In today’s data-driven landscape, organizations are dealing with multiple data sources — from HDFS and S3 to RDBMS and NoSQL systems. Querying these systems separately leads to fragmented analytics and data silos.

Query federation allows you to write SQL queries that span multiple, heterogeneous data sources. By combining the Hive Metastore with Presto (or Trino), you can enable real-time, cross-source analytics without data movement.

In this post, we explore how Hive and Presto work together to provide powerful query federation capabilities, enabling analysts and engineers to unlock value from distributed datasets with ease.

What Is Query Federation?

Query federation allows a single SQL query to access and join data from multiple data stores, such as:

  • Hive on HDFS
  • MySQL or PostgreSQL
  • S3-based Parquet/ORC files
  • Kafka or Delta Lake
  • Elasticsearch and MongoDB

Rather than ETLing data into one place, Presto executes queries across sources in place, merging results at runtime.

How Hive Fits Into Federation

Hive Metastore plays a key role by acting as the central metadata catalog. It stores:

  • Table schemas
  • Data formats (ORC, Parquet, etc.)
  • Locations (HDFS, S3)
  • Partition information

Presto connects to the Hive Metastore to discover and interpret table structures, making it possible to query Hive-managed data directly — or combine it with data from other connectors.

Setting Up Presto with Hive Metastore

To query Hive data using Presto, configure the Hive connector in your Presto deployment.

Example hive.properties:

connector.name=hive-hadoop2
hive.metastore.uri=thrift://hive-metastore:9083
hive.config.resources=/etc/hadoop/conf/core-site.xml,/etc/hadoop/conf/hdfs-site.xml
hive.s3.aws-access-key=...
hive.s3.aws-secret-key=...

Presto now understands Hive tables and partitions, even if the data resides in HDFS, S3, or ADLS.

Example: Federated Query with Hive and MySQL

Assume:

  • orders table is in Hive (stored in Parquet on HDFS)
  • customers table is in MySQL

You can write a federated query like this:

SELECT c.customer_id, c.name, o.order_id, o.amount
FROM mysql.sales.customers c
JOIN hive.retail.orders o
ON c.customer_id = o.customer_id
WHERE o.order_date >= DATE '2024-01-01';

Presto handles:

  • Reading from MySQL via JDBC
  • Reading Parquet from HDFS via Hive connector
  • Pushing filters and joins as close to the data as possible

Benefits of Query Federation with Hive and Presto

  • No ETL needed – Query live data in place
  • Unified metadata via Hive Metastore
  • Low latency – Ideal for interactive analytics
  • Join across sources like Hive + RDBMS or Hive + Kafka
  • Cost-effective – Use compute only at query time

Performance Optimization Tips

  1. Pushdown filters – Use WHERE clauses to minimize scanned data.
  2. Leverage partition pruning – Ensure partitioned Hive tables are properly filtered.
  3. Avoid large cross joins – Ensure join keys are indexed and meaningful.
  4. Use Parquet or ORC for Hive tables to reduce I/O.
  5. Tune memory and parallelism in Presto config for heavy joins.

Real-World Use Cases

1. Unified Analytics Platform

Query customer profiles from MySQL and behavior logs from Hive:

SELECT u.email, COUNT(*) AS clicks
FROM mysql.crm.users u
JOIN hive.analytics.clicks c
ON u.user_id = c.user_id
GROUP BY u.email;

2. Regulatory Reporting

Join compliance data across PostgreSQL (transactional) and Hive (archival):

SELECT p.transaction_id, h.customer_id
FROM postgres.finance.payments p
JOIN hive.archive.transactions h
ON p.transaction_id = h.transaction_id;

3. Time-to-Insight Acceleration

Run ad hoc analytics across cloud and on-prem data without duplicating pipelines.

Tools That Enhance Hive + Presto Integration

  • AWS Athena: Serverless Presto query engine integrated with Hive-compatible Glue Data Catalog
  • Trino: Open-source fork of Presto with enhanced federation support
  • Apache Superset / Tableau: Visualize federated queries via JDBC/ODBC
  • Ranger: Secure data access at table and column levels across Hive and Presto

Security and Governance Considerations

  • Integrate Apache Ranger for consistent access control
  • Use Kerberos or LDAP to enforce authentication
  • Enable data masking and column-level filtering
  • Log access for auditing and compliance

Conclusion

Query federation with Hive and Presto enables unified access to all your organizational data without expensive ETL jobs. By leveraging Hive Metastore for metadata and Presto for fast distributed execution, you can create a flexible, high-performance SQL layer over your data lake and beyond.

Whether you’re running interactive dashboards or exploring large datasets across storage systems, Hive and Presto together offer a scalable solution for cross-source data discovery and analytics.