The document discusses advanced analytics capabilities in Tableau. It summarizes that Tableau allows both technical and non-technical users to perform advanced analytics tasks like segmentation, cohort analysis, scenario analysis, sophisticated calculations, time series analysis, and predictive analysis without requiring programming. It provides intuitive interfaces and drag-and-drop functionality for these advanced tasks. Tableau's calculation language also allows power users to build complex expressions and manipulate result sets.
Tableau interview questions and answerskavinilavuG
Tableau offers five main products including Tableau Desktop, Tableau Server, Tableau Online, Tableau Reader, and Tableau Public. Filters in Tableau come in three types: quick filters, context filters, and data source filters. To remove the "All" option from an auto-filter in Tableau, right click the filter and uncheck the "Show all option" in customize. Tableau extracts can be used anywhere without a connection, allowing visualizations to be built without connecting to the database.
Tableau software is a basic requirement for any business to gain insight into the development of the company. It allows any non-technical user to easily create or develop the customized dashboards that facilitate insight into a broad spectrum of information. It is a must know interactive business intelligence tool in the field of data visualization.
This document provides answers to common interview questions about Tableau. It discusses the differences between .twb and .twbx file extensions, how to join and blend data, how to create calculated fields and sets, and how to schedule automated report refreshes. It also covers topics like shelves, groups, hierarchies, extracts, performance testing, and stories. The document aims to equip job candidates with knowledge of Tableau's core functionality and capabilities.
This presentation "Tableau interview questions and answers" will help you to get prepared for Tableau job interviews. Tableau has become a mission-critical data visualization tool that helps people quickly understand data. The usefulness and popularity of Tableau make it a necessary skill for anyone working with data. As a reflection of the growing importance of data and tools for understanding it, the number of jobs requiring Tableau skills has increased dramatically since 2014.If you’re moving into the field of data analytics or you’re moving up the ladder and need Tableau skills, you’ll probably be interviewing for a job someday soon. We’re here to help, with the key Tableau job interview questions along with their best answers for you to think about ahead of time.
Some of the Tableau interview questions discussed in this presentation are mentioned below. Click on the time stamps to directly jump to that particular question.
1. What are the datatypes supported in Tableau?
2. What do you understand by dimensions and measures?
3. What do you understand by Discrete and Continuous in Tableau?
4. What are filters? Name the different filters in Tableau.
5. There are three customer segments in the Superstore dataset. What percent of the total profits is associated with the Corporate segment?
6. What are the different joins in Tableau? Give example
7. What is the difference between Join and Blending?
8. What is the difference b/w Live and Extract?
9. What is a Calculated Field? How will you create one?
10. How can you display top five and last five sales in the same view ?
11. Is there any difference between Sets and Groups, in Tableau?
12. What is a Parameter in Tableau? Give an example.
13. What is the difference between Tree maps and Heat maps?
14. What is the difference b/w .twbx and .twb?
15. Explain the difference b/w Tableau worksheet, dashboard, story, and workbook?
16. What do you understand by Blended Axis?
17. What is the use of dual axis? How do you create one?
18. What will the following function return? - Left(3, “Tableau”)
19. How do you handle Null and other special values?
20. Find the top product subcategories by Sales within each delivery method. Which sub-category is ranked #2 for first class ship mode?
21. Find the customer with the lowest overall profit. What is his/her profit ratio?
22. What is the Rank function in Tableau?
23. How can you embed a webpage in a dashboard?
24. Design a view to show region wise profit and sales?
25. How can you optimize the performance of a dashboard?
26. Which visualization will be used in the given scenarios:
27. What will you do if some country/province (any geographical entity) is missing and displaying a null when you use map view?
28. What is LOD expression?
29. How can you calculate daily profit measure using LOD?
30. How can you schedule a workbook in Tableau after publishing it?
Learn more at: https://www.simplilearn.com/
This document describes a simulator for database aggregation using metadata. The simulator sits between an end-user application and a database management system (DBMS) to intercept SQL queries and transform them to take advantage of available aggregates using metadata describing the data warehouse schema. The simulator provides performance gains by optimizing queries to use appropriate aggregate tables. It was found to improve performance over previous aggregate navigators by making fewer calls to system tables through the use of metadata mappings. Experimental results showed the simulator solved queries faster than alternative approaches by transforming queries to leverage aggregate tables.
This document provides an overview of data flow diagrams (DFDs):
1. DFDs consist of different levels that show increasing detail, starting with a context diagram showing external entities and high-level data flows, followed by more detailed level 1 and lower level DFDs showing processes and data stores.
2. The components of a DFD are external entities, processes, data stores, and data flows. Processes convert inputs to outputs, data stores hold data, and data flows connect the components.
3. Developing DFDs involves starting with a context diagram, then a level 1 DFD showing how inputs are converted to outputs via processes and data stores, with lower level DFDs
International Refereed Journal of Engineering and Science (IRJES) irjes
International Refereed Journal of Engineering and Science (IRJES)
Ad hoc & sensor networks, Adaptive applications, Aeronautical Engineering, Aerospace Engineering
Agricultural Engineering, AI and Image Recognition, Allied engineering materials, Applied mechanics,
Architecture & Planning, Artificial intelligence, Audio Engineering, Automation and Mobile Robots
Automotive Engineering….
Tableau interview questions and answerskavinilavuG
Tableau offers five main products including Tableau Desktop, Tableau Server, Tableau Online, Tableau Reader, and Tableau Public. Filters in Tableau come in three types: quick filters, context filters, and data source filters. To remove the "All" option from an auto-filter in Tableau, right click the filter and uncheck the "Show all option" in customize. Tableau extracts can be used anywhere without a connection, allowing visualizations to be built without connecting to the database.
Tableau software is a basic requirement for any business to gain insight into the development of the company. It allows any non-technical user to easily create or develop the customized dashboards that facilitate insight into a broad spectrum of information. It is a must know interactive business intelligence tool in the field of data visualization.
This document provides answers to common interview questions about Tableau. It discusses the differences between .twb and .twbx file extensions, how to join and blend data, how to create calculated fields and sets, and how to schedule automated report refreshes. It also covers topics like shelves, groups, hierarchies, extracts, performance testing, and stories. The document aims to equip job candidates with knowledge of Tableau's core functionality and capabilities.
This presentation "Tableau interview questions and answers" will help you to get prepared for Tableau job interviews. Tableau has become a mission-critical data visualization tool that helps people quickly understand data. The usefulness and popularity of Tableau make it a necessary skill for anyone working with data. As a reflection of the growing importance of data and tools for understanding it, the number of jobs requiring Tableau skills has increased dramatically since 2014.If you’re moving into the field of data analytics or you’re moving up the ladder and need Tableau skills, you’ll probably be interviewing for a job someday soon. We’re here to help, with the key Tableau job interview questions along with their best answers for you to think about ahead of time.
Some of the Tableau interview questions discussed in this presentation are mentioned below. Click on the time stamps to directly jump to that particular question.
1. What are the datatypes supported in Tableau?
2. What do you understand by dimensions and measures?
3. What do you understand by Discrete and Continuous in Tableau?
4. What are filters? Name the different filters in Tableau.
5. There are three customer segments in the Superstore dataset. What percent of the total profits is associated with the Corporate segment?
6. What are the different joins in Tableau? Give example
7. What is the difference between Join and Blending?
8. What is the difference b/w Live and Extract?
9. What is a Calculated Field? How will you create one?
10. How can you display top five and last five sales in the same view ?
11. Is there any difference between Sets and Groups, in Tableau?
12. What is a Parameter in Tableau? Give an example.
13. What is the difference between Tree maps and Heat maps?
14. What is the difference b/w .twbx and .twb?
15. Explain the difference b/w Tableau worksheet, dashboard, story, and workbook?
16. What do you understand by Blended Axis?
17. What is the use of dual axis? How do you create one?
18. What will the following function return? - Left(3, “Tableau”)
19. How do you handle Null and other special values?
20. Find the top product subcategories by Sales within each delivery method. Which sub-category is ranked #2 for first class ship mode?
21. Find the customer with the lowest overall profit. What is his/her profit ratio?
22. What is the Rank function in Tableau?
23. How can you embed a webpage in a dashboard?
24. Design a view to show region wise profit and sales?
25. How can you optimize the performance of a dashboard?
26. Which visualization will be used in the given scenarios:
27. What will you do if some country/province (any geographical entity) is missing and displaying a null when you use map view?
28. What is LOD expression?
29. How can you calculate daily profit measure using LOD?
30. How can you schedule a workbook in Tableau after publishing it?
Learn more at: https://www.simplilearn.com/
This document describes a simulator for database aggregation using metadata. The simulator sits between an end-user application and a database management system (DBMS) to intercept SQL queries and transform them to take advantage of available aggregates using metadata describing the data warehouse schema. The simulator provides performance gains by optimizing queries to use appropriate aggregate tables. It was found to improve performance over previous aggregate navigators by making fewer calls to system tables through the use of metadata mappings. Experimental results showed the simulator solved queries faster than alternative approaches by transforming queries to leverage aggregate tables.
This document provides an overview of data flow diagrams (DFDs):
1. DFDs consist of different levels that show increasing detail, starting with a context diagram showing external entities and high-level data flows, followed by more detailed level 1 and lower level DFDs showing processes and data stores.
2. The components of a DFD are external entities, processes, data stores, and data flows. Processes convert inputs to outputs, data stores hold data, and data flows connect the components.
3. Developing DFDs involves starting with a context diagram, then a level 1 DFD showing how inputs are converted to outputs via processes and data stores, with lower level DFDs
International Refereed Journal of Engineering and Science (IRJES) irjes
International Refereed Journal of Engineering and Science (IRJES)
Ad hoc & sensor networks, Adaptive applications, Aeronautical Engineering, Aerospace Engineering
Agricultural Engineering, AI and Image Recognition, Allied engineering materials, Applied mechanics,
Architecture & Planning, Artificial intelligence, Audio Engineering, Automation and Mobile Robots
Automotive Engineering….
Applications of sas and minitab in data analysisVeenaV29
SAS and Minitab are statistical software packages used for data analysis. SAS is used to process raw data, perform a variety of analyses, and generate insights to help organizations make better decisions. It has functions to manipulate text and works through data and procedure steps. Minitab is designed for teaching introductory statistics and solving problems in six sigma projects. It allows users to input, manipulate, visualize, and find patterns in data through various statistical tests and quality control charts. Both SAS and Minitab are widely applied in industry, research, and education.
Weka is a popular open-source machine learning software written in Java. It contains tools for data pre-processing, classification, regression, clustering, and feature selection. The document discusses using Weka for classification and regression tasks. It provides steps to classify bank customer data using J48 decision trees, achieving 89% accuracy. Regression is used to predict CPU performance based on attributes like cycle time and memory, with a correlation of 0.912.
Lumina's Analytica software allows users to create complex business models and simulations visually, without using spreadsheets or code. It supports probabilistic modeling, scenario analysis, and collaboration between managers and analysts. Key benefits include intuitive visual modeling, live testing of assumptions, and validation of decisions. While mastering Analytica is challenging, it handles specialized modeling better than other tools and helps communicate complex analyses. Analytica supports advanced quantitative operations and simulations but could provide more templates and examples for novice users.
This chapter discusses the importance of documenting accounting information systems. It covers various documentation tools used such as flowcharts, process maps, data flow diagrams, and decision tables. It provides guidelines for creating different types of flowcharts and discusses other documentation tools. It also talks about documenting end-user computing and how documentation is important for training, system development and auditing.
The document provides explanations of various SQL concepts including cross join, order by, distinct, union and union all, truncate and delete, compute clause, data warehousing, data marts, fact and dimension tables, snowflake schema, ETL processing, BCP, DTS, multidimensional analysis, and bulk insert. It also discusses the three primary ways of storing information in OLAP: MOLAP, ROLAP, and HOLAP.
The document provides an overview of the topics covered in a systems analysis and design course, including software used, information system components, analyzing the business case, managing projects, requirements modeling, data modeling, object modeling, development strategies, output and interface design, data design, and system architecture. Key concepts discussed include SWOT analysis, business cases, feasibility studies, project management techniques, UML, data flow diagrams, use cases, object-oriented analysis, cost-benefit analysis methods, user interface design, data structure, normalization, and entity relationship diagrams.
Introducing SAS Forecast Studio (2005)Brenda Wolfe
This document introduces SAS Forecast Studio, which facilitates statistical forecasting through a user-friendly interface. It allows users to set up forecasting projects, perform automated forecasting, identify exceptions, and construct custom models. The system supports hierarchical forecasting and generates SAS code to run projects in batch mode. It aims to meet the needs of both novice and experienced forecasters through an automated yet sophisticated approach.
This chapter discusses use case modeling techniques including developing detailed use case descriptions, activity diagrams, system sequence diagrams (SSDs), and integrating requirements models. It covers writing use case descriptions with elements like name, scenario, triggering event, actors, flow of activities, and exceptions. Activity diagrams and SSDs can show the flow of activities and inputs/outputs for a use case. Relating use cases to domain classes through CRUD analysis helps ensure all requirements are addressed.
Optimizing Queries over Partitioned Tables in MPP SystemsEMC
This document discusses techniques for optimizing queries over partitioned tables in Massively Parallel Processing (MPP) database systems. It presents a model for representing partitioned tables and queries using PartitionSelector and DynamicScan operators. The PartitionSelector determines which partitions need to be scanned based on the query predicates, and DynamicScan scans the selected partitions. This allows generating plans that can defer partition selection to query runtime. The techniques were implemented in Pivotal Greenplum Database and shown to outperform conventional approaches by eliminating unnecessary partitions for complex queries like those involving joins.
This document contains questions and answers related to Informatica technical interviews. It discusses concepts like degenerate dimensions, requirements gathering, junk dimensions, staging areas, join types in Informatica and Oracle, file formats for Informatica objects, versioning, tracing levels, performance factors for different join types, databases supported by Informatica server on Windows and UNIX, overview windows, and updating source definitions. The document is a collection of commonly asked Informatica technical interview questions and answers.
This document provides an overview of data flow diagrams (DFDs):
1. DFDs visually represent how information moves through a system and can be used to document current systems or plan new ones. They have four main components: external entities, processes, data stores, and data flows.
2. Multiple levels of DFDs can be created, starting with a high-level context diagram and drilling down into more detail in lower levels. Categories include physical and logical DFDs.
3. An example process of developing DFDs is outlined, starting with gathering requirements and creating a document flow diagram before building the initial context and level 1 DFDs and then more detailed lower levels.
This chapter discusses identifying and modeling functional requirements through use cases and user stories. It describes two techniques for identifying use cases: the user goal technique which identifies user goals and tasks, and the event decomposition technique which identifies system responses to different event types. The chapter also covers modeling use cases with descriptions, diagrams, and relationships to define the system functions and actors.
The document discusses data mining and the Microsoft SQL Server 2005 Data Mining Add-ins for Excel 2007. It provides an overview of data mining, how the add-in works, its prerequisites, who can use it, and how to use its various tools for data preparation, modeling, validation and connection to SQL Server Analysis Services.
This chapter discusses systems design and the major design activities involved in systems development. It outlines the difference between systems analysis and systems design, and describes the key design activities which include designing the environment, application architecture, user interfaces, system interfaces, database, and security controls. The chapter also covers designing for internal deployment on a local network as well as external deployment over the internet, and various hosting alternatives for internet deployment.
The document describes the traditional structured approach to systems design. This includes using data flow diagrams with system boundaries to partition processes. Designers then describe the processes using structured models like system flowcharts, structure charts, and pseudocode. Structure charts can be developed through transaction and transform analysis and may follow a three-layer architecture. The structured design approach aims to produce modular and cohesive system designs.
The document discusses various techniques for modeling software requirements including:
1) Entity-relationship diagrams (ERDs) which model data objects and their relationships to understand the data domain.
2) Use case modeling which describes scenarios of how external actors will use the system through use cases and diagrams.
3) Flow-oriented modeling using data flow diagrams (DFDs) which represent how data objects are transformed as they move through the system.
The document provides an overview of using the SumatraTT application for data mining tasks. It describes basic processing steps like converting between data formats, data understanding/visualization, handling missing values, and creating data sources for modeling. It also covers more advanced techniques like attribute transformation, data enrichment/reduction. Key features of SumatraTT include its modular architecture, rich I/O modules, automatic documentation, and internal SQL database. Example projects described include medical data preprocessing, spa customer analysis, health risk factor analysis, pump diagnostic, and road safety.
The document discusses database design, including transforming entity-relationship diagrams into normalized relations, integrating different user views, choosing data storage formats, designing efficient database tables, file organization, and indexes. It covers key database concepts such as relations, primary keys, normalization, foreign keys, and data types. The goal of database design is to structure data in stable, normalized tables that are efficient for storage and access.
The document discusses various techniques for modeling software requirements including:
1) Entity-relationship diagrams (ERDs) which model data objects and their relationships to understand the data domain.
2) Use case modeling which describes scenarios of how external actors will use the system through use cases and diagrams.
3) Object-oriented modeling which defines classes, objects, attributes, methods, encapsulation, and inheritance.
4) Flow modeling using data flow diagrams (DFDs) which represent how data objects flow through the system as they are transformed.
Partitioning Tables and Indexing Them --- ArticleHemant K Chitale
This is the Article (White Paper) that accompanied my Presentation "Partitioning Tables and Indexing Them" (which, too, is on slideshare) for AIOUG Sangam 11
The document describes different types of partitioning that can be used when creating tables in Oracle including range, list, hash, range-hash, range-list, index organized tables, and clustered tables. Examples are provided for each type of partitioning that show the SQL syntax for creating tables using that partitioning method.
Applications of sas and minitab in data analysisVeenaV29
SAS and Minitab are statistical software packages used for data analysis. SAS is used to process raw data, perform a variety of analyses, and generate insights to help organizations make better decisions. It has functions to manipulate text and works through data and procedure steps. Minitab is designed for teaching introductory statistics and solving problems in six sigma projects. It allows users to input, manipulate, visualize, and find patterns in data through various statistical tests and quality control charts. Both SAS and Minitab are widely applied in industry, research, and education.
Weka is a popular open-source machine learning software written in Java. It contains tools for data pre-processing, classification, regression, clustering, and feature selection. The document discusses using Weka for classification and regression tasks. It provides steps to classify bank customer data using J48 decision trees, achieving 89% accuracy. Regression is used to predict CPU performance based on attributes like cycle time and memory, with a correlation of 0.912.
Lumina's Analytica software allows users to create complex business models and simulations visually, without using spreadsheets or code. It supports probabilistic modeling, scenario analysis, and collaboration between managers and analysts. Key benefits include intuitive visual modeling, live testing of assumptions, and validation of decisions. While mastering Analytica is challenging, it handles specialized modeling better than other tools and helps communicate complex analyses. Analytica supports advanced quantitative operations and simulations but could provide more templates and examples for novice users.
This chapter discusses the importance of documenting accounting information systems. It covers various documentation tools used such as flowcharts, process maps, data flow diagrams, and decision tables. It provides guidelines for creating different types of flowcharts and discusses other documentation tools. It also talks about documenting end-user computing and how documentation is important for training, system development and auditing.
The document provides explanations of various SQL concepts including cross join, order by, distinct, union and union all, truncate and delete, compute clause, data warehousing, data marts, fact and dimension tables, snowflake schema, ETL processing, BCP, DTS, multidimensional analysis, and bulk insert. It also discusses the three primary ways of storing information in OLAP: MOLAP, ROLAP, and HOLAP.
The document provides an overview of the topics covered in a systems analysis and design course, including software used, information system components, analyzing the business case, managing projects, requirements modeling, data modeling, object modeling, development strategies, output and interface design, data design, and system architecture. Key concepts discussed include SWOT analysis, business cases, feasibility studies, project management techniques, UML, data flow diagrams, use cases, object-oriented analysis, cost-benefit analysis methods, user interface design, data structure, normalization, and entity relationship diagrams.
Introducing SAS Forecast Studio (2005)Brenda Wolfe
This document introduces SAS Forecast Studio, which facilitates statistical forecasting through a user-friendly interface. It allows users to set up forecasting projects, perform automated forecasting, identify exceptions, and construct custom models. The system supports hierarchical forecasting and generates SAS code to run projects in batch mode. It aims to meet the needs of both novice and experienced forecasters through an automated yet sophisticated approach.
This chapter discusses use case modeling techniques including developing detailed use case descriptions, activity diagrams, system sequence diagrams (SSDs), and integrating requirements models. It covers writing use case descriptions with elements like name, scenario, triggering event, actors, flow of activities, and exceptions. Activity diagrams and SSDs can show the flow of activities and inputs/outputs for a use case. Relating use cases to domain classes through CRUD analysis helps ensure all requirements are addressed.
Optimizing Queries over Partitioned Tables in MPP SystemsEMC
This document discusses techniques for optimizing queries over partitioned tables in Massively Parallel Processing (MPP) database systems. It presents a model for representing partitioned tables and queries using PartitionSelector and DynamicScan operators. The PartitionSelector determines which partitions need to be scanned based on the query predicates, and DynamicScan scans the selected partitions. This allows generating plans that can defer partition selection to query runtime. The techniques were implemented in Pivotal Greenplum Database and shown to outperform conventional approaches by eliminating unnecessary partitions for complex queries like those involving joins.
This document contains questions and answers related to Informatica technical interviews. It discusses concepts like degenerate dimensions, requirements gathering, junk dimensions, staging areas, join types in Informatica and Oracle, file formats for Informatica objects, versioning, tracing levels, performance factors for different join types, databases supported by Informatica server on Windows and UNIX, overview windows, and updating source definitions. The document is a collection of commonly asked Informatica technical interview questions and answers.
This document provides an overview of data flow diagrams (DFDs):
1. DFDs visually represent how information moves through a system and can be used to document current systems or plan new ones. They have four main components: external entities, processes, data stores, and data flows.
2. Multiple levels of DFDs can be created, starting with a high-level context diagram and drilling down into more detail in lower levels. Categories include physical and logical DFDs.
3. An example process of developing DFDs is outlined, starting with gathering requirements and creating a document flow diagram before building the initial context and level 1 DFDs and then more detailed lower levels.
This chapter discusses identifying and modeling functional requirements through use cases and user stories. It describes two techniques for identifying use cases: the user goal technique which identifies user goals and tasks, and the event decomposition technique which identifies system responses to different event types. The chapter also covers modeling use cases with descriptions, diagrams, and relationships to define the system functions and actors.
The document discusses data mining and the Microsoft SQL Server 2005 Data Mining Add-ins for Excel 2007. It provides an overview of data mining, how the add-in works, its prerequisites, who can use it, and how to use its various tools for data preparation, modeling, validation and connection to SQL Server Analysis Services.
This chapter discusses systems design and the major design activities involved in systems development. It outlines the difference between systems analysis and systems design, and describes the key design activities which include designing the environment, application architecture, user interfaces, system interfaces, database, and security controls. The chapter also covers designing for internal deployment on a local network as well as external deployment over the internet, and various hosting alternatives for internet deployment.
The document describes the traditional structured approach to systems design. This includes using data flow diagrams with system boundaries to partition processes. Designers then describe the processes using structured models like system flowcharts, structure charts, and pseudocode. Structure charts can be developed through transaction and transform analysis and may follow a three-layer architecture. The structured design approach aims to produce modular and cohesive system designs.
The document discusses various techniques for modeling software requirements including:
1) Entity-relationship diagrams (ERDs) which model data objects and their relationships to understand the data domain.
2) Use case modeling which describes scenarios of how external actors will use the system through use cases and diagrams.
3) Flow-oriented modeling using data flow diagrams (DFDs) which represent how data objects are transformed as they move through the system.
The document provides an overview of using the SumatraTT application for data mining tasks. It describes basic processing steps like converting between data formats, data understanding/visualization, handling missing values, and creating data sources for modeling. It also covers more advanced techniques like attribute transformation, data enrichment/reduction. Key features of SumatraTT include its modular architecture, rich I/O modules, automatic documentation, and internal SQL database. Example projects described include medical data preprocessing, spa customer analysis, health risk factor analysis, pump diagnostic, and road safety.
The document discusses database design, including transforming entity-relationship diagrams into normalized relations, integrating different user views, choosing data storage formats, designing efficient database tables, file organization, and indexes. It covers key database concepts such as relations, primary keys, normalization, foreign keys, and data types. The goal of database design is to structure data in stable, normalized tables that are efficient for storage and access.
The document discusses various techniques for modeling software requirements including:
1) Entity-relationship diagrams (ERDs) which model data objects and their relationships to understand the data domain.
2) Use case modeling which describes scenarios of how external actors will use the system through use cases and diagrams.
3) Object-oriented modeling which defines classes, objects, attributes, methods, encapsulation, and inheritance.
4) Flow modeling using data flow diagrams (DFDs) which represent how data objects flow through the system as they are transformed.
Partitioning Tables and Indexing Them --- ArticleHemant K Chitale
This is the Article (White Paper) that accompanied my Presentation "Partitioning Tables and Indexing Them" (which, too, is on slideshare) for AIOUG Sangam 11
The document describes different types of partitioning that can be used when creating tables in Oracle including range, list, hash, range-hash, range-list, index organized tables, and clustered tables. Examples are provided for each type of partitioning that show the SQL syntax for creating tables using that partitioning method.
This document discusses partitioning tables and indexing them in Oracle databases. It covers the different types of partitioning including range, list, hash, and composite partitioning. It provides examples of creating partitioned tables and indexes. It also discusses strategies for maintaining partitioned tables, including adding, dropping, splitting, merging and exchanging partitions. It recommends different partitioning and indexing approaches for optimizing query performance and archiving old data.
Partitioning on Oracle 12c - What changed on the most important Oracle featureLuis Marques
It was introduced in Oracle 8.0 in 1997 and since then Oracle Partitioning is mandatory for a big number Oracle Database architectures and implementations to ensure that high availabity or multi-terabyte systems keep the performance requirements.
This talk will demonstrate the improvements made in Oracle Partition on 12c from new interval reference partitions to partial partitioned and global async global indexes and how the today's critical Oracle databases that still run on 11g can revamp on this set of features.
Topic Objective: This topic is about Oracle Partition, the most used and most important paid option of Oracle Database. Learning how 12c improved it is vital for any Oracle DBA. Using this new set of new features can reduce your downtime, save DBA time and reduce the number of DBA "workarounds" to deal with specific situations when current 11g set of partition features is limited.
Table Partitioning in SQL Server: A Magic Solution for Better Performance? (P...Cathrine Wilhelmsen
The document provides an introduction to table partitioning in SQL Server. It explains what a partitioned table is, the key components like partition key, partition function, and partition scheme. It discusses why table partitioning is used, including benefits like partition elimination to improve query performance, ability to backup and restore partitions individually, and perform maintenance tasks like indexing and statistics updates on partitions. It also covers techniques like partition switching which allows fast loading and archiving of data without physically moving it. The document uses examples and diagrams to illustrate these concepts and components of table partitioning.
Partitioning allows tables and indexes to be subdivided into smaller pieces called partitions. Tables can be partitioned using a partition key which determines which partition each row belongs to. Partitioning provides benefits like improved query performance for large tables, easier management of historical data, and increased high availability. Some disadvantages include additional licensing costs, storage space usage, and administrative overhead to manage partitions. Common partitioning strategies include range, list, hash and interval which divide tables in different ways based on column values.
This document discusses being "data first" when developing applications and systems. It provides examples of collecting data on signup clicks and language selection. It also discusses data strategies of instrumenting everywhere, scaling data collection, and decoupling data gathering from analysis. The overall message is that organizations should adopt a data-driven culture where decisions are informed by objective data and teams work towards shared success.
An overview of the different sets of functionality of Tableau solution suite, and how it can address the many facets of a comprehensive data mining solution.
Designing dashboards for performance shridhar wip 040613Mrunal Shridhar
Session from EUTCC13 London on Designing Dashboards for Performance. The session provides tips, tricks, and skills on how to improve performance of your visual analysis.
How Concur uses Big Data to get you to Tableau Conference On TimeDenny Lee
This is my presentation from Tableau Conference #Data14 as the Cloudera Customer Showcase - How Concur uses Big Data to get you to Tableau Conference On Time. We discuss Hadoop, Hive, Impala, and Spark within the context of Consolidation, Visualization, Insight, and Recommendation.
As a leading data visualization tool Tableau has many desirable and unique features. Its powerful data discovery and exploration application allows you to answer important questions in seconds. You can use Tableau's drag and drop interface to visualize any data, explore different views, and even combine multiple databases together easily. It does not need any complex scripting. Anyone who understands the business problem can address it with a visualization of the relevant data. When the analysis is finished, sharing with others is as easy as publishing to Tableau Server.
Bound Tech is a top institute that provides hands-on Tableau training taught by experienced trainers using real-world scenarios and examples. The training covers fundamental concepts, advanced concepts, and job-oriented skills over 50-60 hours. Students learn how to rapidly analyze data, create dashboards and reports, and share analytics using features of Tableau. The course also provides skills needed for roles like business analyst, data scientist, and Tableau developer.
Tableau is a leading technology that shows data in a much comprehensive and comprehendible way. Comprehensive because you can show multiple things together and can tell a story out of numbers, and comprehendible because it is easier to understand graphically and helps you induce thinking about the graph. However, bringing your visualizations from “better” to “best” takes time, practice, patience and some knowledge of visual analysis best practices
Here are some of the best practices to make your visualization more interactive and creative.
The document discusses embedding Tableau visualizations and filtering capabilities. It covers embedding Tableau using URLs versus objects, URL and object embedding parameters for filtering workbooks and sheets, and formatting filters for dimensions, measures, and dates. Key parameters include :embed, :format, :refresh, and :filter for URLs, and host_url, site_root, and name for objects.
Tableau Software - Business Analytics and Data Visualizationlesterathayde
Tableau boasts drag-and-drop features that allow users to visualize information from any structured format. Tableau is the only provider of data visualization and business intelligence software that can be installed and used by anyone while also adhering to IT standards making it the fastest growing tool on the planet for Business Intelligence. Gartner has recently named us in the magic Quadrant among the Top 27 vendors for BI tool. We are no 1 in ease of use, no 1 in reporting and dashboard creation, interactive visualization, etc.
. Feel free to download the product, see the sample reports & dashboards for other industries from
http://www.tableausoftware.com
Please use the below link to download a 15 Day trial version of Tableau Desktop and Server Versions.
http://www.tableausoftware.com/products/trial
You can also do a self-training by going through the Videos in the below link.
http://www.tableausoftware.com/learn/training.
Step-1 Tableau Introduction
Step-2 Connecting to Data
Step-3 Building basic views
Step-4 Data manipulations and Calculated fields
Step-5 Tableau Dashboards
Step-6 Advanced Data Options
Step-7 Advanced graph Options
Tableau is business intelligence software that was created in 1992 as VizQL and allows users to visualize data through drag-and-drop interfaces to create dashboards, charts, and maps. It has three main products - Tableau Desktop for personal use, Tableau Server for organizations, and Tableau Online for cloud-based offerings. Tableau can connect to different data sources and perform functions like mapping, filtering, and unlimited undo. It is an alternative to using Excel for data analysis and visualization, with pros like ease of use but potential cons around cost and capabilities. The business intelligence software market that Tableau operates in continues to grow.
Top tableau questions and answers in 2019minatibiswal1
At the instant, Tableau Server is Windows and UNIX system compatible. Tableau Training could be a hosted Tableau Server version to skip hardware setup. Tableau Public could be a free computer code that enables anyone to attach to a program or file and build interactive Data visualizations for the online.
Data visualization and storytelling help communicate complex data and insights in an effective and efficient manner. Tableau is a self-service business intelligence tool that allows users to connect to various data sources, perform data preparation tasks, and create interactive visualizations, reports, dashboards, and stories. It provides features like filters, groups, sets, hierarchies, parameters, forecasting, clustering, and what-if analysis to explore and analyze data. Users can build dashboards with well-designed layouts and share reports in different file formats to facilitate data analysis and decision making.
Tableau allows users to create dashboards that display multiple worksheets and views together for easy comparison of data. To create a dashboard, select Dashboard > New Dashboard from the menu. Views and objects can then be added and arranged on the dashboard. Parameters and filters can be used to make dashboards interactive and allow users to dynamically change the data displayed. Maintaining good performance in Tableau requires limiting the amount of data pulled into views through appropriate filtering and aggregation of data.
process that manages the data extracts and caches them in
memory for fast access.
Tableau
26
Architecture
Gateway / Load balancer: distributes requests from clients to application
servers. It provides high availability and scalability.
Customers: Tableau offers desktop, web and mobile clients to access
Tableau Server.
- Tableau Desktop: allows you to create and edit visualizations and
dashboards.
- Web interface: allows you to view and interact with published views.
- Tableau Mobile: optimized for mobile devices to view and interact with
published views.
Tableau
27
Technical Review
Tableau
28
Technical Review
Data Connectivity
- Connects to a
How to Improve Data Analysis Through Visualization in TableauEdureka!
Data Visualization using Tableau will allow one to gain an edge over the other analysts and let you present the data in a much better and insightful manner. It would be easier for the learners to immediately implement it in their workplace and create a real-time dashboard for their management using one of the most sought-after tools.
10 reasons to use analytics canvas for google analytics data in tableaunModal Solutions Inc.
Tableau is an awesome tool for data visualisation, and it has connectors for a wide range of data sources. Its Google analytics connector, however, only goes so far. For serious users of Google Analytics, Analytics Canvas provides a whole next level of data access- find here the top ten ways Analytics Canvas helps get the Google Analytics data you need into Tableau.
The document summarizes and compares four data visualization tools: Tableau, Google Data Studio, Qlikview, and Datawrapper. It provides details on the features, chart types, advantages, and disadvantages of each tool. It also includes examples of how specific companies have used Qlikview and the typical needs of users for each tool. The document aims to help understand the proper use case for each product and what capabilities help make business decisions.
10 Features of Tableau to Smoothen your Data Visualization Tasks.pptxSudhanshiBakre1
Tableau is a popular business intelligence tool that makes data visualization easy through features like drag-and-drop functionality. It allows users to create dashboards and reports from various data sources and share them collaboratively. Key features include advanced visualizations, maps, mobile compatibility, predictive analysis capabilities, and natural language search of data through Ask Data. Tableau's wide range of features make it effective for extracting insights from complex data.
- Tableau 3.0 provides new features that enhance both its analytical and presentation capabilities.
- For analysis, it introduces shortcuts for sorting data, ad-hoc data grouping, automatic reference lines calculated from statistical measures, and faceted displays combining multiple views with global filters.
- For presentations, it includes annotations that are tied to specific data values or regions, so they shift automatically when the data changes, as well as enhanced formatting options.
Data Densification as explained in the popular Tableau Training in Noida or elsewhere is significant because it improves the interpretability of visualizations. It permits individuals to make continuous and detailed charts, that are specifically useful when working with time series data or when there is a requirement for comparing data at a higher level of depth. Without data densification, users may miss out on vital insights and trends that are hidden in the data due to its associated gaps and irregularities.
The document discusses Analance, an analytics platform that integrates multiple modules to provide an end-to-end data solution. It summarizes that maintaining three separate analytics tools leads to high costs, data issues, and performance problems. Analance aims to address these issues by providing seamless integration between its modules for data management, predictive modeling, and business intelligence from a single platform.
This document summarizes Microsoft Office PerformancePoint, a business performance management software. It integrates various Microsoft products like Excel, PowerPoint, and SharePoint to enable metrics-based reporting, planning, analysis, and scorecarding across an organization. Key features include report views, scorecards, dashboards, KPIs, consolidation, and collaboration tools. It provides advanced analytic and visualization capabilities to help users monitor, analyze, and plan business performance and make more informed decisions.
Top 30 Data Analyst Interview Questions.pdfShaikSikindar1
Data Analytics has emerged has one of the central aspects of business operations. Consequently, the quest to grab professional positions within the Data Analytics domain has assumed unimaginable proportions. So if you too happen to be someone who is desirous of making through a Data Analyst .
Ultimate Guide to Tableau: 5 Mind-boggling Advantages!Kavika Roy
https://www.datatobiz.com/blog/ultimate-guide-to-tableau/
For a modern-day business to flourish, data is proving to be an indispensable factor. It is now considered the new fuel for a company’s growth, making data scientists indispensable.
After a schedule breakdown, it was concluded that the data scientist spent only one day a week on average, participating in the activities that truly improve profitability.
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To effectively leverage the power of rich visualizations in making data-driven decisions, you must significantly reduce front-end data preparation time.
In order to create visualizations that lead to answers quickly, you need to prepare your data in the right way. Together, Alteryx and Tableau can help. This paper will show you how.
This document provides an introduction to data visualization using SAP Lumira. It is aimed at beginner students and users with limited business intelligence experience. The tutorial explains how to import data from Excel into SAP Lumira, create visualizations like bar charts and line charts, filter the data using dimensions like time and location, and save visualizations. It also describes how to combine visualizations into a story to describe relationships in the data.
Sql server 2008 r2 data mining whitepaper overviewKlaudiia Jacome
SQL Server 2008 provides powerful predictive analysis tools that are seamlessly integrated into the Microsoft business intelligence platform and Office applications, allowing organizations to gain insights from data and extend predictive capabilities into any application. The tools offer a comprehensive set of algorithms and an intuitive development environment, and can scale to meet the needs of organizations of any size through integration with SQL Server Analysis Services. This predictive analysis functionality enables organizations to incorporate predictive capabilities and data-driven decision making into every step of the data lifecycle and business processes.
Best Tableau course in Chandigarh is provided by Cbitss in Chandigarh which is best institute in Chandigarh for Tableau course
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As per the present circumstances, in India, Diabetic Mellitus (DM) has turned into a major wellbeing peril. Diabetic Mellitus (DM) is arranged as a Non-Communicable Diseases (NCD), and numerous individuals are experiencing it. Consistently vast volume of diabetic information is creating and consequently it is important to do examination on this information and settle on effective choices. In the healing centers different records, for example, patients' profile data, x-beam reports, different therapeutic tests' reports and so on are saved and this structures huge information. Enormous information examination is the procedure which inspects such huge informational collections and reveals shrouded data, concealed examples to find learning from the information. By applying investigation on medicinal services information, essential choice and expectation can be made. The framework proposed in this paper is a propelled answer for investigating the information in light of the information acquired from the past multiyear and after that giving outcomes to the up and coming year in an effective way. The proposed framework influences utilization of programming to device named Tableau to deliver the examination for up and coming year in light of the past five long periods of information.
Similar to whitepaper_advanced_analytics_with_tableau_eng (20)
Tableau for the Enterprise provides an overview of Tableau Server's architecture and capabilities for IT teams. It has a scalable multi-tier client-server architecture that can support mobile, web, and desktop clients. Tableau Server is an enterprise analytics platform that can scale to hundreds of thousands of users, offers mobile and browser-based analytics, and integrates with existing enterprise security, governance, and data architectures. It discusses key components like data connectors, server nodes, load balancing, and deployment models. The document provides information to help IT managers understand and support Tableau Server deployments of any size within their organizations.
Whitepaper Availability complete visibility service providerS. Hanau
The document discusses Veeam's solutions for providing service providers with availability and visibility into backup infrastructure. It describes the Veeam Backup & Replication Plug-in for LabTech which integrates with the LabTech RMM platform and provides dashboard views of backup jobs, infrastructure components, alerts and reports. It also discusses Veeam Endpoint Backup for LabTech which provides similar monitoring, reporting and remote management capabilities for physical endpoints backed up by Veeam Endpoint Backup.
his VeeamUP special edition focuses on Availability for the Always-On Enterprise™. Highlighting key events during VeeamOn 2015 this edition also contains featured articles around:
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You’ll also learn about Trend Micro’s global deployment of Veeam to truly become an Always-on Enterprise.
For always-on businesses, recovery time objectives should be 15 minutes or less to minimize costs from data loss and downtime. Legacy backup solutions often fail, resulting in data loss twice a year on average. Veeam offers 100% automated backup testing and recovery verification with a 0% failure rate to ensure businesses can meet recovery time objectives of less than 15 minutes for all applications and data. This allows businesses to reduce maximum downtime costs to under $500k and be always-on.
Veeam Availability top 10 reasons to choose veeam - longS. Hanau
The document discusses the advantages of Veeam Backup & Replication over legacy backup tools for virtual machine backups. Key advantages include:
1) Being agentless, avoiding the costs and risks of agents within VMs.
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This document discusses rethinking approaches to compliance to focus on business continuity beyond just security. It notes that data breaches are inevitable so compliance activities need to ensure systems can continue running when failures occur. It advocates taking a proactive approach through standards like ISO and continual disaster recovery testing to prove systems can be recovered. It also stresses designating a compliance team and accountability for continually reviewing practices.
This document provides an overview of best practices for data protection at remote branch offices. It discusses how the modern data center has extended to branch offices through virtualization, modern storage solutions, and cloud strategies. It recommends applying the "3-2-1" rule to have 3 copies of data stored on 2 different media with 1 copy offsite. Both onsite backups and offsite replication are suggested to balance availability needs with budgets. Veeam software is presented as a solution that can provide backup, replication, visibility and control for branch offices to achieve the same availability levels as main data centers.
This document provides a summary of key findings from a Veeam report on data center modernization and availability. It finds that most organizations have modernized or plan to modernize their data centers to enable always-on operations. This is driven by demands for mobility, lower costs, innovation and customer experience. Companies are investing heavily in virtualization, storage, data protection and cloud computing. However, many still face an "availability gap" that costs over $10M annually on average due to downtime. Adopting modern availability solutions can help companies meet recovery time objectives of 15 minutes and reduce downtime costs by over half.
2. 2
We used to exist in a world of either-or. Either you knew how to program or advanced
analytics were out your reach. Either you learned to program in R/Python/SAS or you got
someone else to do the heavy lifting. At Tableau we believe that to truly augment human
intelligence, we need to provide rich capabilities for users of all levels of technical ability.
We believe that advanced analytics shouldn’t require programming, that users should get
insights and validation in one place with common skills.
Tableau is unique among analytics platforms in that it serves both business users and
data scientists. Its simplicity empowers non-programmers to conduct deep analysis without
writing code. And its analytical depth augments the workflows of data science groups
at cutting-edge analytics companies like Facebook and Amazon.
With a few clicks, you can create box plots, tree maps, and even predictive visuals.
With just a few more clicks, you can create forecasts or complex cohort analyses.
You can even connect to R and use Tableau as a powerful front-end to visualize model
results. This means non-technical users can ask previously unapproachable questions,
while data scientists can iterate and discover deeper insights faster, yielding better,
more valuable findings.
In this paper we will explore how Tableau can help with all stages of an analytics project,
but focus specifically on a few advanced capabilities. Broadly, we will look at the following
scenarios and the capabilities that support them:
3. 3
Segmentation and cohort analysis: With drag-and-drop segmentation, Tableau promotes
not only an intuitive investigative flow, but also rapid and flexible cohort analysis.
Scenario and what-if analysis: By combining Tableau’s flexible front-end with powerful
input capabilities, you can quickly modify calculations and test different scenarios.
Sophisticated calculations: Tableau possesses a robust calculation language,
which makes it easy to augment your analysis with arbitrary calculations and perform
complex data manipulations with concise expressions.
Time-series analysis: Since much of the world’s data can be modeled by time series,
Tableau natively supports rich time-series analysis, meaning you can explore seasonality,
sample your data, or perform other common time-series operations within a robust UI.
Predictive analysis: Tableau contains out-of-the-box stats and predictive technologies,
which help data experts codify theses and uncover latent variables.
R integration: An R plugin provides the power and ease of use of Tableau’s front-end,
while allowing experts to leverage prior work in other platforms and handle nuanced
statistical needs.
1. Segmentation and Cohort Analysis
2. What-If and Scenario Analysis
3. Sophisticated Calculations
4. Time-Series Analysis
5. Predictive Analysis
6. R Integration
4. 4
Segmentation and Cohort Analysis
To generate an initial hypothesis, business users and data experts often start the same way:
by creating segments and/or conducting an informal cohort analysis. Asking a series of basic questions
about different segments helps analysts understand their data and validate their hypotheses
(e.g. “Do customers who pay with credit retain better than those who pay with check?”). The ability to
iterate rapidly can help drive model development and ensure projects stay on track. The ideal platform
for this phase should support the following:
Rapid ideation:
Provide an intuitive investigation canvas and near-instant feedback to questions asked as part
of the analytical flow.
Simple set operations:
Create and combine cohorts using standard set operations or a simple UI.
Data issue handling:
Correct data errors and adjust cohorts without needing permissions to modify the underlying data source.
Seamless updates when data changes:
Propagate data updates through the analysis without running manual update scripts or refreshing caches.
Figure 1: This interactive dashboard shows sales contribution by country and product.
1.
5. 5
Tableau possesses a rich set of capabilities to enable quick, iterative analysis and comparison of
segments. For example, with just a few calculated fields and some drag-and-drop operations,
you can create a dashboard that breaks down a country’s contribution to total sales across product
categories (Figure 1).
The solution leverages Tableau’s ability to dynamically create segments of data (in this case, sales by
country and product category) and slice-and-dice them with drag and drop. These same capabilities
can be easily coupled with Tableau’s time-series functionality (described in the section on time-series
analysis below) to conduct a more formal cohort analysis.
Tableau’s flexible interface also makes it easy to test different theories and explore distributions
across cohorts. Tableau’s ability to iterate visually saves countless hours of script tweaking and
re-running simulations.
As seen in Figure 2, simply dragging the segmentation fields onto the canvas generates a small multiples
view and trend lines by cohort, highlighting differences in correlation across groups. The trend line is
automatically recomputed for each of the segments of interest without any additional work from the user.
Figure 2: Segment and explore data in seconds. Figure 3: Define Sets graphically.
Using sets, you can define collections of data objects either by manual selection (Figure 3) or using
programmatic logic. Sets can be useful in a number of scenarios including filtering, highlighting, cohort
calculations, and outlier analysis. You can also combine multiple Sets (Figure 4) in order to test different
scenarios or create multiple cohorts for simulations—for example, combining different, independently-
generated customer groups for a retention analysis or applying multiple successive criteria.
6. 6
Figure 4: Combine multiple Sets
To support the need for creating ad-hoc categories and establishing hierarchies, Tableau has a feature
called Groups. Groups can also help with basic data cleaning needs.
Groups let users structure data in an intuitive way for the analysis task at hand—for example, creating
a group of the English-speaking countries as shown in Figure 5. This allows the analyst to customize the
presentation and control the aggregation of data throughout the analysis.
In addition, Groups help when data has consistency and quality issues. For example, California may be
called by its full name, but may also be referred to as CA or Calif. Analysts and business users often do
not have permissions to change source systems directly to clean up issues, meaning small data errors
can greatly encumber exploratory analysis. Having to stop asking questions in order to request data
changes delays projects and disrupts the rapid development of ideas. With Groups, you can quickly
define a new segment that includes all of the alternate names for the purposes of your analysis and
continue to ask questions without disrupting your flow.
Inherent to all of these capabilities are simple updates. In Tableau, if you choose a live connection and
update your data, your analysis and all the underlying components such as Sets and Groups will update
as well. This means that cohort membership updates automatically without manually re-running reports
or dependent scripts. Simple updates help ease the reporting burden and are yet another way to test
scenarios. They make it possible to swap out the underlying data in order to probe the sensitivity to initial
conditions without any need to update the analysis stack.
By letting users quickly segment and categorize their data, Tableau enables business users to perform
cohort analysis with relative ease. These easy cohorting capabilities also help data scientists investigate
initial hypotheses and test scenarios.
Figure 5: Create a Group
7. 7
What-If and Scenario Analysis
Sometimes users want to explore how changing a particular value or set of values affects the output
of their analysis. This could be used to test different theories, to highlight important scenarios for
colleagues, or to investigate new business possibilities. With Tableau, you can experiment with the inputs
of your analysis by providing the following capabilities:
Simple controls:
A flexible set of input controls allows you to add text, numeric inputs, or even more complex controls
such as sliders.
Full platform integration:
You can use the input values across Tableau to control thresholds in expressions, drive the cardinality
of a report, filter data sets, or do any combination of these.
Snapshot interesting results:
Easily flag and share scenarios using Tableau’s ability to store input values but keep analysis live
and updating.
When performing a what-if analysis, you may want to change the base value of a calculation, redefine
a quota, or set initial conditions. Parameters in Tableau make this an easy task. By defining a parameter,
you provide a way to change the input values into your model or dashboard. Parameters can drive
calculations, alter filter thresholds, and even select what data goes into the dashboard. Non-technical
users can leverage parameters to experiment with different inputs and explore possible outputs from
complex models.
In addition to helping you test hypotheses, Tableau’s Parameter feature lets you showcase results from
a what-if analysis in an interactive report. In Figure 6, parameters drive a what-if analysis around sales
commissions. The sales manager can experiment with commission rates, base salaries, and quotas,
all while getting real-time feedback on the impact to key metrics.
2.
8. 8
Figure 6: With this parameter-driven sales report, the interactor can explore the effect of quotas, commissions, and salaries
within the organization.
When combined with Stories (Tableau’s way of building a narrative with data), Parameters allow you to
take snapshots of interesting results and continue exploring. Stories allow you to construct a presentation
that continues to update with data changes and viz modifications. However, Stories are smart to enough
to retain Parameter values, so you can flag scenarios and have confidence you can return to them
without interrupting your analytical flow. You can also compare the results from several different sets of
inputs without worrying about stale screenshots or rerunning simulations.
With Sets, Groups, drag-and-drop segmentation, and Parameters, Tableau makes it possible to move
from theories and questions to a professional-looking dashboard that allows even non-experts to ask
questions and test their own scenarios. Streamlining what-if analysis empowers data professionals to
focus on the more complex aspects of the analysis and deliver greater insight, while simple generation
of intuitive visuals allows end users to engage with the data. This increased engagement helps drive
change and empower better decision-making throughout an organization.
9. 9
Sophisticated Calculations
Typically, source data does not contain all the fields necessary for a comprehensive analysis.
Analysts need a simple yet powerful language to transform data and define intricate logic.
To fully empower analysts, the language should have the following capabilities:
Expressibility:
Author calculations using a robust computational framework backed by a library of functions.
Flexible aggregations:
Support aggregation at multiple levels of detail within the same analysis component.
Result set computations:
Enable complex lags and iterative calculations dependent on the order of data.
Although Tableau is easy to use, we also provide a powerful language backed by a library that can
express complex logic. With calculated fields, you can easily perform arithmetic operations,
express conditional logic, or perform specialized operations on specific data types. Two key
capabilities that enable advanced analysis are Level of Detail (LOD) Expressions and Table Calculations.
A relatively new addition to the calculation language, LOD Expressions have greatly augmented
the power and expressibility of the calculation language. With this new capability, many previously
impossible or challenging scenarios can now be handled with a very simple, concise expression.
LOD Expressions greatly simplify cohort analysis (as described in a previous section) and multi-pass
aggregations. Figure 7 shows the running sum of purchase history for cohorts of customers bucketed by
the quarter of their first purchase. (In the next section on time-series analysis, we’ll look at some of the
other aspects of the calculation language that make this analysis possible.) The chart reveals that the
earliest customers placed the biggest initial orders and remained loyal with subsequent large purchases.
LOD Expressions turn segmentation that would otherwise require complex group-by statements in SQL
into simple, intuitive expressions that are manipulable in Tableau’s front-end.
Figure 7: An LOD Expression is used to calculate the running sum of total sales by first quarter of purchase date.
3.
10. 10
Table Calculations enable computations that are relative in nature. More specifically, Table Calculations
are computations that are applied to all values in a table, and are often dependent on the table structure
itself. This type of calculations includes many time-series operations such as lags or running sums,
but also computations like ranking and weighted averages.
In Tableau, there are two ways to work with table calculations. The first is a collection of commonly-used
table calculations called Quick Table Calculations. These let you define a table calculation with one click
and are a great place to start. In fact, the running sum in Figure 7 was calculated using Quick Table
Calculations. You can also create your own table calculations using the Table Calculation Functions in
calculation language. These functions give workbook authors the power to precisely manipulate their
result sets. Also, since all Table Calculation are expressible in the calculation language, you can use one
of the Quick Table Calculations as a starting point and edit it manually if you need additional complexity.
With Table Calculations, challenging database work—such as manipulating aggregated data,
and creating complex lags and data structure-dependent aggregations—requires just a few clicks
or a simple expression. This both empowers non-technical users and saves experts countless hours
and laborious SQL code.
Figure 8: Down-sampling intraday data reveals possible insights about tipping patterns:
drivers should consider working at night!
11. 11
Time-Series Analysis
From sensor readings to stock market prices to graduation rates, much of the world’s data can be
effectively modeled as time series. As such, time is one of the most common independent variables used
in analytics projects. To work well with time series, an analytics platform should support the following:
Seasonality exploration:
Examine seasonal effects with simple, intuitive tools.
Flexible sampling:
Handle the complexities of sampling elegantly.
Intuitive aggregations:
Combine time series in a manner that respects sampling assumptions.
Windowed calculations:
Perform arbitrary computations on previous values.
Relative date filters:
Quickly filter to relevant ranges based on current values.
In Tableau, a flexible front-end and powerful back-end makes time-series analysis a simple matter
of asking the right questions. Analysis starts by just dragging the fields of interest into the view and
beginning the interrogation process. In Figure 8, we are studying the tipping patterns from all the taxi
rides in New York City. We can easily adjust our sampling to find interesting patterns within the data.
With a single click, you can disaggregate the data or view the entire time series sampled by an arbitrary
window. You can quickly change aggregation frequencies to look for seasonality over different timescales
or even view year-over-year or quarter-over-quarter sales growth.
4.
12. 12
Leveraging the dual axis feature and discretized aggregation, you can start looking at multiple time
series. In this case, the chart indicates that there may be an inverse relationship between the average
number of rides on a given day and the average tip amount (Figure 9). This certainly could be the result
of random variation or driven by another latent variable, but perhaps the quality of service goes down as
volume increases. Without the ability to quickly inspect time series at different levels of granularity and
aggregation, you might not be able to generate the question.
Figure 9: The dual axes plot shows an inverse relationship between rides and tip amount.
To look at a specific time period, you can filter your data to a set of exact dates or take advantage of
Tableau’s relative date filters. With relative date filters, you can look at relative periods, such as last
week or last month. These periods are updated each time you open the view, making them a powerful
tool for reporting.
13. 13
When working with time series, it’s often necessary to smooth or perform other temporal calculations.
Tableau possesses a rich feature set designed to simplify common time-series operations such as
moving averages, year-over-year calculations, and running totals (Figure 10).
As previously discussed, Tableau’s Table Calculations feature lets you choose from a common set
of time-series manipulations (Quick Table Calculations) or to use calculation language to write
custom computations.
Since time-series analysis is so common, Tableau’s functionality helps finish projects faster and deliver
more value to the organization. The intuitive functionality helps both data experts and business analysts
to ask more and better questions of their data.
Figure 10: This time-series analysis shows the moving average of a stock price.
14. 14
Predictive Analysis
Often, after integrating data, forming an initial hypothesis and cleaning up any data quality issues, you
may want to garner further insight by leveraging predictive capabilities. Ideally, you should be able to add
predictive analytics without a large effort so you can explore multiple scenarios quickly. This typically
requires the following capabilities:
Integrated analytics objects:
Analytics objects, such as trend lines and forecasts, should automatically update with the data and
support cohort analysis.
Simple quality metrics:
Quality metrics should be readily accessible for any model.
Advanced predictive capabilities:
Moving beyond simple linear regression should not require complex configuration or coding.
Tableau possesses several native modeling capabilities, including Trending and Forecasting.
You can quickly add a trend line to any chart and view details describing the fit (e.g. p-values and
R-squared) simply by right-clicking on the line. Using Tableau’s drag and drop functionality you can
modeling different groups with a single click as trend lines are fully integrated into the front-end and
can be easily segmented. As seen in Figure 11, Tableau automatically creates three trend lines for
the different segments without any code. Tableau also supports several other types of fits, including
logarithmic, polynomial, and exponential.
Figure 11: Trend lines highlight the relationship between height and weight by sport
5.
15. 15
As shown in Figure 12, Tableau contains a configurable forecasting ability for time-series data.
By default, Forecasting will run several different models in the background and select the best one,
automatically accounting for data issues such as seasonality. Forecasting in Tableau uses a technique
known as exponential smoothing. Exponential smoothing iteratively forecasts future values of a time series
from weighted averages of past values. As mentioned previously, almost everything about the forecast
is configurable, from the length of the forecast to whether or not to account for seasonality, to the type of
model used (additive or multiplicative).
The feature is also very easy to use, so a novice user can create a forecast with just a few clicks, while an
advanced user can configure almost all aspects of the model. As with trend lines, details of the forecast
quality are available with a single click. In addition to the statistical elements, Tableau provides novice
users an estimate of the forecast quality by displaying confidence intervals. Forecasting also fits in
seamlessly with the rest of Tableau, so you can easily segment and manipulate the forecast as you would
any other analytic object in the UI (Figure 12).
Figure 12: Forecasting automatically predicts sales by region.
Easy predictive analytics adds tremendous value to any data project. By supporting both complex
configuration and simple interactive modeling, a platform can serve both the data scientist
and the end user.
16. 16
R Integration
Many organizations have been making investments in analytic platforms and institutional knowledge
for some time; therefore, you may have very specific needs and a valuable corpus of existing work.
Thus, a comprehensive analytics platform must support the ability to integrate with other advanced
analytics technologies, allowing you to expand the possible functionality and leverage existing
investments in other solutions. Supporting the integration with additional technologies enables
the following:
Utilize virtually unlimited choice of methods:
Bring in algorithms and the latest advances from the broader community.
Leverage prior work:
Connect to preexisting logic and models to ensure best institutional practices and avoid
replicating prior work.
Visualize and interrogate model results:
Use an intuitive front-end to help interpret, explore model results, and communicate to your colleagues.
Tableau integrates directly with R to support users with existing models and the leverage the worldwide
statistics community. Tableau can connect to an Rserve process and send data to R via a webAPI.
The results are then returned to Tableau for use by the Tableau visualization engine. This allows a
Tableau user to call any function available in R on data in Tableau and to manipulate models created
in R using Tableau.1
6.
1
Tableau can also read R, SAS, and SPSS data files as a data source. While a complete discussion of data sources is beyond the
scope of this paper, it’s worth nothing that Tableau can directly connect to the file outputs from several common stats programs.
17. 17
In Figures 13 and 14, you can see some examples in which R is used to compute descriptive statistics on
a data set in Tableau, with Tableau used to visualize the results. Figure 13 is a graphical representation
of correlation coefficients and Figure 14 showcases significance testing.
Figure 13: This correlation matrix utilizes R in Tableau
Order Priority
Delivery
Truck
Express
Air
Regular
Air
Critical
High
Medium
Low
Not Specified
8,399
1,672
1,720
1,631
1,768
1,608
6,270
1,277
1,280
1,225
1,308
1,180
983
180
190
201
212
200
1,146
215
250
205
248
228
Contingency Table
Chi-square test of independence
Patient
C E B I O P A F N H M D K J L G
Drug
Placebo
Drug
Placebo
Drug
Placebo
Drug
Placebo
Drug
Placebo
Drug
Placebo
Drug
Placebo
Drug
Placebo
Drug
Placebo
Drug
Placebo
Drug
Placebo
Drug
Placebo
Drug
Placebo
Drug
Placebo
Drug
Placebo
Drug
Placebo
0
5
10
Daystorecover
Is Paired
False
Test Type
Two Sided
Figure 14: R and Tableau were used to calculate and visualize the results of significance testing
Source: boraberan.wordpress.com/
18. 18
The modeling can go much deeper than basic statistics. With R integration, you can visualize results from
clustering (Figure 15), optimizations (Figure 16), or multidimensional scaling (Figure 17).
The integration also supports running R code directly inside Tableau. In Figure 16, you can see an optimized
portfolio computed and simulated in R, but visualized in Tableau.
Figure 15: This visualization shows a class k-means clustering example.
Source: tableausoftware.com/about/blog/2013/10/tableau-81-and-r-25327analytics-in-tableau-with-r/
19. 19
Figure 16 – This visualizes the results of an optimized portfolio.
Source: boraberan.wordpress.com/2014/02/26/prescriptive-analytics-in-tableau-with-r/
Figure 17: These visualizations show the same multidimensional scaling results in two different ways.
Visualizing R results in Tableau often allows the findings to be communicated far more easily to non-
technical audiences. Consider the two visuals below (Figure 17). The image on the left comes from
Wikipedia and shows a classic example of multidimensional scaling to reveal voting patterns.
The second image contains the same results visualized in Tableau on a map. Both tell roughly the same
story, but the map will likely be understood by and appeal to a much broader audience.
The combination of Tableau and R is extremely powerful. You can use Tableau’s advanced analytic
capabilities to create segments with derived metadata and pass them to R for further analysis.
Tableau then helps with understanding by automatically visualizing the results from R. This establishes
a feedback loop, which helps refine the model and prompts further questions. The R model becomes
a component of the analytical workflow as opposed to an end point. Interacting with the model becomes
a visual, iterative process.
20. 20
Conclusion
In many ways, Tableau stands alone among analytics platforms. Because of our mission to augment
human intelligence, we designed Tableau with both the business user and data scientist in mind.
By staying focused on our mission to empower users to ask interesting questions of their data as quickly
as possible, we built a platform that has valuable functionality for users of all levels.
Tableau’s flexible front-end allows business users to ask questions without needing to code or
understand databases. Tableau also has the necessary analytical depth to be a powerful weapon in a
data scientist’s arsenal. By leveraging sophisticated calculations, R integration, rapid cohort analysis,
and predictive capabilities, data scientists can complete complex analyses in Tableau and easily share
the visual results. Whether you use Tableau for data exploration and quality control, or model design and
testing, the interactive nature of the platform saves countless hours across the lifetime of a project.
By making analysis more accessible and faster to complete at all levels, Tableau drives critical
collaboration and better decision-making throughout time enterprise.
21. 21
About Tableau
Tableau helps people see and understand data. Tableau helps anyone quickly analyze, visualize
and share information. More than 29,000 customer accounts get rapid results with Tableau in the
office and on-the-go. And tens of thousands of people use Tableau Public to share data in their blogs
and websites. See how Tableau can help you by downloading the free trial at tableau.com/trial.
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