Why data lake architecture centralizes data in Hadoop - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to read data from a Hadoop data lake using Spark.

Hadoop

df = spark.read.format([1]).load("/data/lake/path")

Drag options to blanks, or click blank then click option'

Acsv

Bjson

Cxml

Dparquet

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing a format not optimized for big data like XML.

The parquet format is commonly used in data lakes for efficient storage and querying.

2fill in blank

medium

Complete the code to filter data for a specific year in the data lake.

Hadoop

filtered_df = df.filter(df.year == [1])

Drag options to blanks, or click blank then click option'

A2020

B"2020"

C'2020'

Dyear

Attempts:

3 left

💡 Hint

Common Mistakes

Using quotes around the year number causing type mismatch.

The year column is numeric, so the filter value should be an integer without quotes.

3fill in blank

hard

Fix the error in the code to write data back to the data lake in parquet format.

Hadoop

filtered_df.write.mode([1]).format("parquet").save("/data/lake/output")

Drag options to blanks, or click blank then click option'

Aappend

Badd

Cinsert

Dupdate

Attempts:

3 left

💡 Hint

Common Mistakes

Using invalid modes like 'add' or 'update' which cause errors.

The write mode 'append' allows adding data without overwriting existing files.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 characters.

Hadoop

lengths = {word: [1] for word in words if len(word) [2] 3}

Drag options to blanks, or click blank then click option'

Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' causing wrong filtering.

The dictionary comprehension maps each word to its length only if the word length is greater than 3.

5fill in blank

hard

Fill all three blanks to create a filtered dictionary with uppercase keys and values greater than 0.

Hadoop

result = [1]: [2] for k, v in data.items() if v [3] 0}

Drag options to blanks, or click blank then click option'

Ak.upper()

Bv

C>

Dk.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using k.lower() instead of k.upper().

Using '<' instead of '>'.

This comprehension creates a dictionary with keys in uppercase and includes only items with values greater than zero.

Practice

1. Why does data lake architecture centralize data in one place?

easy

A. To limit data access to only one user

B. To separate data by type in different locations

C. To store all types of data together for easy access and analysis

D. To reduce the amount of data stored

Solution

1. Step 1: Understand the purpose of data lake centralization

   Data lakes centralize data to keep all types of data in one place, making it easier to manage and analyze.

2. Step 2: Compare options with this purpose

   Only To store all types of data together for easy access and analysis correctly states that centralization is for storing all data types together for easy access and analysis.

3. Final Answer:

   To store all types of data together for easy access and analysis -> Option C

4. Quick Check:

   Centralization means one place for all data = A [OK]

Hint: Centralized means all data in one place for easy use [OK]

Common Mistakes:

• Thinking data lakes separate data by type
• Believing centralization limits access
• Assuming centralization reduces data volume

2. Which of the following is a correct statement about data lakes in Hadoop?

easy

A. Data lakes centralize all data types using Hadoop HDFS

B. Data lakes separate data into many small isolated files

C. Data lakes require data to be pre-processed before storage

D. Data lakes store only structured data in Hadoop HDFS

Solution

1. Step 1: Recall Hadoop HDFS role in data lakes

   Hadoop HDFS stores all types of data (structured, unstructured) centrally in data lakes.

2. Step 2: Evaluate each option

   Data lakes centralize all data types using Hadoop HDFS correctly states that data lakes centralize all data types using Hadoop HDFS. Other options are incorrect because data lakes store more than structured data, do not require pre-processing before storage, and do not isolate data into many small files.

3. Final Answer:

   Data lakes centralize all data types using Hadoop HDFS -> Option A

4. Quick Check:

   Hadoop HDFS stores all data types centrally = D [OK]

Hint: Hadoop HDFS stores all data types centrally in data lakes [OK]

Common Mistakes:

• Thinking data lakes store only structured data
• Believing data must be pre-processed before storage
• Assuming data lakes isolate data into many files

3. Given this code snippet using Hadoop HDFS API to list files in a data lake folder:

from pyarrow import fs
hdfs = fs.HadoopFileSystem('default')
files = hdfs.get_file_info(fs.FileSelector('/data_lake'))
print([file.path for file in files])

What will this code output?

medium

A. An empty list because '/data_lake' is empty

B. A list of file paths stored in the '/data_lake' directory

C. An error because 'get_file_info' does not exist

D. A list of file sizes instead of paths

Solution

1. Step 1: Understand the code functionality

   The code uses HadoopFileSystem to access HDFS, then lists file info in '/data_lake' directory, and prints their paths.

2. Step 2: Predict the output

   The output will be a list of file paths inside '/data_lake'. If the directory has files, their paths will be printed as a list.

3. Final Answer:

   A list of file paths stored in the '/data_lake' directory -> Option B

4. Quick Check:

   Listing files returns paths list = C [OK]

Hint: Listing files returns their paths as a list [OK]

Common Mistakes:

• Assuming method does not exist causing error
• Expecting file sizes instead of paths
• Thinking output is empty without checking directory contents

4. This code tries to read a file from a Hadoop data lake but causes an error:

from pyarrow import fs
hdfs = fs.HadoopFileSystem('default')
with hdfs.open_input_stream('/data_lake/file.txt') as f:
    data = f.read()
print(data)

What is the likely cause of the error?

medium

A. The 'read()' method is missing parentheses

B. The method 'open_input_stream' is not valid for HadoopFileSystem

C. The file is too large to read at once

D. The file path '/data_lake/file.txt' does not exist in HDFS

Solution

1. Step 1: Check method correctness

   'open_input_stream' is a valid method to open files in HadoopFileSystem, and 'read()' is correctly called.

2. Step 2: Identify common error cause

   Most errors here happen if the file path does not exist in HDFS, causing a file not found error.

3. Final Answer:

   The file path '/data_lake/file.txt' does not exist in HDFS -> Option D

4. Quick Check:

   File not found causes error = A [OK]

Hint: File not found is most common read error [OK]

Common Mistakes:

• Thinking method 'open_input_stream' is invalid
• Assuming file size causes immediate error
• Forgetting to use parentheses with read()

5. You want to design a data lake architecture using Hadoop that centralizes data from multiple sources including CSV files, images, and logs. Which approach best supports this goal?

hard

A. Store all data types in Hadoop HDFS without converting formats, enabling flexible access

B. Convert all data to a single format before storing in separate folders in HDFS

C. Store only structured data in HDFS and keep unstructured data outside the lake

D. Use multiple storage systems for each data type and link them with Hadoop

Solution

1. Step 1: Understand data lake centralization goal

   Data lakes centralize all data types in one place to allow flexible storage and easy access.

2. Step 2: Evaluate storage approaches

   Store all data types in Hadoop HDFS without converting formats, enabling flexible access stores all data types in Hadoop HDFS without forcing format conversion, supporting centralization and flexibility. Other options separate data or limit types, which breaks centralization.

3. Final Answer:

   Store all data types in Hadoop HDFS without converting formats, enabling flexible access -> Option A

4. Quick Check:

   Centralize all data types in one system = B [OK]

Hint: Keep all data types together in HDFS for true centralization [OK]

Common Mistakes:

• Converting all data to one format losing flexibility
• Separating unstructured data outside the lake
• Using multiple storage systems breaking centralization