Data lake design patterns in Hadoop - Step-by-Step Execution

Concept Flow - Data lake design patterns

Raw Data Ingested

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Landing Zone: Store Raw Data

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Cleansing & Transformation

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Curated Zone: Clean Data

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Data Serving Layer: Analytics & BI

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Users Access Data

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Feedback & Monitoring

↩Back to Cleansing & Transformation (Iterate)

Data flows from raw ingestion to landing zone, then cleansed and transformed into curated data, finally served for analytics, with feedback loops for improvement.

Execution Sample

Hadoop

1. Ingest raw data into landing zone
2. Clean and transform data
3. Store clean data in curated zone
4. Serve data for analytics
5. Users query and analyze data

This sequence shows the main steps in a data lake design pattern from raw data ingestion to user analytics.

Execution Table

Step	Action	Data State	Storage Zone	Purpose
1	Ingest raw data	Unprocessed, original format	Landing Zone	Capture all incoming data as-is
2	Clean and transform	Filtered, structured, enriched	Processing Layer	Prepare data for analysis
3	Store clean data	Validated and organized	Curated Zone	Reliable data for users
4	Serve data	Ready for queries	Serving Layer	Support analytics and BI tools
5	User access	Data consumed	Serving Layer	Enable insights and decisions
6	Feedback & monitor	Identify issues or improvements	Monitoring System	Improve data quality and processes
7	Iterate cleansing	Refined data	Processing Layer	Continuous improvement loop

💡 Process repeats with feedback to improve data quality and usability

Variable Tracker

Data State	Start	After Step 1	After Step 2	After Step 3	After Step 4	After Step 5	After Step 6
Raw Data	None	Raw ingested	Raw ingested	Raw ingested	Raw ingested	Raw ingested	Raw ingested
Clean Data	None	None	Cleaned & transformed	Stored curated	Available for queries	Queried by users	Refined after feedback
User Access	None	None	None	None	Data served	Data consumed	Data consumed

Key Moments - 3 Insights

Why do we keep raw data in the landing zone instead of cleaning it immediately?

Keeping raw data preserves the original source, allowing reprocessing if needed. See execution_table step 1 where raw data is stored as-is before cleaning.

What is the difference between the curated zone and the serving layer?

The curated zone stores clean, validated data, while the serving layer prepares data specifically for fast queries and analytics. Refer to execution_table steps 3 and 4.

How does feedback improve the data lake?

Feedback identifies data quality issues or process gaps, triggering reprocessing to refine data. This is shown in execution_table steps 6 and 7.

Visual Quiz - 3 Questions

Look at the execution_table, at which step is data first cleaned and transformed?

A Step 2

B Step 3

C Step 1

D Step 4

💡 Hint

Check the 'Action' column for cleaning and transformation in execution_table

According to variable_tracker, what is the state of 'Clean Data' after Step 3?

A None

B Stored curated

C Cleaned & transformed

D Raw ingested

💡 Hint

Look at the 'Clean Data' row under 'After Step 3' in variable_tracker

If feedback is ignored, which step in execution_table would be skipped?

A Step 5

B Step 6

C Step 7

D Step 4

💡 Hint

Feedback triggers iteration shown in Step 7 in execution_table

Concept Snapshot

Data lake design patterns:
1. Ingest raw data into landing zone (store as-is)
2. Clean and transform data in processing layer
3. Store clean data in curated zone
4. Serve data for analytics in serving layer
5. Use feedback loops to improve data quality
Keep raw data for reprocessing and separate zones for clarity.

Full Transcript

Data lake design patterns organize data flow from raw ingestion to user analytics. First, raw data is ingested and stored in the landing zone without changes. Then, data is cleaned and transformed in the processing layer. Clean data is stored in the curated zone for reliability. The serving layer prepares data for fast queries and analytics. Users access data here to gain insights. Feedback and monitoring identify issues and trigger reprocessing to improve data quality continuously. This pattern helps manage large data sets efficiently and keeps original data safe for future use.