Delta Lake is an open format storage layer that delivers reliability, security and performance on your data lake — for both streaming and batch operations;
(please review the document)
Data Engineering A Deep Dive into DatabricksKnoldus Inc.
During this session, you'll gain a comprehensive understanding of Databricks' capabilities for efficiently processing and managing data, with a focus on Apache Spark for data transformation. We'll cover data ingestion methods, storage, orchestration, and best practices to ensure your data engineering workflows are optimized for success.
Analytics and Lakehouse Integration Options for Oracle ApplicationsRay Février
This Red Hot session is designed for customers who are currently using Oracle Cloud applications such as Fusion and EPM, and are interested in gaining a better understanding of the integration options that are available to them.
Here is a high level agenda:
- We will start by discussing the modern data platform on OCI, the Lakehouse architecture and the OCI related services that supports it.
- We will then discuss the data extraction methods available on OCI for Fusion and EPM.
- Last but not least, we will end with a few best practices and possible use cases.
In the interest of time, we will mainly focus on integration patterns that are recommended for Fusion and EPM, but don’t hesitate to reach out if you would to talk to us about other Oracle applications.
Enjoy!
Webinar future dataintegration-datamesh-and-goldengatekafkaJeffrey T. Pollock
The Future of Data Integration: Data Mesh, and a Special Deep Dive into Stream Processing with GoldenGate, Apache Kafka and Apache Spark. This video is a replay of a Live Webinar hosted on 03/19/2020.
Join us for a timely 45min webinar to see our take on the future of Data Integration. As the global industry shift towards the “Fourth Industrial Revolution” continues, outmoded styles of centralized batch processing and ETL tooling continue to be replaced by realtime, streaming, microservices and distributed data architecture patterns.
This webinar will start with a brief look at the macro-trends happening around distributed data management and how that affects Data Integration. Next, we’ll discuss the event-driven integrations provided by GoldenGate Big Data, and continue with a deep-dive into some essential patterns we see when replicating Database change events into Apache Kafka. In this deep-dive we will explain how to effectively deal with issues like Transaction Consistency, Table/Topic Mappings, managing the DB Change Stream, and various Deployment Topologies to consider. Finally, we’ll wrap up with a brief look into how Stream Processing will help to empower modern Data Integration by supplying realtime data transformations, time-series analytics, and embedded Machine Learning from within data pipelines.
GoldenGate: https://www.oracle.com/middleware/tec...
Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/)
Data lakes are central repositories that store large volumes of structured, unstructured, and semi-structured data. They are ideal for machine learning use cases and support SQL-based access and programmatic distributed data processing frameworks. Data lakes can store data in the same format as its source systems or transform it before storing it. They support native streaming and are best suited for storing raw data without an intended use case. Data quality and governance practices are crucial to avoid a data swamp. Data lakes enable end-users to leverage insights for improved business performance and enable advanced analytics.
Data Engineering A Deep Dive into DatabricksKnoldus Inc.
During this session, you'll gain a comprehensive understanding of Databricks' capabilities for efficiently processing and managing data, with a focus on Apache Spark for data transformation. We'll cover data ingestion methods, storage, orchestration, and best practices to ensure your data engineering workflows are optimized for success.
Analytics and Lakehouse Integration Options for Oracle ApplicationsRay Février
This Red Hot session is designed for customers who are currently using Oracle Cloud applications such as Fusion and EPM, and are interested in gaining a better understanding of the integration options that are available to them.
Here is a high level agenda:
- We will start by discussing the modern data platform on OCI, the Lakehouse architecture and the OCI related services that supports it.
- We will then discuss the data extraction methods available on OCI for Fusion and EPM.
- Last but not least, we will end with a few best practices and possible use cases.
In the interest of time, we will mainly focus on integration patterns that are recommended for Fusion and EPM, but don’t hesitate to reach out if you would to talk to us about other Oracle applications.
Enjoy!
Webinar future dataintegration-datamesh-and-goldengatekafkaJeffrey T. Pollock
The Future of Data Integration: Data Mesh, and a Special Deep Dive into Stream Processing with GoldenGate, Apache Kafka and Apache Spark. This video is a replay of a Live Webinar hosted on 03/19/2020.
Join us for a timely 45min webinar to see our take on the future of Data Integration. As the global industry shift towards the “Fourth Industrial Revolution” continues, outmoded styles of centralized batch processing and ETL tooling continue to be replaced by realtime, streaming, microservices and distributed data architecture patterns.
This webinar will start with a brief look at the macro-trends happening around distributed data management and how that affects Data Integration. Next, we’ll discuss the event-driven integrations provided by GoldenGate Big Data, and continue with a deep-dive into some essential patterns we see when replicating Database change events into Apache Kafka. In this deep-dive we will explain how to effectively deal with issues like Transaction Consistency, Table/Topic Mappings, managing the DB Change Stream, and various Deployment Topologies to consider. Finally, we’ll wrap up with a brief look into how Stream Processing will help to empower modern Data Integration by supplying realtime data transformations, time-series analytics, and embedded Machine Learning from within data pipelines.
GoldenGate: https://www.oracle.com/middleware/tec...
Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/)
Data lakes are central repositories that store large volumes of structured, unstructured, and semi-structured data. They are ideal for machine learning use cases and support SQL-based access and programmatic distributed data processing frameworks. Data lakes can store data in the same format as its source systems or transform it before storing it. They support native streaming and are best suited for storing raw data without an intended use case. Data quality and governance practices are crucial to avoid a data swamp. Data lakes enable end-users to leverage insights for improved business performance and enable advanced analytics.
Delta Lake, an open-source innovations which brings new capabilities for transactions, version control and indexing your data lakes. We uncover how Delta Lake benefits and why it matters to you. Through this session, we showcase some of its benefits and how they can improve your modern data engineering pipelines. Delta lake provides snapshot isolation which helps concurrent read/write operations and enables efficient insert, update, deletes, and rollback capabilities. It allows background file optimization through compaction and z-order partitioning achieving better performance improvements. In this presentation, we will learn the Delta Lake benefits and how it solves common data lake challenges, and most importantly new Delta Time Travel capability.
Building Reliable Lakehouses with Apache Flink and Delta LakeFlink Forward
Flink Forward San Francisco 2022.
Apache Flink and Delta Lake together allow you to build the foundation for your data lakehouses by ensuring the reliability of your concurrent streams from processing to the underlying cloud object-store. Together, the Flink/Delta Connector enables you to store data in Delta tables such that you harness Delta’s reliability by providing ACID transactions and scalability while maintaining Flink’s end-to-end exactly-once processing. This ensures that the data from Flink is written to Delta Tables in an idempotent manner such that even if the Flink pipeline is restarted from its checkpoint information, the pipeline will guarantee no data is lost or duplicated thus preserving the exactly-once semantics of Flink.
by
Scott Sandre & Denny Lee
Building Lakehouses on Delta Lake with SQL Analytics PrimerDatabricks
You’ve heard the marketing buzz, maybe you have been to a workshop and worked with some Spark, Delta, SQL, Python, or R, but you still need some help putting all the pieces together? Join us as we review some common techniques to build a lakehouse using Delta Lake, use SQL Analytics to perform exploratory analysis, and build connectivity for BI applications.
SAP Analytics Cloud: Haben Sie schon alle Datenquellen im Live-Zugriff?Denodo
Watch full webinar here: https://bit.ly/3hfEO6d
Die SAP Analytics Cloud (kurz "SAC" genannt) ist ein Service in der Cloud, der umfangreiche Analysefunktionen für Benutzer in einem Produkt bereit stellt. Wie immer bei der SAP ist auch die SAC technologisch gut integriert in die Welt der SAP Systeme.
Doch die Daten, die Unternehmen heutzutage analysieren möchten, befinden sich sehr häufig in den unterschiedlichsten Datenquellen: In relationalen Datenbanken, in Data Lakes, in Webservices, in Dateien, in NoSQL Datenbanken,... Und so stellt sich zwangsläufig die Frage, wie Sie aus der SAC heraus alle Daten konnektieren, transformieren und kombinieren können. Und das möglichst live, d.h. mit Abfragen auf Echtzeit-Daten! Hier kommt die Datenvirtualisierung ins Spiel: Sie bietet Anwendungen (so auch der SAC) einen einheitlichen, integrierten und performanten Zugriff auf SAP Daten und non-SAP Daten.
Erfahren Sie in diesem Webcast:
- Wie die Datenvirtualisierung funktioniert (in a Nutshell)
- Wie Sie aus der SAC heraus auf alle ihre Daten in Echtzeit zugreifen können ("Live Data Connection" genannt)
- Wie die Datenvirtualisierung die Performance auch für Abfragen auf grossen Datenmengen optimiert
CON6619 - OpenWorld Presentation. Oracle data integration, big data, data governance, and cloud integration. Replication, ETL, Data Quality, Streaming Big Data, and Data Preparation
Data Warehouse or Data Lake, Which Do I Choose?DATAVERSITY
Today’s data-driven companies have a choice to make – where do we store our data? As the move to the cloud continues to be a driving factor, the choice becomes either the data warehouse (Snowflake et al) or the data lake (AWS S3 et al). There are pro’s and con’s for each approach. While the data warehouse will give you strong data management with analytics, they don’t do well with semi-structured and unstructured data with tightly coupled storage and compute, not to mention expensive vendor lock-in. On the other hand, data lakes allow you to store all kinds of data and are extremely affordable, but they’re only meant for storage and by themselves provide no direct value to an organization.
Enter the Open Data Lakehouse, the next evolution of the data stack that gives you the openness and flexibility of the data lake with the key aspects of the data warehouse like management and transaction support.
In this webinar, you’ll hear from Ali LeClerc who will discuss the data landscape and why many companies are moving to an open data lakehouse. Ali will share more perspective on how you should think about what fits best based on your use case and workloads, and how some real world customers are using Presto, a SQL query engine, to bring analytics to the data lakehouse.
The BlueData EPIC™ software platform solves the challenges that can slow down and stall Big Data initiatives. It makes deployment of Big Data infrastructure easier, faster, and more
cost-effective – eliminating complexity as a barrier to adoption.
Lyftrondata enables enterprises to load data from 300+ connectors to Google Bigquery in minutes without any engineering requirements. Simply connect, organize, centralize and share your data on Bigquery with zero code data pipeline, ETL & ELT tool.
Today, data lakes are widely used and have become extremely affordable as data volumes have grown. However, they are only meant for storage and by themselves provide no direct value. With up to 80% of data stored in the data lake today, how do you unlock the value of the data lake? The value lies in the compute engine that runs on top of a data lake.
Join us for this webinar where Ahana co-founder and Chief Product Officer Dipti Borkar will discuss how to unlock the value of your data lake with the emerging Open Data Lake analytics architecture.
Dipti will cover:
-Open Data Lake analytics - what it is and what use cases it supports
-Why companies are moving to an open data lake analytics approach
-Why the open source data lake query engine Presto is critical to this approach
What is an Open Data Lake? - Data Sheets | WhitepaperVasu S
A data lake, where data is stored in an open format and accessed through open standards-based interfaces, is defined as an Open Data Lake.
https://www.qubole.com/resources/data-sheets/what-is-an-open-data-lake
Demystifying Data Warehouse as a Service (DWaaS)Kent Graziano
This is from the talk I gave at the 30th Anniversary NoCOUG meeting in San Jose, CA.
We all know that data warehouses and best practices for them are changing dramatically today. As organizations build new data warehouses and modernize established ones, they are turning to Data Warehousing as a Service (DWaaS) in hopes of taking advantage of the performance, concurrency, simplicity, and lower cost of a SaaS solution or simply to reduce their data center footprint (and the maintenance that goes with that).
But what is a DWaaS really? How is it different from traditional on-premises data warehousing?
In this talk I will:
• Demystify DWaaS by defining it and its goals
• Discuss the real-world benefits of DWaaS
• Discuss some of the coolest features in a DWaaS solution as exemplified by the Snowflake Elastic Data Warehouse.
Data Engineering with Databricks PresentationKnoldus Inc.
We will explore how to leverage the Databricks Lakehouse Platform to productionalize ETL pipelines and also learn how to use Delta Live Tables with Spark SQL and PySpark to define and schedule pipelines that incrementally process new data from a variety of data sources into the Lakehouse, orchestrate tasks with Databricks Workflows, and promote code with Databricks Repos.
Building a Logical Data Fabric using Data Virtualization (ASEAN)Denodo
Watch full webinar here: https://bit.ly/3FF1ubd
In the recent Building the Unified Data Warehouse and Data Lake report by leading industry analysts TDWI, we have discovered 64% of organizations stated the objective for a unified Data Warehouse and Data Lakes is to get more business value and 84% of organizations polled felt that a unified approach to Data Warehouses and Data Lakes was either extremely or moderately important.
In this session, you will learn how your organization can apply a logical data fabric and the associated technologies of machine learning, artificial intelligence, and data virtualization can reduce time to value. Hence, increasing the overall business value of your data assets.
KEY TAKEAWAYS:
- How a Logical Data Fabric is the right approach to assist organizations to unify their data.
- The advanced features of a Logical Data Fabric that assist with the democratization of data, providing an agile and governed approach to business analytics and data science.
- How a Logical Data Fabric with Data Virtualization enhances your legacy data integration landscape to simplify data access and encourage self-service.
Delta Lake, an open-source innovations which brings new capabilities for transactions, version control and indexing your data lakes. We uncover how Delta Lake benefits and why it matters to you. Through this session, we showcase some of its benefits and how they can improve your modern data engineering pipelines. Delta lake provides snapshot isolation which helps concurrent read/write operations and enables efficient insert, update, deletes, and rollback capabilities. It allows background file optimization through compaction and z-order partitioning achieving better performance improvements. In this presentation, we will learn the Delta Lake benefits and how it solves common data lake challenges, and most importantly new Delta Time Travel capability.
Building Reliable Lakehouses with Apache Flink and Delta LakeFlink Forward
Flink Forward San Francisco 2022.
Apache Flink and Delta Lake together allow you to build the foundation for your data lakehouses by ensuring the reliability of your concurrent streams from processing to the underlying cloud object-store. Together, the Flink/Delta Connector enables you to store data in Delta tables such that you harness Delta’s reliability by providing ACID transactions and scalability while maintaining Flink’s end-to-end exactly-once processing. This ensures that the data from Flink is written to Delta Tables in an idempotent manner such that even if the Flink pipeline is restarted from its checkpoint information, the pipeline will guarantee no data is lost or duplicated thus preserving the exactly-once semantics of Flink.
by
Scott Sandre & Denny Lee
Building Lakehouses on Delta Lake with SQL Analytics PrimerDatabricks
You’ve heard the marketing buzz, maybe you have been to a workshop and worked with some Spark, Delta, SQL, Python, or R, but you still need some help putting all the pieces together? Join us as we review some common techniques to build a lakehouse using Delta Lake, use SQL Analytics to perform exploratory analysis, and build connectivity for BI applications.
SAP Analytics Cloud: Haben Sie schon alle Datenquellen im Live-Zugriff?Denodo
Watch full webinar here: https://bit.ly/3hfEO6d
Die SAP Analytics Cloud (kurz "SAC" genannt) ist ein Service in der Cloud, der umfangreiche Analysefunktionen für Benutzer in einem Produkt bereit stellt. Wie immer bei der SAP ist auch die SAC technologisch gut integriert in die Welt der SAP Systeme.
Doch die Daten, die Unternehmen heutzutage analysieren möchten, befinden sich sehr häufig in den unterschiedlichsten Datenquellen: In relationalen Datenbanken, in Data Lakes, in Webservices, in Dateien, in NoSQL Datenbanken,... Und so stellt sich zwangsläufig die Frage, wie Sie aus der SAC heraus alle Daten konnektieren, transformieren und kombinieren können. Und das möglichst live, d.h. mit Abfragen auf Echtzeit-Daten! Hier kommt die Datenvirtualisierung ins Spiel: Sie bietet Anwendungen (so auch der SAC) einen einheitlichen, integrierten und performanten Zugriff auf SAP Daten und non-SAP Daten.
Erfahren Sie in diesem Webcast:
- Wie die Datenvirtualisierung funktioniert (in a Nutshell)
- Wie Sie aus der SAC heraus auf alle ihre Daten in Echtzeit zugreifen können ("Live Data Connection" genannt)
- Wie die Datenvirtualisierung die Performance auch für Abfragen auf grossen Datenmengen optimiert
CON6619 - OpenWorld Presentation. Oracle data integration, big data, data governance, and cloud integration. Replication, ETL, Data Quality, Streaming Big Data, and Data Preparation
Data Warehouse or Data Lake, Which Do I Choose?DATAVERSITY
Today’s data-driven companies have a choice to make – where do we store our data? As the move to the cloud continues to be a driving factor, the choice becomes either the data warehouse (Snowflake et al) or the data lake (AWS S3 et al). There are pro’s and con’s for each approach. While the data warehouse will give you strong data management with analytics, they don’t do well with semi-structured and unstructured data with tightly coupled storage and compute, not to mention expensive vendor lock-in. On the other hand, data lakes allow you to store all kinds of data and are extremely affordable, but they’re only meant for storage and by themselves provide no direct value to an organization.
Enter the Open Data Lakehouse, the next evolution of the data stack that gives you the openness and flexibility of the data lake with the key aspects of the data warehouse like management and transaction support.
In this webinar, you’ll hear from Ali LeClerc who will discuss the data landscape and why many companies are moving to an open data lakehouse. Ali will share more perspective on how you should think about what fits best based on your use case and workloads, and how some real world customers are using Presto, a SQL query engine, to bring analytics to the data lakehouse.
The BlueData EPIC™ software platform solves the challenges that can slow down and stall Big Data initiatives. It makes deployment of Big Data infrastructure easier, faster, and more
cost-effective – eliminating complexity as a barrier to adoption.
Lyftrondata enables enterprises to load data from 300+ connectors to Google Bigquery in minutes without any engineering requirements. Simply connect, organize, centralize and share your data on Bigquery with zero code data pipeline, ETL & ELT tool.
Today, data lakes are widely used and have become extremely affordable as data volumes have grown. However, they are only meant for storage and by themselves provide no direct value. With up to 80% of data stored in the data lake today, how do you unlock the value of the data lake? The value lies in the compute engine that runs on top of a data lake.
Join us for this webinar where Ahana co-founder and Chief Product Officer Dipti Borkar will discuss how to unlock the value of your data lake with the emerging Open Data Lake analytics architecture.
Dipti will cover:
-Open Data Lake analytics - what it is and what use cases it supports
-Why companies are moving to an open data lake analytics approach
-Why the open source data lake query engine Presto is critical to this approach
What is an Open Data Lake? - Data Sheets | WhitepaperVasu S
A data lake, where data is stored in an open format and accessed through open standards-based interfaces, is defined as an Open Data Lake.
https://www.qubole.com/resources/data-sheets/what-is-an-open-data-lake
Demystifying Data Warehouse as a Service (DWaaS)Kent Graziano
This is from the talk I gave at the 30th Anniversary NoCOUG meeting in San Jose, CA.
We all know that data warehouses and best practices for them are changing dramatically today. As organizations build new data warehouses and modernize established ones, they are turning to Data Warehousing as a Service (DWaaS) in hopes of taking advantage of the performance, concurrency, simplicity, and lower cost of a SaaS solution or simply to reduce their data center footprint (and the maintenance that goes with that).
But what is a DWaaS really? How is it different from traditional on-premises data warehousing?
In this talk I will:
• Demystify DWaaS by defining it and its goals
• Discuss the real-world benefits of DWaaS
• Discuss some of the coolest features in a DWaaS solution as exemplified by the Snowflake Elastic Data Warehouse.
Data Engineering with Databricks PresentationKnoldus Inc.
We will explore how to leverage the Databricks Lakehouse Platform to productionalize ETL pipelines and also learn how to use Delta Live Tables with Spark SQL and PySpark to define and schedule pipelines that incrementally process new data from a variety of data sources into the Lakehouse, orchestrate tasks with Databricks Workflows, and promote code with Databricks Repos.
Building a Logical Data Fabric using Data Virtualization (ASEAN)Denodo
Watch full webinar here: https://bit.ly/3FF1ubd
In the recent Building the Unified Data Warehouse and Data Lake report by leading industry analysts TDWI, we have discovered 64% of organizations stated the objective for a unified Data Warehouse and Data Lakes is to get more business value and 84% of organizations polled felt that a unified approach to Data Warehouses and Data Lakes was either extremely or moderately important.
In this session, you will learn how your organization can apply a logical data fabric and the associated technologies of machine learning, artificial intelligence, and data virtualization can reduce time to value. Hence, increasing the overall business value of your data assets.
KEY TAKEAWAYS:
- How a Logical Data Fabric is the right approach to assist organizations to unify their data.
- The advanced features of a Logical Data Fabric that assist with the democratization of data, providing an agile and governed approach to business analytics and data science.
- How a Logical Data Fabric with Data Virtualization enhances your legacy data integration landscape to simplify data access and encourage self-service.
Training is intended for business people who may well be doing what data scientists or technical developers would be doing - To Analyze Mounds of Data!
Data Analytics may sound frightening and technical, but this training is to have business-minds really understand data, AND identify some tools that make data analytics look like child's play!
As users spend more time on mobile devices, getting mobile sites right is crucial to success. If your client’s website is too slow to load, users will drop off. On the other hand, a fast-loading site with bad UX design makes it hard for users to complete their desired action. In a mobile-led world, consumer expectations are high.
Win customers with mobile sites - Improve conversions with small, but mighty, mobile site changes
Cut load times with Developer Tools - Identify inefficiencies in the critical rendering path
Speed up mobile site rendering
Key metrics for testing your site
Optimize mobile site transfer size
Optimize images and fonts
Focus on mobile user experience
Deliver user-centered mobile experiences - Craft a mobile site that delivers a great user experience
Make mobile sites drive conversions - Deliver a mobile site that lets users easily convert
Test and optimize mobile experiences - Discover what your users really want with testing
Create super fast sites with AMP - Get your content seen quickly with Accelerated Mobile Pages (AMP)
Create Progressive Web Apps - Learn to build engaging and reliable Progressive Web Apps
Engage users with APIs - Increase user engagement and conversions with new mobile web APIs
Text Analytics (unstructured - Twitter, Facebook posts) :
Information Extraction is the problem of distilling structured information from unstructured text, for example, finding entities such as persons and organizations, and the relationships between them. Using SystemT - a state-of-the-art Information Extraction System.
Text Analytics (unstructured - Twitter, Facebook posts):
Information Extraction is the problem of distilling structured information from unstructured text, for example, finding entities such as persons and organizations, and the relationships between them. Using SystemT - a state-of-the-art Information Extraction System.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
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.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
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.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
1. DOMINIC FERNANDEZ
Accredited Databricks' Delta Lake - Advocate
What Is Delta Lake?
Delta Lake is an open format storage layer that delivers reliability,
security and performance on your data lake — for both streaming and
batch operations. It replaces data silos with a single home - for
structured, semi-structured and unstructured data.
Delta Lake is the foundation of a cost-effective, highly scalable
lakehouse.
Delivers a reliable single source of truth for all of your data, including
real-time streams, so your data teams are always working with the most
current data.
Support for ACID transactions and schema enforcement, Delta Lake
provides the reliability that traditional data lakes lack. This enables you to
scale reliable data insights throughout the organization and run analytics
and other data projects directly on your data lake — for up to 50x faster
time-to-insight.
Industry’s first open protocol for secure data sharing, making it simple to
share data with other organizations regardless of where the data lives.
Native integration with the Unity Catalog allows you to centrally manage
and audit shared data across organizations. This allows you to
confidently share data assets with suppliers and partners for better
coordination of your business while meeting security and compliance
needs.
Integrates with leading tools and platforms that allow you to visualize,
query, enrich, and govern shared data from your tools of choice.
With Apache Spark™ under the hood, Delta Lake delivers massive scale
and speed. And because it’s optimized with performance features like
indexing, Delta Lake customers have seen ETL workloads execute up to
48x faster.
All data in Delta Lake is stored in open Apache Parquet format, allowing
data to be read by any compatible reader. APIs are open and compatible
with Apache Spark. With Delta Lake on Databricks, you have access to a
vast open source ecosystem and avoid data lock-in from proprietary
formats.
Automated and Trusted Data-Engineering
Simplify data engineering with Delta Live Tables – an easy way to build
and manage data pipelines for fresh, high-quality data on Delta Lake. It
helps data engineering teams by simplifying ETL development and
management with declarative pipeline development, improved data
reliability and cloud-scale production operations to help build the
lakehouse foundation.
dscf@computants.org ... Skype: dom.fernandez ... 519.702.0311
2. DOMINIC FERNANDEZ
Accredited Databricks' Delta Lake - Advocate
Let's get IT done .....
Business Intelligence (BI) on Data Make new, real-time data instantly available for querying by data
analysts for immediate insights on your business by running business intelligence workloads directly on
your data lake. Delta Lake allows you to operate a multicloud lakehouse architecture that provides data
warehousing performance at data lake economics for up to 6x better price/performance for SQL workloads
than traditional cloud data warehouses.
Unify Batch and Streaming Run both batch and streaming operations on one simplified architecture that
avoids complex, redundant systems and operational challenges. In Delta Lake, a table is both a batch
table and a streaming source and sink. Streaming data ingest, batch historic backfill and interactive
queries all work out of the box and directly integrate with Spark Structured Streaming.
Meet Regulatory Compliance Delta Lake removes the malformed data ingestion challenges, difficulty
deleting data for compliance, and issues modifying data for change data capture. With support for ACID
transactions on your data lake, Delta Lake ensures that every operation either fully succeeds or fully aborts
for later retries — without requiring new data pipelines to be created. Additionally, Delta Lake records all
past transactions on your data lake, so it’s easy to access and use previous versions of your data to meet
compliance standards like GDPR and CCPA reliably.
dscf@computants.org ... Skype: dom.fernandez ... 519.702.0311