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.