Data Warehousing and Business Intelligence for Managers

Organizations use data warehouses to speed reporting by consolidating data across silos, support complex analytical questions, monitor current operations, and forecast trends with time-based snapshots.

Explore a four-layer data warehousing diagram—sources, staging, data warehouse, and data access—and learn how ETL pulls data from transactional systems to a historical warehouse for reporting and analysis.

Master the iron triangle of cost, scope, and time in data warehousing projects, and apply ETL processes with a specialized team to deliver production reports.

Explore the data journey from capture to usable insights in a data warehouse. Analyze sources and reports to design effective, trusted business insights for managers.

Learn how data models drive data warehousing, from entity relationship diagrams to hub-and-spoke dimensional models and data marts. See how business requirements define keys, attributes, and data integrity.

Learn Kimball dimensional modeling for warehouses, linking facts and dimensions in star schemas. Define grain and facts, and use conformed dimensions for drill up and down across time and location.

Explore Inmon's top-down relational modelling for corporate data warehouses in third normal form, emphasizing subject orientation, time variant data, nonvolatile integration, and data marts for business intelligence.

Explore how big data coexists with data warehousing, and why relational databases can't manage big data. Learn to parse repetitive data, apply textual disambiguation, normalize it, and use parallel processing.

Explore data modeling as the core of the data warehouse, using sql to query sources and create reports, and compare kimball dimensional modeling with third normal form.

Plan and design a data warehouse by mapping data sources to target dimensions and facts, implementing an etl process that extracts, transforms, and loads data using batched sql statements.

Profile data sources through data profiling to determine connectivity to systems, data shape, and available documentation, then map source to target and plan full or incremental extractions with scheduling.

Transforming data in data warehousing and business intelligence for managers cleans and standardizes records before delivery, using filtering, format changes, and deduplication, plus transformations like aggregation, joining, masking, and sorting.

Learn how to load data into a data warehouse using star and snowflake schemas, populate dimension and fact tables, and preserve referential integrity by conformed dimensions and proper sequencing.

A data architect outlines practical steps for starting a data warehousing project, clarifying goals, sourcing data, building a data model, and assembling an interview-ready technical team.

Explain the ETL process—extracting, transforming, and loading data into the data warehouse—and show dimensional modeling by populating the dimensions.

Compare enterprise and scrappy BI tools such as MicroStrategy, IBM Cognos, Oracle BI, and Business Objects. Understand selection factors, users' needs, and why business users rarely create their own reports.

Highlight report development as the data warehouse’s face by delivering early sample reports, testing against existing outputs, and guiding phased delivery to meet real needs.

Design a data mart and data warehouse for Virginia rail incident tracking, organize inspections and reports, manage the final project, and plan for a nationwide business intelligence solution.

Explore how real-time reporting connects operational data to corporate dashboards, enabling fast analytics, data governance, and closer IT and business collaboration to deliver a united version of the truth.

Explore how real-time data streams from multiple sources feed a data lake and an operational data store, applying schema on read to enable rapid business intelligence, alerts, and analytics.

Evaluate real-time reporting by tracing data from source to report and identifying true business use cases behind instantaneous access. Distinguish bottom-line needs from vendor hype or engineering curiosity.

Explore how distributed architectures drive reliable, scalable data delivery through clusters of data warehouses, replication, and failover, while weighing CAP tradeoffs and tool costs for high performance and low latency.

Delivering yesterday's data with 24-hour latency, the traditional data warehouse relies on overnight etl, staging, and centralized processing, limiting timely insights for managers.

Examine how to handle unelaborated real-time requirements by integrating diverse data sources into a data warehouse with a staging area, streaming, caching, and on-demand reporting.

Learn how to gather requirements by listening in meetings, balance real-time needs with performance, and use dashboards to communicate query latency and data access across centralized and decentralized systems.

Refine business requirements for real-time field worker needs and item lists, enable real-time procedure recording, and generate visuals that reveal system performance and slowdown causes under single-server constraints.

See how data flows from sources to streaming or batching paths via Kinesis, stored in a data lake, and queried with Redshift, Glue, and Spectrum across structured and external data.

Examine the on-premises data warehouse approach, including caching and streaming with tools like Spark, and how to choose among many options to process historical data, supporting machine learning and reporting.

Compare Microsoft and AWS cloud architectures to design data pipelines, blending data sources, batch and real-time processing, orchestration, data lakes, data warehouses, and reporting.

Organize a data lake with dedicated zones for ingestion, raw data, staging, and catalogs to support governance, security, and easy data discovery.

Learn the definition of data warehousing, its components and diagram, plus data modeling, the heart of the data warehouse. Apply DTL to load data and business intelligence to extract insights.