�ݺ�ߣshows by User: khuranabalvinder

�ݺ�ߣshows by User: khuranabalvinder / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: khuranabalvinder / Wed, 15 Jun 2022 03:05:07 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: khuranabalvinder Data Quality_ the holy grail for a Data Fluent Organization.pptx /slideshow/data-quality-the-holy-grail-for-a-data-fluent-organizationpptx/251987362 dataqualitytheholygrailforadatafluentorganization-220615030507-3d7072ef
Data quality in big data platforms]]>
Data quality in big data platforms]]> Wed, 15 Jun 2022 03:05:07 GMT /slideshow/data-quality-the-holy-grail-for-a-data-fluent-organizationpptx/251987362 khuranabalvinder@slideshare.net(khuranabalvinder) Data Quality_ the holy grail for a Data Fluent Organization.pptx khuranabalvinder Data quality in big data platforms <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dataqualitytheholygrailforadatafluentorganization-220615030507-3d7072ef-thumbnail.jpg?width=120&height=120&fit=bounds" /> Data quality in big data platforms

Data Quality_ the holy grail for a Data Fluent Organization.pptx from Balvinder Hira

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0 Real time insights for better products, customer experience and resilient platform /slideshow/real-time-insights-for-better-products-customer-experience-and-resilient-platform-250689064/250689064 realtimeinsightsforbetterproductscustomerexperienceandresilientplatform-211120045905
Businesses are building digital platforms with modern architecture principles like domain driven design, microservice based, and event-driven. These platforms are getting ever so modular, flexible and complex. While they are built with architecture principles like - loose coupling, individually scaling, plug-and-play components; regulations and security considerations on data - complexity leads to many unknown and grey areas in the entire architecture. Details on how the different components of this complex architecture interact with each other are lost. Generating insights becomes multi-teams, multi-staged activity and hence multi-days activity. Multiple users and stakeholders of the platform want different and timely insights to take both corrective and preventive actions.Business teams want to know how business is doing in every corner of the country near real time at a zipcode granularity. Tech teams want to correlate flow changes with system health including that of downstream stability as it happens.Knowing these details also helps in providing the feedback to the platform itself, to make it more efficient and also to the underlying business process. In this talk we intend to share how we made all the business and technical insights of a complicated platform available in realtime with limited incremental effort and constant validation of the ideas and slices with business teams. Since the client was a Banking client, we will also touch base handling of financial data in a secure way and still enabling insights for a large group of stakeholders. We kept the self-service aspect at the center of our solution - to accommodate increasing components in the source platform, evolving requirements, even to support new platforms altogether. Configurability and Scalability were key here, it was important that all the data that was collected from the source platform was discoverable and presentable. This also led to evolving the solution in lines of domain data products, where the data is generated and consumed by those who understand it the best.]]>
Businesses are building digital platforms with modern architecture principles like domain driven design, microservice based, and event-driven. These platforms are getting ever so modular, flexible and complex. While they are built with architecture principles like - loose coupling, individually scaling, plug-and-play components; regulations and security considerations on data - complexity leads to many unknown and grey areas in the entire architecture. Details on how the different components of this complex architecture interact with each other are lost. Generating insights becomes multi-teams, multi-staged activity and hence multi-days activity. Multiple users and stakeholders of the platform want different and timely insights to take both corrective and preventive actions.Business teams want to know how business is doing in every corner of the country near real time at a zipcode granularity. Tech teams want to correlate flow changes with system health including that of downstream stability as it happens.Knowing these details also helps in providing the feedback to the platform itself, to make it more efficient and also to the underlying business process. In this talk we intend to share how we made all the business and technical insights of a complicated platform available in realtime with limited incremental effort and constant validation of the ideas and slices with business teams. Since the client was a Banking client, we will also touch base handling of financial data in a secure way and still enabling insights for a large group of stakeholders. We kept the self-service aspect at the center of our solution - to accommodate increasing components in the source platform, evolving requirements, even to support new platforms altogether. Configurability and Scalability were key here, it was important that all the data that was collected from the source platform was discoverable and presentable. This also led to evolving the solution in lines of domain data products, where the data is generated and consumed by those who understand it the best.]]> Sat, 20 Nov 2021 04:59:05 GMT /slideshow/real-time-insights-for-better-products-customer-experience-and-resilient-platform-250689064/250689064 khuranabalvinder@slideshare.net(khuranabalvinder) Real time insights for better products, customer experience and resilient platform khuranabalvinder Businesses are building digital platforms with modern architecture principles like domain driven design, microservice based, and event-driven. These platforms are getting ever so modular, flexible and complex. While they are built with architecture principles like - loose coupling, individually scaling, plug-and-play components; regulations and security considerations on data - complexity leads to many unknown and grey areas in the entire architecture. Details on how the different components of this complex architecture interact with each other are lost. Generating insights becomes multi-teams, multi-staged activity and hence multi-days activity. Multiple users and stakeholders of the platform want different and timely insights to take both corrective and preventive actions.Business teams want to know how business is doing in every corner of the country near real time at a zipcode granularity. Tech teams want to correlate flow changes with system health including that of downstream stability as it happens.Knowing these details also helps in providing the feedback to the platform itself, to make it more efficient and also to the underlying business process. In this talk we intend to share how we made all the business and technical insights of a complicated platform available in realtime with limited incremental effort and constant validation of the ideas and slices with business teams. Since the client was a Banking client, we will also touch base handling of financial data in a secure way and still enabling insights for a large group of stakeholders. We kept the self-service aspect at the center of our solution - to accommodate increasing components in the source platform, evolving requirements, even to support new platforms altogether. Configurability and Scalability were key here, it was important that all the data that was collected from the source platform was discoverable and presentable. This also led to evolving the solution in lines of domain data products, where the data is generated and consumed by those who understand it the best. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/realtimeinsightsforbetterproductscustomerexperienceandresilientplatform-211120045905-thumbnail.jpg?width=120&height=120&fit=bounds" /> Businesses are building digital platforms with modern architecture principles like domain driven design, microservice based, and event-driven. These platforms are getting ever so modular, flexible and complex. While they are built with architecture principles like - loose coupling, individually scaling, plug-and-play components; regulations and security considerations on data - complexity leads to many unknown and grey areas in the entire architecture. Details on how the different components of this complex architecture interact with each other are lost. Generating insights becomes multi-teams, multi-staged activity and hence multi-days activity. Multiple users and stakeholders of the platform want different and timely insights to take both corrective and preventive actions.Business teams want to know how business is doing in every corner of the country near real time at a zipcode granularity. Tech teams want to correlate flow changes with system health including that of downstream stability as it happens.Knowing these details also helps in providing the feedback to the platform itself, to make it more efficient and also to the underlying business process. In this talk we intend to share how we made all the business and technical insights of a complicated platform available in realtime with limited incremental effort and constant validation of the ideas and slices with business teams. Since the client was a Banking client, we will also touch base handling of financial data in a secure way and still enabling insights for a large group of stakeholders. We kept the self-service aspect at the center of our solution - to accommodate increasing components in the source platform, evolving requirements, even to support new platforms altogether. Configurability and Scalability were key here, it was important that all the data that was collected from the source platform was discoverable and presentable. This also led to evolving the solution in lines of domain data products, where the data is generated and consumed by those who understand it the best.

Real time insights for better products, customer experience and resilient platform from Balvinder Hira

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0 Observability in real time at scale /slideshow/observability-in-real-time-at-scale-249771256/249771256 observabilityinrealtimeatscale-210716171234
The rise of cloud and containers has led to systems that are much more distributed and dynamic in nature. Highly elastic microservice and serverless architectures mean containers spin up on demand and scale to zero when that demand goes away. This generates a continous stream of infrastructure data. On the business side, we have started storing lot of data and this data contains enormours information, specially when married with infrastructure data this gives holistic health information of the entire platform. We will talk about how to achieve this kind of fine-grained observability at scale in real-time.]]>
The rise of cloud and containers has led to systems that are much more distributed and dynamic in nature. Highly elastic microservice and serverless architectures mean containers spin up on demand and scale to zero when that demand goes away. This generates a continous stream of infrastructure data. On the business side, we have started storing lot of data and this data contains enormours information, specially when married with infrastructure data this gives holistic health information of the entire platform. We will talk about how to achieve this kind of fine-grained observability at scale in real-time.]]> Fri, 16 Jul 2021 17:12:33 GMT /slideshow/observability-in-real-time-at-scale-249771256/249771256 khuranabalvinder@slideshare.net(khuranabalvinder) Observability in real time at scale khuranabalvinder The rise of cloud and containers has led to systems that are much more distributed and dynamic in nature. Highly elastic microservice and serverless architectures mean containers spin up on demand and scale to zero when that demand goes away. This generates a continous stream of infrastructure data. On the business side, we have started storing lot of data and this data contains enormours information, specially when married with infrastructure data this gives holistic health information of the entire platform. We will talk about how to achieve this kind of fine-grained observability at scale in real-time. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/observabilityinrealtimeatscale-210716171234-thumbnail.jpg?width=120&height=120&fit=bounds" /> The rise of cloud and containers has led to systems that are much more distributed and dynamic in nature. Highly elastic microservice and serverless architectures mean containers spin up on demand and scale to zero when that demand goes away. This generates a continous stream of infrastructure data. On the business side, we have started storing lot of data and this data contains enormours information, specially when married with infrastructure data this gives holistic health information of the entire platform. We will talk about how to achieve this kind of fine-grained observability at scale in real-time.

Observability in real time at scale from Balvinder Hira

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0 Time series analysis 101 /slideshow/time-series-analysis-101/234636326 tw-timeseriesanalysis101-200527153714
An introductory talk to introduce you to Time Series Analysis. We will start with an introduction to time series analysis along with its properties. Going a step further, we will discuss the various components of a time series with various decomposition techniques. We will then explore ARIMA family of models and discuss how they are used in real world. All of the above will be accompanied with Demos to help us understand the concept better.]]>
An introductory talk to introduce you to Time Series Analysis. We will start with an introduction to time series analysis along with its properties. Going a step further, we will discuss the various components of a time series with various decomposition techniques. We will then explore ARIMA family of models and discuss how they are used in real world. All of the above will be accompanied with Demos to help us understand the concept better.]]> Wed, 27 May 2020 15:37:14 GMT /slideshow/time-series-analysis-101/234636326 khuranabalvinder@slideshare.net(khuranabalvinder) Time series analysis 101 khuranabalvinder An introductory talk to introduce you to Time Series Analysis. We will start with an introduction to time series analysis along with its properties. Going a step further, we will discuss the various components of a time series with various decomposition techniques. We will then explore ARIMA family of models and discuss how they are used in real world. All of the above will be accompanied with Demos to help us understand the concept better. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/tw-timeseriesanalysis101-200527153714-thumbnail.jpg?width=120&height=120&fit=bounds" /> An introductory talk to introduce you to Time Series Analysis. We will start with an introduction to time series analysis along with its properties. Going a step further, we will discuss the various components of a time series with various decomposition techniques. We will then explore ARIMA family of models and discuss how they are used in real world. All of the above will be accompanied with Demos to help us understand the concept better.

Time series analysis 101 from Balvinder Hira

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0 Agile, qa and data projects geek night 2020 /slideshow/agile-qa-and-data-projects-geek-night-2020/232570361 agileqaanddataprojects-geeknight2020-200424105143
This presentation talk about QA in data projects using Agile methadologies]]>
This presentation talk about QA in data projects using Agile methadologies]]> Fri, 24 Apr 2020 10:51:43 GMT /slideshow/agile-qa-and-data-projects-geek-night-2020/232570361 khuranabalvinder@slideshare.net(khuranabalvinder) Agile, qa and data projects geek night 2020 khuranabalvinder This presentation talk about QA in data projects using Agile methadologies <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/agileqaanddataprojects-geeknight2020-200424105143-thumbnail.jpg?width=120&height=120&fit=bounds" /> This presentation talk about QA in data projects using Agile methadologies

Agile, qa and data projects geek night 2020 from Balvinder Hira

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0 Pricing Deep learning model /slideshow/pricing-deep-learning-model/190307287 dbpdeeplearningmodel-191104123341
In Retail, Pricing means finding the best Price to sell a Product to maximize Profit or Revenue. We will present, how we can use Deep Learning based models to find the best Price of most of the Products. How our solution evolved and comparison with Traditional approaches.]]>
In Retail, Pricing means finding the best Price to sell a Product to maximize Profit or Revenue. We will present, how we can use Deep Learning based models to find the best Price of most of the Products. How our solution evolved and comparison with Traditional approaches.]]> Mon, 04 Nov 2019 12:33:41 GMT /slideshow/pricing-deep-learning-model/190307287 khuranabalvinder@slideshare.net(khuranabalvinder) Pricing Deep learning model khuranabalvinder In Retail, Pricing means finding the best Price to sell a Product to maximize Profit or Revenue. We will present, how we can use Deep Learning based models to find the best Price of most of the Products. How our solution evolved and comparison with Traditional approaches. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dbpdeeplearningmodel-191104123341-thumbnail.jpg?width=120&height=120&fit=bounds" /> In Retail, Pricing means finding the best Price to sell a Product to maximize Profit or Revenue. We will present, how we can use Deep Learning based models to find the best Price of most of the Products. How our solution evolved and comparison with Traditional approaches.

Pricing Deep learning model from Balvinder Hira

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0 Google Cloud Platform /slideshow/google-cloud-platform-190305335/190305335 gcp-191104122526
Overview of Google Data Platform echosystem for storage, compute and processing. Data engineering use cases and building sample data pipeline on GCP. Learnings and challanges while using its different components. ]]>
Overview of Google Data Platform echosystem for storage, compute and processing. Data engineering use cases and building sample data pipeline on GCP. Learnings and challanges while using its different components. ]]> Mon, 04 Nov 2019 12:25:25 GMT /slideshow/google-cloud-platform-190305335/190305335 khuranabalvinder@slideshare.net(khuranabalvinder) Google Cloud Platform khuranabalvinder Overview of Google Data Platform echosystem for storage, compute and processing. Data engineering use cases and building sample data pipeline on GCP. Learnings and challanges while using its different components. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/gcp-191104122526-thumbnail.jpg?width=120&height=120&fit=bounds" /> Overview of Google Data Platform echosystem for storage, compute and processing. Data engineering use cases and building sample data pipeline on GCP. Learnings and challanges while using its different components.

Google Cloud Platform from Balvinder Hira

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0 Observability in real time at scale /slideshow/observability-in-real-time-at-scale-190304861/190304861 observabilityinrealtimeatscale-191104122317
The rise of cloud and containers has led to systems that are much more distributed and dynamic in nature. Highly elastic microservice and serverless architectures mean containers spin up on demand and scale to zero when that demand goes away. This generates a continous stream of infrastructure data. On the business side, we have started storing lot of data and this data contains enormours information, specially when married with infrastructure data this gives holistic health information of the entire platform. We will talk about how to achieve this kind of fine-grained observability at scale in real-time.]]>
The rise of cloud and containers has led to systems that are much more distributed and dynamic in nature. Highly elastic microservice and serverless architectures mean containers spin up on demand and scale to zero when that demand goes away. This generates a continous stream of infrastructure data. On the business side, we have started storing lot of data and this data contains enormours information, specially when married with infrastructure data this gives holistic health information of the entire platform. We will talk about how to achieve this kind of fine-grained observability at scale in real-time.]]> Mon, 04 Nov 2019 12:23:17 GMT /slideshow/observability-in-real-time-at-scale-190304861/190304861 khuranabalvinder@slideshare.net(khuranabalvinder) Observability in real time at scale khuranabalvinder The rise of cloud and containers has led to systems that are much more distributed and dynamic in nature. Highly elastic microservice and serverless architectures mean containers spin up on demand and scale to zero when that demand goes away. This generates a continous stream of infrastructure data. On the business side, we have started storing lot of data and this data contains enormours information, specially when married with infrastructure data this gives holistic health information of the entire platform. We will talk about how to achieve this kind of fine-grained observability at scale in real-time. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/observabilityinrealtimeatscale-191104122317-thumbnail.jpg?width=120&height=120&fit=bounds" /> The rise of cloud and containers has led to systems that are much more distributed and dynamic in nature. Highly elastic microservice and serverless architectures mean containers spin up on demand and scale to zero when that demand goes away. This generates a continous stream of infrastructure data. On the business side, we have started storing lot of data and this data contains enormours information, specially when married with infrastructure data this gives holistic health information of the entire platform. We will talk about how to achieve this kind of fine-grained observability at scale in real-time.

Observability in real time at scale from Balvinder Hira

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0 https://cdn.slidesharecdn.com/profile-photo-khuranabalvinder-48x48.jpg?cb=1663255529 https://cdn.slidesharecdn.com/ss_thumbnails/dataqualitytheholygrailforadatafluentorganization-220615030507-3d7072ef-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/data-quality-the-holy-grail-for-a-data-fluent-organizationpptx/251987362 Data Quality_ the holy... https://cdn.slidesharecdn.com/ss_thumbnails/realtimeinsightsforbetterproductscustomerexperienceandresilientplatform-211120045905-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/real-time-insights-for-better-products-customer-experience-and-resilient-platform-250689064/250689064 Real time insights for... https://cdn.slidesharecdn.com/ss_thumbnails/observabilityinrealtimeatscale-210716171234-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/observability-in-real-time-at-scale-249771256/249771256 Observability in real ...