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Business Data Management: Overview
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Business Data Management: Overview
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Now · 🎯 Learning Objectives
Business Data Management: Overview
🎯 Learning Objectives
- Understand the role of data management in business intelligence
- Describe data warehouse architecture and ETL processes
- Compare OLAP and OLTP systems
- Introduce Big Data technologies (Hadoop, Spark)
- Understand NoSQL databases and data governance
📖 Core Content
1.1 What is Business Data Management?
Business Data Management (BDM) is the practice of collecting, storing, organizing, and analyzing data to support business decision-making. It encompasses the entire data pipeline from source systems to analytical dashboards.
(Diagram)
1.2 Key Concepts
| Concept | Description | Example |
|---|---|---|
| Data Warehouse | Central repository for structured data from multiple sources | Amazon Redshift, Snowflake |
| Data Lake | Repository for raw data of all types (structured, semi, unstructured) | S3, Azure Data Lake |
| ETL/ELT | Extract, Transform, Load — process to move data | Apache NiFi, dbt |
| OLTP | Transactional systems (high volume, low latency) | Bank transactions |
| OLAP | Analytical systems (complex queries, aggregations) | Sales reporting |
| Data Mart | Subset of data warehouse for one department | Marketing data mart |
1.3 Data Warehouse Architecture
(Diagram)
1.4 OLTP vs OLAP
| Aspect | OLTP | OLAP |
|---|---|---|
| Purpose | Run business (transactions) | Analyze business (decisions) |
| Data | Current, detailed | Historical, aggregated |
| Queries | Simple, frequent | Complex, ad-hoc |
| Updates | Frequent (INSERT, UPDATE) | Periodic (batch loads) |
| Design | Normalized (3NF) | Denormalized (star schema) |
| Users | Clerks, customers | Analysts, managers |
| Example | POS system | Annual sales report |
1.5 Big Data Overview
The 3 V's of Big Data:
- Volume: Terabytes to petabytes
- Velocity: Real-time to streaming
- Variety: Structured, semi-structured, unstructured Technologies:
- Hadoop: HDFS (storage) + MapReduce (processing) + ecosystem
- Spark: In-memory processing (faster than MapReduce)
- NoSQL: MongoDB (document), Cassandra (wide column), Neo4j (graph)
📝 Practice Questions
Q1: What is the difference between a data warehouse and a data lake?Data Warehouse: Structured data only, schema-on-write, cleaned and transformed, optimized for BI queries. Data Lake: All data types (raw), schema-on-read, cheaper storage, used for data science and exploration. Q2: Why is OLAP typically denormalized while OLTP is normalized?OLAP queries are read-heavy aggregations — denormalization reduces JOINs, speeding up queries. OLTP is write-heavy — normalization reduces data redundancy and ensures consistency during updates. Different design optimizes for different workloads. Q3: What does the "T" in ETL stand for and why is it important?Transform — cleaning, deduplication, validation, aggregation, and restructuring data. It's the most important step because raw data from source systems is messy, inconsistent, and needs standardization before analysis. Garbage in = garbage out. Join Discord NextETL Process