SnapLogic Live: Powering Cloud Analytics

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Watch this recorded demonstration of SnapLogic from our team of experts who answer your hybrid cloud and big data integration questions.

SnapLogic Live: Powering Cloud Analytics

2
Welcome to SnapLogic Live
• In 30 minutes we’ll:
– Review specific topics and the role SnapLogic plays
– Dive into demonstrations of our Elastic Integration Platform
– Open it up for questions and discussion
• Today’s featured presenter: Kai Thapa
• Today’s featured topic: Powering Cloud Analytics

Anything
apps | APIs | things | data
Anytime
batch | streaming | real-time
Anywhere
on premises | in the cloud
SnapLogic allows enterprises
to connect data and
applications faster

Why SnapLogic Elastic Integration?
Unified platform
for apps and data
• Batch, streaming,
real-time
• High volume,
variety, and
velocity big data
Easy to use,
intuitive UX
• Intuitive for “citizen
integrators”
• Powerful enough
for experts without
scripting
Hybrid, elastic
architecture
• Scale-out elastic
architecture
• In the cloud, on
prem, on Hadoop
or Spark cluster
Extensive library
of connectors
• Applications,
analytics, big data,
social media
• REST, SOAP,
JSON, transform
Snaps, 300+ more

Modern Architecture: Hybrid and Elastic
Streams: No data is
stored/cached
Secure: 100%
standards-based
Elastic: Scales out &
handles data and
app integration use
cases
Metadata
Data
Databases
On Prem
Apps
Big Data
Cloud Apps
and DataCloud-Based Designer, Manager,
Dashboard
Cloudplex
Groundplex
Hadooplex
Sparkplex
Firewall

Discussion
Unified Platform
snaplogic.com
@SnapLogic
Productive User
Experience
Modern
Architecture
Connected: 300+ Snaps

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Enterprise Resource Planning System includes various modules that reduce any business's workload. Additionally, it organizes the workflows, which drives towards enhancing productivity. Here are a detailed explanation of the ERP modules. Going through the points will help you understand how the software is changing the work dynamics. To know more details here: https://blogs.nyggs.com/nyggs/enterprise-resource-planning-erp-system-modules/

Understanding Globus Data Transfers with NetSage

Globus

NetSage is an open privacy-aware network measurement, analysis, and visualization service designed to help end-users visualize and reason about large data transfers. NetSage traditionally has used a combination of passive measurements, including SNMP and flow data, as well as active measurements, mainly perfSONAR, to provide longitudinal network performance data visualization. It has been deployed by dozens of networks world wide, and is supported domestically by the Engagement and Performance Operations Center (EPOC), NSF #2328479. We have recently expanded the NetSage data sources to include logs for Globus data transfers, following the same privacy-preserving approach as for Flow data. Using the logs for the Texas Advanced Computing Center (TACC) as an example, this talk will walk through several different example use cases that NetSage can answer, including: Who is using Globus to share data with my institution, and what kind of performance are they able to achieve? How many transfers has Globus supported for us? Which sites are we sharing the most data with, and how is that changing over time? How is my site using Globus to move data internally, and what kind of performance do we see for those transfers? What percentage of data transfers at my institution used Globus, and how did the overall data transfer performance compare to the Globus users?

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Globus

COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new relationships among public health practitioners, mathematical modelers, and scientific computing specialists, while revealing critical gaps in exploiting advanced computing systems to support urgent decision making. Informed by our team’s work in applying high-performance computing in support of public health decision makers during the COVID-19 pandemic, we present how Globus technologies are enabling the development of an open science platform for robust epidemic analysis, with the goal of collaborative, secure, distributed, on-demand, and fast time-to-solution analyses to support public health.

GlobusWorld 2024 Opening Keynote session

Globus

Graspan: A Big Data System for Big Code Analysis

Aftab Hussain

We built a disk-based parallel graph system, Graspan, that uses a novel edge-pair centric computation model to compute dynamic transitive closures on very large program graphs. We implement context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases such as Linux shows that their Graspan implementations scale to millions of lines of code and are much simpler than their original implementations. These analyses were used to augment the existing checkers; these augmented checkers found 132 new NULL pointer bugs and 1308 unnecessary NULL tests in Linux 4.4.0-rc5, PostgreSQL 8.3.9, and Apache httpd 2.2.18. - Accepted in ASPLOS ‘17, Xi’an, China. - Featured in the tutorial, Systemized Program Analyses: A Big Data Perspective on Static Analysis Scalability, ASPLOS ‘17. - Invited for presentation at SoCal PLS ‘16. - Invited for poster presentation at PLDI SRC ‘16.

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Cracking the code review at SpringIO 2024

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Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production. Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process? In this session we will cover: - The Art of Effective Code Reviews - Streamlining the Review Process - Elevating Reviews with Automated Tools By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces

First Steps with Globus Compute Multi-User Endpoints

Globus

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.

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SnapLogic Live: Powering Cloud Analytics

1. SnapLogic Live: Powering Cloud Analytics

2. 2 Welcome to SnapLogic Live • In 30 minutes we’ll: – Review specific topics and the role SnapLogic plays – Dive into demonstrations of our Elastic Integration Platform – Open it up for questions and discussion • Today’s featured presenter: Kai Thapa • Today’s featured topic: Powering Cloud Analytics

4. Why SnapLogic Elastic Integration? Unified platform for apps and data • Batch, streaming, real-time • High volume, variety, and velocity big data Easy to use, intuitive UX • Intuitive for “citizen integrators” • Powerful enough for experts without scripting Hybrid, elastic architecture • Scale-out elastic architecture • In the cloud, on prem, on Hadoop or Spark cluster Extensive library of connectors • Applications, analytics, big data, social media • REST, SOAP, JSON, transform Snaps, 300+ more

5. Modern Architecture: Hybrid and Elastic Streams: No data is stored/cached Secure: 100% standards-based Elastic: Scales out & handles data and app integration use cases Metadata Data Databases On Prem Apps Big Data Cloud Apps and DataCloud-Based Designer, Manager, Dashboard Cloudplex Groundplex Hadooplex Sparkplex Firewall

6. Demonstration: Snaplogic.com/live

7. Discussion Unified Platform snaplogic.com @SnapLogic Productive User Experience Modern Architecture Connected: 300+ Snaps

Editor's Notes

Leading enterprises choose SnapLogic because we help them connect data and applications faster. We connect anything: sources including applications, APIs, things, or data We connect anytime: in batches, streaming, or in real time And we connect anywhere: on premises, in the cloud or a combination of both
Unified: At SnapLogic, we’re 100% focused on delivering a single, unified platform that can handle ETL (or ELT) requirements and low-latency ESB requirements. Productive: When we talk about connecting faster, a bit part of that is the productivity gains that our customers tell us about. Integration is a hard problem to solve, and we’re focused on simplifying it as much as possible with a very easy to use, drag and drop design interface for both citizen and advanced integrators. Modern: the SnapLogic modern architecture goes back to our ability to handle both real-time, low-latency integration requirements as well as big data volume, variety and velocity. And finally, Connected. You’ll see in the demonstration the breadth and depth of our Snaps. We have over 300 Snaps and a Snap Development Kit (SDK) for building custom Snaps.
Here is an example of what a SnapLogic deployment looks like. The Integration Cloud does not store your data. It’s metadata only. Once a pipeline is executed, it looks for the associated Snaplex or Hadoplex. The plex, scale out adding more nodes as needed. We like to say that SnapLogic “respects data gravity” and runs as close to the data as need be. If you are integrating only cloud applications, it would make no sense to run your integrations behind the firewall. Similarly, if you’re doing ground to ground or cloud to ground, you may want to run your Snaplex on Window or Linux servers. Note that the dotted line is sending instructions via metadata to the plex, which is waiting to run. The solid line indicates how data movies bi-directionally between systems. The yellow box is an example of the Hadooplex running pipelines and scale out across multiple nodes in a Hadoop cluster. Reference: http://www.snaplogic.com/security
http://video.snaplogic.com/demos/

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Editor's Notes