The client provided KPMG with 3 datasets for analysis: customer demographic data, customer address data, and transactions data from the past 3 months. The transactions data contains 20,000 rows with 26 columns, including customer, product, and transaction information. Some columns have missing values. There are no duplicate rows. Dates in one column are invalid as they all occur on the same day. The new customer list contains 1,000 rows of customer profile data. The data quality assessment found issues with missing values, invalid dates, and potential for further cleaning and analysis.
Simplify Feature Engineering in Your Data WarehouseFeatureByte
Feature Engineering is critical to successful delivery of AI solutions. Crafting relevant features from organization data requires business domain knowledge and creativity, powered by human capital in data science teams.
With growing adoption of machine learning and AI in organizations, there is a pressing need to develop processes around ML development and deployment to maximize productivity with limited resources. While there is no lack of tools for ML model management, solutions for feature engineering remains inadequate.
In this presentation, we outline our approach and design to make feature engineering efficient, repeatable and enjoyable for data science practitioners so they can experiment and iterate fast, without overlooking important issues such as scalability, deployment and auditability.
This document discusses storing product and order data as JSON in a database to support an agile development process. It describes creating tables with JSON columns to store this data, and using JSON functions like JSON_VALUE and JSON_TABLE to query and transform the JSON data. Examples are provided of indexing JSON columns for performance and updating product JSON to include unit costs by joining external data. The goal is to enable flexible and rapid evolution of the application through storing data in JSON.
This document discusses analyzing an e-commerce dataset using Python. It includes:
- Loading and exploring the dataset which has 11 numeric and 4 categorical variables
- Creating a data dictionary to document the variables
- Checking for missing data, outliers, and variable relationships
- Transforming, scaling, and selecting variables for modeling
- Building and comparing various models like linear regression, logistic regression, decision trees, and random forests to predict product weight and whether an order was delivered on time
The key steps are data loading and cleaning, exploratory analysis, feature engineering, and building/comparing predictive models. Various plots are generated to visualize relationships between variables.
Data Exploration (EDA)
- Relationship between variables
- Check for
- Multi-co linearity
- Distribution of variables
- Presence of outliers and its treatment
- Statistical significance of variables
- Class imbalance and its treatment
Feature Engineering
- Whether any transformations required
- Scaling the data
- Feature selection
- Dimensionality reduction
Assumptions
- Check for the assumptions to be satisfied for each of the models in
- Regression – SLR, Multiple Linear Regression, Logistic Regression
- Classification – Decision Tree, Random Forest, SVM, Bagged and boosted models
- Clustering – PCA (multi-co linearity), K-Means (presence of outliers, scaling, conversion to
numerical, etc.)
----------------------------- Interim Presentation Checkpoint----------------------------------------------------------
Model building
- Split the data to train and test.
- Start with a simple model which satisfies all the above assumptions based on your dataset.
- Check for bias and variance errors.
- To improve the performance, try cross-validation, ensemble models, hyperparameter
tuning, grid search
Evaluation of model
- Regression – RMSE, R-Squared value,
- Classification – Classification report with precision, recall, F1-score, Support, AUC, etc.
- Clustering – Inertia value
- Comparison of different models built and discussion of the same
- Time taken for the inferences/ predictions
Business Recommendations & Future enhancements
- How to improve data collection, processing, and model accuracy?
- Commercial value/ Social value / Research value
- Recommendations based on insights
----------------------------- Final Presentation Checkpoint----------------------------------------------------------
Dashboard
- EDA – Correlation matrix, pair plots, box blots, distribution plots
- Model
- Model Parameters
- Visualization of performance of the model with varying parameters
- Visualization of model Metrics
- Testing outcome
- Failure cases and explanation for the same
- Most successful and obvious cases
- Border cases
The document provides details about Kevin Bengtson's SQL portfolio, including several database projects and T-SQL queries projects with examples. It also outlines SQL server administrative tasks performed and an SSIS/SSRS project involving creating a MiniAdventureWorks database. The final section describes a BlockFlix database designed for a video rental store.
The document analyzes a superstore dataset containing sales data from 2015 to 2018 to understand shopping patterns and identify profitable products and regions. Visualizations show that the Western region had the most orders. Quantity ordered was highest for 1 item. Technology was the most profitable category at 36% of sales. The Tree Map showed phones had the highest sales while furniture had losses. Sales and profit were somewhat correlated, while profit and discount were negatively correlated. Word clouds indicated products related to Xerox, binders, chairs and Avery were ordered most frequently.
This is a run-through at a 200 level of the Microsoft Azure Big Data Analytics for the Cloud data platform based on the Cortana Intelligence Suite offerings.
Simplify Feature Engineering in Your Data WarehouseFeatureByte
Feature Engineering is critical to successful delivery of AI solutions. Crafting relevant features from organization data requires business domain knowledge and creativity, powered by human capital in data science teams.
With growing adoption of machine learning and AI in organizations, there is a pressing need to develop processes around ML development and deployment to maximize productivity with limited resources. While there is no lack of tools for ML model management, solutions for feature engineering remains inadequate.
In this presentation, we outline our approach and design to make feature engineering efficient, repeatable and enjoyable for data science practitioners so they can experiment and iterate fast, without overlooking important issues such as scalability, deployment and auditability.
This document discusses storing product and order data as JSON in a database to support an agile development process. It describes creating tables with JSON columns to store this data, and using JSON functions like JSON_VALUE and JSON_TABLE to query and transform the JSON data. Examples are provided of indexing JSON columns for performance and updating product JSON to include unit costs by joining external data. The goal is to enable flexible and rapid evolution of the application through storing data in JSON.
This document discusses analyzing an e-commerce dataset using Python. It includes:
- Loading and exploring the dataset which has 11 numeric and 4 categorical variables
- Creating a data dictionary to document the variables
- Checking for missing data, outliers, and variable relationships
- Transforming, scaling, and selecting variables for modeling
- Building and comparing various models like linear regression, logistic regression, decision trees, and random forests to predict product weight and whether an order was delivered on time
The key steps are data loading and cleaning, exploratory analysis, feature engineering, and building/comparing predictive models. Various plots are generated to visualize relationships between variables.
Data Exploration (EDA)
- Relationship between variables
- Check for
- Multi-co linearity
- Distribution of variables
- Presence of outliers and its treatment
- Statistical significance of variables
- Class imbalance and its treatment
Feature Engineering
- Whether any transformations required
- Scaling the data
- Feature selection
- Dimensionality reduction
Assumptions
- Check for the assumptions to be satisfied for each of the models in
- Regression – SLR, Multiple Linear Regression, Logistic Regression
- Classification – Decision Tree, Random Forest, SVM, Bagged and boosted models
- Clustering – PCA (multi-co linearity), K-Means (presence of outliers, scaling, conversion to
numerical, etc.)
----------------------------- Interim Presentation Checkpoint----------------------------------------------------------
Model building
- Split the data to train and test.
- Start with a simple model which satisfies all the above assumptions based on your dataset.
- Check for bias and variance errors.
- To improve the performance, try cross-validation, ensemble models, hyperparameter
tuning, grid search
Evaluation of model
- Regression – RMSE, R-Squared value,
- Classification – Classification report with precision, recall, F1-score, Support, AUC, etc.
- Clustering – Inertia value
- Comparison of different models built and discussion of the same
- Time taken for the inferences/ predictions
Business Recommendations & Future enhancements
- How to improve data collection, processing, and model accuracy?
- Commercial value/ Social value / Research value
- Recommendations based on insights
----------------------------- Final Presentation Checkpoint----------------------------------------------------------
Dashboard
- EDA – Correlation matrix, pair plots, box blots, distribution plots
- Model
- Model Parameters
- Visualization of performance of the model with varying parameters
- Visualization of model Metrics
- Testing outcome
- Failure cases and explanation for the same
- Most successful and obvious cases
- Border cases
The document provides details about Kevin Bengtson's SQL portfolio, including several database projects and T-SQL queries projects with examples. It also outlines SQL server administrative tasks performed and an SSIS/SSRS project involving creating a MiniAdventureWorks database. The final section describes a BlockFlix database designed for a video rental store.
The document analyzes a superstore dataset containing sales data from 2015 to 2018 to understand shopping patterns and identify profitable products and regions. Visualizations show that the Western region had the most orders. Quantity ordered was highest for 1 item. Technology was the most profitable category at 36% of sales. The Tree Map showed phones had the highest sales while furniture had losses. Sales and profit were somewhat correlated, while profit and discount were negatively correlated. Word clouds indicated products related to Xerox, binders, chairs and Avery were ordered most frequently.
This is a run-through at a 200 level of the Microsoft Azure Big Data Analytics for the Cloud data platform based on the Cortana Intelligence Suite offerings.
Why BI ?
Performance management
Identify trends
Cash flow trend
Fine-tune operations
Sales pipeline analysis
Future projections
business Forecasting
Decision Making Tools
Convert data into information
How to Think ?
What happened?
What is happening?
Why did it happen?
What will happen?
What do I want to happen?
Slidedeck presented at http://devternity.com/ around MongoDB internals. We review the usage patterns of MongoDB, the different storage engines and persistency models as well has the definition of documents and general data structures.
Customer Clustering for Retailer MarketingJonathan Sedar
This was a 90 min talk given to the Dublin R user group in Nov 2013. It describes how one might go about a data analysis project using the R language and packages, using an open source dataset.
This portfolio showcases skills in Microsoft Business Intelligence, including SQL Server Integration Services (SSIS), Analysis Services (SSAS), and Reporting Services (SSRS). The document outlines projects involving:
1) Designing an ETL process in SSIS to load data from various sources into a SQL database.
2) Building a data warehouse cube in SSAS with dimensions, measures, and KPIs.
3) Creating SSRS reports including a sales scorecard, maps, and matrices and displaying them on a PerformancePoint dashboard in SharePoint.
The random forest model generated 182 decision trees from the training data to classify whether users will continue their session or not, with an out-of-bag error rate of 34.17%. Important features were identified using the Gini index. The random forest model was able to successfully build a rule-based classification model with over 70% accuracy on the test data to identify if a user will continue or leave a session based on their behavior metrics.
Squirreling Away $640 Billion: How Stripe Leverages Flink for Change Data Cap...Flink Forward
Flink Forward San Francisco 2022.
Being in the payments space, Stripe requires strict correctness and freshness guarantees. We rely on Flink as the natural solution for delivering on this in support of our Change Data Capture (CDC) infrastructure. We heavily rely on CDC as a tool for capturing data change streams from our databases without critically impacting database reliability, scalability, and maintainability. Data derived from these streams is used broadly across the business and powers many of our critical financial reporting systems totalling over $640 Billion in payment volume annually. We use many components of Flink’s flexible DataStream API to perform aggregations and abstract away the complexities of stream processing from our downstreams. In this talk, we’ll walk through our experience from the very beginning to what we have in production today. We’ll share stories around the technical details and trade-offs we encountered along the way.
by
Jeff Chao
This document summarizes tips for using MongoDB and F# functional programming. It discusses how MongoDB allows for flexible querying of unstructured data using JavaScript and how F# focuses on reasoning through less code and avoiding side effects. It then describes an architecture using F# for data access and processing with MongoDB for storage. Formulas for calculating metrics are refactored into reusable functions for improved testability and understandability. Overall it presents strategies for improving performance, development, and the user experience when integrating functional programming with MongoDB.
45min talk given at LondonR March 2014 Meetup.
The presentation describes how one might go about an insights-driven data science project using the R language and packages, using an open source dataset.
Docker Summit MongoDB - Data Democratization Chris Grabosky
The document summarizes a presentation given by Chris Grabosky, a Solutions Architect at MongoDB. It discusses how containerization and data democratization can help organizations build cloud native applications. It highlights how MongoDB and Docker can be used together to build scalable and portable apps. It also covers data modeling approaches, workload isolation, analytics, and data access controls that MongoDB provides to help democratize data.
Python and MongoDB as a Market Data Platform by James BlackburnPyData
This document discusses using Python and MongoDB as a scalable platform for storing time series market data. It outlines some of the challenges of storing different types and sizes of financial data from various sources. The goals are to access 10 years of 1-minute data in under 1 second, store all data types in a single location, and have a system that is fast, complete, scalable, and supports agile development. MongoDB is chosen as it matches well with Python and allows for fast, low latency access. The system implemented uses MongoDB to store data in a way that supports versioning, arbitrary data types, and efficient storage and retrieval of pandas DataFrames. Performance tests show significant speed improvements over SQL and other tick databases for accessing intra
The document discusses MongoDB's transactions feature. It provides an overview of MongoDB's journey to implementing transactions from versions 3.0 to 4.0. It describes how transactions will work in MongoDB 4.0, including examples of atomic operations across multiple documents using sessions and commit_transaction. The presentation encourages joining the beta program for MongoDB transactions and concludes with announcements about the next session and lunch break.
Developing Microsoft SQL Server 2012 Databases 70-464 Pass GuaranteeSusanMorant
In order to pass the Developing Microsoft SQL Server 2012 Databases 70-464 exam questions in the first attempt to support their preparation process with fravo.com Developing Microsoft SQL Server 2012 Databases 70-464. Your Developing Microsoft SQL Server 2012 Databases 70-464 exam success is guaranteed with a 100% money back guarantee.
For more details visit us today: https://www.fravo.com/70-464-exams.html
INTEGRATE 2022 - Data Mapping in the Microsoft CloudDaniel Toomey
Presentation delivered at INTEGRATE 2022 in London on 15 June 2022.
Data transformation is a key capability of any mature integration platform. The same data may be represented in completely different ways across systems and domains. BizTalk Server provided one of the most intuitive and powerful XML-based mapping tools in the market - but how do to handle other common message formats like JSON or CSV? This session walks through a number of options for performing data mapping in Microsoft Azure, including cloud-hosted BizTalk Maps, Liquid Templates, and more. Dan will also show a great tool for documenting these transformation.
A Novel Secure Cloud SAAS Integration for User Authenticated Informationijtsrd
Organizations are spending a lot of money to maintain their business data using legacy Enterprise content management systems [1], tools, hardware support and maintenance, which are not satisfying the consumers. Box is Novel ECM system which allows the users to store, view, search large value of data with lesser bandwidth to increase user experience for modern business. Modern business running on new enterprise cloud platforms like Amazon, eBay, Salesforce. Persistence of business users data is simpler and faster with this platform and is really an emerging solution for the current industry. The real-time integration between cloud SaaS application with on-premises content management systems (SharePoint [2], box [3], Oracle Web Center [4], Nuxeo [5], Open Text [6], Dell Documentum [7], IBM FileNet p8[8], Docushare [9], On Base [10], Laser Fiche [11], SpringCM [12], Seismic [13], Lexmark [14], M-Files [15], Alfresco [16], Media Bank [17], Veeva Vault [18], etc.) In this paper, we are proposing a new paradigm of how Cloud SaaS applications integrate with novel ECMs for modern business. We performed real-time integration from SFDC to box with a business use case, we did bi-directional data synchronization between SFDC to box and vice versa. C. V. Satyanarayana | Dr. P. Suryanarayana Babu"A Novel Secure Cloud SAAS Integration for User Authenticated Information" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-4 , June 2017, URL: http://www.ijtsrd.com/papers/ijtsrd2227.pdf http://www.ijtsrd.com/computer-science/computer-security/2227/a-novel-secure-cloud-saas-integration-for-user-authenticated-information/c-v-satyanarayana
Project report on the design and build of a data warehouse from unstructured and structured data sources (Quandl, yelp and UK Office for National Statistics) using SQL Server 2016, MongoDB and IBM Watson. Design and implementation of business intelligence visualisations using Tableau to answer cross domain business questions
GraphQL Summit 2019 - Configuration Driven Data as a Service Gateway with Gra...Noriaki Tatsumi
In this talk, you’ll learn about techniques used to build a scalable GraphQL based data gateway with the capability to dynamically on-board various new data sources. They include runtime schema evolution and resolver wiring, abstract resolvers, auto GraphQL schema generation from other schema types, and construction of appropriate cache key-values.
Data Warehousing for students educationpptxjainyshah20
This document discusses data warehousing and OLAP technology. It defines a data warehouse as a subject-oriented, integrated, time-variant, and nonvolatile collection of data used to support management decision making. Key aspects covered include the multi-dimensional data model using cubes and dimensions, various data warehouse architectures like star schemas and snowflake schemas, and OLAP operations for analysis like roll-up, drill-down, slice and dice. Building a data warehouse requires a range of business, technology, and program management skills.
Watch full webinar here: https://buff.ly/2mHGaLA
What started to evolve as the most agile and real-time enterprise data fabric, data virtualization is proving to go beyond its initial promise and is becoming one of the most important enterprise big data fabrics.
Attend this session to learn:
• What data virtualization really is
• How it differs from other enterprise data integration technologies
• Why data virtualization is finding enterprise-wide deployment inside some of the largest organizations
Exploiting data quality tools to meet the expectation of strategic business u...Zubair Abbasi
The document discusses data quality and its importance for business users. It defines data quality as completeness, validity, consistency, timeliness and accuracy. It then outlines key issues, benefits, goals and metrics for ensuring high quality data including completeness, validity, consistency, relevance and availability. Tools like data profiling, standardization, clustering and deduplication help improve data quality. Regular audits and reporting are important for monitoring and improving data quality over time.
The document discusses MongoDB and how it allows storing data in flexible, document-based collections rather than rigid tables. Some key points:
- MongoDB uses a flexible document model that allows embedding related data rather than requiring separate tables joined by foreign keys.
- It supports dynamic schemas that allow fields within documents to vary unlike traditional SQL databases that require all rows to have the same structure.
- Aggregation capabilities allow complex analytics to be performed directly on the data without requiring data warehousing or manual export/import like with SQL databases. Pipelines of aggregation operations can be chained together.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Why BI ?
Performance management
Identify trends
Cash flow trend
Fine-tune operations
Sales pipeline analysis
Future projections
business Forecasting
Decision Making Tools
Convert data into information
How to Think ?
What happened?
What is happening?
Why did it happen?
What will happen?
What do I want to happen?
Slidedeck presented at http://devternity.com/ around MongoDB internals. We review the usage patterns of MongoDB, the different storage engines and persistency models as well has the definition of documents and general data structures.
Customer Clustering for Retailer MarketingJonathan Sedar
This was a 90 min talk given to the Dublin R user group in Nov 2013. It describes how one might go about a data analysis project using the R language and packages, using an open source dataset.
This portfolio showcases skills in Microsoft Business Intelligence, including SQL Server Integration Services (SSIS), Analysis Services (SSAS), and Reporting Services (SSRS). The document outlines projects involving:
1) Designing an ETL process in SSIS to load data from various sources into a SQL database.
2) Building a data warehouse cube in SSAS with dimensions, measures, and KPIs.
3) Creating SSRS reports including a sales scorecard, maps, and matrices and displaying them on a PerformancePoint dashboard in SharePoint.
The random forest model generated 182 decision trees from the training data to classify whether users will continue their session or not, with an out-of-bag error rate of 34.17%. Important features were identified using the Gini index. The random forest model was able to successfully build a rule-based classification model with over 70% accuracy on the test data to identify if a user will continue or leave a session based on their behavior metrics.
Squirreling Away $640 Billion: How Stripe Leverages Flink for Change Data Cap...Flink Forward
Flink Forward San Francisco 2022.
Being in the payments space, Stripe requires strict correctness and freshness guarantees. We rely on Flink as the natural solution for delivering on this in support of our Change Data Capture (CDC) infrastructure. We heavily rely on CDC as a tool for capturing data change streams from our databases without critically impacting database reliability, scalability, and maintainability. Data derived from these streams is used broadly across the business and powers many of our critical financial reporting systems totalling over $640 Billion in payment volume annually. We use many components of Flink’s flexible DataStream API to perform aggregations and abstract away the complexities of stream processing from our downstreams. In this talk, we’ll walk through our experience from the very beginning to what we have in production today. We’ll share stories around the technical details and trade-offs we encountered along the way.
by
Jeff Chao
This document summarizes tips for using MongoDB and F# functional programming. It discusses how MongoDB allows for flexible querying of unstructured data using JavaScript and how F# focuses on reasoning through less code and avoiding side effects. It then describes an architecture using F# for data access and processing with MongoDB for storage. Formulas for calculating metrics are refactored into reusable functions for improved testability and understandability. Overall it presents strategies for improving performance, development, and the user experience when integrating functional programming with MongoDB.
45min talk given at LondonR March 2014 Meetup.
The presentation describes how one might go about an insights-driven data science project using the R language and packages, using an open source dataset.
Docker Summit MongoDB - Data Democratization Chris Grabosky
The document summarizes a presentation given by Chris Grabosky, a Solutions Architect at MongoDB. It discusses how containerization and data democratization can help organizations build cloud native applications. It highlights how MongoDB and Docker can be used together to build scalable and portable apps. It also covers data modeling approaches, workload isolation, analytics, and data access controls that MongoDB provides to help democratize data.
Python and MongoDB as a Market Data Platform by James BlackburnPyData
This document discusses using Python and MongoDB as a scalable platform for storing time series market data. It outlines some of the challenges of storing different types and sizes of financial data from various sources. The goals are to access 10 years of 1-minute data in under 1 second, store all data types in a single location, and have a system that is fast, complete, scalable, and supports agile development. MongoDB is chosen as it matches well with Python and allows for fast, low latency access. The system implemented uses MongoDB to store data in a way that supports versioning, arbitrary data types, and efficient storage and retrieval of pandas DataFrames. Performance tests show significant speed improvements over SQL and other tick databases for accessing intra
The document discusses MongoDB's transactions feature. It provides an overview of MongoDB's journey to implementing transactions from versions 3.0 to 4.0. It describes how transactions will work in MongoDB 4.0, including examples of atomic operations across multiple documents using sessions and commit_transaction. The presentation encourages joining the beta program for MongoDB transactions and concludes with announcements about the next session and lunch break.
Developing Microsoft SQL Server 2012 Databases 70-464 Pass GuaranteeSusanMorant
In order to pass the Developing Microsoft SQL Server 2012 Databases 70-464 exam questions in the first attempt to support their preparation process with fravo.com Developing Microsoft SQL Server 2012 Databases 70-464. Your Developing Microsoft SQL Server 2012 Databases 70-464 exam success is guaranteed with a 100% money back guarantee.
For more details visit us today: https://www.fravo.com/70-464-exams.html
INTEGRATE 2022 - Data Mapping in the Microsoft CloudDaniel Toomey
Presentation delivered at INTEGRATE 2022 in London on 15 June 2022.
Data transformation is a key capability of any mature integration platform. The same data may be represented in completely different ways across systems and domains. BizTalk Server provided one of the most intuitive and powerful XML-based mapping tools in the market - but how do to handle other common message formats like JSON or CSV? This session walks through a number of options for performing data mapping in Microsoft Azure, including cloud-hosted BizTalk Maps, Liquid Templates, and more. Dan will also show a great tool for documenting these transformation.
A Novel Secure Cloud SAAS Integration for User Authenticated Informationijtsrd
Organizations are spending a lot of money to maintain their business data using legacy Enterprise content management systems [1], tools, hardware support and maintenance, which are not satisfying the consumers. Box is Novel ECM system which allows the users to store, view, search large value of data with lesser bandwidth to increase user experience for modern business. Modern business running on new enterprise cloud platforms like Amazon, eBay, Salesforce. Persistence of business users data is simpler and faster with this platform and is really an emerging solution for the current industry. The real-time integration between cloud SaaS application with on-premises content management systems (SharePoint [2], box [3], Oracle Web Center [4], Nuxeo [5], Open Text [6], Dell Documentum [7], IBM FileNet p8[8], Docushare [9], On Base [10], Laser Fiche [11], SpringCM [12], Seismic [13], Lexmark [14], M-Files [15], Alfresco [16], Media Bank [17], Veeva Vault [18], etc.) In this paper, we are proposing a new paradigm of how Cloud SaaS applications integrate with novel ECMs for modern business. We performed real-time integration from SFDC to box with a business use case, we did bi-directional data synchronization between SFDC to box and vice versa. C. V. Satyanarayana | Dr. P. Suryanarayana Babu"A Novel Secure Cloud SAAS Integration for User Authenticated Information" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-4 , June 2017, URL: http://www.ijtsrd.com/papers/ijtsrd2227.pdf http://www.ijtsrd.com/computer-science/computer-security/2227/a-novel-secure-cloud-saas-integration-for-user-authenticated-information/c-v-satyanarayana
Project report on the design and build of a data warehouse from unstructured and structured data sources (Quandl, yelp and UK Office for National Statistics) using SQL Server 2016, MongoDB and IBM Watson. Design and implementation of business intelligence visualisations using Tableau to answer cross domain business questions
GraphQL Summit 2019 - Configuration Driven Data as a Service Gateway with Gra...Noriaki Tatsumi
In this talk, you’ll learn about techniques used to build a scalable GraphQL based data gateway with the capability to dynamically on-board various new data sources. They include runtime schema evolution and resolver wiring, abstract resolvers, auto GraphQL schema generation from other schema types, and construction of appropriate cache key-values.
Data Warehousing for students educationpptxjainyshah20
This document discusses data warehousing and OLAP technology. It defines a data warehouse as a subject-oriented, integrated, time-variant, and nonvolatile collection of data used to support management decision making. Key aspects covered include the multi-dimensional data model using cubes and dimensions, various data warehouse architectures like star schemas and snowflake schemas, and OLAP operations for analysis like roll-up, drill-down, slice and dice. Building a data warehouse requires a range of business, technology, and program management skills.
Watch full webinar here: https://buff.ly/2mHGaLA
What started to evolve as the most agile and real-time enterprise data fabric, data virtualization is proving to go beyond its initial promise and is becoming one of the most important enterprise big data fabrics.
Attend this session to learn:
• What data virtualization really is
• How it differs from other enterprise data integration technologies
• Why data virtualization is finding enterprise-wide deployment inside some of the largest organizations
Exploiting data quality tools to meet the expectation of strategic business u...Zubair Abbasi
The document discusses data quality and its importance for business users. It defines data quality as completeness, validity, consistency, timeliness and accuracy. It then outlines key issues, benefits, goals and metrics for ensuring high quality data including completeness, validity, consistency, relevance and availability. Tools like data profiling, standardization, clustering and deduplication help improve data quality. Regular audits and reporting are important for monitoring and improving data quality over time.
The document discusses MongoDB and how it allows storing data in flexible, document-based collections rather than rigid tables. Some key points:
- MongoDB uses a flexible document model that allows embedding related data rather than requiring separate tables joined by foreign keys.
- It supports dynamic schemas that allow fields within documents to vary unlike traditional SQL databases that require all rows to have the same structure.
- Aggregation capabilities allow complex analytics to be performed directly on the data without requiring data warehousing or manual export/import like with SQL databases. Pipelines of aggregation operations can be chained together.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
1. 8/16/2021 KPMG - TASK 1
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KPMG VIRTUAL INTERNSHIP PROJECT
TASK: 1 - Data Quality Assessment
Assessment of data quality and completeness in preparation for analysis.
The client provided KPMG with 3 datasets:
1.Customer Demographic
2.Customer Addresses
3.Transactions data in the past 3 months
Reading the data
Reading each file separately
Exploring Transactions Data Set
In [14]:
# Importing the required libraries
import pandas as pd
In [113…
data = pd.ExcelFile("KPMG1.xlsx")
In [114…
Transactions = pd.read_excel(data, 'Transactions')
NewCustomerList = pd.read_excel(data, 'NewCustomerList')
CustomerDemographic = pd.read_excel(data, 'CustomerDemographic')
CustomerAddress = pd.read_excel(data, 'CustomerAddress')
In [115…
Transactions.head(5)
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There are missing values in 4 columns. They can be dropped or treated according to the nature of analysis
0
There are no duplicate values.
first_name 940
last_name 961
gender 3
past_3_years_bike_related_purchases 100
DOB 958
job_title 184
job_industry_category 9
wealth_segment 3
deceased_indicator 1
owns_car 2
tenure 23
address 1000
postcode 522
state 3
country 1
property_valuation 12
Rank 324
Value 324
dtype: int64
Exploring the columns
Index(['first_name', 'last_name', 'gender',
'past_3_years_bike_related_purchases', 'DOB', 'job_title',
'job_industry_category', 'wealth_segment', 'deceased_indicator',
'owns_car', 'tenure', 'address', 'postcode', 'state', 'country',
'property_valuation', 'Rank', 'Value'],
dtype='object')
In [61]:
#Checking for duplicate values
NewCustomerList.duplicated().sum()
Out[61]:
In [58]:
#Checking for uniquess of each column
NewCustomerList.nunique()
Out[58]:
In [62]:
NewCustomerList.columns
Out[62]:
11. 8/16/2021 KPMG - TASK 1
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Female 513
Male 470
U 17
Name: gender, dtype: int64
first_name last_name gender past_3_years_bike_related_purchases DOB job_title job_industry_category wealth_segment deceased_
59 Normy Goodinge U 5 NaT
Associate
Professor
IT Mass Customer
226 Hatti Carletti U 35 NaT
Legal
Assistant
IT
Affluent
Customer
324 Rozamond Turtle U 69 NaT
Legal
Assistant
IT Mass Customer
358 Tamas Swatman U 65 NaT
Assistant
Media
Planner
Entertainment
Affluent
Customer
360 Tracy Andrejevic U 71 NaT
Programmer
II
IT Mass Customer
374 Agneta McAmish U 66 NaT
Structural
Analysis
Engineer
IT Mass Customer
434 Gregg Aimeric U 52 NaT
Internal
Auditor
IT Mass Customer
439 Johna Bunker U 93 NaT
Tax
Accountant
IT Mass Customer
574 Harlene Nono U 69 NaT
Human
Resources
Manager
IT Mass Customer
598 Gerianne Kaysor U 15 NaT
Project
Manager
IT
Affluent
Customer
In [63]: NewCustomerList['gender'].value_counts()
Out[63]:
In [66]:
NewCustomerList[NewCustomerList.gender == "U"]
Out[66]:
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There are 17 columns with unknown/unspecified gender.
1993-11-02 2
1994-04-15 2
1963-08-25 2
1995-08-13 2
1987-01-15 2
..
1958-05-14 1
1977-12-08 1
1993-12-19 1
1954-10-06 1
1995-10-19 1
Name: DOB, Length: 958, dtype: int64
first_name last_name gender past_3_years_bike_related_purchases DOB job_title job_industry_category wealth_segment deceased_
664 Chicky Sinclar U 43 NaT Operator IT High Net Worth
751 Adriana Saundercock U 20 NaT Nurse IT High Net Worth
775 Dmitri Viant U 62 NaT Paralegal Financial Services
Affluent
Customer
835 Porty Hansed U 88 NaT
General
Manager
IT Mass Customer
883 Shara Bramhill U 24 NaT NaN IT
Affluent
Customer
904 Roth Crum U 0 NaT
Legal
Assistant
IT Mass Customer
984 Pauline Dallosso U 82 NaT
Desktop
Support
Technician
IT
Affluent
Customer
In [67]:
NewCustomerList['DOB'].value_counts()
Out[67]:
In [68]:
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Financial Services 203
Manufacturing 199
Health 152
Retail 78
Property 64
IT 51
Entertainment 37
Argiculture 26
Telecommunications 25
Name: job_industry_category, dtype: int64
Mass Customer 508
High Net Worth 251
Affluent Customer 241
Name: wealth_segment, dtype: int64
NSW 506
VIC 266
QLD 228
Name: state, dtype: int64
No 507
Yes 493
Name: owns_car, dtype: int64
N 1000
Name: deceased_indicator, dtype: int64
Exploring Customer Demographic Data Set
NewCustomerList['job_industry_category'].value_counts()
Out[68]:
In [69]:
NewCustomerList['wealth_segment'].value_counts()
Out[69]:
In [70]:
NewCustomerList['state'].value_counts()
Out[70]:
In [71]:
NewCustomerList['owns_car'].value_counts()
Out[71]:
In [72]:
NewCustomerList['deceased_indicator'].value_counts()
Out[72]:
In [73]:
CustomerDemographic.head()
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<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4000 entries, 0 to 3999
Data columns (total 13 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 customer_id 4000 non-null int64
1 first_name 4000 non-null object
2 last_name 3875 non-null object
3 gender 4000 non-null object
4 past_3_years_bike_related_purchases 4000 non-null int64
5 DOB 3913 non-null datetime64[ns]
6 job_title 3494 non-null object
7 job_industry_category 3344 non-null object
8 wealth_segment 4000 non-null object
9 deceased_indicator 4000 non-null object
10 default 3698 non-null object
11 owns_car 4000 non-null object
12 tenure 3913 non-null float64
dtypes: datetime64[ns](1), float64(1), int64(2), object(9)
memory usage: 406.4+ KB
customer_id first_name last_name gender past_3_years_bike_related_purchases DOB job_title job_industry_category wealth_segmen
0 1 Laraine Medendorp F 93
1953-
10-12
Executive
Secretary
Health Mass Custome
1 2 Eli Bockman Male 81
1980-
12-16
Administrative
Officer
Financial Services Mass Custome
2 3 Arlin Dearle Male 61
1954-
01-20
Recruiting
Manager
Property Mass Custome
3 4 Talbot NaN Male 33
1961-
10-03
NaN IT Mass Custome
4 5
Sheila-
kathryn
Calton Female 56
1977-
05-13
Senior Editor NaN
Affluen
Custome
Out[73]:
In [74]:
CustomerDemographic.info()
In [129…
#Checking for null values
15. 8/16/2021 KPMG - TASK 1
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customer_id 0
first_name 0
last_name 125
gender 0
past_3_years_bike_related_purchases 0
DOB 87
job_title 506
job_industry_category 656
wealth_segment 0
deceased_indicator 0
default 302
owns_car 0
tenure 87
dtype: int64
There are missing values in 5 columns. They can be dropped or treated according to the nature of analysis
0
There are no duplicate values.
customer_id 4000
first_name 3139
last_name 3725
gender 6
past_3_years_bike_related_purchases 100
DOB 3448
job_title 195
job_industry_category 9
wealth_segment 3
deceased_indicator 2
default 90
owns_car 2
tenure 22
dtype: int64
CustomerDemographic.isnull().sum()
Out[129…
In [79]:
#Checking for duplicate data
CustomerDemographic.duplicated().sum()
Out[79]:
In [78]:
#Checking for uniqueness of each column
CustomerDemographic.nunique()
Out[78]:
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Exploring the columns
Index(['customer_id', 'first_name', 'last_name', 'gender',
'past_3_years_bike_related_purchases', 'DOB', 'job_title',
'job_industry_category', 'wealth_segment', 'deceased_indicator',
'default', 'owns_car', 'tenure'],
dtype='object')
Female 2037
Male 1872
U 88
M 1
Femal 1
F 1
Name: gender, dtype: int64
Certain categories are not correctly titled. The names in these categories are re-named.
Female 2039
Male 1873
Unspecified 88
Name: gender, dtype: int64
19 56
16 56
67 54
20 54
2 50
..
8 28
In [81]:
CustomerDemographic.columns
Out[81]:
In [82]:
CustomerDemographic['gender'].value_counts()
Out[82]:
In [131…
#Re-naming the categories
CustomerDemographic['gender'] = CustomerDemographic['gender'].replace('F','Female').replace('M','Male').replace('Femal','
In [84]:
CustomerDemographic['gender'].value_counts()
Out[84]:
In [85]:
CustomerDemographic['past_3_years_bike_related_purchases'].value_counts()
Out[85]:
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85 27
86 27
95 27
92 24
Name: past_3_years_bike_related_purchases, Length: 100, dtype: int64
1978-01-30 7
1978-08-19 4
1964-07-08 4
1976-09-25 4
1976-07-16 4
..
2001-01-22 1
1955-03-06 1
1966-08-05 1
1968-11-16 1
1958-08-02 1
Name: DOB, Length: 3448, dtype: int64
Business Systems Development Analyst 45
Social Worker 44
Tax Accountant 44
Internal Auditor 42
Legal Assistant 41
..
Staff Accountant I 4
Health Coach III 3
Health Coach I 3
Research Assistant III 3
Developer I 1
Name: job_title, Length: 195, dtype: int64
Manufacturing 799
Financial Services 774
Health 602
Retail 358
Property 267
IT 223
In [86]:
CustomerDemographic['DOB'].value_counts()
Out[86]:
In [87]:
CustomerDemographic['job_title'].value_counts()
Out[87]:
In [88]:
CustomerDemographic['job_industry_category'].value_counts()
Out[88]:
18. 8/16/2021 KPMG - TASK 1
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Entertainment 136
Argiculture 113
Telecommunications 72
Name: job_industry_category, dtype: int64
Mass Customer 2000
High Net Worth 1021
Affluent Customer 979
Name: wealth_segment, dtype: int64
N 3998
Y 2
Name: deceased_indicator, dtype: int64
100 113
1 112
-1 111
-100 99
â°â´âµâââ 53
...
ð¾ ð ð ð ð ð ð ð 31
/dev/null; touch /tmp/blns.fail ; echo 30
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,ãã»:*:ã»ãâ( â» Ï â» )ãã»:*:ã»ãâ 25
Name: default, Length: 90, dtype: int64
The values are inconsistent, hence dropping the column.
customer_id first_name last_name gender past_3_years_bike_related_purchases DOB job_title job_industry_category wealth_segmen
In [89]:
CustomerDemographic['wealth_segment'].value_counts()
Out[89]:
In [90]:
CustomerDemographic['deceased_indicator'].value_counts()
Out[90]:
In [91]:
CustomerDemographic['default'].value_counts()
Out[91]:
In [94]:
CustomerDemographic = CustomerDemographic.drop('default', axis=1)
In [96]:
CustomerDemographic.head(5)
Out[96]:
19. 8/16/2021 KPMG - TASK 1
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Yes 2024
No 1976
Name: owns_car, dtype: int64
7.0 235
5.0 228
11.0 221
10.0 218
16.0 215
8.0 211
18.0 208
12.0 202
14.0 200
9.0 200
6.0 192
4.0 191
13.0 191
17.0 182
15.0 179
1.0 166
3.0 160
customer_id first_name last_name gender past_3_years_bike_related_purchases DOB job_title job_industry_category wealth_segmen
0 1 Laraine Medendorp Female 93
1953-
10-12
Executive
Secretary
Health Mass Custome
1 2 Eli Bockman Male 81
1980-
12-16
Administrative
Officer
Financial Services Mass Custome
2 3 Arlin Dearle Male 61
1954-
01-20
Recruiting
Manager
Property Mass Custome
3 4 Talbot NaN Male 33
1961-
10-03
NaN IT Mass Custome
4 5
Sheila-
kathryn
Calton Female 56
1977-
05-13
Senior Editor NaN
Affluen
Custome
In [92]:
CustomerDemographic['owns_car'].value_counts()
Out[92]:
In [93]:
CustomerDemographic['tenure'].value_counts()
Out[93]:
20. 8/16/2021 KPMG - TASK 1
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19.0 159
2.0 150
20.0 96
22.0 55
21.0 54
Name: tenure, dtype: int64
Exploring Customer Address Data Set
customer_id address postcode state country property_valuation
0 1 060 Morning Avenue 2016 New South Wales Australia 10
1 2 6 Meadow Vale Court 2153 New South Wales Australia 10
2 4 0 Holy Cross Court 4211 QLD Australia 9
3 5 17979 Del Mar Point 2448 New South Wales Australia 4
4 6 9 Oakridge Court 3216 VIC Australia 9
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3999 entries, 0 to 3998
Data columns (total 6 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 customer_id 3999 non-null int64
1 address 3999 non-null object
2 postcode 3999 non-null int64
3 state 3999 non-null object
4 country 3999 non-null object
5 property_valuation 3999 non-null int64
dtypes: int64(3), object(3)
memory usage: 187.6+ KB
In [98]:
CustomerAddress.head(5)
Out[98]:
In [99]:
CustomerAddress.info()
In [132…
#Checking for null values.
CustomerAddress.isnull().sum()
21. 8/16/2021 KPMG - TASK 1
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customer_id 0
address 0
postcode 0
state 0
country 0
property_valuation 0
dtype: int64
There are no null values.
0
There are no duplicate values.
customer_id 3999
address 3996
postcode 873
state 5
country 1
property_valuation 12
dtype: int64
Exploring the columns
2170 31
2145 30
2155 30
2153 29
3977 26
..
3331 1
3036 1
3321 1
3305 1
Out[132…
In [133…
#Checking for duplicate values
CustomerAddress.duplicated().sum()
Out[133…
In [100…
#Checking for uniqueness of each column
CustomerAddress.nunique()
Out[100…
In [105…
CustomerAddress['postcode'].value_counts()
Out[105…
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2143 1
Name: postcode, Length: 873, dtype: int64
NSW 2054
VIC 939
QLD 838
New South Wales 86
Victoria 82
Name: state, dtype: int64
Australia 3999
Name: country, dtype: int64
9 647
8 646
10 577
7 493
11 281
6 238
5 225
4 214
12 195
3 186
1 154
2 143
Name: property_valuation, dtype: int64
All the columns appear to have consistent and correct information.
In [106…
CustomerAddress['state'].value_counts()
Out[106…
In [107…
CustomerAddress['country'].value_counts()
Out[107…
In [108…
CustomerAddress['property_valuation'].value_counts()
Out[108…
In [ ]:
In [ ]: