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Business intelligence tools for small companies a guide to free and low-cost solutions /
Business intelligence tools for small companies a guide to free and low-cost solutions /
Learn how to transition from Excel-based business intelligence (BI) analysis to enterprise stacks of open-source BI tools. Select and implement the best free and freemium open-source BI tools for your company's needs and design, implement, and integrate BI automation across the full stack using...
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| Main Author: | |
|---|---|
| Corporate Author: | |
| Other Authors: | |
| Format: | eBook |
| Language: | English |
| Published: |
Berkeley, CA :
Apress,
2017.
|
| Physical Description: |
1 online resource. |
| Subjects: | |
| Online Access: | SpringerLink - Click here for access |
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| 100 | 1 | |a Nogués, Albert |q (Nogués Sabater) |0 https://id.loc.gov/authorities/names/no2017116805. | |
| 245 | 1 | 0 | |a Business intelligence tools for small companies : |b a guide to free and low-cost solutions / |c Albert Nogués, Juan Valladares. |
| 264 | 1 | |a Berkeley, CA : |b Apress, |c 2017. | |
| 300 | |a 1 online resource. | ||
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| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a At a Glance; Contents; About the Authors; About the Technical Reviewer; Acknowledgments; Introduction; Chapter 1: Business Intelligence for Everybody; What Is Business Intelligence?; BI Evolution; From Strategic to Tactical; Big Data; Internet of Things; BI Characteristics; Hidden Relevant Information; Accuracy; Significant KPIs; On Time; Company Analytics Life Cycle and Continuous Improvement; Benefits of BI; Direct Benefits; Indirect Benefits; Objectives; Who Can Get Benefit from BI?; BI Platform Components; Source ERP; Database; ETL; Front-End Tool; Budgeting Tool. | |
| 505 | 8 | |a BI Platform LocationBI Concepts; Datawarehouse; DataMart; Logical Model; Relational Model; Normalized Model; Denormalized Model; Star Model; Snowflake Model; Physical Model; BI Main Objects; Project; Tables; Facts; Dimensions; Reports; BI Approaches; Query and Reporting; Information Sharing; Dashboarding; Data Import; Data Discovery; MOLAP; Data Mining; Incoming Approaches; BI Capabilities; Drilling; Pivoting; Visualizations; Map Visualizations; Sorting; Grouping; Filtering; Conditional Expressions; Subtotals; Administration; User Interface; Conclusion. | |
| 505 | 8 | |a Chapter 2: Agile Methodologies for BI ProjectsIntroduction to Agile Methodologies; Agile Approaches; Our Recommended Mix between Scrum and Kanban; Developing Projects with Scrum; Roles; Product Owner; Scrum Master; Development Team; Stakeholders; Users; Product Owner Assistant; Sprint; Specific Scrum Meetings; Release; Artifacts Used in Scrum; User Story; Developer Story; Product Backlog; Definition of Done; When to Start -- Ready Definition; Product Backlog Burndown Chart; Sprint Backlog; Sprint Backlog Burndown Chart; Impediment List; Improvement List; Limiting Spent Time with Timeboxing. | |
| 505 | 8 | |a SpikeMaintenance with Kanban; Kanban Concepts; Task Workflow; Task Board and Visual Management; Work In Progress; Lead Time; Mix of Both Methodologies, Scrumban; Peculiarities of Scrum for BI; Sprint 0 -- Longer Initial Analysis; BI Project Segmentation; Front-End vs. Back-End Activities; Role Isolation -- Specific Knowledge; Developer Story Types in BI; Data Modeling Developer Stories; ETL Developer Stories; BI Model Developer Stories; Report and Dashboarding Developer Stories; MOLAP Developer Stories; Agile Management Tools; Trello; JIRA Software; Conclusion; Chapter 3: SQL Basics. | |
| 505 | 8 | |a What Is SQL?The Relational Model; Databases and Database Vendors; ACID Compliance; Types of SQL Statements; SQL Datatypes; Numeric Datatypes; Text Datatypes; Date Datatypes; Other Datatypes; Retrieving Data from a Table; The select * Statement; The select column Statement; The select count (*) or count (column) Statement; The select distinct Clause; Sorting; Filtering; The Where Clause; Operators; Logical Operators; Grouping Data; Using Subqueries; Joining Tables; Types of Joins; Cartesian Join; Inner Join; Outer Join; Left Outer Join (or Left Join); Right Outer Join (or Right Join). | |
| 500 | |a Full Outer Join (or Full Join). | ||
| 500 | |a Includes index. | ||
| 520 | |a Learn how to transition from Excel-based business intelligence (BI) analysis to enterprise stacks of open-source BI tools. Select and implement the best free and freemium open-source BI tools for your company's needs and design, implement, and integrate BI automation across the full stack using agile methodologies. Business Intelligence Tools for Small Companies provides hands-on demonstrations of open-source tools suitable for the BI requirements of small businesses. The authors draw on their deep experience as BI consultants, developers, and administrators to guide you through the extract-transform-load/data warehousing (ETL/DWH) sequence of extracting data from an enterprise resource planning (ERP) database freely available on the Internet, transforming the data, manipulating them, and loading them into a relational database. The authors demonstrate how to extract, report, and dashboard key performance indicators (KPIs) in a visually appealing format from the relational database management system (RDBMS). They model the selection and implementation of free and freemium tools such as Pentaho Data Integrator and Talend for ELT, Oracle XE and MySQL/MariaDB for RDBMS, and Qliksense, Power BI, and MicroStrategy Desktop for reporting. This richly illustrated guide models the deployment of a small company BI stack on an inexpensive cloud platform such as AWS. You will learn how to manage, integrate, and automate the processes of BI by selecting and implementing tools to: Implement and manage the business intelligence/data warehousing (BI/DWH) infrastructure Extract data from any enterprise resource planning (ERP) tool Process and integrate BI data using open-source extract-transform-load (ETL) tools Query, report, and analyze BI data using open-source visualization and dashboard tools Use a MOLAP tool to define next year's budget, integrating real data with target scenarios Deploy BI solutions and big data experiments inexpensively on cloud platforms. | ||
| 650 | 0 | |a Management information systems. |0 https://id.loc.gov/authorities/subjects/sh85080359. | |
| 650 | 0 | |a Cloud computing. |0 https://id.loc.gov/authorities/subjects/sh2008004883. | |
| 650 | 0 | |a Business |x Data processing. |0 https://id.loc.gov/authorities/subjects/sh85018264. | |
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| 650 | 1 | 0 | |a Computer science. |0 https://id.loc.gov/authorities/subjects/sh89003285. |
| 650 | 2 | 4 | |a Open Source. |
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| 650 | 2 | 2 | |a Information Storage and Retrieval. |0 https://id.nlm.nih.gov/mesh/D016247. |
| 650 | 2 | 0 | |a Big data. |0 https://id.loc.gov/authorities/subjects/sh2012003227. |
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| 650 | 7 | |a Management |x Data processing. |2 fast. | |
| 650 | 7 | |a Business |x Data processing. |2 fast. | |
| 650 | 7 | |a Cloud computing. |2 fast. | |
| 650 | 7 | |a Management information systems. |2 fast. | |
| 700 | 1 | |a Valladares, Juan. | |
| 710 | 2 | |a SpringerLink (Online service) |0 https://id.loc.gov/authorities/names/no2005046756. | |
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