Data warehousing Training

Data warehousing Training in Chennai:

Training in Tambaram is one of the best Data warehousing Training Institutes in Chennai Tambaram. We are focusing Placement based hands-on training for Datawarehousing Course. Our Data warehousing course Trainers are taking the classes from basic level to Advanced Level. So, our Students can get the job easily in MNC companies in Chennai and Bangalore once complete their Data warehousing Classes. Our Data warehousing Course content and syllabus are revised monthly once by our experienced trainers. Our Data warehousing training syllabus is designed for fulfilled to our student’s needs. So, you can get the up-to-date technical knowledge in Data warehousing field. Our 6+ years of experienced trainers are working hard to improve their student knowledge and helps to attend the interviews confidently.

Our Training center offering Data warehousing Training in various places around the Chennai. We are providing well infrastructure with fully equipped lab facilities to our students. We pay more attention to the needs of our students. Already we have trained more than 200+ Students in Chennai with placement. Data warehousing Training timing schedule on weekday classes, Weekend classes, Evening classes and Fast Track classes. Our Data warehousing Course fees is based on student’s requirement and value for money. After completing Data warehousing Classes, providing free resume building classes, Aptitude classes, Mockup Interview Sessions.

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Data Warehousing Training in Chennai syllabus:

Dimensional modeling is the proven technique for designing comprehensive, high-performance Business Intelligence/Data Warehouse (BI/DW) systems. Agile dimensional modeling is dimensional modeling done iteratively, incrementally and collaboratively.

This course provides in depth coverage of agile dimensional modeling techniques for improving communication between BI/DW teams and business stakeholders as well as responding to changing DW requirements. Specifically the course covers BEAM*, the powerful, yet simple method for ModelStorming directly with BI stakeholders described in the book Agile Data Warehouse Design: collaborative dimensional modeling, from whiteboard to star schema

Objectives

In this agile data warehouse training, you will learn to:

• Model data requirements directly with stakeholders
• Translate common cross-industry data analysis requirements into efficient, flexible dimensional models
• Maximize the usability and performance of your data warehouse or data mart designs
• Plan, design and incrementally develop agile data warehouse solutions

Audience

This course is for anyone interested in learning the latest agile techniques for designing and managing dimensional data warehouses and ETL processes. Beginner, intermediate and experienced data warehouse practitioners, data architects, DBA’s and ETL designers & developers will all benefit from this course.

Agile Data Warehousing

• What the Agile manifesto means for business intelligence/data warehousing
• The challenge of proactive data warehouse development: the need for agile data modeling techniques
• Becoming agile: supporting iterative and incremental data warehouse development

Dimensional Modeling Fundamentals

• Data warehousing requirements, challenges and opportunities
• Modeling for measurement: the case for dimensional modeling
• Star schema, snowflake schema, facts and dimensions
• Modeling with BI stakeholders: the case for Agile dimensional modeling

Dimensional ModelStorming

• Business Event Analysis and Modeling (BEAM*): an agile dimensional modeling method
• Creating inclusive data models using the BEAM* notation
• ModelStorming: practical techniques for facilitating interactive modeling
• Using the 7Ws framework (who, what, when, where, how many, why and how) to discover and document BI data requirements
• Telling business event stories: modeling by example
• Drawing hierarchy charts: modeling dimensional attributes and hierarchical relationships
• Sketching timelines: modeling events sequences and business processes
• Identifying Key Performance Indicators (KPIs) and Metrics: aggregation level, comparisons and query by exception
• Test-driven design: using data profiling results to validate requirements models
• Designing high performance star schemas
• Refactoring stars: welcoming change and responding to it

Agile Date Warehouse Design

• Agile data marts vs. enterprise data warehouses
• ‘Just enough design up front’ (JEDUF) and ‘just in time’ (JIT) modeling
• Incremental data warehouse development using data marts: the Data Warehouse Bus Architecture and event matrix planning
• Using event matrices: Documenting high-level data warehouse design. Planning and estimating their delivery
• Data Warehouse reuse: conformed dimensions and facts, role playing dimensions

Dimensional Design Patters Using the 7 Ws

Who and What: Customer, employee, product and service dimensions

• Handling Large customer populations with rapidly changing demographic profiles: tracking millions of detail changes per year
• Customer relationships and organization structures: using hierarchy maps to handle recursive relationships and variable-depth hierarchies
• Mixed business models: Business to Business (B2B) and Business to Consumer (B2C)
• Heterogeneous products: dealing with many dimensional attributes and ragged hierarchies
• Decomposing products and services: bill of materials, components, product bundles

When and Where: Date, time and location dimensions

• Flexible date handling, ad-hoc ranges and multiple simultaneous calendar events
• Understanding journeys and trajectories: modeling events with multiple geographies
• Multinational support: national languages reporting, multi-currencies time zones and local calendars

How Many: Facts, Measures and KPIs

• Fact table types: transactional, periodic snapshots, accumulating snapshots
• Fact additivity: additive, semi-additive and non-additive measures
• Fact table performance and usability optimization: indexing, partitioning, aggregation and consolidated fact tables

Why and How: Causal (promotions, weather, reasons) and Event description dimensions

• Allocation problems: multi-valued dimensions, bridge tables, weighting factors, impact reports and ‘correctly weighted’ analysis
• Supporting complex combination analysis with pivoted dimensions
• Combining and separating dimensions
• Modeling sequential behavior, causation and outcome using dimensional overloading and step dimensions