When working with large-scale data lakes and distributed storage systems like HDFS or Amazon S3, two of the most popular SQL query engines are Apache Hive and Presto (now Trino). Both support SQL analytics on massive datasets but serve different use cases and performance profiles.

This blog offers a deep comparison between Hive and Presto, covering architecture, execution engines, performance, compatibility, and best-fit scenarios to help you choose the right tool for your analytics workloads.

What is Apache Hive?

Apache Hive is a data warehouse infrastructure built on top of Hadoop. It translates SQL queries into MapReduce, Tez, or Spark jobs and is designed for batch-oriented processing.

  • Designed for long-running analytical queries
  • Integrates tightly with HDFS, ORC, Parquet
  • Supports ACID and transactional tables
  • Works well in ETL pipelines and reporting workloads

What is Presto (Trino)?

Presto, now rebranded as Trino, is a distributed SQL engine designed for low-latency, interactive analytics on large datasets. It doesn’t rely on MapReduce or Tez but instead uses its own massively parallel processing (MPP) engine.

  • Ideal for interactive SQL queries
  • Supports federated querying across data sources (Hive, Kafka, MySQL, S3)
  • In-memory, real-time execution
  • Faster for ad-hoc exploration and dashboards

Architectural Comparison

Feature Hive Presto (Trino)
Execution Engine MapReduce, Tez, or Spark Custom MPP engine
Query Latency Seconds to minutes Milliseconds to seconds
Processing Style Batch Interactive / Low-latency
Storage Support HDFS, ORC, Parquet, Avro HDFS, S3, Hive Metastore, JDBC, etc.
ACID Support Yes (with Hive 3+) Limited (read-only on Hive tables)
Federation Limited Native federation across multiple sources
ANSI SQL Support Partial More compliant with ANSI SQL

Performance Considerations

Presto excels at:

  • Fast joins, aggregations, and filters
  • Federated queries (e.g., joining S3 with MySQL)
  • Serving BI tools like Tableau or Superset

Hive performs better when:

  • You need complex ETL transformations
  • Queries run on transactional or ACID-compliant tables
  • You’re working with massive, regularly scheduled jobs

Use Cases for Hive

  • Scheduled ETL pipelines in enterprise environments
  • Batch jobs processing terabytes or petabytes
  • Historical reporting and data warehousing
  • ACID transactional workloads (e.g., slowly changing dimensions)

Example Hive query:

INSERT INTO sales_summary
SELECT region, SUM(amount)
FROM sales
WHERE year = 2024
GROUP BY region;

Hive is better suited for data transformation and longer-running aggregations.

Use Cases for Presto

  • Interactive queries with sub-second response time
  • Data exploration and business intelligence
  • Cross-source joins (e.g., Hive + Kafka + PostgreSQL)
  • Real-time dashboard backends

Example Presto query (joining Hive and PostgreSQL):

SELECT h.user_id, h.event_time, p.email
FROM hive.logs h
JOIN postgresql.users p
ON h.user_id = p.id
WHERE h.event_type = 'click';

Presto’s ability to query across sources in real-time gives it a major edge for analytics.

Compatibility with Hive Metastore

Presto can read Hive tables by integrating with the Hive Metastore. You can reuse schemas and table definitions without duplicating metadata.

However, Presto doesn’t support inserting into Hive ACID tables — it’s primarily for read-only access.

Security and Governance

Both tools support:

  • Ranger for authorization
  • Kerberos or LDAP-based authentication
  • Role-based access control

Hive may offer deeper integration with enterprise data governance platforms due to its longer Hadoop lineage.

Cost and Resource Considerations

  • Hive is more resource-intensive but fits batch-oriented architectures well.
  • Presto requires more memory but uses fewer cores and runs faster for most queries.

In cloud environments, Presto is often more cost-efficient for ad-hoc workloads, whereas Hive is better for scheduled jobs and data lakes with heavy transformation logic.

Choosing the Right Tool

Scenario Recommended Tool
Scheduled ETL job Hive
Ad-hoc data analysis Presto
Federated query across data sources Presto
Long-running batch aggregations Hive
Real-time dashboards Presto
ACID transactional table support Hive

Conclusion

Both Hive and Presto are powerful tools for querying big data — but they solve different problems.

  • Choose Hive for batch ETL, ACID compliance, and large-scale historical queries.
  • Choose Presto for fast, interactive analysis, federated data access, and modern BI use cases.

Understanding their strengths and limitations will help you optimize data workflows and make the right architectural decisions for your analytics platform.