D608: Data Processing

D608 Data Processing is about automating how data moves into and out of the parts of an analytics system. At Western Governors University it belongs to the M.S. Data Analytics, Data Engineering specialization.

It is often seen as one of the more practical courses because you design and implement a pipeline. The challenge is communicating the design: diagrams, justification and a plan to monitor the pipeline once it runs.

What D608 Covers

The guide describes ETL pipeline operations at scale, variations of ETL depending on data repositories such as warehouses and lakes, streaming versus batch, and implementing pipelines in cloud-native environments.

Key Concepts Explained

ETL and Its Variations

ETL extracts data from sources, transforms it (clean, join, reshape) and loads it to a target. In a data lake approach, data is often loaded first and transformed later, which is commonly called ELT.

Batch Versus Streaming

Batch processing handles data in scheduled chunks and suits reporting that tolerates delay. Streaming handles records continuously and suits needs such as fraud alerts.

Data Flow Versus Process Flow

A data flow diagram shows movement of data between sources, processes and stores. A process flow diagram shows the sequence of steps and decisions. They answer different questions, so both are needed.

Typical Assignments and How to Approach Them

Designing the Pipeline Step by Step

A clear pipeline description follows the data. Name each source, say what is extracted and how often, list the transformations in order, and identify the target and who uses it.

Incremental Loads

Loading everything every time is simple but slow. An incremental load brings in only new or changed records, often identified by a timestamp or an ID, and is a common talking point in pipeline design.

Monitoring That Is Concrete

Useful checks include job success or failure, run duration, row counts, null rates in key fields and data freshness. State a threshold and an action for each, such as "alert the data team if the nightly job has not finished by 06:00".

The Three Competencies in Depth

The rubric separates design, build and operation. Make each easy to find in your document and avoid blending them.

• Design. Provide both diagrams and a paragraph explaining why this pipeline suits the scenario, including the batch or streaming decision.
• Build. Show the extract, transform and load steps working, with row counts or a sample proving the data arrived correctly.
• Operate. Present monitoring as a plan someone else could follow, with checks, thresholds and responses.

Warehouses and lakes deserve a sentence each: warehouses favor modeled, query-ready data, lakes keep raw data flexible. Say which your scenario needs, and why the ETL steps change as a result.

Where Students Get Stuck

• Choosing batch or streaming by habit. Let the latency requirement decide.
• Diagrams that mix two purposes. Keep data flow and process flow separate.
• No data quality checks. Validation belongs in the transform and monitoring steps.
• Thin monitoring. Cover failure detection, retries and logging.

Study Tips for D608

• Practice drawing both diagram types for a simple scenario.
• Keep sample rows before and after each transform.
• Write the monitoring plan as a short table.
• Check each rubric line against your document.

How We Help with D608

Send the scenario, any data description, rubric and your draft. We can explain ETL concepts, review diagrams and write-ups, or prepare a model example. The implementation and submission stay with you.

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