DATA MINING AND DATA WAREHOUSING TOOLS .pptx

DATA VIRTUALIZATION TOOLS

1. 1 Data V irtualization tools This presentation explores the exciting world of data virtualization tools. These powerful tools allow organizations to extract valuable insights from vast datasets, driving better decision-making. B y PONMAYIL KARTHIK

2. CONTENTS Datawarehouse Tools Amazon Redshift Google Bigquery Microsoft Azure Synapse Analytics Oracle Exadata IBM DB2 warewhouse DataMining Tool R PYTHON LIBRARIES SAS SPSS TABLEAU VEKA RAPID MINA S

3. 3 Data Warehousing Tools :Types

4. 4 Integrating Data Mining and Data Warehousing Data Preparation Data stored in the data warehouse is cleansed, transformed, and structured to meet the requirements of data mining algorithms. Data Mining Data mining algorithms are applied to the prepared data, uncovering patterns, insights, and relationships. Data Visualization The results of data mining are visualized in charts, graphs, or dashboards for easy understanding and communication.

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6. 6 Overview of Data Mining Techniques : Classification This technique categorizes data into predefined classes, like identifying fraudulent transactions or classifying customers based on their purchasing behavior. Regression Regression analysis predicts continuous values, such as predicting future sales or estimating customer lifetime value. Clustering Clustering groups similar data points together, enabling businesses to identify customer segments or discover patterns in product usage. Association Rule Mining This technique discovers relationships between different data items, uncovering patterns like "customers who buy product A also tend to purchase product B."

7. 7 Data Warehousing Concepts and Architecture Data Warehouse A data warehouse is a centralized repository of integrated data from multiple sources, optimized for analytical queries. It provides a single, consistent view of the organization's data. Data Mart A data mart is a smaller, focused subset of a data warehouse, tailored to a specific business function, like marketing or finance. It simplifies data access and analysis for specific departments. ETL Process Extract, Transform, Load (ETL) is a crucial process that extracts data from various sources, cleanses and transforms it into a consistent format, and loads it into the data warehouse or data mart.

8. 8 Data Warehousing Concepts and Architecture Data Warehouse Data Mart ETL Process A data warehouse is a centralized repository of integrated data from multiple sources, optimized for analytical queries. It provides a single, consistent view of the organization's data . A data mart is a smaller, focused subset of a data warehouse, tailored to a specific business function, like marketing or finance. It simplifies data access and analysis for specific departments. Extract, Transform, Load (ETL) is a crucial process that extracts data from various sources, cleanses and transforms it into a consistent format, and loads it into the data warehouse or data mart.

9. 9 Data Warehousing Tools : Amazon Redshift

10. Feature Description Petabyte -scale data warehousing Handles massive amounts of data efficiently Columnar storage Stores data by columns for faster query performance Massively parallel processing (MPP) Distributes data and processing across multiple nodes Compression Reduces storage costs and improves query performance Scalability Adjusts cluster size based on workload Cost-effective Pay-per-use model without upfront costs Integration Seamlessly integrates with other AWS services 10 Data Warehousing Tools : Amazon Redshift Features Feature Description Petabyte -scale data warehousing Handles massive amounts of data efficiently Columnar storage Stores data by columns for faster query performance Massively parallel processing (MPP) Distributes data and processing across multiple nodes Compression Reduces storage costs and improves query performance Scalability Adjusts cluster size based on workload Cost-effective Integration Pay-per-use model without upfront costs Seamlessly integrates with other AWS services

11. 11 Data Warehousing Tools : Amazon Redshift Amazon Redshift Key Feature Real-time Applications Petabyte -scale data warehousing Fraud detection, e-commerce analytics, IoT data processing Columnar storage Financial data analysis, customer segmentation, scientific simulations Massively parallel processing (MPP) Fraud detection, financial market analysis, social media sentiment analysis Compression Cost reduction, query performance improvement, data transfer optimization Scalability Peak load handling, cost optimization, business growth adaptation Cost-effectiveness Resource optimization, reduced infrastructure costs, faster time-to-market Integration with other AWS services Serverless data processing, data storage and retrieval, real-time data ingestion and processing

12. Application Area Specific Use Cases Real-time data analytics Financial services, e-commerce, telecommunications, healthcare Machine learning and AI Model training, NLP, image/video analysis Data warehousing and reporting Data consolidation, data warehousing, reporting and dashboards Geospatial analysis Geospatial data processing, visualization Data integration and ETL Data ingestion, transformation, loading, pipeline automation Data governance and security Data access control, auditing, compliance 12 Data Warehousing Tools: Google Bigquery Application Area Specific Use Cases Real-time data analytics Financial services, e-commerce, telecommunications, healthcare Machine learning and AI Model training, NLP, image/video analysis Data warehousing and reporting Data consolidation, data warehousing, reporting and dashboards Geospatial analysis Geospatial data processing, visualization Data integration and ETL Data ingestion, transformation, loading, pipeline automation Data governance and security Data access control, auditing, compliance

13. 13 Application Area Specific Use Cases Real-time data analytics Financial services, e-commerce, telecommunications, healthcare Machine learning and AI Model training and deployment, natural language processing, image and video analysis Data warehousing and reporting Data consolidation, data warehousing, reporting and dashboarding Geospatial analysis Geospatial data processing, visualization Data integration and ETL Data ingestion, transformation, loading, pipeline automation Data governance and security Data access control, auditing, compliance Data Warehousing Tools : Microsoft Azure Synapse Analytics

14. 14 Application Area Specific Use Cases Real-time data analytics Financial services, e-commerce, telecommunications, healthcare Machine learning and AI Model training and deployment, natural language processing, image and video analysis Data warehousing and reporting Data consolidation, data warehousing, reporting and dashboarding Geospatial analysis Geospatial data processing, visualization Data integration and ETL Data ingestion, transformation, loading, pipeline automation Data governance and security Data access control, auditing, compliance Additional Applications Fraud detection, customer insights, inventory management, supply chain optimization, risk management Data Warehousing Tools : Oracle Exadata DATA WAREHOUSING TOOLS ORACLE EXADATA

15. 15 Industry Real-time Applications Financial Services High-frequency trading, fraud detection, risk management Telecommunications Network monitoring, customer churn prediction, fraud prevention Retail Inventory management, customer analytics, supply chain management Healthcare Patient monitoring, supply chain management, fraud prevention Other Industries Manufacturing, gaming, IoT Data Warehousing Tools : Ibm Db2

16. 16 Popular Data Mining Tools RapidMiner A user-friendly tool that provides a comprehensive set of data mining algorithms and a visual workflow interface. Weka A powerful open-source tool offering a wide range of data mining algorithms and visualizations, commonly used for research and education. Orange A visual programming environment for data analysis, offering easy-to-use visual widgets for data mining tasks. KNIME A platform for data analytics, featuring a modular approach that allows users to create custom workflows with a wide range of nodes for data mining and machine learning tasks.

17. 17 Application Area Description Financial Modeling and Risk Assessment Model building for market trends, risk metrics, and real-time alerts, often requiring integration with faster systems. Data Streaming and Analysis Initial exploration and analysis of streaming data, but real-time decision-making needs specialized tools. Interactive Data Visualization Real-time monitoring with R Shiny dashboards, but potential for slight data update delays. Key Considerations Latency requirements, integration with other tools, impact of data volume on performance Data Mining Tools : R

18. 18 Feature Description Primary Focus Offline data analysis and modeling Real-time Suitability Not optimized Reasons Batch processing focus, interpreted language, lack of streaming capabilities Potential Workarounds Offline model training, online scoring; Integration with streaming platforms Limitations Significant engineering required, not ideal for true real-time applications Data Mining Tool : Weka

19. 19 Feature Description Real-time Capability RapidMiner Real-time Scoring, RapidMiner Server, RapidMiner Web Apps Key Considerations Latency, scalability, integration with other tools Overall Assessment Suitable for some real-time scenarios, but specialized tools might be better for critical applications Data Mining Tool : Rapid Miner

20. 20 Feature Description Real-time Capabilities Live connections, incremental refresh, Tableau Server/Online Limitations Data volume, latency Ideal Use Cases Interactive dashboards, operational analytics, customer analytics Considerations Data extraction and preparation for high-demand scenarios Data Mining Tool : Table

21. Feature Description Core Components SAS Event Stream Processing, SAS Real-time Decision Manager, SAS Micro Analytic Service Key Considerations Latency, scalability, integration Real-world Applications Fraud detection, customer churn prediction, risk management, supply chain optimization 21 Feature Description Core Components SAS Event Stream Processing, SAS Real-time Decision Manager, SAS Micro Analytic Service Key Considerations Latency, scalability, integration Real-world Applications Fraud detection, customer churn prediction, risk management, supply chain optimization Data Mining Tool : SAS (Statistical Analysis System)

22. 22 Library Real-time Applications Scikit -learn, TensorFlow Fraud detection, customer churn prediction, real-time recommendation systems, anomaly detection, sentiment analysis, image and video analysis Data Mining Tool : Python Libraries Scikit , Tensorflow

23. 23 Technique Description Common Algorithms Classification Predicts categorical outcomes Decision trees, Naive Bayes, SVM, Neural Networks Clustering Groups similar data points K-means, Hierarchical clustering, DBSCAN Association Rule Mining Identifies relationships between items Data Mining Techniques

24. 24 Challenges and Best Practices 1 Data Quality Ensuring data accuracy, completeness, and consistency is crucial for reliable insights. Implementing data validation and cleaning procedures is essential. 2 Data Governance Establishing clear data ownership, access controls, and security measures protects data integrity and privacy. 3 Scalability Handling large volumes of data efficiently requires scalable data warehouse architectures and data mining tools. 4 Performance Optimization Optimizing query performance and minimizing data processing time is critical for timely and effective data analysis.

25. 25 Conclusion and Future Trends Advanced Analytics The focus is shifting towards more advanced analytics techniques like machine learning and artificial intelligence, enabling deeper insights and more predictive capabilities. Cloud Adoption Cloud-based data warehousing and data mining solutions are becoming increasingly popular, offering scalability, flexibility, and cost-effectiveness. Automation Automating data mining and data warehousing tasks improves efficiency and allows data scientists to focus on higher-level analysis and interpretation. Interactive Visualization The demand for interactive data visualizations that provide dynamic insights and allow users to explore data in real-time is growing.

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