When to use Hadoop in modern data stacks - Step-by-Step Execution

Concept Flow - When to use Hadoop in modern data stacks

Start: Need to process big data?

↓

Is data size > few TBs?

No→Use simpler tools

Yes↓

Is data mostly batch and unstructured?

No→Consider other tools like Spark

Yes↓

Is cost and scalability a concern?

No→Use cloud managed services

Yes↓

Use Hadoop: Distributed storage + batch processing

↓

Integrate with modern tools (Spark, Hive, etc.)

↓

Analyze and process big data efficiently

↓

End

This flow shows when Hadoop is a good choice: for very large, mostly batch, unstructured data where cost and scalability matter.

Execution Sample

Hadoop

if data_size > 1000 and data_type == 'batch':
    use_hadoop = True
else:
    use_hadoop = False

This simple code decides to use Hadoop if data size is very large and data is batch type.

Execution Table

Step	Condition Checked	Value	Decision	Next Step
1	data_size > 1000TB	False (e.g. 500TB)	No	Use simpler tools
2	data_size > 1000TB	True (e.g. 2000TB)	Yes	Check data_type
3	data_type == 'batch'	False (e.g. 'stream')	No	Consider other tools
4	data_type == 'batch'	True	Yes	Check cost and scalability
5	cost and scalability concern	No	No	Use cloud managed services
6	cost and scalability concern	Yes	Yes	Use Hadoop
7	Integrate with modern tools	N/A	Done	Analyze data

💡 Decision made based on data size, type, and cost/scalability needs.

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	Final
data_size	500TB	500TB	2000TB	2000TB	2000TB
data_type	batch	batch	batch	stream	batch
use_hadoop	False	False	Undecided	False	True

Key Moments - 3 Insights

Why do we check data size before deciding to use Hadoop?

Because Hadoop is designed for very large data sets (usually terabytes or more). If data is small, simpler tools are better. See execution_table rows 1 and 2.

Why is data type important (batch vs stream)?

Hadoop works best with batch processing of large, unstructured data. For streaming data, other tools like Spark Streaming are better. See execution_table rows 3 and 4.

Why consider cost and scalability before choosing Hadoop?

Hadoop clusters can be costly to maintain. If cost or scalability is not a concern, cloud managed services might be easier. See execution_table rows 5 and 6.

Visual Quiz - 3 Questions

Look at the execution table, what is the decision when data_size is 500TB?

A Use Hadoop

B Use simpler tools

C Consider other tools

D Use cloud managed services

💡 Hint

Check row 1 in the execution_table where data_size is 500TB.

At which step does the decision depend on data_type?

A Step 3

B Step 1

C Step 5

D Step 7

💡 Hint

Look at the execution_table row where data_type is checked.

If cost and scalability are not concerns, what is the recommended choice?

A Use Hadoop

B Use simpler tools

C Use cloud managed services

D Consider other tools

💡 Hint

See execution_table row 5 for cost and scalability decision.

Concept Snapshot

When to use Hadoop:
- Use for very large data (terabytes+)
- Best for batch, unstructured data
- Consider cost and scalability
- Integrate with modern tools like Spark
- Not ideal for small or streaming data

Full Transcript

This visual guide helps decide when to use Hadoop in modern data stacks. First, check if data size is very large, typically over a terabyte. If not, simpler tools are better. Next, check if data is batch type, as Hadoop is designed for batch processing. Streaming data is better handled by other tools. Then, consider cost and scalability needs. If these are concerns, Hadoop is a good choice. Otherwise, cloud managed services might be easier. Finally, Hadoop can be integrated with modern tools like Spark and Hive for efficient big data analysis.