe Wu (Graph Architect) will share a brief intro to what property graphs can do for you, and take your questions - on property graphs or any other aspect of Oracle Database Spatial and Graph features. With property graphs, you can analyze relationships in Big Data like social networks, financial transactions, or IoT sensor networks; identify influencers; discover patterns of fraudulent behavior; recommend products, and much more -- right inside Oracle Database.
4th in the AskTOM Office Hours series on graph database technologies. https://devgym.oracle.com/pls/apex/dg/office_hours/3084
Learn how to visualize graphs – a powerful, intuitive way to interact with data. Using open source tools like Cytoscape or third party tools, you have several choices on how to visualize and interact with graphs from Oracle Database and big data platforms. Albert Godfrind (EMEA Solutions Architect) and Gabriela Montiel-Moreno (Software Development Manager) share all you need to get started, with detailed demos using a banking customer data set.
3rd in the AskTOM Office Hours series on graph database technologies. https://devgym.oracle.com/pls/apex/dg/office_hours/3084
See the magic of graphs in this session. Graph analysis can answer questions like detecting patterns of fraud or identifying influential customers - and do it quickly and efficiently. We’ll show you the APIs for accessing graphs and running analytics such as finding influencers, communities, anomalies, and how to use them from various languages including Groovy, Python, and Javascript, with Jupiter and Zeppelin notebooks.
Albert Godfrind (EMEA Solutions Architect), Zhe Wu (Architect), and Jean Ihm (Product Manager) walk you through, and take your questions.
5th in the AskTOM Office Hours series on graph database technologies. https://devgym.oracle.com/pls/apex/dg/office_hours/3084
PGQL: A Query Language for Graphs
Learn how to query graphs using PGQL, an expressive and intuitive graph query language that's a lot like SQL. With PGQL, it's easy to get going writing graph analysis queries to the database in a very short time. Albert and Oskar show what you can do with PGQL, and how to write and execute PGQL code.
Oracle Spatial Studio: Fast and Easy Spatial Analytics and MapsJean Ihm
Learn about a new tool, Spatial Studio, that lets you quickly and easily do spatial analytics and create maps, even if you don't have GIS or Spatial knowledge. Now business users and non-GIS developers have a simple user interface to access the spatial features in Oracle Database.
Spatial Studio lets you prepare your data for spatial analysis, perform spatial analysis operations, publish, and share the results – as well access spatial analyses results via REST and incorporate in applications and workflows. Presented by Carol Palmer, Sr. Principal Product Manager, and David Lapp, Sr. Principal Product Manager, Oracle Spatial and Graph.
Presentation video including demo and resources available here: https://devgym.oracle.com/pls/apex/dg/office_hours/3084 .
Learn how graph technologies can be applied to real-world use cases, using medical, network security, and financial data. By combining graph models and machine learning techniques, we can discover relationships, classify information, and identify patterns and anomalies in data. We can answer questions such as “How did other investigators approach similar cases?” and “Do these symptoms seem similar to ones we’ve seen in other diseases?” Presented by Sungpack Hong, Research Director, Oracle Labs.
An Introduction to Graph: Database, Analytics, and Cloud ServicesJean Ihm
Graph analysis employs powerful algorithms to explore and discover relationships in social network, IoT, big data, and complex transaction data. Learn how graph technologies are used in applications such as fraud detection for banking, customer 360, public safety, and manufacturing. This session will provide an overview and demos of graph technologies for Oracle Cloud Services, Oracle Database, NoSQL, Spark and Hadoop, including PGX analytics and PGQL property graph query language.
Presented at Analytics and Data Summit, March 20, 2018
Large Scale Graph Analytics with RDF and LPG Parallel ProcessingCambridge Semantics
Analytics that traverse large portions of large graphs have been problematic for both RDF and LPG graph engines. In this webinar Barry Zane, former co-founder of Netezza, Paraccel and SPARQL City and current VP of Engineering at Cambridge Semantics, discusses the native parallel-computing approach taken in AnzoGraph to yield interactive, scalable performance for RDF and LPG graphs.
The slides give an overview of how Spark can be used to tackle Machine learning tasks, such as classification, regression, clustering, etc., at a Big Data scale.
8th TUC Meeting - Zhe Wu (Oracle USA). Bridging RDF Graph and Property Graph...LDBC council
During the 8th TUC Meeting held at Oracle’s facilities in Redwood City, California, Zhe Wu, Software Architect at Oracle Spatial and Graph, explained how is his team trying to bridge RDF Graph and Property Data Models.
8th TUC Meeting – Yinglong Xia (Huawei), Big Graph Analytics EngineLDBC council
Yinlong started his talk with an introduction of his new position at Huawei, what is the company doing and more specifically how is it involved with Big Data Research and graphs. He also explained that his research center is currently working on Big Data Analytics and Management from 4 sides: Natural Language Processing, Graph analyrics, Machine Learning and Deep Learning.
In this webinar Thomas Cook, Sales Director, AnzoGraph DB, provides a history lesson on the origins of SPARQL, including its roots in the Semantic Web, and how linked open data is used to create Knowledge Graphs. Then, he dives into "What is RDF?", "What is a URI?" and "What is SPARQL?", wrapping up with a real-world demonstration via a Zeppelin notebook.
At Data-centric Architecture Forum 2020 Thomas Cook, our Sales Director of AnzoGraph DB, gave his presentation "Knowledge Graph for Machine Learning and Data Science". These are his slides.
What you need to know to start an AI company?Mo Patel
An overview of why AI and Deep Learning are hot now? Overview f Machine Intelligence startups. What are the key ingredients for AI startup? How can AI startups compete with big tech companies and areas to focus on for differentiation?
Joseph Bradley, Software Engineer, Databricks Inc. at MLconf SEA - 5/01/15MLconf
Spark DataFrames and ML Pipelines: In this talk, we will discuss two recent efforts in Spark to scale up data science: distributed DataFrames and Machine Learning Pipelines. These components allow users to manipulate distributed datasets and handle complex ML workflows, using intuitive APIs in Python, Java, and Scala (and R in development).
Data frames in R and Python have become standards for data science, yet they do not work well with Big Data. Inspired by R and Pandas, Spark DataFrames provide concise, powerful interfaces for structured data manipulation. DataFrames support rich data types, a variety of data sources and storage systems, and state-of-the-art optimization via the Spark SQL Catalyst optimizer.
On top of DataFrames, we have built a new ML Pipeline API. ML workflows often involve a complex sequence of processing and learning stages, including data cleaning, feature extraction and transformation, training, and hyperparameter tuning. With most current tools for ML, it is difficult to set up practical pipelines. Inspired by scikit-learn, we built simple APIs to help users quickly assemble and tune practical ML pipelines.
Apache Atlas is the governance and metadata framework for Hadoop. This presentation outlines the need for a tool like Atlas to solve compliance and governance use cases. It provides an overview of architecture and design choices. Then, a popular Hadoop component Spark is modeled. Roadmap and project details are also outlined
Data Science for Dummies - Data Engineering with Titanic dataset + Databricks...Rodney Joyce
Number 2 in the Data Science for Dummies series - We'll predict Titanic survival with Databricks, python and MLSpark.
These are the slides only (excuse the Powerpoint animation issues) - check out the actual tech talk on YouTube: https://rodneyjoyce.home.blog/2019/05/03/data-science-for-dummies-machine-learning-with-databricks-python-sparkml-tech-talk-1-of-7/)
If you have not used Databricks before check out the first talk - Databricks for Dummies.
Here's the rest of the series: https://rodneyjoyce.home.blog/tag/data-science-for-dummies/
1) Data Science overview with Databricks
2) Titanic survival prediction with Azure Machine Learning Studio + Kaggle
3) Data Engineering with Titanic dataset + Databricks + Python
4) Titanic with Databricks + Spark ML
5) Titanic with Databricks + Azure Machine Learning Service
6) Titanic with Databricks + MLS + AutoML
7) Titanic with Databricks + MLFlow
8) Titanic with .NET Core + ML.NET
9) Deployment, DevOps/MLOps and Productionisation
Complex analytics should work as nimbly on extremely large data sets as on small ones. You don’t want to think about whether your data fits in-memory, about parallelism, or formatting data for math packages. You’d like to use your favorite analytical language and have it transparently scale up to Big Data volumes.
Paradigm4 presents a webinar about SciDB—the massively scalable, open source, array database with native complex analytics, integrated with R and Python.
Details:
Presenter: Bryan Lewis, Chief Data Scientist, Paradigm4
Day/Time: Tuesday November 12th, 2013 at 1pm EST
Learn how SciDB enables you to:
-Explore rich data sets interactively
-Do complex math in-database—without being constrained -by memory limitations
-Perform multi-dimensional windowing, filtering, and aggregation
-Offload large computations to a commodity hardware cluster—on-premise or in a cloud
-Use R and Python to analyze SciDB arrays as if they were R or Python objects.
-Share data among users, with multi-user data integrity guarantees and version control
Webinar Agenda:
-Introduction to SciDB
-Demo
-Live Q&A
Predicting Influence and Communities Using Graph AlgorithmsDatabricks
Relationships are one of the most predictive indicators of behavior and preferences. Communities detection based on relationships is a powerful tool for inferring similar preferences in peer groups, anticipating future behavior, estimating group resiliency, finding hierarchies, and preparing data for other analysis. Centrality measures based on relationships identify the most important items in a network and help us understand group dynamics such as influence, accessibility, the speed at which things spread, and bridges between groups. Data scientists use graph algorithms to identify groups and estimate important entities based on their interactions. In this session, we'll cover the common uses of community detection and centrality measures and how some of the iconic graph algorithms compute values. We'll show examples of how to run community detection and centrality algorithms in Apache Spark including using the AggregateMessages function to add your own algorithms. You'll learn best practices and tips for tricky situations. For those that want to run graph algorithms in a graph platform, we'll also illustrate a few examples in Neo4j. Some of the Community Detection Algorithms included: * Triangle Count and Clustering Coefficient to estimate network cohesiveness * Strongly Connected Components and Connected Components to find clusters * Label Propagation to quickly infer groups and data cleans with semi-supervised learning * Louvain Modularity to uncover at group hierarchies Balanced Triad to identify unstable groups * PageRank to reveal influencers * Betweenness Centrality to predict bottlenecks and bridges.
Authors: Amy Hodler, Sören Reichardt
AnzoGraph DB: Driving AI and Machine Insights with Knowledge Graphs in a Conn...Cambridge Semantics
Thomas Cook, director of sales, Cambridge Semantics, offers a primer on graph database technology and the rapid growth of knowledge graphs at Data Summit 2020 in his presentation titled "AnzoGraph DB: Driving AI and Machine Insights with Knowledge Graphs in a Connected World".
Large Scale Graph Analytics with RDF and LPG Parallel ProcessingCambridge Semantics
Analytics that traverse large portions of large graphs have been problematic for both RDF and LPG graph engines. In this webinar Barry Zane, former co-founder of Netezza, Paraccel and SPARQL City and current VP of Engineering at Cambridge Semantics, discusses the native parallel-computing approach taken in AnzoGraph to yield interactive, scalable performance for RDF and LPG graphs.
The slides give an overview of how Spark can be used to tackle Machine learning tasks, such as classification, regression, clustering, etc., at a Big Data scale.
8th TUC Meeting - Zhe Wu (Oracle USA). Bridging RDF Graph and Property Graph...LDBC council
During the 8th TUC Meeting held at Oracle’s facilities in Redwood City, California, Zhe Wu, Software Architect at Oracle Spatial and Graph, explained how is his team trying to bridge RDF Graph and Property Data Models.
8th TUC Meeting – Yinglong Xia (Huawei), Big Graph Analytics EngineLDBC council
Yinlong started his talk with an introduction of his new position at Huawei, what is the company doing and more specifically how is it involved with Big Data Research and graphs. He also explained that his research center is currently working on Big Data Analytics and Management from 4 sides: Natural Language Processing, Graph analyrics, Machine Learning and Deep Learning.
In this webinar Thomas Cook, Sales Director, AnzoGraph DB, provides a history lesson on the origins of SPARQL, including its roots in the Semantic Web, and how linked open data is used to create Knowledge Graphs. Then, he dives into "What is RDF?", "What is a URI?" and "What is SPARQL?", wrapping up with a real-world demonstration via a Zeppelin notebook.
At Data-centric Architecture Forum 2020 Thomas Cook, our Sales Director of AnzoGraph DB, gave his presentation "Knowledge Graph for Machine Learning and Data Science". These are his slides.
What you need to know to start an AI company?Mo Patel
An overview of why AI and Deep Learning are hot now? Overview f Machine Intelligence startups. What are the key ingredients for AI startup? How can AI startups compete with big tech companies and areas to focus on for differentiation?
Joseph Bradley, Software Engineer, Databricks Inc. at MLconf SEA - 5/01/15MLconf
Spark DataFrames and ML Pipelines: In this talk, we will discuss two recent efforts in Spark to scale up data science: distributed DataFrames and Machine Learning Pipelines. These components allow users to manipulate distributed datasets and handle complex ML workflows, using intuitive APIs in Python, Java, and Scala (and R in development).
Data frames in R and Python have become standards for data science, yet they do not work well with Big Data. Inspired by R and Pandas, Spark DataFrames provide concise, powerful interfaces for structured data manipulation. DataFrames support rich data types, a variety of data sources and storage systems, and state-of-the-art optimization via the Spark SQL Catalyst optimizer.
On top of DataFrames, we have built a new ML Pipeline API. ML workflows often involve a complex sequence of processing and learning stages, including data cleaning, feature extraction and transformation, training, and hyperparameter tuning. With most current tools for ML, it is difficult to set up practical pipelines. Inspired by scikit-learn, we built simple APIs to help users quickly assemble and tune practical ML pipelines.
Apache Atlas is the governance and metadata framework for Hadoop. This presentation outlines the need for a tool like Atlas to solve compliance and governance use cases. It provides an overview of architecture and design choices. Then, a popular Hadoop component Spark is modeled. Roadmap and project details are also outlined
Data Science for Dummies - Data Engineering with Titanic dataset + Databricks...Rodney Joyce
Number 2 in the Data Science for Dummies series - We'll predict Titanic survival with Databricks, python and MLSpark.
These are the slides only (excuse the Powerpoint animation issues) - check out the actual tech talk on YouTube: https://rodneyjoyce.home.blog/2019/05/03/data-science-for-dummies-machine-learning-with-databricks-python-sparkml-tech-talk-1-of-7/)
If you have not used Databricks before check out the first talk - Databricks for Dummies.
Here's the rest of the series: https://rodneyjoyce.home.blog/tag/data-science-for-dummies/
1) Data Science overview with Databricks
2) Titanic survival prediction with Azure Machine Learning Studio + Kaggle
3) Data Engineering with Titanic dataset + Databricks + Python
4) Titanic with Databricks + Spark ML
5) Titanic with Databricks + Azure Machine Learning Service
6) Titanic with Databricks + MLS + AutoML
7) Titanic with Databricks + MLFlow
8) Titanic with .NET Core + ML.NET
9) Deployment, DevOps/MLOps and Productionisation
Complex analytics should work as nimbly on extremely large data sets as on small ones. You don’t want to think about whether your data fits in-memory, about parallelism, or formatting data for math packages. You’d like to use your favorite analytical language and have it transparently scale up to Big Data volumes.
Paradigm4 presents a webinar about SciDB—the massively scalable, open source, array database with native complex analytics, integrated with R and Python.
Details:
Presenter: Bryan Lewis, Chief Data Scientist, Paradigm4
Day/Time: Tuesday November 12th, 2013 at 1pm EST
Learn how SciDB enables you to:
-Explore rich data sets interactively
-Do complex math in-database—without being constrained -by memory limitations
-Perform multi-dimensional windowing, filtering, and aggregation
-Offload large computations to a commodity hardware cluster—on-premise or in a cloud
-Use R and Python to analyze SciDB arrays as if they were R or Python objects.
-Share data among users, with multi-user data integrity guarantees and version control
Webinar Agenda:
-Introduction to SciDB
-Demo
-Live Q&A
Predicting Influence and Communities Using Graph AlgorithmsDatabricks
Relationships are one of the most predictive indicators of behavior and preferences. Communities detection based on relationships is a powerful tool for inferring similar preferences in peer groups, anticipating future behavior, estimating group resiliency, finding hierarchies, and preparing data for other analysis. Centrality measures based on relationships identify the most important items in a network and help us understand group dynamics such as influence, accessibility, the speed at which things spread, and bridges between groups. Data scientists use graph algorithms to identify groups and estimate important entities based on their interactions. In this session, we'll cover the common uses of community detection and centrality measures and how some of the iconic graph algorithms compute values. We'll show examples of how to run community detection and centrality algorithms in Apache Spark including using the AggregateMessages function to add your own algorithms. You'll learn best practices and tips for tricky situations. For those that want to run graph algorithms in a graph platform, we'll also illustrate a few examples in Neo4j. Some of the Community Detection Algorithms included: * Triangle Count and Clustering Coefficient to estimate network cohesiveness * Strongly Connected Components and Connected Components to find clusters * Label Propagation to quickly infer groups and data cleans with semi-supervised learning * Louvain Modularity to uncover at group hierarchies Balanced Triad to identify unstable groups * PageRank to reveal influencers * Betweenness Centrality to predict bottlenecks and bridges.
Authors: Amy Hodler, Sören Reichardt
AnzoGraph DB: Driving AI and Machine Insights with Knowledge Graphs in a Conn...Cambridge Semantics
Thomas Cook, director of sales, Cambridge Semantics, offers a primer on graph database technology and the rapid growth of knowledge graphs at Data Summit 2020 in his presentation titled "AnzoGraph DB: Driving AI and Machine Insights with Knowledge Graphs in a Connected World".
Graph Analytics on Data from Meetup.comKarin Patenge
How to improve your Meetup experience by using Graph Analytics on data from Meetup.com. Slides from my session with "Women Who Code" group in Berlin on May 23, 2018.
Applying large scale text analytics with graph databasesData Ninja API
Data Ninja Services collaborated with Oracle to reach a major milestone in the integration of text analytics with Oracle Spatial and Graph. The Data Ninja Services client in Java can be used to analyze free texts, extract entities, generate RDF semantic graphs, and choose from a number of graph analytics to infer entity relationships. We demonstrated two case studies involving mining health news and detecting anomalies in product reviews.
MySQL JSON Document Store - A Document Store with all the benefits of a Trans...Olivier DASINI
SQL + NoSQL = MySQL
MySQL Document Store allows developers to work with SQL relational tables and schema-less JSON collections. To make that possible MySQL has created the X Dev API which puts a strong focus on CRUD by providing a fluent API allowing you to work with JSON documents in a natural way. The X Protocol is a highly extensible and is optimized for CRUD as well as SQL API operations.
Oracle Code Online: Building a Serverless State Service for the CloudEd Burns
While application architectures are evolving to become stateless, application state and state management are naturally emerging as a service in themselves. This session outlines the development, operation, and maintenance of an application state service for the cloud with Java 9, using a serverless strategy. The presentation investigates some of the challenges of designing an infinite-capacity, infinite-processing platform capable of reliably running everything from the smallest application to a globally distributed enterprise-class infrastructure for the mobile and IoT domains.
SRV307 Applying AWS Purpose-Built Database Strategy: Match Your Workload to ...Amazon Web Services
In this session, Tony Petrossian, director of engineering, AWS Database Services, dives deep into what databases to use for which components of your application. Learn how to evaluate a new workload for the best managed database option based on specific application needs related to data shape, data size at limit, computational requirements, programmability, throughput and latency needs, etc. This session explains the ideal use cases for relational and non-relational database services, including Amazon Aurora, Amazon DynamoDB, Amazon ElastiCache for Redis, Amazon Neptune, and Amazon Redshift.
Cómo Oracle ha logrado separar el motor SQL de su emblemática base de datos para procesar las consultas y los drivers de acceso que permiten leer datos, tanto de ficheros sobre el Hadoop Distributed File System, como de la herramienta de Data Warehousing, HIVE.
aRangodb, un package per l'utilizzo di ArangoDB con RGraphRM
Lingua talk: Italiano.
Descrizione:
In questo talk parleremo di come integrare e utilizzare ArangoDB, un database multi-modello con supporto nativo ai grafi, con R. Presenteremo quindi aRangodb, il package che abbiamo sviluppato per interfacciarsi in modo più semplice e intuitivo al database. Nel corso del talk mostreremo come il package possa essere utilizzato in ambito data science usando alcuni case studies concreti.
Speaker:
Gabriele Galatolo - Data Scientist - Kode srl
Oracle ADF Architecture TV - Development - Naming Conventions & Project LayoutsChris Muir
Slides from Oracle's ADF Architecture TV series covering the Development phase of ADF projects, a discussion on naming and project layout conventions for your ADF projects.
Like to know more? Check out:
- Subscribe to the YouTube channel - http://bit.ly/adftvsub
- Development Playlist - http://www.youtube.com/playlist?list=PLJz3HAsCPVaQfFop-QTJUE6LtjkyP_SOp
- Read the episode index on the ADF Architecture Square - http://bit.ly/adfarchsquare
How to Survive as a Data Architect in a Polyglot Database WorldKaren Lopez
Karen Lopez talks to data architects and data moders how they can best deliver value on modern data drive projects beyond relational database technologies. She covers NoSQL Databases and Datastores, which data stories they best fit and which ones they don't. She ends with 10 tips for adding more value to ployschematic database solutions.
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.
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.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
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