Presto vs Apache Hive

Developers should learn Presto when they need to perform high-speed, interactive SQL queries on massive, heterogeneous datasets, such as in data warehousing, log analysis, or real-time analytics meets developers should learn apache hive when working with big data ecosystems, especially for data warehousing and analytics tasks on hadoop, as it simplifies querying large datasets with sql-like syntax, reducing the need for complex mapreduce programming. Here's our take.

Presto

Nice Pick

Developers should learn Presto when they need to perform high-speed, interactive SQL queries on massive, heterogeneous datasets, such as in data warehousing, log analysis, or real-time analytics

Pros

• It is particularly valuable in environments with data stored in multiple systems (e
• Related to: sql, hadoop

Cons

• Specific tradeoffs depend on your use case

Apache Hive

Developers should learn Apache Hive when working with big data ecosystems, especially for data warehousing and analytics tasks on Hadoop, as it simplifies querying large datasets with SQL-like syntax, reducing the need for complex MapReduce programming

Pros

• It is ideal for use cases like log analysis, business intelligence reporting, and data summarization where structured querying is required over petabytes of data stored in HDFS or cloud storage
• Related to: apache-hadoop, hiveql

Cons

• Specific tradeoffs depend on your use case

The Verdict

These tools serve different purposes. Presto is a database while Apache Hive is a tool. We picked Presto based on overall popularity, but your choice depends on what you're building.

The Bottom Line

Presto wins

Based on overall popularity. Presto is more widely used, but Apache Hive excels in its own space.