Apache Hive vs Databricks Lakehouse Platform

• Hive syntax is almost like SQL, so for someone already familiar with SQL it takes almost no effort to pick up Hive.
• To be able to run map reduce jobs using json parsing and generate dynamic partitions in parquet file format.
• Simplifies your experience with Hadoop especially for non-technical/coding partners.

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• Extremely Flexible in Data Scenarios
• Fantastic Performance
• DB is always updating the system so we can have latest features.

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• Use Hive for analytical work loads. Write once and read many scenarios. Do not prefer updates and deletes.
• Behind scenes Hive creates map reduce jobs. Hive performance is slow compared to Apache Spark.
• Map reduce writes the intermediate outputs to dial whereas Spark operates in in-memory and uses DAG.

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• The navigation through which one would create a workspace is a bit confusing at first. It takes a couple minutes to figure out how to create a folder and upload files since it is not the same as traditional file systems such as box.com
• Also, when you create a table, if you forgot to copy the link where the table is stored, it is hard to relocate it. Most of the time I would have to delete the table and re-created.

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• It exposes the distributed calculation world (Hadoop) to the users but doesn't require the user to have the in-depth understanding of boilerplate details, it reduces the time of learning and let the data analyst can focus their efforts on the core business.

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• Rapid growth of analytics within our company.
• Cost model aligns with usage allowing us to make a reasonable initial investment and scale the cost as we realize the value.
• Platform is easy to learn and Databricks provides excellent support and training.
• Platform does not require a large DevOPs investment

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