Hadoop - Modern Data Architecture with Hadoop
You want to design a data lake architecture using Hadoop that centralizes data from multiple sources including CSV files, images, and logs. Which approach best supports this goal?
You want to design a data lake architecture using Hadoop that centralizes data from multiple sources including CSV files, images, and logs. Which approach best supports this goal?
hard📝 Application Q15 of 15
Step-by-Step Solution
Solution:
Step 1: Understand data lake centralization goal
Data lakes centralize all data types in one place to allow flexible storage and easy access.Step 2: Evaluate storage approaches
Store all data types in Hadoop HDFS without converting formats, enabling flexible access stores all data types in Hadoop HDFS without forcing format conversion, supporting centralization and flexibility. Other options separate data or limit types, which breaks centralization.Final Answer:
Store all data types in Hadoop HDFS without converting formats, enabling flexible access -> Option AQuick Check:
Centralize all data types in one system = B [OK]
Quick Trick: Keep all data types together in HDFS for true centralization [OK]
Common Mistakes:
- Converting all data to one format losing flexibility
- Separating unstructured data outside the lake
- Using multiple storage systems breaking centralization
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