Building Advanced Analytics Pipelines with Azure DatabricksLace Lofranco
Participants will get a deep dive into one of Azure’s newest offering: Azure Databricks, a fast, easy and collaborative Apache® Spark™ based analytics platform optimized for Azure. In this session, we start with a technical overview of Spark and quickly jump into Azure Databricks’ key collaboration features, cluster management, and tight data integration with Azure data sources. Concepts are made concrete via a detailed walk through of an advance analytics pipeline built using Spark and Azure Databricks.
Full video of the presentation: https://www.youtube.com/watch?v=14D9VzI152o
Presentation demo: https://github.com/devlace/azure-databricks-anomaly
Spark as a Service with Azure DatabricksLace Lofranco
Presented at: Global Azure Bootcamp (Melbourne)
Participants will get a deep dive into one of Azure’s newest offering: Azure Databricks, a fast, easy and collaborative Apache® Spark™ based analytics platform optimized for Azure. In this session, we will go through Azure Databricks key collaboration features, cluster management, and tight data integration with Azure data sources. We’ll also walk through an end-to-end Recommendation System Data Pipeline built using Spark on Azure Databricks.
Building Advanced Analytics Pipelines with Azure DatabricksLace Lofranco
Participants will get a deep dive into one of Azure’s newest offering: Azure Databricks, a fast, easy and collaborative Apache® Spark™ based analytics platform optimized for Azure. In this session, we start with a technical overview of Spark and quickly jump into Azure Databricks’ key collaboration features, cluster management, and tight data integration with Azure data sources. Concepts are made concrete via a detailed walk through of an advance analytics pipeline built using Spark and Azure Databricks.
Full video of the presentation: https://www.youtube.com/watch?v=14D9VzI152o
Presentation demo: https://github.com/devlace/azure-databricks-anomaly
Spark as a Service with Azure DatabricksLace Lofranco
Presented at: Global Azure Bootcamp (Melbourne)
Participants will get a deep dive into one of Azure’s newest offering: Azure Databricks, a fast, easy and collaborative Apache® Spark™ based analytics platform optimized for Azure. In this session, we will go through Azure Databricks key collaboration features, cluster management, and tight data integration with Azure data sources. We’ll also walk through an end-to-end Recommendation System Data Pipeline built using Spark on Azure Databricks.
Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics platform optimized for Azure. Designed in collaboration with the founders of Apache Spark, Azure Databricks combines the best of Databricks and Azure to help customers accelerate innovation with one-click set up, streamlined workflows, and an interactive workspace that enables collaboration between data scientists, data engineers, and business analysts. As an Azure service, customers automatically benefit from the native integration with other Azure services such as Power BI, SQL Data Warehouse, and Cosmos DB, as well as from enterprise-grade Azure security, including Active Directory integration, compliance, and enterprise-grade SLAs.
This presentation focuses on the value proposition for Azure Databricks for Data Science. First, the talk includes an overview of the merits of Azure Databricks and Spark. Second, the talk includes demos of data science on Azure Databricks. Finally, the presentation includes some ideas for data science production.
ETL Made Easy with Azure Data Factory and Azure DatabricksDatabricks
Data Engineers are responsible for data cleansing, prepping, aggregating, and loading analytical data stores, which is often difficult and time-consuming. Azure Data Factory makes this work easy and expedites solution development. We’ll demonstrate how Azure Data Factory can enable a new UI-driven ETL design paradigm on top of Azure Databricks for building scaled-out data transformation pipelines.
The Developer Data Scientist – Creating New Analytics Driven Applications usi...Microsoft Tech Community
The developer world is changing as we create and generate new data patterns and handling processes within our applications. Additionally, with the massive interest in machine learning and advanced analytics how can we as developers build intelligence directly into our applications that can integrate with the data and data paths we are creating? The answer is Azure Databricks and by attending this session you will be able to confidently develop smarter and more intelligent applications and solutions which can be continuously built upon and that can scale with the growing demands of a modern application estate.
Microsoft Ignite AU 2017 - Orchestrating Big Data Pipelines with Azure Data F...Lace Lofranco
Data orchestration is the lifeblood of any successful data analytics solution. Take a deep dive into Azure Data Factory's data movement and transformation activities, particularly its integration with Azure's Big Data PaaS offerings such as HDInsight, SQL Data warehouse, Data Lake, and AzureML. Participants will learn how to design, build and manage big data orchestration pipelines using Azure Data Factory and how it stacks up against similar Big Data orchestration tools such as Apache Oozie.
Video of presentation:
https://channel9.msdn.com/Events/Ignite/Australia-2017/DA332
New Developments in the Open Source Ecosystem: Apache Spark 3.0, Delta Lake, ...Databricks
<p>In this talk, we will highlight major efforts happening in the Spark ecosystem. In particular, we will dive into the details of adaptive and static query optimizations in Spark 3.0 to make Spark easier to use and faster to run. We will also demonstrate how new features in Koalas, an open source library that provides Pandas-like API on top of Spark, helps data scientists gain insights from their data quicker.</p>
Inteligencia de Negocios en SQL Server 2008 y Minería de Datos.
Ing. Eduardo Castro Martinez, PhD
Microsoft SQL Server MVP
http://ecastrom.blogspot.com
http://comunidadwindows.org
From Idea to Model: Productionizing Data Pipelines with Apache AirflowDatabricks
When supporting a data science team, data engineers are tasked with building a platform that keeps a wide range of stakeholders happy. Data scientists want rapid iteration, infrastructure engineers want monitoring and security controls, and product owners want their solutions deployed in time for quarterly reports.
My talk at Data Science Labs conference in Odessa.
Training a model in Apache Spark while having it automatically available for real-time serving is an essential feature for end-to-end solutions.
There is an option to export the model into PMML and then import it into a separated scoring engine. The idea of interoperability is great but it has multiple challenges, such as code duplication, limited extensibility, inconsistency, extra moving parts. In this talk we discussed an alternative solution that does not introduce custom model formats and new standards, not based on export/import workflow and shares Apache Spark API.
Sql Bits 2020 - Designing Performant and Scalable Data Lakes using Azure Data...Rukmani Gopalan
Cloud Storage is evolving rapidly, and our Azure Storage portfolio has added a ton of new industry leading capabilities. In this session you will learn the do's and don'ts of building data lakes on Azure Data Lake Storage. You will learn about the commonly used patterns, how to set up your accounts and pipelines to maximize performance, how to organize your data and various options to secure access to your data. We will also cover customer use cases and highlight planned enhancements and upcoming features.
Analyzing StackExchange data with Azure Data LakeBizTalk360
Big data is the new big thing where storing the data is the easy part. Gaining insights in your pile of data is something different. Based on a data dump of the well-known StackExchange websites, we will store & analyse 150+ GB of data with Azure Data Lake Store & Analytics to gain some insights about their users. After that we will use Power BI to give an at a glance overview of our learnings.
If you are a developer that is interested in big data, this is your time to shine! We will use our existing SQL & C# skills to analyse everything without having to worry about running clusters.
Spark and Couchbase– Augmenting the Operational Database with SparkMatt Ingenthron
How do NoSQL Document-Oriented Databases like Couchbase fit in with Apache Spark? This set of slides gives a couple of use cases, shows why Couchbase works great with Spark, and sets up a scenario for a demo.
Apache® Spark™ MLlib: From Quick Start to Scikit-LearnDatabricks
These are the slides to support the Apache® Spark™ MLlib: From Quick Start to Scikit-Learn webinar.
In this webcast, Joseph Bradley from Databricks will be speaking about Apache Spark’s distributed Machine Learning Library - MLlib.
We will start off with a quick primer on machine learning, Spark MLlib, and a quick overview of some Spark machine learning use cases. We will continue with multiple Spark MLlib quick start demos. Afterwards, the talk will transition toward the integration of common data science tools like Python pandas, scikit-learn, and R with MLlib
Managing your ML lifecycle with Azure Databricks and Azure MLParashar Shah
Machine learning development has new complexities beyond software development. There are a myriad of tools and frameworks which make it hard to track experiments, reproduce results and deploy machine learning models. Learn how you can accelerate and manage your end-to-end machine learning lifecycle on Azure Databricks using MLflow and Azure ML to reliably build, share and deploy machine learning applications using Azure Databricks. This is based on our talk at //build - https://www.youtube.com/watch?v=pe_OH07wAYc and https://mybuild.techcommunity.microsoft.com/sessions/76976
Machine Learning Data Lineage with MLflow and Delta LakeDatabricks
Many organizations using machine learning are facing challenges storing and versioning their complex ML data as well as a large number of models generated from those data. To simplify this process, organizations tend to start building their customized ‘ML platforms.’
What to Expect for Big Data and Apache Spark in 2017 Databricks
Big data remains a rapidly evolving field with new applications and infrastructure appearing every year. In this talk, Matei Zaharia will cover new trends in 2016 / 2017 and how Apache Spark is moving to meet them. In particular, he will talk about work Databricks is doing to make Apache Spark interact better with native code (e.g. deep learning libraries), support heterogeneous hardware, and simplify production data pipelines in both streaming and batch settings through Structured Streaming.
Speaker: Matei Zaharia
Video: http://go.databricks.com/videos/spark-summit-east-2017/what-to-expect-big-data-apache-spark-2017
This talk was originally presented at Spark Summit East 2017.
Architecting an Open Source AI Platform 2018 editionDavid Talby
How to build a scalable AI platform using open source software. The end-to-end architecture covers data integration, interactive queries & visualization, machine learning & deep learning, deploying models to production, and a full 24x7 operations toolset in a high-compliance environment.
Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics platform optimized for Azure. Designed in collaboration with the founders of Apache Spark, Azure Databricks combines the best of Databricks and Azure to help customers accelerate innovation with one-click set up, streamlined workflows, and an interactive workspace that enables collaboration between data scientists, data engineers, and business analysts. As an Azure service, customers automatically benefit from the native integration with other Azure services such as Power BI, SQL Data Warehouse, and Cosmos DB, as well as from enterprise-grade Azure security, including Active Directory integration, compliance, and enterprise-grade SLAs.
This presentation focuses on the value proposition for Azure Databricks for Data Science. First, the talk includes an overview of the merits of Azure Databricks and Spark. Second, the talk includes demos of data science on Azure Databricks. Finally, the presentation includes some ideas for data science production.
ETL Made Easy with Azure Data Factory and Azure DatabricksDatabricks
Data Engineers are responsible for data cleansing, prepping, aggregating, and loading analytical data stores, which is often difficult and time-consuming. Azure Data Factory makes this work easy and expedites solution development. We’ll demonstrate how Azure Data Factory can enable a new UI-driven ETL design paradigm on top of Azure Databricks for building scaled-out data transformation pipelines.
The Developer Data Scientist – Creating New Analytics Driven Applications usi...Microsoft Tech Community
The developer world is changing as we create and generate new data patterns and handling processes within our applications. Additionally, with the massive interest in machine learning and advanced analytics how can we as developers build intelligence directly into our applications that can integrate with the data and data paths we are creating? The answer is Azure Databricks and by attending this session you will be able to confidently develop smarter and more intelligent applications and solutions which can be continuously built upon and that can scale with the growing demands of a modern application estate.
Microsoft Ignite AU 2017 - Orchestrating Big Data Pipelines with Azure Data F...Lace Lofranco
Data orchestration is the lifeblood of any successful data analytics solution. Take a deep dive into Azure Data Factory's data movement and transformation activities, particularly its integration with Azure's Big Data PaaS offerings such as HDInsight, SQL Data warehouse, Data Lake, and AzureML. Participants will learn how to design, build and manage big data orchestration pipelines using Azure Data Factory and how it stacks up against similar Big Data orchestration tools such as Apache Oozie.
Video of presentation:
https://channel9.msdn.com/Events/Ignite/Australia-2017/DA332
New Developments in the Open Source Ecosystem: Apache Spark 3.0, Delta Lake, ...Databricks
<p>In this talk, we will highlight major efforts happening in the Spark ecosystem. In particular, we will dive into the details of adaptive and static query optimizations in Spark 3.0 to make Spark easier to use and faster to run. We will also demonstrate how new features in Koalas, an open source library that provides Pandas-like API on top of Spark, helps data scientists gain insights from their data quicker.</p>
Inteligencia de Negocios en SQL Server 2008 y Minería de Datos.
Ing. Eduardo Castro Martinez, PhD
Microsoft SQL Server MVP
http://ecastrom.blogspot.com
http://comunidadwindows.org
From Idea to Model: Productionizing Data Pipelines with Apache AirflowDatabricks
When supporting a data science team, data engineers are tasked with building a platform that keeps a wide range of stakeholders happy. Data scientists want rapid iteration, infrastructure engineers want monitoring and security controls, and product owners want their solutions deployed in time for quarterly reports.
My talk at Data Science Labs conference in Odessa.
Training a model in Apache Spark while having it automatically available for real-time serving is an essential feature for end-to-end solutions.
There is an option to export the model into PMML and then import it into a separated scoring engine. The idea of interoperability is great but it has multiple challenges, such as code duplication, limited extensibility, inconsistency, extra moving parts. In this talk we discussed an alternative solution that does not introduce custom model formats and new standards, not based on export/import workflow and shares Apache Spark API.
Sql Bits 2020 - Designing Performant and Scalable Data Lakes using Azure Data...Rukmani Gopalan
Cloud Storage is evolving rapidly, and our Azure Storage portfolio has added a ton of new industry leading capabilities. In this session you will learn the do's and don'ts of building data lakes on Azure Data Lake Storage. You will learn about the commonly used patterns, how to set up your accounts and pipelines to maximize performance, how to organize your data and various options to secure access to your data. We will also cover customer use cases and highlight planned enhancements and upcoming features.
Analyzing StackExchange data with Azure Data LakeBizTalk360
Big data is the new big thing where storing the data is the easy part. Gaining insights in your pile of data is something different. Based on a data dump of the well-known StackExchange websites, we will store & analyse 150+ GB of data with Azure Data Lake Store & Analytics to gain some insights about their users. After that we will use Power BI to give an at a glance overview of our learnings.
If you are a developer that is interested in big data, this is your time to shine! We will use our existing SQL & C# skills to analyse everything without having to worry about running clusters.
Spark and Couchbase– Augmenting the Operational Database with SparkMatt Ingenthron
How do NoSQL Document-Oriented Databases like Couchbase fit in with Apache Spark? This set of slides gives a couple of use cases, shows why Couchbase works great with Spark, and sets up a scenario for a demo.
Apache® Spark™ MLlib: From Quick Start to Scikit-LearnDatabricks
These are the slides to support the Apache® Spark™ MLlib: From Quick Start to Scikit-Learn webinar.
In this webcast, Joseph Bradley from Databricks will be speaking about Apache Spark’s distributed Machine Learning Library - MLlib.
We will start off with a quick primer on machine learning, Spark MLlib, and a quick overview of some Spark machine learning use cases. We will continue with multiple Spark MLlib quick start demos. Afterwards, the talk will transition toward the integration of common data science tools like Python pandas, scikit-learn, and R with MLlib
Managing your ML lifecycle with Azure Databricks and Azure MLParashar Shah
Machine learning development has new complexities beyond software development. There are a myriad of tools and frameworks which make it hard to track experiments, reproduce results and deploy machine learning models. Learn how you can accelerate and manage your end-to-end machine learning lifecycle on Azure Databricks using MLflow and Azure ML to reliably build, share and deploy machine learning applications using Azure Databricks. This is based on our talk at //build - https://www.youtube.com/watch?v=pe_OH07wAYc and https://mybuild.techcommunity.microsoft.com/sessions/76976
Machine Learning Data Lineage with MLflow and Delta LakeDatabricks
Many organizations using machine learning are facing challenges storing and versioning their complex ML data as well as a large number of models generated from those data. To simplify this process, organizations tend to start building their customized ‘ML platforms.’
What to Expect for Big Data and Apache Spark in 2017 Databricks
Big data remains a rapidly evolving field with new applications and infrastructure appearing every year. In this talk, Matei Zaharia will cover new trends in 2016 / 2017 and how Apache Spark is moving to meet them. In particular, he will talk about work Databricks is doing to make Apache Spark interact better with native code (e.g. deep learning libraries), support heterogeneous hardware, and simplify production data pipelines in both streaming and batch settings through Structured Streaming.
Speaker: Matei Zaharia
Video: http://go.databricks.com/videos/spark-summit-east-2017/what-to-expect-big-data-apache-spark-2017
This talk was originally presented at Spark Summit East 2017.
Architecting an Open Source AI Platform 2018 editionDavid Talby
How to build a scalable AI platform using open source software. The end-to-end architecture covers data integration, interactive queries & visualization, machine learning & deep learning, deploying models to production, and a full 24x7 operations toolset in a high-compliance environment.
H2O Rains with Databricks Cloud - Parisoma SFSri Ambati
Michal Malohlava's meetup on H2O Rains with Databricks Cloud at Parisoma SF, 02.02.16
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
H2O Rains with Databricks Cloud - NY 02.16.16Sri Ambati
Michal Malohlava's presentation on H2O Rains with Databricks Cloud, New York, NY 02.16.16
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Bring Your SAP and Enterprise Data to Hadoop, Kafka, and the CloudDataWorks Summit
The world’s largest enterprises run their infrastructure on Oracle, DB2 and SQL and their critical business operations on SAP applications. Organisations need this data to be available in real-time to conduct necessary analytics. However, delivering this heterogeneous data at the speed it’s required can be a huge challenge because of the complex underlying data models and structures and legacy manual processes which are prone to errors and delays.
Unlock these silos of data and enable the new advanced analytics platforms by attending this session.
Find out how to:
• To overcome common challenges faced by enterprises trying to access their SAP data
• You can integrate SAP data in real-time with change data capture (CDC) technology
• Organisations are using Attunity Replicate for SAP to stream SAP data in to Kafka
Speakers:
John Hol, Regional Director, Attunity
Mike Hollobon, Director Business Development, IBT
Denodo Design Studio: Modeling and Creation of Data ServicesDenodo
Watch full webinar here: https://bit.ly/39T7SON
Change is the only constant and it is very important for enterprises to keep up with the changing times in an agile fashion. To ensure faster time to market, quick business insights and rapid data driven decision making, it is important that the Data Delivery channel is optimized in the best way possible.
With the advent of API Management technologies the demand for data being delivered in the form of a Data Service/APIs is increasing. The ability to make data available in an API format at the click of a button is the need of the hour. Join us to see how easy it is to make enterprise wide data available as Data Services/APIs no matter what format the data is stored in with no prior coding experience. Faster development, zero learning curve and huge value.
Watch on-demand this webinar to learn:
- How to explore datasets available using Denodo Data Catalog
- How to build new data sets using Denodo Design Studio, drag and drop interface
- How to make datasets available in RESTful, OData 4, GeoJSON, GraphQL.
- How to enable different authentication protocols including OAuth 2.0.
- Automatic documentation (Open API) and availability in the Denodo Data Catalog.
• Capable of processing large sets of structured, semi-structured and unstructured data and supporting system architecture
• Implemented Proof of concepts on Hadoop stack and different big data analytic tools, migration from different databases to Hadoop.
• Developed multiple Map Reduce jobs in java for data cleaning and pre-processing according to the business requirements, Importing and exporting data into HDFS and Hive using Sqoop.
Having Experience in writing HIVE queries & Pig scripts.
Architecting Agile Data Applications for ScaleDatabricks
Data analytics and reporting platforms historically have been rigid, monolithic, hard to change and have limited ability to scale up or scale down. I can’t tell you how many times I have heard a business user ask for something as simple as an additional column in a report and IT says it will take 6 months to add that column because it doesn’t exist in the datawarehouse. As a former DBA, I can tell you the countless hours I have spent “tuning” SQL queries to hit pre-established SLAs. This talk will talk about how to architect modern data and analytics platforms in the cloud to support agility and scalability. We will include topics like end to end data pipeline flow, data mesh and data catalogs, live data and streaming, performing advanced analytics, applying agile software development practices like CI/CD and testability to data applications and finally taking advantage of the cloud for infinite scalability both up and down.
Top Features to Include in Your Winzo Clone App for Business Growth (4).pptxrickgrimesss22
Discover the essential features to incorporate in your Winzo clone app to boost business growth, enhance user engagement, and drive revenue. Learn how to create a compelling gaming experience that stands out in the competitive market.
Accelerate Enterprise Software Engineering with PlatformlessWSO2
Key takeaways:
Challenges of building platforms and the benefits of platformless.
Key principles of platformless, including API-first, cloud-native middleware, platform engineering, and developer experience.
How Choreo enables the platformless experience.
How key concepts like application architecture, domain-driven design, zero trust, and cell-based architecture are inherently a part of Choreo.
Demo of an end-to-end app built and deployed on Choreo.
Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...Globus
The Earth System Grid Federation (ESGF) is a global network of data servers that archives and distributes the planet’s largest collection of Earth system model output for thousands of climate and environmental scientists worldwide. Many of these petabyte-scale data archives are located in proximity to large high-performance computing (HPC) or cloud computing resources, but the primary workflow for data users consists of transferring data, and applying computations on a different system. As a part of the ESGF 2.0 US project (funded by the United States Department of Energy Office of Science), we developed pre-defined data workflows, which can be run on-demand, capable of applying many data reduction and data analysis to the large ESGF data archives, transferring only the resultant analysis (ex. visualizations, smaller data files). In this talk, we will showcase a few of these workflows, highlighting how Globus Flows can be used for petabyte-scale climate analysis.
A Comprehensive Look at Generative AI in Retail App Testing.pdfkalichargn70th171
Traditional software testing methods are being challenged in retail, where customer expectations and technological advancements continually shape the landscape. Enter generative AI—a transformative subset of artificial intelligence technologies poised to revolutionize software testing.
May Marketo Masterclass, London MUG May 22 2024.pdfAdele Miller
Can't make Adobe Summit in Vegas? No sweat because the EMEA Marketo Engage Champions are coming to London to share their Summit sessions, insights and more!
This is a MUG with a twist you don't want to miss.
Unleash Unlimited Potential with One-Time Purchase
BoxLang is more than just a language; it's a community. By choosing a Visionary License, you're not just investing in your success, you're actively contributing to the ongoing development and support of BoxLang.
top nidhi software solution freedownloadvrstrong314
This presentation emphasizes the importance of data security and legal compliance for Nidhi companies in India. It highlights how online Nidhi software solutions, like Vector Nidhi Software, offer advanced features tailored to these needs. Key aspects include encryption, access controls, and audit trails to ensure data security. The software complies with regulatory guidelines from the MCA and RBI and adheres to Nidhi Rules, 2014. With customizable, user-friendly interfaces and real-time features, these Nidhi software solutions enhance efficiency, support growth, and provide exceptional member services. The presentation concludes with contact information for further inquiries.
Enhancing Research Orchestration Capabilities at ORNL.pdfGlobus
Cross-facility research orchestration comes with ever-changing constraints regarding the availability and suitability of various compute and data resources. In short, a flexible data and processing fabric is needed to enable the dynamic redirection of data and compute tasks throughout the lifecycle of an experiment. In this talk, we illustrate how we easily leveraged Globus services to instrument the ACE research testbed at the Oak Ridge Leadership Computing Facility with flexible data and task orchestration capabilities.
Software Engineering, Software Consulting, Tech Lead.
Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Security,
Spring Transaction, Spring MVC,
Log4j, REST/SOAP WEB-SERVICES.
Quarkus Hidden and Forbidden ExtensionsMax Andersen
Quarkus has a vast extension ecosystem and is known for its subsonic and subatomic feature set. Some of these features are not as well known, and some extensions are less talked about, but that does not make them less interesting - quite the opposite.
Come join this talk to see some tips and tricks for using Quarkus and some of the lesser known features, extensions and development techniques.
Globus Connect Server Deep Dive - GlobusWorld 2024Globus
We explore the Globus Connect Server (GCS) architecture and experiment with advanced configuration options and use cases. This content is targeted at system administrators who are familiar with GCS and currently operate—or are planning to operate—broader deployments at their institution.
Prosigns: Transforming Business with Tailored Technology SolutionsProsigns
Unlocking Business Potential: Tailored Technology Solutions by Prosigns
Discover how Prosigns, a leading technology solutions provider, partners with businesses to drive innovation and success. Our presentation showcases our comprehensive range of services, including custom software development, web and mobile app development, AI & ML solutions, blockchain integration, DevOps services, and Microsoft Dynamics 365 support.
Custom Software Development: Prosigns specializes in creating bespoke software solutions that cater to your unique business needs. Our team of experts works closely with you to understand your requirements and deliver tailor-made software that enhances efficiency and drives growth.
Web and Mobile App Development: From responsive websites to intuitive mobile applications, Prosigns develops cutting-edge solutions that engage users and deliver seamless experiences across devices.
AI & ML Solutions: Harnessing the power of Artificial Intelligence and Machine Learning, Prosigns provides smart solutions that automate processes, provide valuable insights, and drive informed decision-making.
Blockchain Integration: Prosigns offers comprehensive blockchain solutions, including development, integration, and consulting services, enabling businesses to leverage blockchain technology for enhanced security, transparency, and efficiency.
DevOps Services: Prosigns' DevOps services streamline development and operations processes, ensuring faster and more reliable software delivery through automation and continuous integration.
Microsoft Dynamics 365 Support: Prosigns provides comprehensive support and maintenance services for Microsoft Dynamics 365, ensuring your system is always up-to-date, secure, and running smoothly.
Learn how our collaborative approach and dedication to excellence help businesses achieve their goals and stay ahead in today's digital landscape. From concept to deployment, Prosigns is your trusted partner for transforming ideas into reality and unlocking the full potential of your business.
Join us on a journey of innovation and growth. Let's partner for success with Prosigns.
First Steps with Globus Compute Multi-User EndpointsGlobus
In this presentation we will share our experiences around getting started with the Globus Compute multi-user endpoint. Working with the Pharmacology group at the University of Auckland, we have previously written an application using Globus Compute that can offload computationally expensive steps in the researcher's workflows, which they wish to manage from their familiar Windows environments, onto the NeSI (New Zealand eScience Infrastructure) cluster. Some of the challenges we have encountered were that each researcher had to set up and manage their own single-user globus compute endpoint and that the workloads had varying resource requirements (CPUs, memory and wall time) between different runs. We hope that the multi-user endpoint will help to address these challenges and share an update on our progress here.
In software engineering, the right architecture is essential for robust, scalable platforms. Wix has undergone a pivotal shift from event sourcing to a CRUD-based model for its microservices. This talk will chart the course of this pivotal journey.
Event sourcing, which records state changes as immutable events, provided robust auditing and "time travel" debugging for Wix Stores' microservices. Despite its benefits, the complexity it introduced in state management slowed development. Wix responded by adopting a simpler, unified CRUD model. This talk will explore the challenges of event sourcing and the advantages of Wix's new "CRUD on steroids" approach, which streamlines API integration and domain event management while preserving data integrity and system resilience.
Participants will gain valuable insights into Wix's strategies for ensuring atomicity in database updates and event production, as well as caching, materialization, and performance optimization techniques within a distributed system.
Join us to discover how Wix has mastered the art of balancing simplicity and extensibility, and learn how the re-adoption of the modest CRUD has turbocharged their development velocity, resilience, and scalability in a high-growth environment.
Exploring Innovations in Data Repository Solutions - Insights from the U.S. G...Globus
The U.S. Geological Survey (USGS) has made substantial investments in meeting evolving scientific, technical, and policy driven demands on storing, managing, and delivering data. As these demands continue to grow in complexity and scale, the USGS must continue to explore innovative solutions to improve its management, curation, sharing, delivering, and preservation approaches for large-scale research data. Supporting these needs, the USGS has partnered with the University of Chicago-Globus to research and develop advanced repository components and workflows leveraging its current investment in Globus. The primary outcome of this partnership includes the development of a prototype enterprise repository, driven by USGS Data Release requirements, through exploration and implementation of the entire suite of the Globus platform offerings, including Globus Flow, Globus Auth, Globus Transfer, and Globus Search. This presentation will provide insights into this research partnership, introduce the unique requirements and challenges being addressed and provide relevant project progress.
Experience our free, in-depth three-part Tendenci Platform Corporate Membership Management workshop series! In Session 1 on May 14th, 2024, we began with an Introduction and Setup, mastering the configuration of your Corporate Membership Module settings to establish membership types, applications, and more. Then, on May 16th, 2024, in Session 2, we focused on binding individual members to a Corporate Membership and Corporate Reps, teaching you how to add individual members and assign Corporate Representatives to manage dues, renewals, and associated members. Finally, on May 28th, 2024, in Session 3, we covered questions and concerns, addressing any queries or issues you may have.
For more Tendenci AMS events, check out www.tendenci.com/events
Corporate Management | Session 3 of 3 | Tendenci AMS
Sparkflows.io
1. Reducing cost and time-to-market for Big Data Analytics
& Applications by 10X
Self-Service Big Data Analytics & Applications
Cut down from months to hours
3. 3
Data Analysts
Data
Engineers
Data
Scientists
Its challenging for users and get value out of the Data Lake
Data Lake
● Data Analytics, Data Preparation &
Blending
● Machine Learning
● Streaming Applications
● Batch Applications
● Dashboards & Visualization
Needs a lot of coding on Big
Data
5. 5
Long time to
Production & Value
Hard to maintain and extend
the pipelines/applications
Very Hard to
Collaborate
Business Data Scientist Data Engineer IT
Very Complex
Deployment
Hard to handover code
Results In
8. Workflow Editor
How Sparkflows Works
Rich Visualizations &
Dashboards
100’s of Nodes
Batch & Streaming
Engine
Interactive Execution
Easy Deployment &
Configuration
Pre-built Workflows
Telco Churn Pred
Housing Price Pred
Bike Sharing Analysis
NY Taxi Data Analysis
Movie Lens
Recommendations
9. Confidential Property of Sparkflows.io
Sparkflows Product Stack
Streaming
Data
Kafka
Flume
Data
Sources
HIVE/HBase
HDFS/S3
Solr
RDBMS
Apache Spark Cluster
Databricks AWS
IBM
Bluemix
On
Prem
Azur
e
Visualizations
ETL/NLP/OCR
Model Building
Workflow Execution
Scala/SQL/Python
Data Wrangling
Data Analysis
Data Pipelines
Big Data Analytics /Applications
Visualization
Data Sinks
HIVE/HBase
HDFS/S3
Solr
RDBMS
10. 10
Business Analyst
Data
Scientist
Data Engineer IT
Data Analytics for Business Use
Cases by dragging and
dropping nodes and using
various datasets.
Visualization and deep
understanding of the data
Build predictive models and apply
predictions
Do predictive and analytical
modeling with the drag-and-
drop capabilities
Write custom SQL, Scala, Python
to close the gaps
Blend static and real-time streams
to build complex data
pipelines
Build and deploy complex
pipelines in minutes.
Connect to various sources and
sinks including Kafaka, HDFS,
S3, HBase, Solr.
Build and expose custom nodes
in Sparkflows for others to
use
Embed SQL, Scala, Python within
the workflow.
Easily configure multi-tenancy
and security for
Sparkflows users
Connect workflow results to
platform of choice for
visualization
Provision Hadoop
infrastructure, monitor
workflow jobs, and tune
performance
11. Why Now?
Big Trend towards building with Templates
11
Streamsets
iPhone Apps
Building Website
nifi
StreamAnalytix
Impetus
Alteryx
13. Core Differentiators
13
Easy & Natural to use and Deploy
Deep Integration with Hadoop -
Security/Impersonation/HIVE/HBase/Solr
Custom Nodes - Users can write their own
Nodes and plug into the UI
Schema Propagation
Interactive Execution at Design Time
Rich Application Dashboards
Growing Repository of Workflows for
various Solutions
Building out of Complex Nodes by
Sparkflows - Dedup, Drools,
OpenNLP, StanfordNLP, Tesseract
etc.
Batch & Streaming - Nodes support
both Batch & Streaming workloads
Support for SQL, Scala, Jython as
Nodes of the workflow
14. Line of Products
14
Data Analytics
(Analytics / Wrangling
/ Machine Learning)
Streaming
Analytics
Applications
16. Building Big Data Analytics & Applications is very costly & time
consuming
16
Customer
360
Fraud
Detection
Operations
Analytics
Cyber
Security
IoT
Analytics
Analytics
Application
s
Not enough users are able to extract great value from the Data Lake
17. Needs a lot of coding on Big
Data
17
Data Analytics, Data
Preparation &
Blending
Machine LearningStreaming Applications
Batch Applications
Visualizations
Editor's Notes
Makes building Big Data Applications Agile, much, much faster and predictable
Benefits:
Business Users Can Really Interact with Data & Experiment with Building Applications
Rich Dashboards - Make day-to-day operations more efficient and provide insights into data and workflow performance
Pre-Built Applications which can be easily extended or changed
Use Cases Easy to Visualize and Implement
