Why ingestion pipelines feed the data lake in Hadoop - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to read data from a source into the ingestion pipeline.

Hadoop

data = spark.read.format([1]).load("/source/data")

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

Acsv

Bxml

Cjson

Dtxt

Attempts:

3 left

💡 Hint

Common Mistakes

Using unsupported formats like 'txt' or 'xml' without proper parsing.

The ingestion pipeline reads data in JSON format from the source.

2fill in blank

medium

Complete the code to write data into the data lake in parquet format.

Hadoop

data.write.format([1]).save("/data_lake/raw")

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

Acsv

Bparquet

Cjson

Dorc

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing CSV which is row-based and less efficient for big data.

Parquet is a columnar storage format optimized for big data and commonly used in data lakes.

3fill in blank

hard

Fix the error in the code to append new data to the existing data lake folder.

Hadoop

data.write.mode([1]).format("parquet").save("/data_lake/raw")

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

Aerror

Boverwrite

Cignore

Dappend

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'overwrite' which deletes existing data.

Using 'append' mode adds new data without deleting existing data in the data lake.

4fill in blank

hard

Fill both blanks to filter data before saving to the data lake.

Hadoop

filtered_data = data.filter(data.[1] [2] 100)
filtered_data.write.format("parquet").save("/data_lake/filtered")

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

Ascore

B>

C<

Dage

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong column names or operators that do not filter correctly.

The code filters rows where the 'score' column is greater than 100 before saving.

5fill in blank

hard

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

Hadoop

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

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

Aword

Blen(word)

Dlen(words)

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong variable or length function.

The dictionary comprehension maps each word to its length for words longer than 3 characters.

Practice

1. Why do ingestion pipelines feed data into a data lake?

easy

A. To collect raw data from multiple sources in one place

B. To delete old data automatically

C. To convert all data into a single format immediately

D. To prevent data from being accessed by users

Solution

1. Step 1: Understand the role of ingestion pipelines

   Ingestion pipelines gather data from many different sources.

2. Step 2: Understand the purpose of a data lake

   A data lake stores raw data centrally for easy access and analysis later.

3. Final Answer:

   To collect raw data from multiple sources in one place -> Option A

4. Quick Check:

   Ingestion pipelines feed raw data to data lakes = A [OK]

Hint: Remember: pipelines bring raw data together [OK]

Common Mistakes:

• Thinking pipelines delete data
• Assuming data is converted immediately
• Believing data lakes block user access

2. Which of the following is a correct tool used in ingestion pipelines for feeding data lakes?

easy

A. WordPress

B. Photoshop

C. Excel

D. Hadoop

Solution

1. Step 1: Identify tools related to data ingestion

   Hadoop is a big data tool used for storing and processing large datasets.

2. Step 2: Check other options

   Photoshop, Excel, and WordPress are not designed for data ingestion pipelines.

3. Final Answer:

   Hadoop -> Option D

4. Quick Check:

   Hadoop is a data ingestion tool = A [OK]

Hint: Pick big data tools, not office or design apps [OK]

Common Mistakes:

• Confusing data tools with office software
• Choosing popular but unrelated software
• Ignoring Hadoop's role in data pipelines

3. Consider this code snippet using Apache NiFi to ingest data:

flowFile = session.get()
if flowFile is None:
    session.commit()
    exit()
flowFile = session.putAttribute(flowFile, "source", "sensor1")
session.transfer(flowFile, REL_SUCCESS)

What does this pipeline step do?

medium

A. It deletes the data from the pipeline

B. It adds a source attribute and sends data forward

C. It converts data to CSV format

D. It stops the pipeline permanently

Solution

1. Step 1: Analyze the code actions

   The code gets a data flow file, adds an attribute named 'source' with value 'sensor1'.

2. Step 2: Understand the transfer step

   It then transfers the flow file to the success relationship, moving data forward.

3. Final Answer:

   It adds a source attribute and sends data forward -> Option B

4. Quick Check:

   Attribute added + transfer = C [OK]

Hint: Look for attribute changes and transfer commands [OK]

Common Mistakes:

• Thinking data is deleted
• Assuming format conversion happens here
• Believing pipeline stops permanently

4. This ingestion pipeline code snippet has an error:

data = kafka_consumer.poll()
if data:
print('Data received:', data)
process(data)

What is the error and how to fix it?

medium

A. Indentation error; indent the print and process lines

B. Syntax error; replace poll() with consume()

C. Runtime error; data variable is undefined

D. Logical error; should check data length instead of truthiness

Solution

1. Step 1: Identify the error in code structure

   The print and process lines are not indented under the if statement, causing an indentation error.

2. Step 2: Fix the indentation

   Indent these lines to be inside the if block to run only when data exists.

3. Final Answer:

   Indentation error; indent the print and process lines -> Option A

4. Quick Check:

   Indentation fixes code error = B [OK]

Hint: Check indentation after if statements [OK]

Common Mistakes:

• Confusing method names
• Assuming variable is undefined
• Overthinking data checks

5. You want to build an ingestion pipeline that feeds a data lake with streaming data from multiple sensors. Which approach best ensures data is stored raw and ready for future analysis?

hard

A. Use batch uploads once a day to reduce pipeline complexity

B. Transform data into summaries before storing to reduce storage needs

C. Use Kafka to collect streams, then store raw data in Hadoop HDFS without transformation

D. Store data only after converting to JSON format and filtering out duplicates

Solution

1. Step 1: Understand raw data storage needs

   Storing raw data means saving original data without changes for flexible analysis later.

2. Step 2: Evaluate options for streaming ingestion

   Kafka is designed for streaming data collection; Hadoop HDFS stores large raw datasets efficiently.

3. Step 3: Compare other options

   Transforming or filtering data early loses raw details; batch uploads delay data availability.

4. Final Answer:

   Use Kafka to collect streams, then store raw data in Hadoop HDFS without transformation -> Option C

5. Quick Check:

   Raw streaming data + Hadoop storage = D [OK]

Hint: Keep data raw and streaming for best analysis [OK]

Common Mistakes:

• Filtering data too early
• Using batch instead of streaming
• Converting formats before storage