Lambda architecture (batch + streaming) in Hadoop - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main goal of Lambda architecture?

To process large amounts of data by combining batch processing for accuracy and streaming processing for low latency.

beginner

Name the three layers of Lambda architecture.

Batch layer, speed (streaming) layer, and serving layer.

beginner

What does the batch layer do in Lambda architecture?

It stores all the raw data and computes batch views to provide accurate and comprehensive results.

beginner

Why is the speed layer important in Lambda architecture?

It processes data in real-time to provide quick updates and low-latency views, complementing the batch layer.

intermediate

How does the serving layer function in Lambda architecture?

It merges batch views and real-time views to answer queries with both accuracy and freshness.

Which layer in Lambda architecture handles real-time data processing?

A Batch layer

B Storage layer

C Speed layer

D Serving layer

The speed layer processes data in real-time to provide low-latency results.

What is the main responsibility of the batch layer?

A Serve queries to users

B Store raw data and compute batch views

C Process streaming data

D Clean data before processing

The batch layer stores all raw data and computes batch views for accurate results.

In Lambda architecture, which layer combines batch and real-time views?

A Serving layer

B Batch layer

C Speed layer

D Processing layer

The serving layer merges batch and speed layer outputs to answer queries.

Why do we need both batch and speed layers in Lambda architecture?

A To balance accuracy and latency

B To reduce storage costs

C To simplify data pipelines

D To avoid using Hadoop

Batch layer ensures accuracy, speed layer ensures low latency.

Which technology is commonly used for batch processing in Lambda architecture?

A Apache Kafka

B Apache Storm

C Apache Flink

D Apache Hadoop

Hadoop is widely used for batch processing in Lambda architecture.

Explain the roles of the batch, speed, and serving layers in Lambda architecture.

Think about accuracy, speed, and how results are combined.

Describe why Lambda architecture uses both batch and streaming processing instead of just one.

Consider the trade-off between speed and accuracy.

Practice

1. What is the main purpose of the batch layer in Lambda architecture?

easy

A. To process all data for accurate and complete results

B. To handle real-time data streams quickly

C. To store raw data without processing

D. To visualize data for end users

Solution

1. Step 1: Understand batch layer role

   The batch layer processes all accumulated data to provide accurate and comprehensive views.

2. Step 2: Compare with other layers

   The speed layer handles real-time data, not the batch layer.

3. Final Answer:

   To process all data for accurate and complete results -> Option A

4. Quick Check:

   Batch layer = accurate full data processing [OK]

Hint: Batch layer = full data, accurate results [OK]

Common Mistakes:

• Confusing batch layer with speed layer
• Thinking batch layer processes data in real-time
• Assuming batch layer only stores raw data

2. Which of the following is the correct component of Lambda architecture for handling real-time data?

easy

A. Batch layer

B. Speed layer

C. Serving layer

D. Storage layer

Solution

1. Step 1: Identify real-time processing component

   The speed layer is designed to process data streams in real-time for quick insights.

2. Step 2: Eliminate other options

   Batch layer processes full data, serving layer provides query results, storage layer is not a standard Lambda component.

3. Final Answer:

   Speed layer -> Option B

4. Quick Check:

   Speed layer = real-time data processing [OK]

Hint: Speed layer = real-time data [OK]

Common Mistakes:

• Choosing batch layer for real-time data
• Confusing serving layer with speed layer
• Thinking storage layer is a Lambda component

3. Consider this simplified pseudocode for Lambda architecture layers:

batch_result = batch_layer(data)
speed_result = speed_layer(stream)
final_result = merge(batch_result, speed_result)
print(final_result)

If batch_layer returns {'count': 100} and speed_layer returns {'count': 5}, what will final_result be after merging by summing counts?

medium

A. {'count': 105}

B. {'count': 100}

C. {'count': 5}

D. Error: Cannot merge dictionaries

Solution

1. Step 1: Understand merging logic

   Merging sums the 'count' values from batch and speed results: 100 + 5 = 105.

2. Step 2: Confirm final result

   The final dictionary after merging is {'count': 105}.

3. Final Answer:

   {'count': 105} -> Option A

4. Quick Check:

   100 + 5 = 105 [OK]

Hint: Sum batch and speed counts for final result [OK]

Common Mistakes:

• Ignoring speed layer result
• Assuming merge replaces batch with speed
• Thinking merge causes error

4. This code snippet tries to implement the speed layer but has an error:

def speed_layer(stream):
    for record in stream:
        process(record)
    return processed_data

stream = get_stream()
result = speed_layer(stream)
print(result)

What is the likely error here?

medium

A. process() function is missing

B. stream is not iterable

C. processed_data is not defined before return

D. print() syntax error

Solution

1. Step 1: Check variable initialization

   The variable processed_data is returned but never defined or updated inside the function.

2. Step 2: Verify other parts

   Assuming stream is iterable and process() exists, the main error is missing processed_data definition.

3. Final Answer:

   processed_data is not defined before return -> Option C

4. Quick Check:

   Undefined variable causes error [OK]

Hint: Check all variables are defined before return [OK]

Common Mistakes:

• Assuming process() is missing without checking
• Thinking stream is not iterable
• Ignoring variable initialization

5. In a Lambda architecture, if the batch layer updates data every 24 hours and the speed layer processes data every second, what is the best way to ensure the serving layer shows the most accurate and up-to-date results?

hard

A. Use the speed layer results immediately and replace them with batch results after 24 hours

B. Only show batch layer results to avoid inconsistency

C. Ignore speed layer and rely on batch layer for all queries

D. Merge speed and batch results continuously, updating serving layer with combined data

Solution

1. Step 1: Understand serving layer role

   The serving layer combines batch and speed layer outputs to provide accurate and timely results.

2. Step 2: Evaluate options

   Merge speed and batch results continuously, updating serving layer with combined data merges both results continuously, ensuring the serving layer is both up-to-date and accurate.

3. Final Answer:

   Merge speed and batch results continuously, updating serving layer with combined data -> Option D

4. Quick Check:

   Serving layer = combined batch + speed data [OK]

Hint: Serving layer merges batch and speed data continuously [OK]

Common Mistakes:

• Using only speed or batch layer results alone
• Replacing speed results abruptly without merging
• Ignoring serving layer's combining role