DBST 665 is a three-credit graduate course at the University of Maryland Global Campus, with DBST 651 as the prerequisite.
UMGC describes an introduction to technology approaches for designing and implementing a data warehouse for structured and unstructured data, covering modeling techniques, extraction, transformation and loading, performance challenges and system tradeoffs.
A warehouse is built for analysis rather than transactions, which changes design choices. Students often find the course demanding because modeling, data movement and performance all interact, and assignments require a remote access laboratory.
Course at a Glance
| Item | Details |
|---|---|
| University | University of Maryland Global Campus (UMGC) |
| Code and title | DBST 665 Data Warehouse Technologies |
| Credits | 3 |
| Prerequisite | DBST 651 |
| Lab | Assignments require use of a remote access laboratory |
| Subject area | Data warehousing |
What DBST 665 Covers
| Topic (from UMGC) | In plain language |
|---|---|
| Data modeling techniques | Designing warehouse structures such as facts and dimensions |
| Extraction, transformation and loading (ETL) | Moving data from sources, cleaning it and loading it |
| Performance challenges | Keeping queries and loads fast as data grows |
| System tradeoffs | Choosing between designs with different costs and benefits |
| Structured and unstructured data | Including data that does not fit tidy tables |
Key Concepts Explained
Facts and Dimensions
A fact table records measurable events, such as sales amounts. Dimension tables describe the context, such as product, store and date. Queries then summarize facts by dimension attributes.
Illustrative star schema: FactSales(DateKey, ProductKey, StoreKey, Quantity, Amount) joined to DimDate, DimProduct and DimStore. A question like "monthly sales by product category" joins the fact table to two dimensions and groups the result.
ETL Steps
Extraction pulls data from sources; transformation cleans, converts and conforms it; loading writes it to the warehouse. Quality checks between steps catch errors early.
Grain
The grain states what one fact row represents, such as one line item on one invoice. Deciding it first prevents double counting and mixed levels of detail.
Typical Assignments and How to Approach Them
| Assignment type | What it tests | How to approach it |
|---|---|---|
| Warehouse design | Modeling facts and dimensions | Declare the grain, then list measures and dimensions |
| ETL design or lab | Planning data movement and cleaning | Document source, rule and target for each field |
| Performance exercise | Indexing, partitioning and aggregation | Compare timings before and after |
| Tradeoff report | Evaluating architectures | Tabulate cost, speed, flexibility and maintenance |
Where Students Get Stuck
- Unclear grain. Write the grain sentence before adding columns.
- Dirty data. Plan for missing values, duplicates and mismatched formats.
- Slow loads. Think about incremental loads rather than full reloads.
- Over-normalizing. Warehouse dimensions are often deliberately denormalised.
Planning Your Work
| Stage | Do this | Output |
|---|---|---|
| Requirements | List the business questions | Question list |
| Design | Fix grain, facts and dimensions | Star schema diagram |
| ETL | Map source to target fields | Mapping table |
| Test | Run reconciliation checks | Row counts and totals compared with sources |
Reconciliation is a habit that impresses markers: after loading, compare row counts and key totals with the source to prove nothing was lost or duplicated.
A Worked ETL Mapping
| Source field | Rule | Target field |
|---|---|---|
| Order date (text) | Convert to date; reject invalid values to an error table | Date dimension, full date |
| Customer state | Trim spaces; map variants to standard codes | Customer dimension, state code |
| Order line quantity and price | Amount = qty x price; exclude canceled lines | Sales fact, amount |
A mapping table like this shows source, rule and target on one line, so a marker can follow your thinking. Add a column for data quality checks, such as null counts and duplicate keys, if the assignment asks for them.
Think too about slowly changing dimensions. If a customer moves state, should old sales stay with the old state or move to the new one? Naming the options (overwrite, add a new row with dates, or keep a previous-value column) and picking one for a reason is exactly the kind of tradeoff the course asks you to discuss.
Finally, document load frequency. A nightly batch, an hourly micro-batch and a streaming feed have different costs and freshness, and a short comparison table of these options makes a strong addition to a design report.
Final Checks Before Submitting
Before you submit, confirm that your diagram shows the grain in a note, that every measure has a stated aggregation rule (sum, average, count), and that dimension keys in the fact table match the dimension tables.
Also re-run your load scripts from an empty state to prove they work in order, and keep the output of your reconciliation checks as evidence.
Study Tips for DBST 665
- Revisit dimensional modeling from DBST 660 if you have taken it.
- Use real-looking sample data, not just a few rows.
- Write the business question next to each query.
- Keep ETL mappings in a table, one row per field.
How We Help with DBST 665
Send the assignment, rubric, schema and feedback. We can explain concepts, prepare custom worked solutions, or review your own design and mapping for errors and clarity. We do not log in to lab environments for you.
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Frequently Asked Questions
DBST 651.
Three credits.
Yes, UMGC says assignments require use of a remote access laboratory.
Extraction, transformation and loading: moving data from sources into the warehouse after cleaning and conforming it.
UMGC says the technology approaches apply to structured and unstructured data.
The course covers modeling techniques generally; check your assignment instructions for the required design style.