Why Apache Hive?
Apache Hive is an open-source data warehouse platform that manages large, distributed data sets. Hive was formerly a Hadoop sub-project, which has now “graduated” to a full-fledged project in its own right. It supports standard SQL queries and enables extract/transform/load (ETL) tasks, reporting, and data analysis. Hive supports data partitioning at the table level (sharding), which can significantly improve performance and scalability. Meta store or Metadata store is a big plus in the architecture, which makes the lookup easy. Hive supports file formats such as textile, Sequence File, ORC, RCFile, Avro Files, Parquet, LZO Compression, etc.
Hive has plenty of support on most major cloud platforms, like Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure. From a business perspective, what is of potential interest is that it can also run on-premises using inexpensive off-the-shelf servers and directly attached storage arrays. This means you can handle massive amounts of data without worrying about cloud storage and usage costs. Hive can also easily integrate with other Apache Software Foundation projects, including Airflow, Spark, MapReduce, and Slider.
The underlying data layer, Hadoop, can easily handle hundreds of terabytes of data and can scale to dozens of petabytes. However, Hadoop has a big disadvantage: a noticeable start-up overhead, which makes it a poor choice for low-latency queries.
Comparing Snowflake and Apache Hive
While both technologies are commonly used for data warehousing and analytics, they have some key differences. Snowflake is a fully managed cloud platform, while Apache Hive is an open-source tool that runs on top of a Hadoop cluster. Snowflake uses a proprietary language called SQL, while Apache Hive uses an SQL-like language called HiveQL. Snowflake is designed to be highly scalable and flexible, while Apache Hive is more focused on data processing and analysis.