DP-203 Design and implement data storage Practice Question
A company is migrating an on-premises Hadoop cluster to Azure. The cluster uses Hive tables stored as Parquet files on HDFS. They want to minimize changes to existing Hive queries and continue using HiveQL. Which Azure storage solution should they choose?
⚠ Common exam trap
It's easy for candidates to confuse 'supporting HiveQL' with 'running Hive on any Azure service,' but only HDInsight provides the native Hive runtime and HDFS-compatible storage (ADLS Gen2) needed to run existing HiveQL queries unchanged.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Azure HDInsight with Hive and Azure Data Lake Storage Gen2
Azure HDInsight with Hive and Azure Data Lake Storage Gen2 (ADLS Gen2) is the correct choice because it provides a fully managed Hadoop service that supports HiveQL with minimal changes. ADLS Gen2 offers a hierarchical namespace and is optimized for Hadoop workloads, allowing the existing Parquet files on HDFS to be directly mounted and queried without data movement or schema changes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Azure HDInsight with Hive and Azure Data Lake Storage Gen2
Why this is correct
HDInsight provides Hive-compatible environment; ADLS Gen2 replaces HDFS seamlessly.
- ✗
Azure SQL Database with PolyBase
Why it's wrong here
PolyBase queries external data but uses T-SQL; not HiveQL.
- ✗
Azure Databricks with Delta Lake
Why it's wrong here
Delta Lake can read Parquet but Hive queries would require changes; not a direct Hive replacement.
- ✗
Azure Synapse Analytics with external tables
Why it's wrong here
Synapse uses T-SQL, not HiveQL; requires query modification.
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