Data lake design patterns in Hadoop - Mini Project: Build & Apply

Data Lake Design Patterns with Hadoop

📖 Scenario: You work at a company that collects lots of data from different sources like sales, customer feedback, and website logs. You want to organize this data in a Hadoop data lake so it is easy to find and use later.

🎯 Goal: Build a simple data lake structure using Hadoop folders and files that follow common design patterns: raw data, cleaned data, and aggregated data.

📋 What You'll Learn

Create a dictionary called data_lake with keys for raw, cleaned, and aggregated data folders

Add a configuration variable called file_format set to parquet

Use a dictionary comprehension to create file paths for each data type using the file_format

Print the final data_lake_paths dictionary showing the full paths

💡 Why This Matters

🌍 Real World

Data lakes store large amounts of raw and processed data in Hadoop systems. Organizing data with clear folder and file naming helps teams find and use data efficiently.

💼 Career

Understanding data lake design patterns is important for data engineers and analysts working with big data platforms like Hadoop.

1

Create the initial data lake structure

Create a dictionary called data_lake with these exact keys and values: 'raw': '/data/raw', 'cleaned': '/data/cleaned', and 'aggregated': '/data/aggregated'.

Hadoop

# Create the data_lake dictionary with raw, cleaned, and aggregated paths
# Your code here

Hint

Use curly braces {} to create a dictionary with the specified keys and values.

2

Add a file format configuration

Create a variable called file_format and set it to the string 'parquet'.

Hadoop

data_lake = {'raw': '/data/raw', 'cleaned': '/data/cleaned', 'aggregated': '/data/aggregated'}
# Create the file_format variable and set it to 'parquet'
# Your code here

Hint

Use a simple assignment statement to create the variable file_format.

3

Create file paths using dictionary comprehension

Use a dictionary comprehension to create a new dictionary called data_lake_paths. For each key and path in data_lake.items(), create a new path by adding a slash and the file name data. plus the file_format extension. For example, the raw path should be /data/raw/data.parquet.

Hadoop

data_lake = {'raw': '/data/raw', 'cleaned': '/data/cleaned', 'aggregated': '/data/aggregated'}
file_format = 'parquet'
# Create data_lake_paths dictionary using dictionary comprehension
# Your code here

Hint

Use {key: value for key, value in dictionary.items()} and f-strings to build the new paths.

4

Print the final data lake paths

Write a print statement to display the data_lake_paths dictionary.

Hadoop

data_lake = {'raw': '/data/raw', 'cleaned': '/data/cleaned', 'aggregated': '/data/aggregated'}
file_format = 'parquet'
data_lake_paths = {key: f"{path}/data.{file_format}" for key, path in data_lake.items()}
# Print the data_lake_paths dictionary
# Your code here

Hint

Use print(data_lake_paths) to show the dictionary.

Practice

1. What is the main purpose of organizing data into zones like raw, processed, and curated in a data lake?

easy

A. To increase the size of the data lake

B. To keep data clean, easy to find, and ready for analysis

C. To make data harder to access

D. To reduce the number of users accessing data

Solution

1. Step 1: Understand the role of data zones

   Raw, processed, and curated zones help organize data by its state and quality.

2. Step 2: Identify the benefit of this organization

   This organization keeps data clean, easy to find, and ready for analysis.

3. Final Answer:

   To keep data clean, easy to find, and ready for analysis -> Option B

4. Quick Check:

   Data zones improve data usability = A [OK]

Hint: Think about why clean data helps analysis fast [OK]

Common Mistakes:

• Confusing size increase with organization benefits
• Assuming data zones restrict access
• Thinking zones reduce user numbers

2. Which of the following is the correct way to represent the 'raw' data zone in Hadoop folder structure?

easy

A. /data/curated/

B. /data/processed/

C. /data/raw/

D. /data/analytics/

Solution

1. Step 1: Recall standard folder naming for data zones

   The raw data zone is usually stored in a folder named 'raw' to indicate unprocessed data.

2. Step 2: Match folder names to zones

   /data/raw/ matches the raw data zone, while others represent different zones or purposes.

3. Final Answer:

   /data/raw/ -> Option C

4. Quick Check:

   Raw data folder = /data/raw/ [OK]

Hint: Raw data folder always named 'raw' [OK]

Common Mistakes:

• Choosing processed or curated folder names for raw data
• Using unrelated folder names like analytics
• Confusing folder names with data types

3. Given the following Hadoop folder structure:

/data/raw/sales.csv
/data/processed/sales_clean.csv
/data/curated/sales_summary.csv

Which file would most likely contain cleaned and transformed sales data ready for analysis?

medium

A. /data/processed/sales_clean.csv

B. /data/raw/sales_summary.csv

C. /data/curated/sales_summary.csv

D. /data/raw/sales.csv

Solution

1. Step 1: Understand the meaning of each zone

   Raw zone has original data, processed zone has cleaned/transformed data, curated zone has aggregated or summarized data.

2. Step 2: Identify the file with cleaned and transformed data

   /data/processed/sales_clean.csv indicates cleaned sales data ready for further use.

3. Final Answer:

   /data/processed/sales_clean.csv -> Option A

4. Quick Check:

   Processed zone = cleaned data = D [OK]

Hint: Processed folder means cleaned and transformed data [OK]

Common Mistakes:

• Choosing raw data file as cleaned data
• Confusing curated summary with cleaned data
• Selecting non-existent file paths

4. You have a data lake with folders: /data/raw/, /data/processed/, and /data/curated/. You accidentally saved uncleaned data in /data/curated/. What is the best fix?

medium

A. Move the uncleaned data to /data/raw/ and reprocess it

B. Leave the data as is in /data/curated/

C. Delete the /data/raw/ folder

D. Rename /data/curated/ to /data/raw/

Solution

1. Step 1: Identify the problem with data placement

   Uncleaned data should not be in the curated zone, which is for clean, ready data.

2. Step 2: Choose the correct fix

   Moving uncleaned data back to raw zone and reprocessing it ensures proper data flow and quality.

3. Final Answer:

   Move the uncleaned data to /data/raw/ and reprocess it -> Option A

4. Quick Check:

   Raw zone = uncleaned data fix = B [OK]

Hint: Unclean data belongs in raw zone, not curated [OK]

Common Mistakes:

• Ignoring data quality rules
• Deleting unrelated folders
• Renaming folders without moving data

5. You want to design a data lake in Hadoop that supports fast analytics and easy data governance. Which design pattern should you follow?

hard

A. Store all data in a single folder without zones

B. Keep only raw data and process it on-demand

C. Use random folder names for each data type

D. Organize data into raw, processed, and curated zones with clear folder structures

Solution

1. Step 1: Understand the goal of fast analytics and governance

   Fast analytics needs clean, organized data; governance needs clear structure and control.

2. Step 2: Identify the best design pattern

   Organizing data into raw, processed, and curated zones with clear folders supports both goals effectively.

3. Final Answer:

   Organize data into raw, processed, and curated zones with clear folder structures -> Option D

4. Quick Check:

   Zones + clear folders = fast analytics + governance = C [OK]

Hint: Zones and clear folders improve analytics and governance [OK]

Common Mistakes:

• Storing all data together causing confusion
• Processing data only on-demand slowing queries
• Using unclear folder names causing management issues