Why ingestion pipelines feed the data lake in Hadoop - Visual Breakdown

Concept Flow - Why ingestion pipelines feed the data lake

Raw Data Sources

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Ingestion Pipelines

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Data Lake Storage

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Data Processing & Analysis

Data flows from raw sources through ingestion pipelines into the data lake, where it is stored for later processing and analysis.

Execution Sample

Hadoop

hadoop fs -put /local/path/data.csv /data_lake/raw/
# Ingest data file into data lake raw zone

This command uploads a local data file into the raw data zone of the data lake using Hadoop.

Execution Table

Step	Action	Source	Destination	Result
1	Read raw data from source system	Sensor logs	Ingestion pipeline	Data collected
2	Transform and clean data	Ingestion pipeline	Temporary staging	Data cleaned
3	Load data into data lake	Temporary staging	Data lake raw zone	Data stored
4	Verify data availability	Data lake raw zone	Data analysts	Data ready for use
5	End	-	-	Pipeline complete

💡 Pipeline ends after data is stored and verified in the data lake for analysis.

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	Final
raw_data	empty	collected	cleaned	stored	available

Key Moments - 2 Insights

Why do we need ingestion pipelines before storing data in the data lake?

Ingestion pipelines collect, clean, and prepare raw data before storing it in the data lake, ensuring data quality and structure as shown in steps 1-3 of the execution table.

What happens if data is stored directly without ingestion pipelines?

Without ingestion pipelines, raw data may be unorganized or dirty, making analysis difficult. The execution table shows ingestion steps that clean and prepare data before storage.

Visual Quiz - 3 Questions

Look at the execution table, what is the result after step 2?

A Data collected

B Data cleaned

C Data stored

D Pipeline complete

💡 Hint

Check the 'Result' column for step 2 in the execution table.

At which step is data loaded into the data lake?

A Step 1

B Step 2

C Step 3

D Step 4

💡 Hint

Look at the 'Action' and 'Destination' columns in the execution table.

If the ingestion pipeline skipped cleaning, how would the 'raw_data' variable change after step 2?

A It would remain 'collected'

B It would become 'cleaned'

C It would become 'stored'

D It would be 'available'

💡 Hint

Refer to the variable_tracker row for 'raw_data' and the role of step 2.

Concept Snapshot

Ingestion pipelines collect and prepare raw data
They clean and transform data before storage
Data is loaded into the data lake for analysis
Pipelines ensure data quality and availability
Without pipelines, data may be unusable

Full Transcript

Ingestion pipelines are essential because they take raw data from various sources and prepare it for storage in the data lake. The process involves collecting data, cleaning it to remove errors or inconsistencies, and then loading it into the data lake's raw zone. This preparation ensures that the data stored is usable and ready for analysis. The execution table shows each step clearly: data is collected, cleaned, stored, and then verified for availability. The variable tracker shows how the data changes state through these steps. Without ingestion pipelines, raw data might be messy and hard to analyze, so pipelines help maintain data quality and usability.