Why ingestion pipelines feed the data lake in Hadoop

Introduction

Ingestion pipelines move data from many sources into a data lake. This helps keep all data in one place for easy use.

When collecting data from different systems like databases, logs, or sensors.

When you want to store raw data before cleaning or analysis.

When you need to update the data lake regularly with new data.

When you want to prepare data for big data tools like Hadoop or Spark.

When you want to keep a history of all incoming data for future use.

Syntax

Hadoop

hadoop fs -put <local_file_path> <hdfs_path>

# Or using Apache NiFi or Apache Kafka for streaming ingestion pipelines

Ingestion pipelines can be batch (moving data in chunks) or streaming (real-time data flow).

Data lakes store raw data in its original format, so ingestion pipelines must handle different data types.

Examples

This command uploads a local CSV file into the data lake's raw data folder on HDFS.

Hadoop

hadoop fs -put /home/user/data.csv /data_lake/raw/

NiFi can automate data ingestion by connecting to databases and sending data directly to the data lake.

Hadoop

# Using Apache NiFi to ingest data from a database to HDFS
# NiFi flow reads from DB and writes to HDFS directory

Kafka helps stream data continuously into the data lake for real-time processing.

Hadoop

# Kafka streaming ingestion example
# Producers send data to Kafka topics
# Consumers write data from Kafka to HDFS

Sample Program

This script reads a CSV file locally and writes it to the data lake on HDFS in parquet format using PySpark.

Hadoop

# This example shows a simple Python script using PySpark to ingest a CSV file into a data lake (HDFS)
from pyspark.sql import SparkSession

spark = SparkSession.builder.appName('IngestToDataLake').getOrCreate()

# Read local CSV file
input_path = '/home/user/data.csv'
df = spark.read.csv(input_path, header=True, inferSchema=True)

# Write data to HDFS data lake in parquet format
output_path = 'hdfs:///data_lake/raw/data.parquet'
df.write.mode('overwrite').parquet(output_path)

print(f'Data ingested to {output_path}')

spark.stop()

Important Notes

Data ingestion pipelines must handle errors like missing files or network issues.

Choosing the right ingestion method depends on data size, speed, and format.

Data lakes allow storing all data types, so ingestion pipelines must be flexible.

Summary

Ingestion pipelines bring data from many sources into a single data lake.

This helps store raw data for easy access and analysis later.

Tools like Hadoop, NiFi, and Kafka help build these pipelines efficiently.

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