Are you tired of tedious and long data-to-insights journey, siloed data and unleveraged Data? Would you like existing demographic data help you drive business outcome? Would you like NOT to create any data lake and direct insights on data with pre-fabricated data structure without any efforts?
Exploring the Wider World of Big Data- Vasalis KapsalisNetAppUK
Every second of every day you hear about Electronic systems creating ever increasing quantities of data. Systems in markets such as finance, media, healthcare, government and scientific research feature strongly in the Big Data processing conversation. While extracting business value from Big Data is forecast to bring customer and competitive advantage and benefits. In this session hear Vas Kapsalis, NetApp Big Data Business Development Manager, discuss his views and experience on the wider world of Big Data.
The CSC Big Data Analytics Insights service enables clients who do not have an analytics capability to implement the business, data and technology changes to gain business benefit from an initial set of analytics based on a roadmap of changes created by CSC or provided from a compatible set of inputs.
CSC Analytic Insights Implementation has four phases:
Stage 1: Analytic Engagement
Stage 2: Analytic Discovery
Stage 3: Implementation Planning
Stage 4: Embedding Analysis .
Understand the Big Data ecosystem on the Cloud and the building blocks that help you build application for Data Mining and Visualization. Also learn from Latentview Analytics on how they built “PanelMiner” a Platform That Efficiently Transforms Unstructured HTML Data to Structured Data to gain Insights about consumer behavior from large data sets.
Presenter:
Ganesh Raja, Solution Architect, Amazon Internet Services
Ganesh Sankarlingam, Head of Delivery (US West Coast), LatentView Analytics
Shrirang Bapat, Vice President – Engineering, Pubmatic
Amazon Redshift Update and How Equinox Fitness Clubs Migrated to a Modern Dat...Amazon Web Services
In this session, we provide an update on Amazon Redshift, and look at a case study from Equinox Fitness Clubs. We show you how Amazon Redshift queries data across your data warehouse and data lake, without the need or delay of loading data, to deliver insights you cannot obtain by querying independent data silos. Discover how Equinox Fitness Clubs transitioned from on-premises data warehouses and data marts to a cloud-based, integrated data platform, built on AWS and Amazon Redshift. Learn about their journey from static reports, redundant data, and inefficient data integration to a modern and flexible data lake and data warehouse architecture that delivers dynamic reports based on trusted data.
Driving Business Outcomes with a Modern Data Architecture - Level 100Amazon Web Services
Your business data contains critical information about customer behaviors, operational decisions, and many factors that have financial impact on your organisation. Increasingly though, this data is too big, too fast, and too complex for existing systems to handle. AWS Data and Analytics services are designed to ingest, store, analyse, and consume information at record-breaking scale. In this session you will learn how these services work together to deliver business automation, enhance customer engagement and intelligence.
Speaker: Craig Stires, APAC Business Development - Big Data & Analytics, Amazon Web Services
Exploring the Wider World of Big Data- Vasalis KapsalisNetAppUK
Every second of every day you hear about Electronic systems creating ever increasing quantities of data. Systems in markets such as finance, media, healthcare, government and scientific research feature strongly in the Big Data processing conversation. While extracting business value from Big Data is forecast to bring customer and competitive advantage and benefits. In this session hear Vas Kapsalis, NetApp Big Data Business Development Manager, discuss his views and experience on the wider world of Big Data.
The CSC Big Data Analytics Insights service enables clients who do not have an analytics capability to implement the business, data and technology changes to gain business benefit from an initial set of analytics based on a roadmap of changes created by CSC or provided from a compatible set of inputs.
CSC Analytic Insights Implementation has four phases:
Stage 1: Analytic Engagement
Stage 2: Analytic Discovery
Stage 3: Implementation Planning
Stage 4: Embedding Analysis .
Understand the Big Data ecosystem on the Cloud and the building blocks that help you build application for Data Mining and Visualization. Also learn from Latentview Analytics on how they built “PanelMiner” a Platform That Efficiently Transforms Unstructured HTML Data to Structured Data to gain Insights about consumer behavior from large data sets.
Presenter:
Ganesh Raja, Solution Architect, Amazon Internet Services
Ganesh Sankarlingam, Head of Delivery (US West Coast), LatentView Analytics
Shrirang Bapat, Vice President – Engineering, Pubmatic
Amazon Redshift Update and How Equinox Fitness Clubs Migrated to a Modern Dat...Amazon Web Services
In this session, we provide an update on Amazon Redshift, and look at a case study from Equinox Fitness Clubs. We show you how Amazon Redshift queries data across your data warehouse and data lake, without the need or delay of loading data, to deliver insights you cannot obtain by querying independent data silos. Discover how Equinox Fitness Clubs transitioned from on-premises data warehouses and data marts to a cloud-based, integrated data platform, built on AWS and Amazon Redshift. Learn about their journey from static reports, redundant data, and inefficient data integration to a modern and flexible data lake and data warehouse architecture that delivers dynamic reports based on trusted data.
Driving Business Outcomes with a Modern Data Architecture - Level 100Amazon Web Services
Your business data contains critical information about customer behaviors, operational decisions, and many factors that have financial impact on your organisation. Increasingly though, this data is too big, too fast, and too complex for existing systems to handle. AWS Data and Analytics services are designed to ingest, store, analyse, and consume information at record-breaking scale. In this session you will learn how these services work together to deliver business automation, enhance customer engagement and intelligence.
Speaker: Craig Stires, APAC Business Development - Big Data & Analytics, Amazon Web Services
Webinar: Transforming Customer Experience Through an Always-On Data PlatformDataStax
According to Forrester Research, leaders in customer experience drive 5.1X revenue growth over laggards. And although 84% of companies aspire to be a leader in this space, only 1 in 5 successfully delivers good or great customer experience. Join us for our next webinar where Mike Gualtieri, VP and Principal Analyst at Forrester Research and Rajay Rai, Head of Digital Engineering at Macquarie Bank will share how Customer Experience can drive business results such as faster revenue growth, longer customer retention, greater employee engagement and improved profit margins.
View webinar recording: https://youtu.be/eEc5tx-nHvI
Explore past DataStax webinars: http://www.datastax.com/resources/webinars
Self-Service Data Science for Leveraging ML & AI on All of Your DataMapR Technologies
MapR has launched the MapR Data Science Refinery which leverages a scalable data science notebook with native platform access, superior out-of-the-box security, and access to global event streaming and a multi-model NoSQL database.
Non-interactive big-data analysis prohibits experimentation and can interrupt the analyst’s train of thoughts but analyzing and drawing insights in real time is no easy task with jobs often taking minutes/hours to complete. What if you want to put a interactive interface in front of that data that allows iterative insights? What if you need that interactive experience to be sub second?
Traditional SQL and most MPP/NoSQL databases cannot run complex calculations over large data in a performant manner. Popular distributed systems such as Hadoop or Spark can execute jobs but their job overhead prohibits sub second response times. Learn how an in-memory computing framework enabled us to perform complex analysis jobs on massive data points with sub second response times — allowing us to plug it into a simple, drag-and-drop web 2.0 interface.
Moving to the Cloud: Modernizing Data Architecture in HealthcarePerficient, Inc.
Constant changes in the healthcare industry continue to drive innovation in technology and serve as a catalyst for cloud adoption. This trend will continue to evolve and accelerate in the coming years with the increasing need to store and analyze vast amounts of information for personal and population health initiatives.
We joined guest speaker from HIMSS Analytics, James Gaston, to discuss the impact of the cloud on data architecture in healthcare. Topics included:
-The benefits and risks of moving data and analytics environments to the cloud
-Main healthcare use cases for cloud migration
-Deep dive into two leading healthcare organizations’ cloud journeys including drivers, challenges, benefits, and lessons learned
We are already in the era of ‘connected things’ -- our mobile phones, computers, tablets and wearables sync and talk via ‘cloud’.
IoT presents a lot of challenges, especially data coming in at high volume and high velocity - imagine billions of devices each sending readings every few seconds or minutes. All this data need to be stored and analyzed. Plus we need to think about security, privacy and compliance.
In this talk we will present a reference data architecture for IoT. We draw on the industry’s best practices on Big Data including Lambda architecture patterns. Lambda architecture outlines generic, scalable and fault-tolerant data processing. We will illustrate a potential data pipeline and discuss how each stage might be implemented and technology choices at each stage.
Discover HPE Software
Technology and business are changing at an unprecedented rate.
New ways of doing business from streamlining processes, fasttracking
innovation, and delivering amazing customer experience
all come from the convergence of IT and business strategy. But
you need to be fast to win. At HPE Software we can accelerate
your digital transformation.
Change is at our core. On 7 September, Hewlett Packard
Enterprise announced plans for a spin-off and merger of our
software business unit with Micro Focus, a global software
company dedicated to delivering and supporting enterprise
software solutions. The combination of HPE software assets
with Micro Focus will create one of the world’s largest pure-play
enterprise software companies. We will remain focused on helping
you get the most out of the software that runs your business.
Discover how HPE Software can help you thrive in a world of
digital transformation.
How Starbucks Forecasts Demand at Scale with Facebook Prophet and DatabricksNavin Albert
Performing fine-grained forecasts on day-store-SKU is beyond the ability of legacy, data warehousing based forecasting tools. Demand for products varies by product, store and day, and yet traditional demand forecasting solutions perform their forecasts at the aggregate market, week and promo group levels.
With the introduction of the Databricks Unified Data Analytics Platform, retailers are able to see double-digit improvements in their forecast accuracy. They can perform fine-grained forecasts at the SKU, store and day as well as include hundreds of additional features to improve the accuracy of models. They can further enhance their forecasts with localization and the easy inclusion of additional data sets. And they’re running these forecasts daily, providing their planners and retail operations team with timely data for better execution.
In this webinar, we reviewed:
How to perform fine-grained demand forecasts on a day/store/SKU level with Databricks
How to forecast time series data precisely using Facebook’s Prophet
Also, how Starbucks does custom forecasting with relative ease
How to train a large number of models using the defacto distributed data processing engine, Apache Spark™
Finally, we then presented this data to analysts and managers using BI tools to enable the decision making required to drive the required business outcomes
Achieving Business Value by Fusing Hadoop and Corporate DataInside Analysis
The Briefing Room with Richard Hackathorn and Teradata
Live Webcast March 25, 2015
Watch the Archive: https://bloorgroup.webex.com/bloorgroup/onstage/g.php?MTID=e7254708146d056339a0974f097f569b2
Hadoop data lakes are emerging as peers to corporate data warehouses. However, successful analytic solutions require a fusion of all relevant data, big and small, which has proven challenging for many companies. By allowing business analysts to quickly access data wherever it rests, success factors shift to focus on three key aspects: 1) business objectives, 2) organizational workflow, and 3) data placement.
Register for this Special Edition of The Briefing Room to hear veteran Analyst Richard Hackathorn as he provides details from his recent research report focused on success stories using Teradata QueryGrid. Examples of use cases described will include:
Joining sensor data in Hadoop with data warehouse labor schedules in seconds
How bridging corporate cultures and systems creates new business opportunities
The 360 view of customer journeys using weblogs in Hadoop via BI tools
How can you put the data where you want and query it however you want
Virtualizing Hadoop data with Teradata QueryGrid
Visit InsideAnalysis.com for more information.
Use Cases from Batch to Streaming, MapReduce to Spark, Mainframe to Cloud: To...Precisely
So you built your Hadoop cluster. How do you get data from hundreds of database tables, streaming Kafka sources, and data shared by 20-year-old COBOL programs, all in there and working together quickly, efficiently and securely? With many customers asking this same question, Hortonworks recently expanded its partnership with Syncsort to provide optimized ETL onboarding for Hadoop. During this talk, we'll discuss how a next-generation ETL tool, built on contributions to the open source community and natively integrated in Hadoop, can drive lasting value for your organization. 1) Seamlessly onboard data from all your enterprise sources – batch and streaming -- into Hadoop for fast and easy analytics. 2) Stay agile and simplify your environment with a "design once, deploy anywhere" approach that minimizes disruption and risk in the face of a rapidly evolving big data ecosystem. 3) Secure, govern and manage your data with full integration with Apache Ambari, Apache Ranger, and more. These benefits come to life with real customer case studies. Learn how a national insurance company and global hotel chain are using Hortonworks HDP and Syncsort DMX-h to get bigger insights from their enterprise data, securely, efficiently, and cost-effectively, without spending hundreds of man-hours.
Lightning-Fast, Interactive Business Intelligence Performance with MicroStrat...Tyler Wishnoff
See how extreme query speeds and ultra-high concurrency on MicroStrategy, and any other business intelligence (BI) tool, on Big Data is possible through the Kyligence platform. Learn more here: https://kyligence.io/
Transforming GE Healthcare with Data Platform StrategyDatabricks
Data and Analytics is foundational to the success of GE Healthcare’s digital transformation and market competitiveness. This use case focuses on a heavy platform transformation that GE Healthcare drove in the last year to move from an On prem legacy data platforming strategy to a cloud native and completely services oriented strategy. This was a huge effort for an 18Bn company and executed in the middle of the pandemic. It enables GE Healthcare to leap frog in the enterprise data analytics strategy.
Build Next Generation Real-time Applications with SAP HANA on AWS (BDT211) | ...Amazon Web Services
"(Presented by SAP) SAP HANA, available on the AWS Cloud, is an industry transforming in-memory platform, which has been adopted by many startups and ISVs, as well as traditional SAP enterprise customers. SAP HANA converges database and application platform capabilities in-memory to transform transactions, analytics, text analysis, predictive, and spatial processing so businesses can operate in real-time. Please join us to learn what SAP HANA can do for you!
Doug Turner, CEO of Mantis Technologies, and an early adopter of SAP HANA One on AWS, will present and share his experience migrating his Sentiment Analysis solution from MySQL to SAP HANA One. He will talk about following benefits that he achieved with this migration:
-Dramatic simplification of his system architecture and landscape
-System consolidation by moving from 23 MySQL instances to one SAP HANA One instance
-Reduced overall AWS infrastructure cost as well as reduced admin effort and efficiency
We will conclude with an overview of all the key SAP HANA capabilities on the AWS Cloud like text analysis, predictive analytics, geospatial, data integration. We will round out the session with an in-depth view of what new HANA deployment options are available on the AWS Cloud like customers’ ability to bring their own licenses (BYOL) of SAP HANA to run on AWS in a variety of configurations ranging from 244GB up to 1.22TB. "
How to Power Innovation with Geo-Distributed Data Management in Hybrid CloudDataStax
Most enterprises understand the value of hybrid cloud. In fact, your enterprise is already working in a multi-cloud or hybrid cloud environment, whether you know it or not. View this SlideShare to gain a greater understanding of the requirements of a geo-distributed cloud database in hybrid and multi-cloud environments.
View recording: https://youtu.be/tHukS-p6lUI
Explore all DataStax webinars: https://www.datastax.com/resources/webinars
Deep Learning Image Processing Applications in the EnterpriseGanesan Narayanasamy
The presentation has many use cases covering the following Image classification: "The process of identifying and detecting an object or a feature in a digital image or video," the report states. In retail, deep learning models "quickly scan and analyze in-store imagery to intuitively determine inventory movement."
Voice recognition: "The ability to receive and interpret dictation or to understand and carry out spoken commands. Models are able to convert captured voice commands to text and then use natural language processing to understand what is being said and in what context." In transportation, deep learning "uses voice commands to enable drivers to make phone calls and adjust internal controls - all without taking their hands off the steering wheel."
Anomaly detection: "Deep learning technique strives to recognize abnormal patterns which don't match the behaviors expected for a particular system, out of millions of different transactions. These applications can lead to the discovery of an attack on financial networks, fraud detection in insurance filings or credit card purchases, even isolating sensor data in industrial facilities signifying a safety issue."
Recommendation engines: "Analyze user actions in order to provide recommendations based on user behavior."
Sentiment analysis: "Leverages deep learning-heavy techniques such as natural language processing, text analysis, and computational linguistics to gain clear insight into customer opinion, understanding of consumer sentiment, and measuring the impact of marketing strategies."
Video analysis: "Process and evaluate vast streams of video footage for a range of tasks including threat detection, which can be used in airport security, banks, and sporting events."
Using AWS to design and build your data architecture has never been easier to gain insights and uncover new opportunities to scale and grow your business. Join this workshop to learn how you can gain insights at scale with the right big data applications.
Using AWS to design and build your data architecture has never been easier to gain insights and uncover new opportunities to scale and grow your business. Join this workshop to learn how you can gain insights at scale with the right big data applications.
Webinar: Transforming Customer Experience Through an Always-On Data PlatformDataStax
According to Forrester Research, leaders in customer experience drive 5.1X revenue growth over laggards. And although 84% of companies aspire to be a leader in this space, only 1 in 5 successfully delivers good or great customer experience. Join us for our next webinar where Mike Gualtieri, VP and Principal Analyst at Forrester Research and Rajay Rai, Head of Digital Engineering at Macquarie Bank will share how Customer Experience can drive business results such as faster revenue growth, longer customer retention, greater employee engagement and improved profit margins.
View webinar recording: https://youtu.be/eEc5tx-nHvI
Explore past DataStax webinars: http://www.datastax.com/resources/webinars
Self-Service Data Science for Leveraging ML & AI on All of Your DataMapR Technologies
MapR has launched the MapR Data Science Refinery which leverages a scalable data science notebook with native platform access, superior out-of-the-box security, and access to global event streaming and a multi-model NoSQL database.
Non-interactive big-data analysis prohibits experimentation and can interrupt the analyst’s train of thoughts but analyzing and drawing insights in real time is no easy task with jobs often taking minutes/hours to complete. What if you want to put a interactive interface in front of that data that allows iterative insights? What if you need that interactive experience to be sub second?
Traditional SQL and most MPP/NoSQL databases cannot run complex calculations over large data in a performant manner. Popular distributed systems such as Hadoop or Spark can execute jobs but their job overhead prohibits sub second response times. Learn how an in-memory computing framework enabled us to perform complex analysis jobs on massive data points with sub second response times — allowing us to plug it into a simple, drag-and-drop web 2.0 interface.
Moving to the Cloud: Modernizing Data Architecture in HealthcarePerficient, Inc.
Constant changes in the healthcare industry continue to drive innovation in technology and serve as a catalyst for cloud adoption. This trend will continue to evolve and accelerate in the coming years with the increasing need to store and analyze vast amounts of information for personal and population health initiatives.
We joined guest speaker from HIMSS Analytics, James Gaston, to discuss the impact of the cloud on data architecture in healthcare. Topics included:
-The benefits and risks of moving data and analytics environments to the cloud
-Main healthcare use cases for cloud migration
-Deep dive into two leading healthcare organizations’ cloud journeys including drivers, challenges, benefits, and lessons learned
We are already in the era of ‘connected things’ -- our mobile phones, computers, tablets and wearables sync and talk via ‘cloud’.
IoT presents a lot of challenges, especially data coming in at high volume and high velocity - imagine billions of devices each sending readings every few seconds or minutes. All this data need to be stored and analyzed. Plus we need to think about security, privacy and compliance.
In this talk we will present a reference data architecture for IoT. We draw on the industry’s best practices on Big Data including Lambda architecture patterns. Lambda architecture outlines generic, scalable and fault-tolerant data processing. We will illustrate a potential data pipeline and discuss how each stage might be implemented and technology choices at each stage.
Discover HPE Software
Technology and business are changing at an unprecedented rate.
New ways of doing business from streamlining processes, fasttracking
innovation, and delivering amazing customer experience
all come from the convergence of IT and business strategy. But
you need to be fast to win. At HPE Software we can accelerate
your digital transformation.
Change is at our core. On 7 September, Hewlett Packard
Enterprise announced plans for a spin-off and merger of our
software business unit with Micro Focus, a global software
company dedicated to delivering and supporting enterprise
software solutions. The combination of HPE software assets
with Micro Focus will create one of the world’s largest pure-play
enterprise software companies. We will remain focused on helping
you get the most out of the software that runs your business.
Discover how HPE Software can help you thrive in a world of
digital transformation.
How Starbucks Forecasts Demand at Scale with Facebook Prophet and DatabricksNavin Albert
Performing fine-grained forecasts on day-store-SKU is beyond the ability of legacy, data warehousing based forecasting tools. Demand for products varies by product, store and day, and yet traditional demand forecasting solutions perform their forecasts at the aggregate market, week and promo group levels.
With the introduction of the Databricks Unified Data Analytics Platform, retailers are able to see double-digit improvements in their forecast accuracy. They can perform fine-grained forecasts at the SKU, store and day as well as include hundreds of additional features to improve the accuracy of models. They can further enhance their forecasts with localization and the easy inclusion of additional data sets. And they’re running these forecasts daily, providing their planners and retail operations team with timely data for better execution.
In this webinar, we reviewed:
How to perform fine-grained demand forecasts on a day/store/SKU level with Databricks
How to forecast time series data precisely using Facebook’s Prophet
Also, how Starbucks does custom forecasting with relative ease
How to train a large number of models using the defacto distributed data processing engine, Apache Spark™
Finally, we then presented this data to analysts and managers using BI tools to enable the decision making required to drive the required business outcomes
Achieving Business Value by Fusing Hadoop and Corporate DataInside Analysis
The Briefing Room with Richard Hackathorn and Teradata
Live Webcast March 25, 2015
Watch the Archive: https://bloorgroup.webex.com/bloorgroup/onstage/g.php?MTID=e7254708146d056339a0974f097f569b2
Hadoop data lakes are emerging as peers to corporate data warehouses. However, successful analytic solutions require a fusion of all relevant data, big and small, which has proven challenging for many companies. By allowing business analysts to quickly access data wherever it rests, success factors shift to focus on three key aspects: 1) business objectives, 2) organizational workflow, and 3) data placement.
Register for this Special Edition of The Briefing Room to hear veteran Analyst Richard Hackathorn as he provides details from his recent research report focused on success stories using Teradata QueryGrid. Examples of use cases described will include:
Joining sensor data in Hadoop with data warehouse labor schedules in seconds
How bridging corporate cultures and systems creates new business opportunities
The 360 view of customer journeys using weblogs in Hadoop via BI tools
How can you put the data where you want and query it however you want
Virtualizing Hadoop data with Teradata QueryGrid
Visit InsideAnalysis.com for more information.
Use Cases from Batch to Streaming, MapReduce to Spark, Mainframe to Cloud: To...Precisely
So you built your Hadoop cluster. How do you get data from hundreds of database tables, streaming Kafka sources, and data shared by 20-year-old COBOL programs, all in there and working together quickly, efficiently and securely? With many customers asking this same question, Hortonworks recently expanded its partnership with Syncsort to provide optimized ETL onboarding for Hadoop. During this talk, we'll discuss how a next-generation ETL tool, built on contributions to the open source community and natively integrated in Hadoop, can drive lasting value for your organization. 1) Seamlessly onboard data from all your enterprise sources – batch and streaming -- into Hadoop for fast and easy analytics. 2) Stay agile and simplify your environment with a "design once, deploy anywhere" approach that minimizes disruption and risk in the face of a rapidly evolving big data ecosystem. 3) Secure, govern and manage your data with full integration with Apache Ambari, Apache Ranger, and more. These benefits come to life with real customer case studies. Learn how a national insurance company and global hotel chain are using Hortonworks HDP and Syncsort DMX-h to get bigger insights from their enterprise data, securely, efficiently, and cost-effectively, without spending hundreds of man-hours.
Lightning-Fast, Interactive Business Intelligence Performance with MicroStrat...Tyler Wishnoff
See how extreme query speeds and ultra-high concurrency on MicroStrategy, and any other business intelligence (BI) tool, on Big Data is possible through the Kyligence platform. Learn more here: https://kyligence.io/
Transforming GE Healthcare with Data Platform StrategyDatabricks
Data and Analytics is foundational to the success of GE Healthcare’s digital transformation and market competitiveness. This use case focuses on a heavy platform transformation that GE Healthcare drove in the last year to move from an On prem legacy data platforming strategy to a cloud native and completely services oriented strategy. This was a huge effort for an 18Bn company and executed in the middle of the pandemic. It enables GE Healthcare to leap frog in the enterprise data analytics strategy.
Build Next Generation Real-time Applications with SAP HANA on AWS (BDT211) | ...Amazon Web Services
"(Presented by SAP) SAP HANA, available on the AWS Cloud, is an industry transforming in-memory platform, which has been adopted by many startups and ISVs, as well as traditional SAP enterprise customers. SAP HANA converges database and application platform capabilities in-memory to transform transactions, analytics, text analysis, predictive, and spatial processing so businesses can operate in real-time. Please join us to learn what SAP HANA can do for you!
Doug Turner, CEO of Mantis Technologies, and an early adopter of SAP HANA One on AWS, will present and share his experience migrating his Sentiment Analysis solution from MySQL to SAP HANA One. He will talk about following benefits that he achieved with this migration:
-Dramatic simplification of his system architecture and landscape
-System consolidation by moving from 23 MySQL instances to one SAP HANA One instance
-Reduced overall AWS infrastructure cost as well as reduced admin effort and efficiency
We will conclude with an overview of all the key SAP HANA capabilities on the AWS Cloud like text analysis, predictive analytics, geospatial, data integration. We will round out the session with an in-depth view of what new HANA deployment options are available on the AWS Cloud like customers’ ability to bring their own licenses (BYOL) of SAP HANA to run on AWS in a variety of configurations ranging from 244GB up to 1.22TB. "
How to Power Innovation with Geo-Distributed Data Management in Hybrid CloudDataStax
Most enterprises understand the value of hybrid cloud. In fact, your enterprise is already working in a multi-cloud or hybrid cloud environment, whether you know it or not. View this SlideShare to gain a greater understanding of the requirements of a geo-distributed cloud database in hybrid and multi-cloud environments.
View recording: https://youtu.be/tHukS-p6lUI
Explore all DataStax webinars: https://www.datastax.com/resources/webinars
Deep Learning Image Processing Applications in the EnterpriseGanesan Narayanasamy
The presentation has many use cases covering the following Image classification: "The process of identifying and detecting an object or a feature in a digital image or video," the report states. In retail, deep learning models "quickly scan and analyze in-store imagery to intuitively determine inventory movement."
Voice recognition: "The ability to receive and interpret dictation or to understand and carry out spoken commands. Models are able to convert captured voice commands to text and then use natural language processing to understand what is being said and in what context." In transportation, deep learning "uses voice commands to enable drivers to make phone calls and adjust internal controls - all without taking their hands off the steering wheel."
Anomaly detection: "Deep learning technique strives to recognize abnormal patterns which don't match the behaviors expected for a particular system, out of millions of different transactions. These applications can lead to the discovery of an attack on financial networks, fraud detection in insurance filings or credit card purchases, even isolating sensor data in industrial facilities signifying a safety issue."
Recommendation engines: "Analyze user actions in order to provide recommendations based on user behavior."
Sentiment analysis: "Leverages deep learning-heavy techniques such as natural language processing, text analysis, and computational linguistics to gain clear insight into customer opinion, understanding of consumer sentiment, and measuring the impact of marketing strategies."
Video analysis: "Process and evaluate vast streams of video footage for a range of tasks including threat detection, which can be used in airport security, banks, and sporting events."
Using AWS to design and build your data architecture has never been easier to gain insights and uncover new opportunities to scale and grow your business. Join this workshop to learn how you can gain insights at scale with the right big data applications.
Using AWS to design and build your data architecture has never been easier to gain insights and uncover new opportunities to scale and grow your business. Join this workshop to learn how you can gain insights at scale with the right big data applications.
It is a fascinating, explosive time for enterprise analytics.
It is from the position of analytics leadership that the mission will be executed and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data, and projects that will deliver analytics. After all, no matter what business you’re in, you’re in the business of analytics.
The coming years will be full of big changes in enterprise analytics and Data Architecture. William will kick off the fourth year of the Advanced Analytics series with a discussion of the trends winning organizations should build into their plans, expectations, vision, and awareness now.
This overview presentation discusses big data challenges and provides an overview of the AWS Big Data Platform by covering:
- How AWS customers leverage the platform to manage massive volumes of data from a variety of sources while containing costs.
- Reference architectures for popular use cases, including, connected devices (IoT), log streaming, real-time intelligence, and analytics.
- The AWS big data portfolio of services, including, Amazon S3, Kinesis, DynamoDB, Elastic MapReduce (EMR), and Redshift.
- The latest relational database engine, Amazon Aurora— a MySQL-compatible, highly-available relational database engine, which provides up to five times better performance than MySQL at one-tenth the cost of a commercial database.
Created by: Rahul Pathak,
Sr. Manager of Software Development
This session provides an introduction to the AWS platform and services. It explains how you can get started on your cloud journey and what resources you can use build sophisticated applications with increased flexibility, scalability and reliability. The session also covers the benefits customers are enjoying by moving to AWS cloud; increased agility, faster decision making and the ability to fail fast and innovate.
Businesses are generating more data than ever before.
Doing real time data analytics requires IT infrastructure that often needs to be scaled up quickly and running an on-premise environment in this setting has its limitations.
Organisations often require a massive amount of IT resources to analyse their data and the upfront capital cost can deter them from embarking on these projects.
What’s needed is scalable, agile and secure cloud-based infrastructure at the lowest possible cost so they can spin up servers that support their data analysis projects exactly when they are required. This infrastructure must enable them to create proof-of-concepts quickly and cheaply – to fail fast and move on.
Modern Data Architectures for Business Insights at Scale Amazon Web Services
Using AWS to design and build your data architecture has never been easier to gain insights and uncover new opportunities to scale and grow your business. Join this workshop to learn how you can gain insights at scale with the right big data applications.
Building a real-time analytics solution has never been faster or more cost-efficient. Most organizations are trying to find a way to improve customer experience and respond to business events in real time. Importantly, to do this quickly and at a fraction of the price of traditional approaches. In this session we will look at how to use the AWS services to best meet your real-time analytics needs.
When and How Data Lakes Fit into a Modern Data ArchitectureDATAVERSITY
Whether to take data ingestion cycles off the ETL tool and the data warehouse or to facilitate competitive Data Science and building algorithms in the organization, the data lake – a place for unmodeled and vast data – will be provisioned widely in 2020.
Though it doesn’t have to be complicated, the data lake has a few key design points that are critical, and it does need to follow some principles for success. Avoid building the data swamp, but not the data lake! The tool ecosystem is building up around the data lake and soon many will have a robust lake and data warehouse. We will discuss policy to keep them straight, send data to its best platform, and keep users’ confidence up in their data platforms.
Data lakes will be built in cloud object storage. We’ll discuss the options there as well.
Get this data point for your data lake journey.
Accelerate Self-Service Analytics with Data Virtualization and VisualizationDenodo
Watch full webinar here: https://bit.ly/39AhUB7
Enterprise organizations are shifting to self-service analytics as business users need real-time access to holistic and consistent views of data regardless of its location, source or type for arriving at critical decisions.
Data Virtualization and Data Visualization work together through a universal semantic layer. Learn how they enable self-service data discovery and improve performance of your reports and dashboards.
In this session, you will learn:
- Challenges faced by business users
- How data virtualization enables self-service analytics
- Use case and lessons from customer success
- Overview of the highlight features in Tableau
AWS Summit Berlin 2013 - Big Data AnalyticsAWS Germany
Learn more about the tools, techniques and technologies for working productively with data at any scale. This session will introduce the family of data analytics tools on AWS which you can use to collect, compute and collaborate around data, from gigabytes to petabytes. We'll discuss Amazon Elastic MapReduce, Hadoop, structured and unstructured data, and the EC2 instance types which enable high performance analytics.
Cloud Service Provider in India | Cloud Solution and ConsultingKAMLESHKUMAR471
The innovative technologies will make businesses to re-think about their strategies and infrastructure. Cloud providers also need to re-invent their mindsets. While the businesses move towards agility and versatility, efficiency will be an achievement. With the support of a cloud services provider like Teleglobal International , stay updated and ahead of your competitors in the market with a cloud
FSI201 FINRA’s Managed Data Lake – Next Gen Analytics in the CloudAmazon Web Services
FINRA’s Data Lake unlocks the value in its data to accelerate analytics and machine learning at scale. FINRA's Technology group has changed its customer's relationship with data by creating a Managed Data Lake that enables discovery on Petabytes of capital markets data, while saving time and money over traditional analytics solutions. FINRA’s Managed Data Lake includes a centralized data catalog and separates storage from compute, allowing users to query from petabytes of data in seconds. Learn how FINRA uses Spot instances and services such as Amazon S3, Amazon EMR, Amazon Redshift, and AWS Lambda to provide the 'right tool for the right job' at each step in the data processing pipeline. All of this is done while meeting FINRA’s security and compliance responsibilities as a financial regulator.
Real life use cases from across Europe (Walid Aoudi - Cognizant)
This presentation will present some Cognizant Big Data clients return on experiences on continental Europe and UK. The main focus will be centered on use cases through the presentation of the business drivers behind these projects. Key highlights around the big data architecture and approach solutions will be presented. Finally, the business outcomes in terms of ROI provided by the solutions implementations will be discussed.
Similar to MongoDB World 2019: re:Innovate from Siloed to Deep Insights on Your Data (20)
MongoDB SoCal 2020: Migrate Anything* to MongoDB AtlasMongoDB
During this talk we'll navigate through a customer's journey as they migrate an existing MongoDB deployment to MongoDB Atlas. While the migration itself can be as simple as a few clicks, the prep/post effort requires due diligence to ensure a smooth transfer. We'll cover these steps in detail and provide best practices. In addition, we’ll provide an overview of what to consider when migrating other cloud data stores, traditional databases and MongoDB imitations to MongoDB Atlas.
MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!MongoDB
These days, everyone is expected to be a data analyst. But with so much data available, how can you make sense of it and be sure you're making the best decisions? One great approach is to use data visualizations. In this session, we take a complex dataset and show how the breadth of capabilities in MongoDB Charts can help you turn bits and bytes into insights.
MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...MongoDB
MongoDB Kubernetes operator and MongoDB Open Service Broker are ready for production operations. Learn about how MongoDB can be used with the most popular container orchestration platform, Kubernetes, and bring self-service, persistent storage to your containerized applications. A demo will show you how easy it is to enable MongoDB clusters as an External Service using the Open Service Broker API for MongoDB
MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDBMongoDB
Are you new to schema design for MongoDB, or are you looking for a more complete or agile process than what you are following currently? In this talk, we will guide you through the phases of a flexible methodology that you can apply to projects ranging from small to large with very demanding requirements.
MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...MongoDB
Humana, like many companies, is tackling the challenge of creating real-time insights from data that is diverse and rapidly changing. This is our journey of how we used MongoDB to combined traditional batch approaches with streaming technologies to provide continues alerting capabilities from real-time data streams.
MongoDB SoCal 2020: Best Practices for Working with IoT and Time-series DataMongoDB
Time series data is increasingly at the heart of modern applications - think IoT, stock trading, clickstreams, social media, and more. With the move from batch to real time systems, the efficient capture and analysis of time series data can enable organizations to better detect and respond to events ahead of their competitors or to improve operational efficiency to reduce cost and risk. Working with time series data is often different from regular application data, and there are best practices you should observe.
This talk covers:
Common components of an IoT solution
The challenges involved with managing time-series data in IoT applications
Different schema designs, and how these affect memory and disk utilization – two critical factors in application performance.
How to query, analyze and present IoT time-series data using MongoDB Compass and MongoDB Charts
At the end of the session, you will have a better understanding of key best practices in managing IoT time-series data with MongoDB.
Join this talk and test session with a MongoDB Developer Advocate where you'll go over the setup, configuration, and deployment of an Atlas environment. Create a service that you can take back in a production-ready state and prepare to unleash your inner genius.
MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]MongoDB
Our clients have unique use cases and data patterns that mandate the choice of a particular strategy. To implement these strategies, it is mandatory that we unlearn a lot of relational concepts while designing and rapidly developing efficient applications on NoSQL. In this session, we will talk about some of our client use cases, the strategies we have adopted, and the features of MongoDB that assisted in implementing these strategies.
MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2MongoDB
Encryption is not a new concept to MongoDB. Encryption may occur in-transit (with TLS) and at-rest (with the encrypted storage engine). But MongoDB 4.2 introduces support for Client Side Encryption, ensuring the most sensitive data is encrypted before ever leaving the client application. Even full access to your MongoDB servers is not enough to decrypt this data. And better yet, Client Side Encryption can be enabled at the "flick of a switch".
This session covers using Client Side Encryption in your applications. This includes the necessary setup, how to encrypt data without sacrificing queryability, and what trade-offs to expect.
MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...MongoDB
MongoDB Kubernetes operator is ready for prime-time. Learn about how MongoDB can be used with most popular orchestration platform, Kubernetes, and bring self-service, persistent storage to your containerized applications.
MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!MongoDB
These days, everyone is expected to be a data analyst. But with so much data available, how can you make sense of it and be sure you're making the best decisions? One great approach is to use data visualizations. In this session, we take a complex dataset and show how the breadth of capabilities in MongoDB Charts can help you turn bits and bytes into insights.
MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your MindsetMongoDB
When you need to model data, is your first instinct to start breaking it down into rows and columns? Mine used to be too. When you want to develop apps in a modern, agile way, NoSQL databases can be the best option. Come to this talk to learn how to take advantage of all that NoSQL databases have to offer and discover the benefits of changing your mindset from the legacy, tabular way of modeling data. We’ll compare and contrast the terms and concepts in SQL databases and MongoDB, explain the benefits of using MongoDB compared to SQL databases, and walk through data modeling basics so you feel confident as you begin using MongoDB.
MongoDB .local San Francisco 2020: MongoDB Atlas JumpstartMongoDB
Join this talk and test session with a MongoDB Developer Advocate where you'll go over the setup, configuration, and deployment of an Atlas environment. Create a service that you can take back in a production-ready state and prepare to unleash your inner genius.
MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...MongoDB
Query performance should be the unsung hero of an application, but without proper configuration, can become a constant headache. When used properly, MongoDB provides extremely powerful querying capabilities. In this session, we'll discuss concepts like equality, sort, range, managing query predicates versus sequential predicates, and best practices to building multikey indexes.
MongoDB .local San Francisco 2020: Aggregation Pipeline Power++MongoDB
Aggregation pipeline has been able to power your analysis of data since version 2.2. In 4.2 we added more power and now you can use it for more powerful queries, updates, and outputting your data to existing collections. Come hear how you can do everything with the pipeline, including single-view, ETL, data roll-ups and materialized views.
MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...MongoDB
Are you new to schema design for MongoDB, or are you looking for a more complete or agile process than what you are following currently? In this talk, we will guide you through the phases of a flexible methodology that you can apply to projects ranging from small to large with very demanding requirements.
MongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep DiveMongoDB
MongoDB Atlas Data Lake is a new service offered by MongoDB Atlas. Many organizations store long term, archival data in cost-effective storage like S3, GCP, and Azure Blobs. However, many of them do not have robust systems or tools to effectively utilize large amounts of data to inform decision making. MongoDB Atlas Data Lake is a service allowing organizations to analyze their long-term data to discover a wealth of information about their business.
This session will take a deep dive into the features that are currently available in MongoDB Atlas Data Lake and how they are implemented. In addition, we'll discuss future plans and opportunities and offer ample Q&A time with the engineers on the project.
MongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & GolangMongoDB
Virtual assistants are becoming the new norm when it comes to daily life, with Amazon’s Alexa being the leader in the space. As a developer, not only do you need to make web and mobile compliant applications, but you need to be able to support virtual assistants like Alexa. However, the process isn’t quite the same between the platforms.
How do you handle requests? Where do you store your data and work with it to create meaningful responses with little delay? How much of your code needs to change between platforms?
In this session we’ll see how to design and develop applications known as Skills for Amazon Alexa powered devices using the Go programming language and MongoDB.
MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...MongoDB
aux Core Data, appréciée par des centaines de milliers de développeurs. Apprenez ce qui rend Realm spécial et comment il peut être utilisé pour créer de meilleures applications plus rapidement.
MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...MongoDB
Il n’a jamais été aussi facile de commander en ligne et de se faire livrer en moins de 48h très souvent gratuitement. Cette simplicité d’usage cache un marché complexe de plus de 8000 milliards de $.
La data est bien connu du monde de la Supply Chain (itinéraires, informations sur les marchandises, douanes,…), mais la valeur de ces données opérationnelles reste peu exploitée. En alliant expertise métier et Data Science, Upply redéfinit les fondamentaux de la Supply Chain en proposant à chacun des acteurs de surmonter la volatilité et l’inefficacité du marché.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
MongoDB World 2019: re:Innovate from Siloed to Deep Insights on Your Data
1. Jignesh Desai - Partner Solutions Architect @ aws
re:Innovate from Siloed to Deep Insights on Your Data
jigs_1979 | jigned@amazon.com
2. What to expect
Hear
about the aws
approach to services,
products and solutions
1
Understand
our partners and
analytics strategy;
our portfolio of
various services &
how they work
together
2
Plan
how you would use
the solution by
appreciating how
others use them
3
3. Our strategy & our beliefs
1. There is going to be an explosion in data.
2. Cloud will enable a different architecture.
3. One size does not fit all—innovate with purpose-
built.
2010
5. Analytics
Our portfolio
Broad and deep portfolio, purpose-built for builders
Redshift
Data warehousing
EMR
Hadoop + Spark
Athena
Interactive analytics
Kinesis Data Analytics Real
time
Elasticsearch Service
Operational Analytics
QuickSight SageMaker
S3/Glacier
Glue
ETL & Data Catalog
Lake Formation
Data Lakes
Database Migration Service | Snowball | Snowmobile | Kinesis Data Firehose | Kinesis Data Streams
Data Movement
Business Intelligence & Machine Learning
Data Lake
6. Analytics
Our portfolio
Broad and deep portfolio, purpose-built for builders
QuickSight SageMaker
S3/Glacier
Glue
ETL & Data Catalog
Lake Formation
Data Lakes
Database Migration Service | Snowball | Snowmobile | Kinesis Data Firehose | Kinesis Data Streams
Data Movement
Business Intelligence & Machine Learning
Data Lake
Redshift
Data warehousing
EMR
Hadoop + Spark
Kinesis Data Analytics Real
time
Elasticsearch Service
Operational Analytics
Athena
Interactive analytics
RDS
MySQL, PostgreSQL, MariaDB,
Oracle, SQL Server
Aurora
MySQL, PostgreSQL
DynamoDB
Key value, Document
ElastiCache
Redis, Memcached
Neptune
Graph
Timestream
Time Series
QLDB
Ledger Database
RDS on VMware
Databases
7. Our portfolio
Broad and deep portfolio, purpose-built for builders
Redshift
Data warehousing
EMR
Hadoop + Spark
Athena
Interactive analytics
Kinesis Data Analytics
Real time
Elasticsearch Service
Operational Analytics
RDS
MySQL, PostgreSQL, MariaDB,
Oracle, SQL Server
Aurora
MySQL, PostgreSQL
QuickSight SageMaker
DynamoDB
Key value, Document
ElastiCache
Redis, Memcached
Neptune
Graph
Timestream
Time Series
QLDB
Ledger Database
S3/Glacier
Glue
ETL & Data Catalog
Lake Formation
Data Lakes
Database Migration Service | Snowball | Snowmobile | Kinesis Data Firehose | Kinesis Data Streams
Data Movement
Analytics Databases
Business Intelligence & Machine Learning
Data Lake
Managed
Blockchain
Blockchain
Templates
Blockchain
RDS on VMware
8. Three type of projects
Quickly build new
apps in the cloud
Gain new
insights
“Lift and shift” existing
apps to the cloud
9. Traditionally, analytics looked like this
Relational data
GBs-TBs scale [not designed for PB/EBs]
Expensive: Large initial capex + $10K-$50K/TB/year
90% of data was thrown away because of cost
OLTP ERP CRM LOB
Data Warehouse
Business Intelligence
10. Our beliefs
1. All data has value. No data should be thrown
away.
2. All employees should have access to all data
(subject to company access rules).
2010
11. Snowball
Snowmobile Kinesis
Data Firehose
Kinesis
Data Streams
S3
Redshift
EMR
Athena Kinesis
Elasticsearch Service
Data lakes on AWS
Kinesis
Video Streams
AI Services
QuickSight
Exabyte scale
Store and analyze relational and non-relational data
Purpose-built analytics tools
Cost effective
• Store at 2.3 cents per GB-month in Amazon S3
• Query with Amazon Athena at ½ cent per GB scanned
• DW with Amazon Redshift for $1,000/TB/year
Give access to everyone
• Amazon QuickSight: $0.30 for 30 minutes of use
12. CHALLENGE
Need to create constant feedback loop
for designers.
Gain up-to-the-minute understanding
of gamer satisfaction to guarantee
gamers are engaged, resulting in the
most popular game played in the
world.
Fortnite | 125+ million players
14. MongoDB Atlas takes the innovation to the cloud
Database as a service for MongoDB
• Automated: Create and deploy production ready cluster in minutes. Modify your
cluster with zero downtime
• Scalable and high performance: Full elastic scalability with the performance you need
for your most demanding workloads
• Highly available: Each deployment is geographically distributed, fault-tolerant, and
self-healing by default
• Visibility: Optimized dashboards highlight key historical metrics. View metric in real-
time, customize alerts, or dig into the details with ease
• Continuous Backup: Continuous backup with point-in-time restores and queryable
snapshots to ensure no data loss
• Secured: Authentication, network isolation, encryption, and role-based access
controls keep your data protected
15. Enterprises Data Strategy
15
15
Analytics will play a central role across all business processes to drive outcomes
2
Financial
Services
Retail, CPG &
Logistics
Energy,
Communication
Services
Healthcare &
Lifesciences
Manufacturing
Marketing & Sales Analytics
• Improve Campaign ROI
• Enhance sales volume
Servicing Analytics
• Reduce AHT
• Reduce Employee Attrition
Supply Chain Analytics
• Reduce Safety stock
• Improve Working Capital
Customer Analytics
• Improve NPS/CLTV
• Reduce Churn
Risk and Compliance
• Reduce Fraud
• Real time transaction monitoring
Personalized Experience with
targeted recommendations
Develop the relationship by
understanding Social and
digital behavior & life events
Synthesize external data with
enterprise data to generate
Revenue opportunities
Eg Geo-spatial data(Weather,
News, Commodity, Political)
Optimize servicing cost
(Channel/Self-service)
Improve Supply Chain efficiencies
Smart Factories (IOT based
Automation)
Fraud monitoring
Improving Speed of
compliance
Customer
Intimacy
New Revenue
Models
Risk &
Compliance
Operational
Excellence
Monetize data to drive business outcomes
CHALLENGES : Tedious and cumbersome process across IT, Data Science and Business Users, Long Time to Market, Huge TCO
Product and Process based data fragmentation New Age Data sourcesData Science Lifecycle Management
§ Use case based approach
§ REPETITION OF DATA & ANALYTICS LIFE
CYCLE for every use case
§ Limited standardization & reuse
§ PRODUCT & PROCESS DATA MART across sales, marketing &
operations limit the ability to create a unified view of data in an
accelerated manner
§ PRE-AGGREGATED DATA limits the ability to dynamically create new
behavioral attributes
§ CONNECT THE UNCONNECTED DATA -
Existing enterprise data structures
unable to wrangle with new age data
sources ; digital, social & external
(Geo-spatial data)
16. Cloud ready Data
Security
Backup and Recovery
Monitoring
Networking Migration
High availability, load balancing, failover
Set-up storage
Access Controls
Data
NEW DEV or ON-
PREMISES
CLOUD
Provision Compute
17. 17
360 View : Genome
Can be seamlessly
augmented based on
client’s business needs for
§ Product 360
§ Account 360
§ …
Custome
r 360
§ Prebuilt for Customer ,
Household
§ To be customized as per
clients needs
Gene Blocks
Customer
(1…n)
Account
(1…n)
Transaction
Collection
Aggregation
based on
“RFMQ”
AutomatedDerivationonBehavioralDimensions
Recency
Frequency
Monetary
Quantitative
Genome Transformation Engine (GTE)
Genome Management Console (GMC)
Genome Query Engine (GQE)
Raw Layer
Customer
(1…n)
Account
(1…n)
Transaction
Collection
Customer Type
Account Type
Transaction Type
Channel
Dimension (n)
Curated Layer
Dimension (n)
+ Exception Handling
+ Audit Processing
Validation
Deduplication
Transformation
Standardization&Processing(RuleBased)
Master
Transaction
Dimension
Legend CDC/SCD2 (Rule Based)
• Capture Change records
• Performs SCD2
Indicative. Not
Exhaustive
ETL
Subject Area
Table
Attribute
Changes
Dimensions
Customer
(1…n)
Account
(1…n)
Transaction
Collection
Customer Type
Account Type
Transaction Type
Channel
ETL
Solution
18. AWS + Infosys – a Strategic Partnership
• Relationship driven by CEOs of both companies
• 4500+ Infosys employees trained in AWS; 550 Certified resources
• AWS Dedicated architect and GTM sales support
• Joint AWS and Infosys investments in solution development,
Sales incentives and customer delivery
• Exclusive access to joint collaboration and funding to accelerate
cloud adoption program for clients
• Infosys is part of a small set of delivery partners
certified to help customers with every stage of their cloud
migration.
Infosys is one of only 8 Migration Acceleration Program partners•
Big Data Competency
• Infosys is AWS’s Premier Consulting Partner
19. 19
Curated
Data
Derived
Attributes -
Features
M
odel
Outcom
es
Acquisition
&
Ingestion
Transformation
Distribution
Create
UI
Manage UI
Access
UI
CONFIGURABLE DATA ASSETS
AUTOMATED PROCESS
INTERACTIVE UI
Identity &
Personality
Actions Taken
For Entity
Actions
Performed By
Entity
GENOME
User Interface
Genome Management Console
Genome Marketplace
Data Pipeline / Code
Genome Transformation Engine
Genome Query Engine
Genome Metadata Repository
Data Models : Logical &
Physical
Gene Blocks
Genome
Infosys Information Grid
Analytics & Visualization
Library
Analytical Models
Dashboards
GTE (UI)
EnterpriseCommunity
Operations
Compliance
Servicing
Sales
Marketing
Leadership
Data Scientists
& Analysts
Business Users
IT Data
Engineers
Data Monetization Challenges
§ Process based data fragmentation across
products and processes
§ New age data sources and external data
§ Varied data formats
§ Repetition of data life cycle for insight and
analytical use cases
How Infosys Genome Solution can help?
20. Customer & Marketing Analytics Operations Analytics Risk & Compliance
Customer
segmentation
Market basket
analytics
Sentiment
analytics
Call intent
prediction
Supply shortage
prediction
Provide fraud
Monitoring
Probability
to default
20
Get Away from Silos
21. Genome solution’s architecture and data process flow..
Provisioning
Data
Governance
Provisioning, Mgmt.
Monitoring
Monitoring Workflow Retention
Metadata Lineage SecurityEncryptionA
B
Curated LayerSource Systems RAW Layer
Conformed Zone
Consumption/
VisualizationData Intelligence Grid
Landing
Zone
Ingestion Framework
• AS-IS data
from source
systems
• Master,
Reference data
Gene Blocks
Aggregated
Data (GENOME)
• Data enrichment
• Standardized and curated
data
• Flatten data in Gene Blocks.
• Aggregated view of each advisor,
product, customer etc. in Genome
Real Time Gene
Blocks
Speed Zone
Internal Data
External Data
1
2
3
4
5
6
Extracts
Data Exploration
Dashboards
Canned Reports
Self Service
Analytics
Analytical Tools
ReportingSemantic
Layer
22. Genome Architecture on AWS & Mongo DB Atlas
22
Batch
Real-Time
Real-Time
Sources
Internal
Sources
External
Sources
Spark Streaming
Speed Zone
Ingestion Zone Curated
Zone
Distribution Zone
Visualize
Kafka
Real-Time Serving
Real-Time
Gene Blocks
Sqoop
Gene
Blocks
GQE
Genome
GMP
IIG – Infosys Information Grid
GQE – Genome Query Engine
GMP – Genome Market Place
Genome
Management
Console
• Provides UI to define structures for Gene
Blocks and genome as well as Dimension
data
• Generates the metadata that is leveraged
by Genome Transformation Engine to
define data pipeline & build Gene Blocks.
• Genome query engine uses same
metadata to for automated generation of
Genome Features (derived attributes in
Genome)
Real-Time/Near Real Time Flow
Batch Transactional Flow
AWS
Direct
Connect
AWS EMR
AWS EC2
AWS EC2
AWS EMR
Amazon KinesisInfosysInformationGrid
IIG
AWS Glue
23. Any type of data, structured, unstructured Scale to any demands with no downtime
§ Performance
§ Data
§ Cluster (geographies)
Zero lock-in
§ Run on choice of cloud: AWS, GCP and Azure
§ Frictionless migration from or to on-premises deployments
General purpose to wide variety of use-cases, including blockchain, real time analytics,
single view and more!
One click upgrade to latest and greatest version of MongoDB as soon as it is available
Why MongoDB ATLAS for Genome?
24. Highly Available by Default
● A minimum of three data nodes per replica
set/shard are automatically deployed across
zones for high availability
● If your primary node does go down for any
reason, the self healing recovery process in
MongoDB Atlas will typically occur in under 2
seconds
● MongoDB Atlas automatically applies patches
and enables 1 touch upgrades with no
downtime
25. Live Migration Migrate existing deployments running anywhere into
MongoDB Atlas with minimal impact to your
application. Live migration works by:
● Performing a sync between your source
database and a target database hosted in
MongoDB Atlas
● Syncing live data between your source database
and the target database by tailing the oplog
● Notifying you when its time to cut over to the
MongoDB Atlas cluster
26. MongoDB Atlas on AWS
Cost of Migration +
Post Migration OpEx
Current CapEx
and OpEX<
Ø Production ready cluster in minutes
Ø Built on modern best practices and aligns with DevOps practices and tools (CI/CD)
Ø Automates and simplifies operational tasks
Ø Flexible schema and aggregation framework to enable rapid development
Ø Tools to explore data and schema, and optimize performance
27. Query-able Backups
MongoDB Atlas gives you the ability to query your backup snapshots
and restore data at the document level in minutes.
Queryable backups significantly reduces the operational overhead
associated with:
• Identifying whether data of interest has been altered
• Pinpointing the best point in time to restore a database by
comparing data across multiple snapshots
28. Continuous Backup / Point-in-time Restore
● MongoDB Atlas continuously backs up your data, ensuring your
backups are typically just a few seconds behind the operational system
● Point-in-time backup of replica sets and consistent, cluster-wide
snapshots of sharded clusters. With MongoDB Atlas, you can easily
and safely restore to precisely the moment you need
● Compression and block-level deduplication technology keeps your
backup processes as efficient as possible
● Backups are securely stored in North America, Ireland, Germany,
United Kingdom, or Australia*. For more location flexibility of your
backup data, you can utilize MongoDB’s mongodump / mongorestore
tools
*Additional regions for backup coming soon
29. Track Everything
● Monitoring and alerts provide full metrics on the state of your
cluster’s database and server usage
● Automatic notifications when your database operations or
server usage reach defined thresholds that affect your cluster's
performance
● Combining our automated alerting with the flexible scale-up-
and-out options in MongoDB Atlas, we can keep your
database-supported applications always performing as well as
they should
30. Security is job zero
All MongoDB Atlas nodes are single-tenant and deployed into their own VPC for
security isolation.
VPC Peering is available between AWS VPCs in the same AWS region.
In-flight security:
● TLS/SSL for in-flight data encryption
● Authentication and authorization access controls with SCRAM-SHA1
● IP whitelists
At-rest security:
● Encrypted storage volumes
● AES-256 (CBC mode) hardware encryption with Seagate Self-Encrypting
Drives
31. Solutions across Industries
• Retail: Retail Analytics Solution (customer)
• Telco: Telco Analytics Solution (customer)
• Financial Services:
o Corporate Banking Analytics Solution (corporate)
o Credit Card Analytics Solution (Customer, Merchant)
o Wealth Management Analytics Solution (Customer, Security, Advisor)
o Consumer banking Analytics Solution
• Insurance, Healthcare & Life Science
o Healthcare Analytics Solution ( Member, Payer, Provider, Claim , Corporate)
o Insurance Analytics Solution ( Customer, Household)
o Life Science Analytics Solution ( HCP)
• Automobile Analytics Solution ( Customer)
• Horizontal Solutions
o Supply chain Analytics Solution (Supplier, Product)
o HR Analytics Solution ( Employee)
o Asset Analytics Solution (Oil Well)
o Assets Analytics Solution ( Chiller plant – Equipment component)
31
32. Business benefits are customers are seeing
• One process, platform and reference model across enterprises. Customer, Account,
Revenue, Usage, payment, Interaction, Clickstream to create a 360 view of customer for a
New Zealand based Telco client . Created Customer and Product genome table to enable
micro segmentation, call reduction and real time visualization
1
• Time saving in insight generation insights for the campaign management team such as
classification of ecomm customers, creating of propensity models for a brand etc for a
European based retailer ;
80%
• Predicting shipment delay 7 days in advance lead to a revenue benefits by way of
released working capital currently tied up in safety stocks10%
• Unified Data and Analytical platform (Genome) for a US based Insurance firm across
personal and commercial lines for all the Insurance products
32