Phoenix - Your AI AssistantHadoop Hive Warehouse: Partitions & Dimensional Model
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4 days
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What you get with this Offer
I will design and build a Hive data warehouse structure for your analytical use case — covering a dimensional model (fact and dimension tables), Hive DDL for all tables with ORC format and appropriate compression, partition strategy for the fact table, bucketing for frequently joined dimensions, statistics computation for CBO, and a data loading pipeline populating the warehouse from your HDFS raw data. A Hive data warehouse designed with a coherent dimensional model, correct partitioning, and ORC format delivers query performance that matches user expectations; a warehouse designed as a flat file dump with no partitioning or bucketing delivers the performance profile that makes stakeholders lose confidence in Hadoop as a platform.
The warehouse design covers star or snowflake schema for your domain, Hive DDL with CREATE TABLE statements, STORED AS ORC with SNAPPY compression, partition column selection, bucketed dimension tables, INSERT OVERWRITE population pipeline, and ANALYZE TABLE statistics computation. A schema documentation document is included.
This service suits data engineering teams building analytical capability on Hadoop who need a properly designed Hive warehouse rather than a collection of raw HDFS files accessed with ad hoc queries.
The warehouse design covers star or snowflake schema for your domain, Hive DDL with CREATE TABLE statements, STORED AS ORC with SNAPPY compression, partition column selection, bucketed dimension tables, INSERT OVERWRITE population pipeline, and ANALYZE TABLE statistics computation. A schema documentation document is included.
This service suits data engineering teams building analytical capability on Hadoop who need a properly designed Hive warehouse rather than a collection of raw HDFS files accessed with ad hoc queries.
What the Freelancer needs to start the work
Please describe your business domain and the analytical questions the warehouse should answer, your source data in HDFS (format, structure, volume), your Hive version, cluster resources for warehouse sizing, and your reporting and query tool (Hive CLI, Beeline, Tableau, Power BI, etc.).