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Hive Workload Adaptation:
- Challenge: Adapting existing Hadoop workloads to Databricksโ advanced analytics framework.
- Mitigation: Rework and fine-tune your Hadoop workloads to leverage Databricksโ speedy, in-memory processing.
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Architecture Differences:
- Challenge: Hadoop and Databricks have distinct architectures.
- Mitigation: Understand the differences. Hadoop operates across multiple hardware systems, while Databricks offers a unified analytics platform built on Apache Spark.
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Hive Metastore and UDFs:
- Challenge: Hive SerDe and UDFs need adjustments.
- Mitigation:
- Update Hive SerDe to use Databricks-native file codecs (change DDL from
STORED AS
to USING
).
- Install Hive UDFs as libraries or refactor them to native Spark.
- Adjust directory structure for tables (Databricks uses partitions differently).
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SQL Workloads:
- Challenge: Migrating SQL workloads from other systems.
- Mitigation:
- Refactor SQL pipelines as needed (Databricks uses Delta Lake by default).
- Configure access to external data sources.
If you encounter specific issues during the migration, feel free to ask for further assistance! ๐