Apache Hive vs Presto

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 meets 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. Here's our take.

Apache Hive

Nice Pick

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

Presto

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

The Verdict

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

The Bottom Line

Apache Hive wins

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