In analytics world, when you need to process many millions or billions of documents to generate a single report. Novel techniques have been developed for exploiting modern processor architecture (larger on-chip cache, SIMD processing, compression, vector processing, columnar approach). Now, this technology is available to process your large JSON data. This talk will discuss analysis of JSON data using advanced data warehousing techniques and make it simple and seamless for the application/tool developer.
Redundancy and high availability are the basis for all production deployments. With MongoDB this can be achieved by deploying replica set. In this slides we are exploring how the replication works with MongoDB, why you should use replication, what are the features and go over different deployment use cases. At the end we are comparing some features with MySQL replication and what are the differences between the two
Webinar: Schema Patterns and Your Storage EngineMongoDB
How do MongoDB’s different storage options change the way you model your data?
Each storage engine, WiredTiger, the In-Memory Storage engine, MMAP V1 and other community supported drivers, persists data differently, writes data to disk in different formats and handles memory resources in different ways.
This webinar will go through how to design applications around different storage engines based on your use case and data access patterns. We will be looking into concrete examples of schema design practices that were previously applied on MMAPv1 and whether those practices still apply, to other storage engines like WiredTiger.
Topics for review: Schema design patterns and strategies, real-world examples, sizing and resource allocation of infrastructure.
Working with MongoDB as MySQL DBA. Comparing commands from MongoDB to MySQL, similarities and differences. Exploring replication features, failover and recovery, adjusting the variables and checking status and using DML, DDL with different storage engines
Redundancy and high availability are the basis for all production deployments. With MongoDB this can be achieved by deploying replica set. In this slides we are exploring how the replication works with MongoDB, why you should use replication, what are the features and go over different deployment use cases. At the end we are comparing some features with MySQL replication and what are the differences between the two
Webinar: Schema Patterns and Your Storage EngineMongoDB
How do MongoDB’s different storage options change the way you model your data?
Each storage engine, WiredTiger, the In-Memory Storage engine, MMAP V1 and other community supported drivers, persists data differently, writes data to disk in different formats and handles memory resources in different ways.
This webinar will go through how to design applications around different storage engines based on your use case and data access patterns. We will be looking into concrete examples of schema design practices that were previously applied on MMAPv1 and whether those practices still apply, to other storage engines like WiredTiger.
Topics for review: Schema design patterns and strategies, real-world examples, sizing and resource allocation of infrastructure.
Working with MongoDB as MySQL DBA. Comparing commands from MongoDB to MySQL, similarities and differences. Exploring replication features, failover and recovery, adjusting the variables and checking status and using DML, DDL with different storage engines
Has your app taken off? Are you thinking about scaling? MongoDB makes it easy to horizontally scale out with built-in automatic sharding, but did you know that sharding isn't the only way to achieve scale with MongoDB?
In this webinar, we'll review three different ways to achieve scale with MongoDB. We'll cover how you can optimize your application design and configure your storage to achieve scale, as well as the basics of horizontal scaling. You'll walk away with a thorough understanding of options to scale your MongoDB application.
MongoDB was designed for humongous amounts of data, with the ability to scale horizontally via sharding. In this session, we’ll look at MongoDB’s approach to partitioning data, and the architecture of a sharded system. We’ll walk you through configuration of a sharded system, and look at how data is balanced across servers and requests are routed.
Learn about the various approaches to sharding your data with MongoDB. This presentation will help you answer questions such as when to shard and how to choose a shard key.
Breaking the Oracle Tie; High Performance OLTP and Analytics Using MongoDBMongoDB
This talk is the story of design and implementation of Marketing Communication Suite at Persado. Marketing Communication Suite is a platform serving tens of customers ranging from telecoms to finance and web properties with persuasion marketing language messaging. Our platform uses a range of technologies with the most important being MongoDB for the online transactional and analytical processing of messages. Topics this talk will be about: MongoDB Aggregation vs. Mapreduce Data Modeling Deployment Architecture Migration Scenarios Hybrid Solutions
Are you in the process of evaluating or migrating to MongoDB? We will cover key aspects of migrating to MongoDB from a RDBMS, including Schema design, Indexing strategies, Data migration approaches as your implementation reaches various SDLC stages, Achieving operational agility through MongoDB Management Services (MMS).
Exploring the replication and sharding in MongoDBIgor Donchovski
Redundancy and high availability are the basis for all production deployments. Database systems with large data sets or high throughput applications can challenge the capacity of a single server like CPU for high query rates or RAM for large working sets. Adding more CPU and RAM for vertical scaling is limited. Systems need horizontal scaling by distributing data across multiple servers. MongoDB supports horizontal scaling through sharding. Each shard consist of replica set that provides Redundancy and high availability.
Redundancy and high availability are the basis for all production deployments. With MongoDB high availability is achieved with replica sets which provides automatic fail-over in case the Primary goes down. In this session we will review multiple maintenance scenarios that will include the proper steps for keeping the high availability while we perform the maintenance steps without causing downtime.
This session will cover Database upgrades, OS server patching, Hardware upgrades, Network maintenance and more.
How MongoDB HA works
Replica sets components/deployment typologies
Database upgrades
System patching/upgrade
Network maintenance
Add/Remove members to the replica set
Reconfiguring replica set members
Building indexes
Backups and restores
Choosing a shard key can be difficult, and the factors involved largely depend on your use case. In fact, there is no such thing as a perfect shard key; there are design tradeoffs inherent in every decision. This presentation goes through those tradeoffs, as well as the different types of shard keys available in MongoDB, such as hashed and compound shard keys
Sharding allows you to distribute load across multiple servers and keep your data balanced across those servers. This session will review MongoDB’s sharding support, including an architectural overview, design principles, and automation.
MongoDB sharded cluster. How to design your topology ?Mydbops
This slides was presented at Mydbops Database Meetup 4 on Aug-03 2019 by Vinodh Krishnaswamy ( Percona ). This talk focuses on when to go for sharing topology in MongoDB and their benefits and impact.
MongoDB's architecture features built-in support for horizontal scalability, and high availability through replica sets. Auto-sharding allows users to easily distribute data across many nodes. Replica sets enable automatic failover and recovery of database nodes within or across data centers. This session will provide an introduction to scaling with MongoDB by one of MongoDB's early adopters.
Back to Basics 2017: Introduction to ShardingMongoDB
Sharding is a method for distributing data across multiple machines. MongoDB uses sharding to support deployments with very large data sets and high throughput operations by providing the capability for horizontal scaling.
memcached Distributed Cache. memcached is the most popular cache solution for low latency high throughput websites. improves the read timings drastically.
Back to Basics Webinar 6: Production DeploymentMongoDB
This is the final webinar of a Back to Basics series that will introduce you to the MongoDB database. This webinar will guide you through production deployment.
Dan Sullivan - Data Analytics and Text Mining with MongoDB - NoSQL matters Du...NoSQLmatters
Data analysis is an exploratory process that requires a variety of tools and a flexible data store. Data analysis projects are easy to start but quickly become difficult to manage and error prone when depending on file-based data storage. Relational databases are poorly equipped to accommodate the dynamic demands complex analysis. This talk describes best practices for using MongoDB for analytics projects. Examples will be drawn from a large scale text mining project (approximately 25 million documents) that applies machine learning (neural networks and support vector machines) and statistical analysis. Tools discussed include R, Spark, Python scientific stack, and custom pre-processing scripts but the focus is on using these with the document database.
Has your app taken off? Are you thinking about scaling? MongoDB makes it easy to horizontally scale out with built-in automatic sharding, but did you know that sharding isn't the only way to achieve scale with MongoDB?
In this webinar, we'll review three different ways to achieve scale with MongoDB. We'll cover how you can optimize your application design and configure your storage to achieve scale, as well as the basics of horizontal scaling. You'll walk away with a thorough understanding of options to scale your MongoDB application.
MongoDB was designed for humongous amounts of data, with the ability to scale horizontally via sharding. In this session, we’ll look at MongoDB’s approach to partitioning data, and the architecture of a sharded system. We’ll walk you through configuration of a sharded system, and look at how data is balanced across servers and requests are routed.
Learn about the various approaches to sharding your data with MongoDB. This presentation will help you answer questions such as when to shard and how to choose a shard key.
Breaking the Oracle Tie; High Performance OLTP and Analytics Using MongoDBMongoDB
This talk is the story of design and implementation of Marketing Communication Suite at Persado. Marketing Communication Suite is a platform serving tens of customers ranging from telecoms to finance and web properties with persuasion marketing language messaging. Our platform uses a range of technologies with the most important being MongoDB for the online transactional and analytical processing of messages. Topics this talk will be about: MongoDB Aggregation vs. Mapreduce Data Modeling Deployment Architecture Migration Scenarios Hybrid Solutions
Are you in the process of evaluating or migrating to MongoDB? We will cover key aspects of migrating to MongoDB from a RDBMS, including Schema design, Indexing strategies, Data migration approaches as your implementation reaches various SDLC stages, Achieving operational agility through MongoDB Management Services (MMS).
Exploring the replication and sharding in MongoDBIgor Donchovski
Redundancy and high availability are the basis for all production deployments. Database systems with large data sets or high throughput applications can challenge the capacity of a single server like CPU for high query rates or RAM for large working sets. Adding more CPU and RAM for vertical scaling is limited. Systems need horizontal scaling by distributing data across multiple servers. MongoDB supports horizontal scaling through sharding. Each shard consist of replica set that provides Redundancy and high availability.
Redundancy and high availability are the basis for all production deployments. With MongoDB high availability is achieved with replica sets which provides automatic fail-over in case the Primary goes down. In this session we will review multiple maintenance scenarios that will include the proper steps for keeping the high availability while we perform the maintenance steps without causing downtime.
This session will cover Database upgrades, OS server patching, Hardware upgrades, Network maintenance and more.
How MongoDB HA works
Replica sets components/deployment typologies
Database upgrades
System patching/upgrade
Network maintenance
Add/Remove members to the replica set
Reconfiguring replica set members
Building indexes
Backups and restores
Choosing a shard key can be difficult, and the factors involved largely depend on your use case. In fact, there is no such thing as a perfect shard key; there are design tradeoffs inherent in every decision. This presentation goes through those tradeoffs, as well as the different types of shard keys available in MongoDB, such as hashed and compound shard keys
Sharding allows you to distribute load across multiple servers and keep your data balanced across those servers. This session will review MongoDB’s sharding support, including an architectural overview, design principles, and automation.
MongoDB sharded cluster. How to design your topology ?Mydbops
This slides was presented at Mydbops Database Meetup 4 on Aug-03 2019 by Vinodh Krishnaswamy ( Percona ). This talk focuses on when to go for sharing topology in MongoDB and their benefits and impact.
MongoDB's architecture features built-in support for horizontal scalability, and high availability through replica sets. Auto-sharding allows users to easily distribute data across many nodes. Replica sets enable automatic failover and recovery of database nodes within or across data centers. This session will provide an introduction to scaling with MongoDB by one of MongoDB's early adopters.
Back to Basics 2017: Introduction to ShardingMongoDB
Sharding is a method for distributing data across multiple machines. MongoDB uses sharding to support deployments with very large data sets and high throughput operations by providing the capability for horizontal scaling.
memcached Distributed Cache. memcached is the most popular cache solution for low latency high throughput websites. improves the read timings drastically.
Back to Basics Webinar 6: Production DeploymentMongoDB
This is the final webinar of a Back to Basics series that will introduce you to the MongoDB database. This webinar will guide you through production deployment.
Dan Sullivan - Data Analytics and Text Mining with MongoDB - NoSQL matters Du...NoSQLmatters
Data analysis is an exploratory process that requires a variety of tools and a flexible data store. Data analysis projects are easy to start but quickly become difficult to manage and error prone when depending on file-based data storage. Relational databases are poorly equipped to accommodate the dynamic demands complex analysis. This talk describes best practices for using MongoDB for analytics projects. Examples will be drawn from a large scale text mining project (approximately 25 million documents) that applies machine learning (neural networks and support vector machines) and statistical analysis. Tools discussed include R, Spark, Python scientific stack, and custom pre-processing scripts but the focus is on using these with the document database.
MongoDB Days UK: Using MongoDB and Python for Data Analysis PipelinesMongoDB
Presented by Eoin Brazil, Proactive Technical Services Engineer, MongoDB
Experience level: Advanced
MongoDB offers a flexible, scalable, and easy way to store your large data set. Python provides many useful data science tools (e.g. NumPy, SciPy, Scikit-learn, etc.). This talk will discuss the concerns for creating operational data analytic pipelines, introduce Monary as alternative for loading data into NumPy, and give examples of accessing data with Monary, as well as how to build scalable data analysis pipelines using these open source tools.
JSON-stat & JS: the JSON-stat Javascript ToolkitXavier Badosa
This presentation was part of the introductory JSON-stat presentation (July, 2013):
http://www.slideshare.net/badosa/json-stat
That presentation was split into three presentations between December 2015 and January 2016 and mainly updated to reflect the latest changes in the format (v. 2.0).
New usage model for real-time analytics by Dr. WILLIAM L. BAIN at Big Data S...Big Data Spain
Operational systems manage our finances, shopping, devices and much more. Adding real-time analytics to these systems enables them to instantly respond to changing conditions and provide immediate, targeted feedback. This use of analytics is called “operational intelligence,” and the need for it is widespread.
StatisticalTable, a JSON-stat-based vocabularyXavier Badosa
This presentation was part of the introductory JSON-stat presentation (July, 2013):
http://www.slideshare.net/badosa/json-stat
That presentation was split into three presentations between December 2015 and January 2016 and only updated to reflect the latest changes in the format (v. 2.0).
JSON-stat, a simple light standard for all kinds of data disseminatorsXavier Badosa
An introduction to the JSON-stat ecosystem. Originally published in July 2013, it was edited in 2015: it was updated to the latest changes in the standard and aspects not directly related to the JSON-stat document format were removed).
A very brief version of this presentation was used at the Data Tuesday BCN (Sept. 17th, 2013).
Webinar: High Performance MongoDB Applications with IBM POWER8MongoDB
Innovative companies are building Internet of Things, mobile, content management, single view, and big data apps on top of MongoDB. In this session, we'll explore how the IBM POWER8 platform brings new levels of performance and ease of configuration to these solutions which already benefit from easier and faster design and development using MongoDB.
MongoDB has taken a clear lead in adoption among the new generation of databases, including the enormous variety of NoSQL offerings. A key reason for this lead has been a unique combination of agility and scalability. Agility provides business units with a quick start and flexibility to maintain development velocity, despite changing data and requirements. Scalability maintains that flexibility while providing fast, interactive performance as data volume and usage increase. We'll address the key organizational, operational, and engineering considerations to ensure that agility and scalability stay aligned at increasing scale, from small development instances to web-scale applications. We will also survey some key examples of highly-scaled customer applications of MongoDB.
Postgres eliminates the complexity and the pain of creating a single view of the customer. With recent advances, Postgres can support semi-structured, unstructured and structured data in the same environment, employing relational qualities and ACID compliance.
This presentation reviews:
- How advances in Postgres enable it to match capabilities of NoSQL-only niche solutions·
- How the ETL process in Postgres is simple compared to undoing tables and schemas in order to transfer data to a NoSQL-only system
- How Foreign Data Wrappers – essentially pipelines between Postgres and other databases – work and how they help bridge the gap between disparate systems faster than an ETL process
Visit Enterprisedb.com and go to our Resources section, then Webcasts to listen to the presentation recording.
This session is recommended for anyone interested in understanding how to use AWS big data services to develop real-time analytics applications. In this session, you will get an overview of a number of Amazon's big data and analytics services that enable you to build highly scaleable cloud applications that immediately and continuously analyze large sets of distributed data. We'll explain how services like Amazon Kinesis, EMR and Redshift can be used for data ingestion, processing and storage to enable real-time insights and analysis into customer, operational and machine generated data and log files. We'll explore system requirements, design considerations, and walk through a specific customer use case to illustrate the power of real-time insights on their business.
You know what iMEAN? Using MEAN stack for application dev on InformixKeshav Murthy
You know what iMEAN? Using MEAN stack for application dev on Informix. MongoDB, ExpressJS, AngularJS, NodeJS combine to form a MEAN stack for quick appdev. iMEAN is using the same stack to develop applications on Informix.
In the world of big data, legacy modernization, siloed organizations, empowered customers, and mobile devices, making informed choices about your enterprise infrastructure has become more important than ever. The alternatives are abundant, and the successful Enterprise Architect must constantly discern which new technology is just a shiny object and which will add true business value.
The proliferation of data from new data sources has generated greater demand for technologies that can handle and harvest value from unstructured data. Postgres is leading the movement of integrating unstructured data with the relational environment.
Postgres first added JSON and then enhanced it with new data types, functions and operators in recent releases. Now in beta is the JSONB “binary JSON” type. These advances follow the longstanding HStore data type added in 2006 to support key/value stores in Postgres. Now Postgres users can learn how to harness these capabilities to master unstructured data challenges with Postgres.
The presentation also covers:
* An overview of JSON data types and operators
* Examples of SELECT, UPDATE, etc
* An examination of performance considerations
For more information, please email sales@enterprisedb.com
In the age of digital transformation and disruption, your ability to thrive depends on how you adapt to the constantly changing environment. MongoDB 3.4 is the latest release of the leading database for modern applications, a culmination of native database features and enhancements that will allow you to easily evolve your solutions to address emerging challenges and use cases.
In this webinar, we introduce you to what’s new, including:
- Multimodel Done Right. Native graph computation, faceted navigation, rich real-time analytics, and powerful connectors for BI and Apache Spark bring additional multimodel database support right into MongoDB.
- Mission-Critical Applications. Geo-distributed MongoDB zones, elastic clustering, tunable consistency, and enhanced security controls bring state-of-the-art database technology to your most mission-critical applications.
- Modernized Tooling. Enhanced DBA and DevOps tooling for schema management, fine-grained monitoring, and cloud-native integration allow engineering teams to ship applications faster, with less overhead and higher quality.
NoSQL Now: Postgres - The NoSQL Cake You Can EatDATAVERSITY
The path to creating a single view of your customers or your transactional systems is overflowing with high costs and complexity. Major vendors have built massive, million-dollar systems that are too expensive and too complicated for most. NoSQL-only solutions seem to have promise, but simply do not necessarily have what you need. Learn what Postgres can do for you that NoSQL-only solutions can't.
Using a NoSQL-only solution and dumping gigabytes of data from multiple disparate systems into gigantic documents is complicated. And it forces tough choices—group all data by customer, by transaction, or by policy? You must choose, and this can be a hard process for some organizations. And almost always, organizations later learn they need relationships among the data, which NoSQL-only solutions cannot support.
Postgres eliminates the complexity and the pain of creating a single view of the customer. With recent advances, Postgres can support semi-structured, unstructured and structured data in the same environment, employing relational qualities and ACID compliance.
During this presentation, Marc Linster, SVP Products & Services, will review: ·
How to do more with Postgres
Open source alternative to RDBMS and more...
The NoSQL Conundrum
Why do developers like NoSQL Only solutions?
Problems and fallacies of NoSQL (only)
Data Standards
Data Islands
NoSQL Data Models include data access paths
Not Only SQL - Technology Innovation on a Robust Platform
Document Store
See JSON Examples
360 Degree view of the customer
Data Integration
Slidedeck presented at http://devternity.com/ around MongoDB internals. We review the usage patterns of MongoDB, the different storage engines and persistency models as well has the definition of documents and general data structures.
Similar to NoSQL Analytics: JSON Data Analysis and Acceleration in MongoDB World (20)
Discover the power of Recursive SQL and query transformation with Informix da...Ajay Gupte
This presentation will provide an overview of the Recursive SQL with the CONNECT BY clause feature. We will provide examples of typical practical database problems and describe in detail how they can be solved with recursive SQL. The problems discussed include for bill of materials, obtaining the number of employees for each manager in a particular sub-organization, converting linked dimension hierarchies in a star schema to fixed dimension hierarchies, tracking packages, and generating test data. This presentation compares the new solutions with traditional solutions of these problems and discusses the advantages and disadvantages of the various methods. This presentation will also discuss the query transformation techniques with Informix 12.10 features which will focus on how query blocks are moved between different levels and optimized using examples and diagrams. Users will learn how to analyze complex examples based on various Informix 12.10 features. Examples included in this session are query block movement, table re-ordering, complex ANSI joins, sub-queries, derived tables, views, connect by, OLAP functions, setops cases.
Using Lateral derived table in Informix databaseAjay Gupte
This presentation will focus on Lateral derived table concept along with various examples. It will cover lateral correlation overview and user scenarios with views, stored procedures and complex queries. It will show how Informix Server execute Lateral correlation in different cases. Users will learn how to build Lateral correlation in application development.
Building a Hierarchical Data Model Using the Latest IBM Informix FeaturesAjay Gupte
Learn about developing Hierarchical queries using Informix features such as OLAP functions, setops operators and query rewrite. This presentation will cover building the hierarchical data model using existing relational schema in IDS. You learn about customer scenarios for designing hierarchical data model, in-depth knowledge of complex hierarchical queries, performance tips and references. This talk will provide details on how to identify hierarchical relationship and take advantage of using existing relational model.
Using JSON/BSON types in your hybrid application environmentAjay Gupte
This presentation will cover overview of
JSON/BSON types along with various SQL
features. It will cover JSON/BSON data extraction, performance & tips for hybrid environment.
Examples will have SQL features such as Views,
Derived Tables, Stored Procedure, Hierarchical
queries
How IBM API Management use Informix and NoSQLAjay Gupte
IBM API Management product version 3 (V3) has been
re-design and re- architected from ground up to
be able to handle scale in a cloud environment as
well as in an on-premise environment, but also to
be able to deliver features at a faster pace. As
part of this process. This session will cover Programming Model Best Practices with NoSQL Technology and Informix Database.
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.
Providing Globus Services to Users of JASMIN for Environmental Data AnalysisGlobus
JASMIN is the UK’s high-performance data analysis platform for environmental science, operated by STFC on behalf of the UK Natural Environment Research Council (NERC). In addition to its role in hosting the CEDA Archive (NERC’s long-term repository for climate, atmospheric science & Earth observation data in the UK), JASMIN provides a collaborative platform to a community of around 2,000 scientists in the UK and beyond, providing nearly 400 environmental science projects with working space, compute resources and tools to facilitate their work. High-performance data transfer into and out of JASMIN has always been a key feature, with many scientists bringing model outputs from supercomputers elsewhere in the UK, to analyse against observational or other model data in the CEDA Archive. A growing number of JASMIN users are now realising the benefits of using the Globus service to provide reliable and efficient data movement and other tasks in this and other contexts. Further use cases involve long-distance (intercontinental) transfers to and from JASMIN, and collecting results from a mobile atmospheric radar system, pushing data to JASMIN via a lightweight Globus deployment. We provide details of how Globus fits into our current infrastructure, our experience of the recent migration to GCSv5.4, and of our interest in developing use of the wider ecosystem of Globus services for the benefit of our user community.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Globus Compute wth IRI Workflows - GlobusWorld 2024Globus
As part of the DOE Integrated Research Infrastructure (IRI) program, NERSC at Lawrence Berkeley National Lab and ALCF at Argonne National Lab are working closely with General Atomics on accelerating the computing requirements of the DIII-D experiment. As part of the work the team is investigating ways to speedup the time to solution for many different parts of the DIII-D workflow including how they run jobs on HPC systems. One of these routes is looking at Globus Compute as a way to replace the current method for managing tasks and we describe a brief proof of concept showing how Globus Compute could help to schedule jobs and be a tool to connect compute at different facilities.
Check out the webinar slides to learn more about how XfilesPro transforms Salesforce document management by leveraging its world-class applications. For more details, please connect with sales@xfilespro.com
If you want to watch the on-demand webinar, please click here: https://www.xfilespro.com/webinars/salesforce-document-management-2-0-smarter-faster-better/
Cyaniclab : Software Development Agency Portfolio.pdfCyanic lab
CyanicLab, an offshore custom software development company based in Sweden,India, Finland, is your go-to partner for startup development and innovative web design solutions. Our expert team specializes in crafting cutting-edge software tailored to meet the unique needs of startups and established enterprises alike. From conceptualization to execution, we offer comprehensive services including web and mobile app development, UI/UX design, and ongoing software maintenance. Ready to elevate your business? Contact CyanicLab today and let us propel your vision to success with our top-notch IT solutions.
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.
Top 7 Unique WhatsApp API Benefits | Saudi ArabiaYara Milbes
Discover the transformative power of the WhatsApp API in our latest SlideShare presentation, "Top 7 Unique WhatsApp API Benefits." In today's fast-paced digital era, effective communication is crucial for both personal and professional success. Whether you're a small business looking to enhance customer interactions or an individual seeking seamless communication with loved ones, the WhatsApp API offers robust capabilities that can significantly elevate your experience.
In this presentation, we delve into the top 7 distinctive benefits of the WhatsApp API, provided by the leading WhatsApp API service provider in Saudi Arabia. Learn how to streamline customer support, automate notifications, leverage rich media messaging, run scalable marketing campaigns, integrate secure payments, synchronize with CRM systems, and ensure enhanced security and privacy.
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/
Developing Distributed High-performance Computing Capabilities of an Open Sci...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.
Understanding Globus Data Transfers with NetSageGlobus
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?
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.
Custom Healthcare Software for Managing Chronic Conditions and Remote Patient...Mind IT Systems
Healthcare providers often struggle with the complexities of chronic conditions and remote patient monitoring, as each patient requires personalized care and ongoing monitoring. Off-the-shelf solutions may not meet these diverse needs, leading to inefficiencies and gaps in care. It’s here, custom healthcare software offers a tailored solution, ensuring improved care and effectiveness.
How Recreation Management Software Can Streamline Your Operations.pptxwottaspaceseo
Recreation management software streamlines operations by automating key tasks such as scheduling, registration, and payment processing, reducing manual workload and errors. It provides centralized management of facilities, classes, and events, ensuring efficient resource allocation and facility usage. The software offers user-friendly online portals for easy access to bookings and program information, enhancing customer experience. Real-time reporting and data analytics deliver insights into attendance and preferences, aiding in strategic decision-making. Additionally, effective communication tools keep participants and staff informed with timely updates. Overall, recreation management software enhances efficiency, improves service delivery, and boosts customer satisfaction.
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.
Innovating Inference - Remote Triggering of Large Language Models on HPC Clus...Globus
Large Language Models (LLMs) are currently the center of attention in the tech world, particularly for their potential to advance research. In this presentation, we'll explore a straightforward and effective method for quickly initiating inference runs on supercomputers using the vLLM tool with Globus Compute, specifically on the Polaris system at ALCF. We'll begin by briefly discussing the popularity and applications of LLMs in various fields. Following this, we will introduce the vLLM tool, and explain how it integrates with Globus Compute to efficiently manage LLM operations on Polaris. Attendees will learn the practical aspects of setting up and remotely triggering LLMs from local machines, focusing on ease of use and efficiency. This talk is ideal for researchers and practitioners looking to leverage the power of LLMs in their work, offering a clear guide to harnessing supercomputing resources for quick and effective LLM inference.
Innovating Inference - Remote Triggering of Large Language Models on HPC Clus...
NoSQL Analytics: JSON Data Analysis and Acceleration in MongoDB World
1. NoSQL Analytics: JSON Data
Analysis and Acceleration in
MongoDB World
Ajaykumar Gupte
IBM
1
2. 2
Agenda
Basic overview of JSON data management.
Overview of IBM in-memory accelerator
Performance using in-memory accelerator with JSON data
3. 3
Explosion of mobile
devices – gaming
and social apps
Advertising:
serving ads and
real-time
bidding
Social networking,
online
communities
E-commerce, social
commerce
Machine data and
real-time
operational
decisions
Smart
Devices
Internet of Data
Internet of
Things
SQL
SQL, {JSON}, Spatial
{JSON},
TimeSeries
SQL, {JSON}
Simple,
{JSON},
Timeseries
SQL, {JSON}
4. 4
Schema-less app development lowers costs
– Simplicity and agility to develop apps quickly
– Inter operate with modern applications, especially mobile
– Applications address schema migrations
– Sometimes referred to a “flexible schema” development
While fixed schema is required for traditional
relational
– Transaction volumes growing
– Operational data, analytics, static records
– Creates greater value through “360 degree” view of the
business
Demand is growing in both areas
These approaches are
complementary
NoSQL Compared to “Traditional” DBMS
5. 5
Real Time Analytics
•
Customer Issues
– Several different models of data (SQL,
NoSQL, TimeSeries/Sensor)
– NoSQL is not strong building relations
between collections
– Most valuable analytics combine the results of
all data models
– Most prominent analytic system written using
standard SQL
7. 7
IBM Informix database 12.1
Relational, Embeddable
Real-time Analytics
Fast, Always-on Transactions
NoSQL capability
Multi-Tenancy
Sensor data management
High availability
Easy to Use
8. 8
Tier 2
Smart Gateway / Aggregator
Tier 3
Sensor Operational
Analytics Zone
Tier 1
Devices/Sensors
AnalyticsTier 4
Deep Analytics Zone
Simple
Customer
Portal
REST (https)
Time Series
REST (https)
MAC Addr. MAC Addr. MAC Addr. MAC Addr. MAC Addr. MAC Addr.
Informix TS IWA
Streams
Logic
dist.
engine
Cloudant
MessageSight
Lightweight
Analytics
Context
The IoT Architecture
IBM has opportunity in multiple Tiers of IoT
9. 9
•
Relational and non-relational data in one system
•
NoSQL/MongoDB Apps can access Informix Relational Tables
•
Distributed Queries
•
Multi-statement Transactions
•
Enterprise Proven Reliability
•
Enterprise Scalability
•
Enterprise Level Availability
Informix provides the capability to leverage
the abilities of both relational DBMS and document store
systems.
The Hybrid Solution
Informix has the Best of Both Worlds
10. 10
Informix 12.1 & MongoDB Clients
•
New Wire Protocol Listener supports
existing MongoDB drivers
•
Connect to MongoDB or Informix with
same application!
MongoDB
native Client
MongoDB
web browser
Mobile
Applications
MongoDB
Wire
Protocol
Informix
12.1
MongoDB
driver
11. 11
Informix JSON Store Benefits
•
Row locking on the individual JSON document
•
Large documents, up to 2GB maximum size
•
Ability to compress documents
•
Ability to intelligently cache commonly used
documents
•
Use existing storage options and management tools.
12. 12
Two New Data Types JSON and BSON
•
Native JSON and BSON data types
•
Index support for NoSQL data types
•
Native operators and comparator
functions allow for direct
manipulation of the BSON data type
•
Database Server seamlessly
converts to and from
• JSON BSON
• Character data JSON
13. 13
Informix: All Together Now!
SQL Tables
JSON Collections
TimeSeries
MQ Series
SQL APIs
JDBC, ODBC
Informix
IWA – BLU ACCELERATION
GENBSON: SQL to {BSON}
MongoDB
Drivers
TEXT SEARCH
SPATIAL
TIME SERIES {BSON}
REST API
14. 14
RelationalTable JSON Timeseries SpatialText
SQL API
StandardODBC, JDBC,.NET, OData,etc.Language SQL.
Direct SQLAccess.Dynamic ViewsRow types
StandardSQL/extJDBC/ODBCJSON Support
Standard SQLJDBC/ODBCJSON Support
Mongo API(NoSQL)
Mongo APIs forJava,
Javascript, C++, C#,...
Mongo APIs forJava,Javascript, C++, C#,...
Virtual TableJSON support JSON Support
Hybrid Access:SQL, JSON, Timeseries &
Spatial
15. 15
Benefits of Hybrid Power
Access consistent data from its source
Avoid ETL, continuous data sync and conflicts.
Exploit the power of SQL, MongoAPI seamlessly
Exploit the power of RDBMS technologies in MongoAPI:
– Informix Warehouse accelerator,
– Cost based Optimizer & power of SQL
– R-tree indices for spatial, Lucene text indexes, and more.
Access all your data thru any interface: MongoAPI & SQL
Store data in one place and efficiently transform and use them
on demand.
Existing SQL based tools and APIs can access new data in
JSON
16. 16
How to Convert Relational Data as JSON Documents
•
Relational data can be treated as structured JSON documents; column
name-value becomes key-value pair.
•
select partner, pnum, country from partners;
partner pnum Country
Acme 1748 Australia
Vernco 1746 USA
Baker 1472 Spain
Contrex 1742 France
{parnter: “Acme”, pnum:1748, Country: “Australia”}
{parnter: “Vernco”, pnum:1746, Country: “USA”}
{parnter: “Backer”, pnum:1472, Country: “Spain”}
{parnter: “Contrex”, pnum:1742, Country: “France”}
•
GENBSON function
Method for transforming existing SQL data into a JSON or BSON
document store format
•
select GENBSON( partners )::JSON from partners;
16
17. 17
•
Supports B-Tree indexes on any key-value pairs.
•
Typed indices could be on simple basic type (int, decimal,)
•
Type-less indices could be created on BSON and use BSON type comparison
•
Informix translates ensureIndex() to CREATE INDEX
•
Informix translates dropIndex() to DROP INDEX
Indexing
Mongo Operation SQL Operation
db.customers.ensureIndex(
{orderDate:1, zip:-1})
CREATE INDEX IF NOT EXISTS v_customer_2 ONcustomer (bson_get(data,‘orderDate') ASC,bson_get(data,‘zip') DESC) USING BSON
CREATE UNIQUE INDEX IF NOT EXISTS
v_customer_3 ON customer
(bson_get(data,'orderDate')ASC) USING BSON
18. Flexible Grid + Sharding
Informix
Shard 1
Informix/1
Secondary
Disk or Diskless
Informix/1
Secondary
Disk or Diskless
Informix
Secondary 1
Disk or Diskless
Informix
Shard 2
Informix/1
Secondary
Disk or Diskless
Informix/1
Secondary
Disk or Diskless
Informix
Secondary 2
Disk or Diskless
Informix
Shard 3
Informix/1
Secondary
Disk or Diskless
Informix/1
Secondary
Disk or Diskless
Informix
Secondary 3
Disk or Diskless
Informix
Shard 4
Informix/1
Secondary
Disk or Diskless
Informix/1
Secondary
Disk or Diskless
Informix
Secondary 4
Disk or Diskless
Informix NoSQL Cluster Architecture Overview
Scaling in both directions
Shard Disk Secondary
Secondary server(s) provide HA and scaling
Allow write on Secondary
19. 19
Mongo Application
IBM Wire Listener
IDXs
Logs
Enterprise replication + Flexible Grid + Sharding
Distributed
Queries
Informix Dynamic Server
Tables
Tables
IDXs
Relational
Tables
JSON
Collections
SELECT bson_new(bson, ‘{}’) FROM customer WHERE
data.state::varchar(128) =“MO”
db.customer.find({state:”MO”}) db.partners.find({state:”CA”})
SELECT * FROM partners WHERE state=“CA”
Customer
partners
JSON JSON
Access RelationalAccess JSON
MongoAPI Accessing Both NoSQL and Relational Tables
20. 20
MongoAPI Accessing Both NoSQL and Relational Tables
•
Typically NoSQL does not involve transactions
– In many cases, a document update is atomic, but not the application
statement (Example :7 targeted for deletion, but only 4 are removed )
•
Informix-NoSQL provides transactions on all application statements
– Each server operation INSERT, UPDATE, DELETE, SELECT will
automatically be committed after each operation.
•
Default isolation level is DIRTY READ
•
All standard isolation level support
•
$sql operator – execute SQL commands within Informix database
db.getCollection("system.sql").find({ "$sql":
"select c.customer_num, p.customer_num as p_cust from customer c left
outer join partners p on c.customer_num = p.customer_num order by 1" })
23. 23
You can use IWA's In-Memory Analytics to Speed Up queries on…
Local or remote views
HA Clusters
24. IWA Overview and Seamless Integration with
Informix/IDS
• Before IWA…
Informix
Receives analytic query from client
Spends some time doing intensive I/O
Returns results back to the client
Informix 12.1
Results
SQL
25. Informix/IWA Setup and Workflow
• Using IWA: Process is transparent to Informix client
Results
SQL
Informix
Receives analytic query from client
If query uses data matching an IWA datamart
and can be accelerated, route/offload it to IWA
Returns results back to the client
If query is not based on an IWA datamart or
cannot be accelerated, Informix will resolve it
Informix 12.1
The Accelerator
Processes the routed SQL query extremely
fast and returns answer back to Informix
Linux on
Intel/AMD 64-bit
Bulk Loader Compressed
Database
Partition
TCP/IP
Compression
In-Memory Columnar Storage
Frequency Partitioning
Parallelism
Predicate evaluation on compressed data
Multi-core and Vector optimized algorithms
SIMD
Query Router
Query Processor
26. 26
Informix Dynamic Server
Tables
Tables
Relational
Tables and
views
JSON
Collections {Customer}
partners
SQL & BI Applications
{Orders}
Inventory
Tables
Timeseries
Tables
{Orders}
Text index (BTS)
spatial indices
Informix Warehouse Accelerator (In-Memory Query Engine)
ODBC, JDBC connections
SQL Apps/Tools
MongoDB Drivers
NoSQL Apps/Tools
27. 27
Informix Database Server
Informix warehouse Accelerator
BI Applications
Step 1. Install, configure,
start Informix
Step 2. Install, configure,
start Accelerator
Step 3. Connect Studio to
Informix & add accelerator
Step 4. Design, validate,
Deploy Data mart
Step 5. Load data to
accelerator
Ready for Queries
IBM Smart
Analytics Studio
Step 1
Step 2
Step 3
Step 4
Step 5
Ready
IWA – BLU ACCELERATION
28. 28
All NOSQL Marts (all views based on JSON collections )
Hybrid Marts (subset of views based on JSON collections)
TPCDS 10GB workload
web_returns fact table along with 13 dimension tables
Total Memory on the machine: 250GB
Total nodes: 5
Coordinator nodes: 1
Coordinator node Memory: 20000 MB
Worker nodes : 4
Worker nodes memory : 100000 MB
IWA DRDA Interface: eth1 (IWA running on a different machine
than IDS server)
JSON Data Acceleration
29. 29
Using genbson to create web_returns, we can literally accomplish this
with just two statements and with good performance
create table json_web_returns_coll(c1 serial, c2 bson);
insert into json_web_returns_coll select 0, genbson( web_returns_ext ) from
web_returns_ext;
719964 row(s) inserted.
create table json_customer_address_coll(c1 serial, c2 bson);
insert into json_customer_address_coll select 0, genbson( customer_address_ext )
from customer_address_ext;
250000 row(s) inserted.
create table json_date_dim_coll(c1 serial, c2 bson);
insert into json_date_dim_coll select 0, genbson( date_dim_ext ) from date_dim_ext;
73049 row(s) inserted.
create table json_time_dim_coll(c1 serial, c2 bson);
insert into json_time_dim_coll select 0, genbson( time_dim_ext ) from time_dim_ext;
86400 row(s) inserted.
JSON Data Acceleration
33. 33
demo_database –
create view vcomments(uid,pid,comment) AS SELECT
data.uid::INT,
data.pid::INT,
data.comment::VARCHAR(128)
from comments;
create view vusers(uid,name) AS SELECT
data.uid::int,
data.name::varchar(128)
from users;
Create the SQL Views & analyze workload
34. 34
set environment use_dwa 'probe cleanup';
set environment use_dwa 'probe start';
select {+ avoid_execute} * from vcomments c,vusers u
where c.uid=u.uid;
set environment use_dwa 'probe stop';
execute procedure
ifx_probe2mart('demo_database','noSQL_mart');
execute function ifx_createmart('demo_dwa','noSQL_mart');
execute function ifx_loadmart('demo_dwa','noSQL_mart','NONE');
Create the SQL Views & analyze workload
35. 35
set environment use_dwa 'accelerate on';
select c.uid,name,comment from vcomments c,vusers u
where c.uid=u.uid and pid=444;
uid 12345
name john
comment first
uid 99999
name mia
comment third
Deploy NoSQL data mart & Issue queries
Informix (left side):
Object-Relational Database for OLTP & OLAP
Provides extreme performance for transactions
Best database for Time-stamped (sensor) data
Best in market Cluster / HA and Grids / Data Replication technology
Support on Cloud and Virtual environments
Hybrid SQL and NoSQL database - Big-Data and IOT ready platform
Easy to use and administer (GUI, commands, SQL functions)
Enterprise-class Autonomics and Embeddable Database
IWA (right side):
In-Memory compressed parallel columnar Database software
Combines multiple state-of-the-art IMBD technologies for OLAP speed
Plugs-in to an Informix database server via TCP/IP
Leverages existing Informix database environment and schema
Keeps an in-memory columnar copy of Informix data relevant for analytics
Works behind Informix database, tightly integrated, transparent to users
Provides Extreme Performance for I/O intensive and Analytic queries
Uses low-cost commodity hardware and O/S: Linux on Intel x86_64