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1. Streaming ingest of data.  Many users have tools such as Apache Flume, Apache Storm, or Apache Kafka that they use to stream data into their Hadoop cluster.  While these tools can write data at rates of hundreds or more rows per second, Hive can only add partitions every fifteen minutes to an hour.  Adding partitions more often leads quickly to an overwhelming number of partitions in the table.  These tools could stream data into existing partitions, but this would cause readers to get dirty reads (that is, they would see data written after they had started their queries) and leave many small files in their directories that would put pressure on the NameNode.  With this new functionality this use case will be supported while allowing readers to get a consistent view of the data and avoiding too many files.
2. Slow changing dimensions.  In a typical star schema data warehouse, dimensions tables change slowly over time.  For example, a retailer will open new stores, which need to be added to the stores table, or an existing store may change its square footage or some other tracked characteristic.  These changes lead to inserts of individual records or updates of records (depending on the strategy chosen).  Starting with 0.14, Hive is able to support this.
3. Data restatement.  Sometimes collected data is found to be incorrect and needs correction.  Or the first instance of the data may be an approximation (90% of servers reporting) with the full data provided later.  Or business rules may require that certain transactions be restated due to subsequent transactions (e.g., after making a purchase a customer may purchase a membership and thus be entitled to discount prices, including on the previous purchase).  Or a user may be contractually required to remove their customer’s data upon termination of their relationship.  Starting with Hive 0.14 these use cases can be supported via INSERT, UPDATE, and DELETE.
4. Bulk updates using SQL MERGE statement.

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