Apache Hive: A Comprehensive Guide 🐝
Apache Hive is an open-source data warehouse system built on top of Hadoop for providing data query and analysis. Hive gives an SQL-like interface to query data stored in various databases and file systems that integrate with Hadoop.
What is Apache Hive? 🤔
✅ Apache Hive is a distributed, fault-tolerant data warehouse that enables analytics at a massive scale. It facilitates querying petabytes of data residing in distributed storage using Hive Query Language — HQL, which has an SQL-like syntax.
A Hive table comprises of two main components:
✅ Actual Data : This is the raw data that is present in distributed storage.
✅ Metadata : This is the schema or information of data present in the Metastore DB.
Why is Metadata stored in a Database? 📚
➡️ Metadata requires frequent modifications/updates and should be accessible very quickly. Datalakes, while offering high throughput, are not capable of providing low latency and updates are hard to perform. Therefore, it is best to store the metadata in a metastore which is a Database and not in a Datalake.
Schema on Read vs Schema on Write 📝
🔵 Traditional RDBMS systems follow a Schema on Write approach, where the table is created first, then data is inserted, and data validation takes place while writing the data.
🔵 On the other hand, Hive, a data warehouse, follows a Schema on Read approach. The data is present in a Distributed Storage like HDFS / Datalake in the form of files. Tables are created on top of the data for a tabular view to query the data using SQL kind of syntax. The table structure gets imposed while reading the data, and data validation takes place at this stage.
Hive Services 🛠️
Hive provides several services, including:
🔑 Hive Metastore (HMS): Stores the Metadata / Schema.
🔑 HiveServer2 : Enables clients to execute queries against hive tables.
🔑 JDBC Client (Beeline) : Used to interact with HiveServer2.
Conclusion 🎯
🎉 Apache Hive is a powerful tool for high throughput processing of large volumes of data for analytical purposes. It separates the storage of actual data and metadata, providing flexibility and efficiency. Whether you’re querying petabytes of data or performing complex data analysis, Hive is a reliable, scalable solution for your data warehousing needs.
What challenges have you faced when working with Apache Hive? Share your experiences in comments.
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