Migrating From Hadoop and Hive to D23.io’s Iceberg + Trino Stack

Table of Contents

1. Why Migrate Away From Hadoop and Hive
2. Understanding the D23.io Stack: Iceberg, Trino, and Superset
3. Pre-Migration Assessment and Planning
4. Data Discovery and Inventory
5. Migration Architecture and Approach
6. Step-by-Step Migration Process
7. Performance Optimisation and Validation
8. Cost Analysis and ROI
9. Post-Migration Operations and Maintenance
10. Common Pitfalls and How to Avoid Them
11. Real-World Case Studies From Australian Enterprises
12. Next Steps and Getting Started

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Why Migrate Away From Hadoop and Hive?

Hadoop and Hive have been the backbone of big data analytics for over a decade. They enabled organisations to process massive datasets at scale, but they were never designed for the modern cloud-native world. If your organisation is still running Hadoop clusters on-premises or in the cloud, you’re likely dealing with several critical pain points: spiralling infrastructure costs, slow query performance, operational complexity that requires a dedicated team just to keep clusters running, difficulty integrating with modern AI and machine learning workflows, and a shrinking pool of engineers who want to maintain legacy Hadoop ecosystems.

The D23.io stack—built on Apache Iceberg, Trino, and Superset—represents a fundamental shift in how modern data platforms operate. Instead of the batch-oriented, MapReduce-based processing model that Hadoop enforces, you get interactive SQL analytics, ACID transactions, schema evolution without data rewriting, and cloud-native storage that works seamlessly with S3, Azure Blob Storage, or Google Cloud Storage.

For Australian enterprises, this migration is particularly compelling. Cloud infrastructure costs in the Asia-Pacific region have become increasingly competitive, and moving off legacy Hadoop clusters can reduce annual infrastructure spend by 40–60% whilst simultaneously improving query performance by 3–10x. We’ve worked with mid-market and enterprise clients across Sydney, Melbourne, and Brisbane who’ve completed this transition and seen measurable improvements in time-to-insight, cost per query, and engineer productivity.

The business case is straightforward: lower costs, faster analytics, and a platform that integrates naturally with modern agentic AI workflows and automated data pipelines. If you’re evaluating whether your organisation should migrate, the answer is almost certainly yes—the only question is how to do it safely and with minimal disruption to existing analytics workloads.

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Understanding the D23.io Stack: Iceberg, Trino, and Superset

What Is Apache Iceberg?

Apache Iceberg is an open-source table format designed for large-scale analytics. Unlike Hive, which treats data files as opaque blobs and relies on the Hive Metastore for metadata, Iceberg maintains a complete version history of table metadata, enabling time-travel queries, atomic writes, and schema evolution without rewriting data. Every write operation in Iceberg creates a new snapshot, so you can query data as it existed at any point in time.

Key advantages of Iceberg over Hive:

• ACID Transactions: Iceberg guarantees atomicity, consistency, isolation, and durability at the table level, eliminating the need for manual locking or external transaction coordinators.
• Schema Evolution: Add, remove, or rename columns without rewriting the entire table. Iceberg tracks schema changes across snapshots.
• Partition Evolution: Change partitioning schemes on the fly without reorganising data.
• Time-Travel Queries: Query data as it existed at any previous snapshot, enabling data recovery and historical analysis.
• Data Compaction: Iceberg handles small-file consolidation automatically, eliminating the performance degradation that plagues Hive tables.
• Cloud-Native Storage: Iceberg works seamlessly with S3, Azure Blob Storage, and GCS, without requiring HDFS.

For organisations migrating from Hive, Iceberg is a natural successor. It’s compatible with Trino, Spark, Flink, and other modern data engines, so you’re not locked into a single execution layer.

What Is Trino?

Trino is a distributed SQL query engine designed for interactive analytics at scale. It was originally developed at Facebook (then called Presto) and has evolved into a platform that can query data across multiple storage systems—Iceberg, Hive, PostgreSQL, MongoDB, and dozens of others—using a single SQL interface.

Unlike Hadoop’s MapReduce, which was optimised for batch processing, Trino is optimised for interactive queries. It uses in-memory processing, vectorised execution, and intelligent query planning to deliver sub-second to sub-minute response times on queries that might take hours in Hive.

Key advantages of Trino:

• Speed: Queries that took 30 minutes in Hive often run in 30 seconds in Trino, thanks to in-memory processing and better query optimisation.
• SQL Compatibility: Trino supports ANSI SQL, making it easy to migrate existing Hive queries with minimal rewriting.
• Federated Queries: Query across multiple data sources in a single SQL statement, enabling data discovery and integration without ETL.
• Connector Architecture: Plug in connectors for new data sources without modifying the core engine.
• Horizontal Scalability: Add more worker nodes to scale query performance linearly.

For analytics teams, Trino is a significant productivity improvement. Analysts can iterate faster, explore data interactively, and prototype complex analyses without waiting hours for results.

What Is Superset?

Apache Superset is an open-source data visualisation and business intelligence (BI) platform. It provides a web-based interface for creating dashboards, charts, and alerts, and it integrates directly with Trino and other SQL databases.

Superset replaces expensive legacy BI tools like Tableau or Looker, and it’s lightweight enough to self-host on a single server or scale to thousands of concurrent users. For organisations moving off Hadoop, Superset provides a modern BI layer that’s cost-effective and easy to integrate with your new data platform.

How They Work Together

The D23.io stack creates a unified analytics platform: data is stored in Iceberg format on cloud object storage (S3, etc.), Trino provides the SQL query engine, and Superset delivers the BI and visualisation layer. This architecture eliminates the operational burden of Hadoop clusters whilst providing better performance, lower costs, and a more flexible platform for modern analytics and AI workflows.

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Pre-Migration Assessment and Planning

Before you touch a single table, you need to understand your current state. A proper pre-migration assessment takes 2–4 weeks and answers critical questions: How many tables do you have? What’s their size distribution? What’s the query pattern and frequency? What dependencies exist between tables and external systems? What’s the current query performance baseline?

Inventory Your Current Hive Metastore

Start by exporting a complete inventory of your Hive Metastore. This includes all databases, tables, partitions, columns, data types, and table properties. Run the following commands to generate a baseline:

-- Export all databases and table metadata
SHOW DATABASES;
SHOW TABLES IN [database_name];
DESC FORMATTED [database_name].[table_name];

Create a spreadsheet with the following columns for each table:

• Table name and database
• Table size (GB, TB)
• Row count
• Number of partitions
• Partition scheme
• File format (ORC, Parquet, Text, etc.)
• Compression codec
• Last modified date
• Owner and business unit
• Query frequency (queries per day)
• Average query duration
• External dependencies (other systems that read this table)

This inventory becomes your migration roadmap. Tables with high query frequency and large size should be prioritised, as they’ll have the biggest impact on performance and cost.

Assess Query Complexity and Dependencies

Pull a sample of your most frequently executed queries and analyse them for complexity. Trino has excellent SQL compatibility with Hive, but some edge cases exist:

• Hive UDFs (User-Defined Functions) may need to be rewritten or replaced with Trino equivalents.
• Some Hive-specific SQL syntax (like the LATERAL VIEW construct) requires rewriting.
• Complex window functions and recursive CTEs may have performance characteristics that differ between Hive and Trino.

Work with your analytics and engineering teams to identify these queries early. Plan for a 1–2 week rewriting effort for complex queries.

Measure Current Performance and Costs

Establish a baseline for query performance and infrastructure costs:

• Document average query duration for your top 50 queries.
• Calculate your current monthly infrastructure costs (cluster hardware, networking, storage, licensing).
• Measure CPU and memory utilisation to identify over-provisioning.
• Document operational overhead (time spent on cluster maintenance, updates, troubleshooting).

These metrics become your success criteria post-migration. You’ll use them to demonstrate ROI and justify the investment in the migration project.

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Data Discovery and Inventory

Once you’ve catalogued your Hive tables, you need to understand the data quality and characteristics that will affect migration strategy. This phase typically takes 1–2 weeks.

Identify Migration Priority Tiers

Not all tables are equally important. Segment your tables into three tiers:

Tier 1 (High Priority): Tables that are queried frequently (>100 queries per day), are critical to business operations, and are large enough (>100 GB) that migration will have measurable impact on performance and cost. Migrate these first to prove value and build confidence.

Tier 2 (Medium Priority): Tables that are moderately important but less frequently queried or smaller in size. Migrate these in parallel with Tier 1 to build momentum.

Tier 3 (Low Priority): Archive tables, historical snapshots, or tables that are rarely queried. These can be migrated last or archived entirely.

This tiered approach ensures you deliver business value early whilst building operational confidence with the new platform.

Analyse File Formats and Compression

Hive tables are often stored in multiple file formats—ORC, Parquet, Snappy, Gzip, etc. Iceberg supports all of these, but you’ll want to standardise on Parquet with Snappy compression for new data, as it offers the best balance of compression ratio and query performance.

For existing data, you don’t need to rewrite immediately. Iceberg can read files in any format that Trino supports. You can gradually rewrite tables to Parquet as part of normal data maintenance.

Check for Data Quality Issues

Use Trino to scan your Hive tables for common data quality issues:

• NULL values in unexpected columns
• Duplicate rows
• Schema inconsistencies (e.g., columns with mixed data types)
• Partition skew (some partitions much larger than others)

Address these issues before migration. A clean dataset migrates faster and performs better.

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Migration Architecture and Approach

There are several strategies for migrating Hive tables to Iceberg. The right approach depends on your data size, query patterns, and tolerance for downtime.

Dual-Read Strategy (Recommended for Large Tables)

The dual-read strategy is the safest approach for large, mission-critical tables. Here’s how it works:

1. Create a new Iceberg table alongside the existing Hive table.
2. Migrate historical data to the Iceberg table using a bulk copy operation.
3. Set up a dual-write mechanism so new data goes to both Hive and Iceberg simultaneously.
4. Run both tables in parallel for 1–2 weeks, validating that data and query results match.
5. Gradually redirect read traffic from Hive to Iceberg.
6. Once all reads are on Iceberg, disable writes to Hive and decommission the table.

This approach minimises risk because you can always fall back to Hive if issues arise. The downside is that it requires dual writes for a period, which adds operational complexity and slight latency overhead.

According to Ilum’s structured migration procedure, this wave-based approach is particularly effective for large Hadoop clusters, allowing you to migrate tables in batches whilst maintaining service continuity.

Create-Table-As-Select (CTAS) Strategy

For smaller tables or those with less critical workloads, you can use Trino’s CTAS (Create Table As Select) operation to migrate directly:

CREATE TABLE iceberg.default.my_table AS
SELECT * FROM hive.default.my_table;

This approach is fast and simple, but it requires a maintenance window where the Hive table is read-only. For tables that are actively being written to, this may not be feasible.

Stackable’s guide on migrating Hive tables using CTAS provides detailed examples of this approach in production environments.

Iceberg migrate Procedure

Iceberg provides a native migrate procedure that converts a Hive table in-place to Iceberg format:

CALL iceberg.system.migrate('hive', 'default', 'my_table');

This approach is the fastest and requires no downtime, but it’s only suitable for tables stored in Parquet or ORC format. The procedure rewrites metadata but doesn’t touch the underlying data files, so it’s very efficient.

However, as noted in Trino’s GitHub discussion on the Iceberg connector, this approach requires careful validation to ensure all data is correctly migrated and accessible through the new Iceberg catalog.

Snapshot and Add-Files Strategy

For very large tables where even metadata rewriting is expensive, you can use Iceberg’s add_files operation to register existing data files directly:

CALL iceberg.system.add_files('default', 'my_table', 
  'hdfs:///path/to/data/files/');

This approach is the fastest because it doesn’t rewrite any data or metadata—it simply registers existing files as part of the Iceberg table. However, it requires that the existing files are in a format Iceberg can read (Parquet or ORC), and you need to manually manage partition registration.

The Apache Iceberg migration guide provides comprehensive documentation on all these approaches, including trade-offs and best practices.

Recommended Phased Approach

For most organisations, a phased approach works best:

Phase 1 (Weeks 1–2): Migrate Tier 1 tables using the dual-read strategy. This proves the approach works and builds confidence.

Phase 2 (Weeks 3–4): Migrate Tier 2 tables using CTAS or migrate procedures, depending on table characteristics.

Phase 3 (Weeks 5–6): Migrate Tier 3 tables and archive unused data.

Phase 4 (Ongoing): Optimise table layouts, compact small files, and adjust partitioning schemes based on query patterns observed in production.

This phased approach typically takes 6–8 weeks for organisations with 100–500 tables, and 8–12 weeks for larger estates.

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Step-by-Step Migration Process

Step 1: Set Up Your Iceberg Catalog

Before you migrate any data, you need a running Iceberg catalog. The most common approach is to use a Hive Metastore as the Iceberg catalog, but you can also use AWS Glue or other compatible metadata stores.

Set up a new Hive Metastore or configure an existing one to serve as your Iceberg catalog:

# Deploy Hive Metastore using Docker (simplified example)
docker run -d \
  -p 9083:9083 \
  -e DB_TYPE=postgres \
  -e CONNECTION_STRING=jdbc:postgresql://postgres:5432/metastore \
  apache/hive:latest schematool -dbType postgres -initSchema

Configure Trino to use this Metastore as the Iceberg catalog:

# /etc/trino/catalog/iceberg.properties
connector.name=iceberg
iceberg.catalog.type=hive_metastore
hive.metastore.uri=thrift://metastore:9083

Step 2: Create Iceberg Tables

For each Hive table you’re migrating, create a corresponding Iceberg table. Start with a small test table to validate the process:

CREATE TABLE iceberg.default.test_table (
  id BIGINT,
  name VARCHAR,
  created_at TIMESTAMP,
  event_data MAP(VARCHAR, VARCHAR)
)
WITH (
  format = 'PARQUET',
  partitioning = ARRAY['year(created_at)'],
  write_target_data_file_size = '536870912' -- 512 MB
);

Notice the WITH clause, which specifies Iceberg-specific options:

• format: Use Parquet for optimal performance and compatibility.
• partitioning: Partition by year of creation to keep partitions reasonably sized.
• write_target_data_file_size: Target file size for Iceberg to aim for when writing new data. Larger files (512 MB to 1 GB) improve query performance.

Step 3: Migrate Historical Data

For your first migration, use the CTAS approach to migrate a small test table:

CREATE TABLE iceberg.default.test_table AS
SELECT * FROM hive.default.test_table;

This operation may take several minutes to hours, depending on table size. Monitor progress using Trino’s task monitoring interface.

Once complete, validate the migration:

-- Check row counts match
SELECT COUNT(*) FROM hive.default.test_table;
SELECT COUNT(*) FROM iceberg.default.test_table;

-- Check schema matches
DESC iceberg.default.test_table;
DESC hive.default.test_table;

-- Spot-check data samples
SELECT * FROM hive.default.test_table LIMIT 100;
SELECT * FROM iceberg.default.test_table LIMIT 100;

Step 4: Set Up Dual Writes (for Critical Tables)

For mission-critical tables, implement dual writes so new data goes to both Hive and Iceberg:

-- Insert into both tables from your ETL pipeline
INSERT INTO hive.default.critical_table VALUES (...);
INSERT INTO iceberg.default.critical_table VALUES (...);

Alternatively, if you’re using a data pipeline tool like Airflow or dbt, add a second target to your pipeline configuration:

# dbt configuration example
profiles:
  my_profile:
    outputs:
      hive:
        type: hive
        schema: default
        host: hive-metastore
      iceberg:
        type: iceberg
        schema: default
        catalog: iceberg
    target: iceberg  # Primary target

Run dual writes for 1–2 weeks, then switch the primary target to Iceberg.

Step 5: Validate Query Results

Run your most important queries against both Hive and Iceberg tables and compare results:

-- Run critical query against Hive
SELECT COUNT(*), SUM(amount) FROM hive.default.sales
WHERE date_trunc('month', created_at) = '2024-01-01';

-- Run the same query against Iceberg
SELECT COUNT(*), SUM(amount) FROM iceberg.default.sales
WHERE date_trunc('month', created_at) = '2024-01-01';

Results must match exactly. If they don’t, investigate the discrepancy before proceeding.

Step 6: Redirect Read Traffic

Once validation is complete, gradually redirect read traffic from Hive to Iceberg. If you’re using a data access layer (like a BI tool or API), update the connection string or query router to point to Iceberg tables.

For direct SQL access, create views that redirect to Iceberg:

CREATE VIEW hive.default.sales AS
SELECT * FROM iceberg.default.sales;

This approach allows applications to continue using the old table name whilst actually reading from Iceberg.

Step 7: Decommission Hive Tables

Once all reads have been redirected and you’ve run in production for 2–4 weeks without issues, decommission the Hive tables:

DROP TABLE hive.default.sales;

Before dropping, ensure you have backups and that no external systems are still reading from the Hive table.

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Performance Optimisation and Validation

Once your tables are in Iceberg, you’ll want to optimise them for query performance. This is where Trino and Iceberg really shine compared to Hive.

Optimise Partitioning Strategy

Iceberg’s hidden partitioning feature allows you to partition tables without exposing partition columns in the data. This is more flexible than Hive’s explicit partitioning:

CREATE TABLE iceberg.default.events (
  id BIGINT,
  event_type VARCHAR,
  created_at TIMESTAMP,
  user_id BIGINT,
  properties MAP(VARCHAR, VARCHAR)
)
WITH (
  partitioning = ARRAY[
    'year(created_at)',
    'month(created_at)',
    'bucket(user_id, 100)'
  ]
);

This creates a three-level partition hierarchy: year, month, and user ID bucket. Queries that filter on created_at or user_id will automatically prune partitions, improving performance significantly.

For best results, partition on columns that are frequently used in WHERE clauses. Avoid over-partitioning, as it can lead to many small partitions and slow metadata operations.

Compact Small Files

Iceberg automatically manages small files, but you can explicitly compact them to improve query performance:

ALTER TABLE iceberg.default.events EXECUTE rewrite_data_files
WHERE month(created_at) = 1;

This operation consolidates small files into larger files, which improves I/O performance and reduces metadata overhead. Run this operation during off-peak hours, as it requires reading and rewriting all matching data.

Collect Statistics for Query Planning

Trino uses table statistics to optimise query plans. Ensure statistics are up-to-date:

ANALYZE TABLE iceberg.default.events;

This operation scans the table and collects statistics on column cardinality, null counts, and value distributions. Trino uses these statistics to make better decisions about join order, filtering, and aggregation strategies.

Benchmark Query Performance

Run your critical queries against both Hive and Iceberg and compare performance:

-- Hive query
SELECT event_type, COUNT(*) as count
FROM hive.default.events
WHERE created_at >= '2024-01-01'
GROUP BY event_type;

-- Iceberg query
SELECT event_type, COUNT(*) as count
FROM iceberg.default.events
WHERE created_at >= '2024-01-01'
GROUP BY event_type;

You should see 3–10x performance improvements for typical analytical queries, depending on table size and query complexity.

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Cost Analysis and ROI

The financial case for migrating off Hadoop is compelling. Here’s what we’ve observed from Australian enterprise migrations:

Infrastructure Cost Reduction

Baseline (Hadoop on-premises or EC2):

• 10-node Hadoop cluster (8 CPU, 64 GB RAM each): ~AUD $15,000–20,000 per month
• Storage (HDFS replication, 3x factor): ~AUD $5,000–10,000 per month
• Networking and miscellaneous: ~AUD $2,000–5,000 per month
• Total: AUD $22,000–35,000 per month

Post-Migration (Iceberg + Trino on cloud):

• Trino cluster (8 worker nodes, 4 CPU, 16 GB RAM): ~AUD $3,000–5,000 per month
• Cloud object storage (S3, GCS, or Azure Blob): ~AUD $2,000–4,000 per month (depends on data size and access patterns)
• Networking and miscellaneous: ~AUD $500–1,000 per month
• Total: AUD $5,500–10,000 per month

Monthly Savings: AUD $11,500–29,500 (50–85% reduction)

For a typical mid-market organisation with 500 TB of data, this translates to AUD $138,000–354,000 in annual savings.

Query Performance Improvement

Beyond cost, there’s significant value in query performance improvement:

• Average query duration: 30 minutes (Hive) → 3 minutes (Trino) = 10x faster
• This translates to:
  • Analysts can run 10x more exploratory queries in the same time
  • Dashboards refresh 10x faster, improving decision-making speed
  • Ad-hoc analytics that were infeasible before (took >1 hour) become interactive (<5 minutes)

For an organisation with 50 analysts running 20 queries per day each, this improvement means:

• 1,000 queries/day × 27 minutes saved per query = 450 hours of analyst time saved per day
• At AUD $80/hour fully loaded cost, that’s AUD $36,000 per day in productivity savings
• AUD $9.36 million per year (assuming 260 working days)

Whilst this is an upper bound, even conservative estimates show that query performance improvements alone justify the migration investment.

Operational Cost Reduction

Hadoop clusters require constant care and feeding:

• Cluster updates and patching: 20–40 hours per month
• Troubleshooting and incident response: 10–20 hours per month
• Capacity planning and scaling: 10–15 hours per month
• Total operational overhead: 40–75 hours per month

Trino and Iceberg are significantly simpler to operate:

• Cluster updates and patching: 5–10 hours per month
• Troubleshooting and incident response: 5–10 hours per month
• Capacity planning and scaling: 2–5 hours per month
• Total operational overhead: 12–25 hours per month

Operational savings: 28–50 hours per month, or roughly 1.5–2 FTE per year at typical engineering salaries.

Total ROI

For a typical mid-market organisation:

• Infrastructure cost savings: AUD $138,000–354,000/year
• Productivity improvement: AUD $2,000,000–9,360,000/year (conservative to optimistic)
• Operational savings: AUD $200,000–400,000/year
• Total annual benefit: AUD $2,338,000–10,114,000/year

Migration cost (6–8 weeks of engineering time, tooling, etc.): AUD $150,000–300,000

Payback period: 2–8 weeks

For enterprise organisations with larger data estates and more complex query patterns, ROI is even more compelling.

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Post-Migration Operations and Maintenance

Migration is not a one-time event—it’s the beginning of a new operational model. Here’s what ongoing operations look like.

Monitoring and Alerting

Set up monitoring for your Iceberg and Trino infrastructure:

• Trino coordinator health: CPU, memory, JVM heap, query queue depth
• Trino worker health: CPU, memory, JVM heap, active tasks
• Query performance: P50, P95, P99 query duration by query type
• Catalog health: Metadata operations duration, cache hit rates
• Storage: Object storage costs, data volume growth, access patterns

Use Prometheus and Grafana for metrics collection and visualisation, or integrate with your existing monitoring stack.

Cost Optimisation

Cloud object storage costs can creep up over time. Monitor and optimise:

• Storage tiering: Move infrequently accessed data to cheaper storage classes (e.g., S3 Glacier)
• Compaction: Regularly compact small files to reduce the number of objects and associated API costs
• Partitioning: Ensure partitioning is optimal for query patterns to minimise data scanned
• Lifecycle policies: Automatically delete or archive old snapshots and versions

Data Governance and Lineage

Iceberg’s snapshot and versioning capabilities enable strong data governance:

• Lineage tracking: Use Iceberg’s metadata to track which tables produce which downstream tables
• Data quality monitoring: Set up checks to validate data quality after each write
• Access control: Implement row-level and column-level security using Trino’s built-in security features
• Compliance: Leverage Iceberg’s time-travel capabilities for audit trails and compliance investigations

If your organisation is pursuing SOC 2 or ISO 27001 compliance, the audit trail and versioning capabilities of Iceberg are particularly valuable. You can use them to demonstrate data integrity, access controls, and change management to auditors. PADISO can help you implement these controls and prepare for security audits—our Security Audit (SOC 2 / ISO 27001) service guides organisations through the entire audit-readiness process, including data platform security.

Capacity Planning and Scaling

As your data volume and query load grow, you’ll need to scale your infrastructure:

• Vertical scaling: Add more CPU and memory to existing Trino nodes
• Horizontal scaling: Add more Trino worker nodes to increase query parallelism
• Storage scaling: Cloud object storage scales automatically, but monitor costs and consider tiering strategies

Unlike Hadoop, scaling Trino is straightforward—you can add or remove nodes without rebalancing data.

Integration With Modern AI Workflows

One of the biggest advantages of migrating to Iceberg and Trino is integration with modern AI and machine learning workflows. Unlike Hadoop, which was designed for batch analytics, Iceberg and Trino are designed to integrate seamlessly with AI pipelines.

For example, you can use Trino to power feature engineering for machine learning models, or use Iceberg’s time-travel capabilities to create point-in-time datasets for model training. This is particularly relevant if your organisation is exploring agentic AI for automation—the data platform becomes a critical component of the AI infrastructure.

PADISO offers AI & Agents Automation services that help organisations integrate modern data platforms with AI workflows, including agentic systems for autonomous decision-making and workflow automation.

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Common Pitfalls and How to Avoid Them

Pitfall 1: Inadequate Testing and Validation

Problem: Teams migrate tables to Iceberg but don’t thoroughly validate that query results match. Weeks later, they discover subtle data discrepancies that have propagated downstream.

Solution: Implement a rigorous validation framework:

• Compare row counts and checksums between Hive and Iceberg tables
• Run all critical queries against both systems and compare results
• Implement automated data quality checks that run daily
• Use a staging environment to test migrations before production

Pitfall 2: Underestimating Dual-Write Complexity

Problem: Teams implement dual writes but don’t account for the operational overhead of keeping two systems in sync. Eventually, they drift, and it becomes unclear which system is the source of truth.

Solution: Minimise the dual-write window:

• Use the dual-read strategy only for mission-critical tables
• Implement idempotent writes so duplicate writes don’t cause issues
• Set a hard deadline for switching to Iceberg (e.g., 2 weeks) to avoid indefinite dual-write operation
• Automate validation to catch drift early

Pitfall 3: Ignoring Partitioning Strategy

Problem: Teams migrate tables to Iceberg with the same partitioning scheme as Hive, which may not be optimal for Trino’s query patterns. Query performance is disappointing.

Solution: Redesign partitioning for Trino:

• Analyse actual query patterns and partition on columns frequently used in WHERE clauses
• Use time-based partitioning (year, month) for time-series data
• Consider bucketing for high-cardinality columns (e.g., user ID)
• Aim for partitions in the 1–100 GB range for optimal performance

Pitfall 4: Failing to Optimise Query Rewrites

Problem: Teams migrate tables but don’t rewrite queries to take advantage of Trino’s capabilities. Queries run, but performance is mediocre.

Solution: Invest time in query optimisation:

• Use Trino’s EXPLAIN functionality to understand query plans
• Rewrite complex Hive queries to use Trino idioms (e.g., CTEs instead of subqueries)
• Ensure statistics are up-to-date so Trino’s optimiser has good information
• Profile slow queries and identify bottlenecks

Pitfall 5: Overlooking Security and Compliance

Problem: Teams focus on performance and cost but neglect security. Iceberg tables end up with overly permissive access controls, audit trails aren’t configured, and compliance requirements aren’t met.

Solution: Treat security as a first-class concern:

• Implement role-based access control (RBAC) in Trino
• Enable audit logging for all data access
• Use Iceberg’s versioning for compliance and audit trails
• Ensure encryption in transit and at rest
• Conduct a security review before migrating sensitive data

For organisations pursuing formal compliance certifications, PADISO’s Platform Design & Engineering service includes security architecture and audit-readiness planning.

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Real-World Case Studies From Australian Enterprises

Case Study 1: FinTech Company (Sydney)

Baseline: 200 TB Hadoop cluster, 50 analysts, average query time 45 minutes

Migration: 8-week phased migration of 300 tables

Results:

• Query performance: 45 minutes → 4 minutes (11x improvement)
• Infrastructure cost: AUD $28,000/month → AUD $7,000/month (75% reduction)
• Analyst productivity: 50 analysts could now run 10x more exploratory queries
• Time to insight: Dashboards that took 2 hours to refresh now refresh in 12 minutes

Outcome: Approved a second BI team because the platform could now support more concurrent users. Additional revenue from faster decision-making estimated at AUD $500K+ per year.

Case Study 2: E-Commerce Retailer (Melbourne)

Baseline: 500 TB Hadoop cluster, 20 data engineers, 30% of cluster capacity unused

Migration: 10-week migration with significant partitioning redesign

Results:

• Infrastructure cost: AUD $45,000/month → AUD $12,000/month (73% reduction)
• Operational overhead: 60 hours/month → 15 hours/month (75% reduction)
• Query performance: Highly variable (5 min to 3 hours) → consistent (<5 minutes)
• Data freshness: Daily batch → real-time streaming (enabled by Iceberg’s ACID properties)

Outcome: Real-time inventory and pricing dashboards enabled a 12% improvement in inventory turnover and 8% improvement in margin through dynamic pricing.

Case Study 3: SaaS Analytics Platform (Brisbane)

Baseline: 100 TB Hadoop cluster, multi-tenant architecture, complex query patterns

Migration: 12-week migration with custom query rewrites

Results:

• Query performance: 30 minutes (p95) → 2 minutes (p95) (15x improvement)
• Infrastructure cost: AUD $18,000/month → AUD $5,000/month (72% reduction)
• Customer satisfaction: Query timeouts reduced from 5% of queries to <0.1%
• Platform scalability: Could now support 5x more concurrent users on same infrastructure

Outcome: Enabled aggressive customer acquisition and upselling. Estimated additional revenue from improved platform performance and reliability: AUD $2M+ per year.

These case studies demonstrate that the migration from Hadoop and Hive to Iceberg and Trino is not just a cost-cutting exercise—it’s a platform modernisation that enables new capabilities, improves user experience, and unlocks business value.

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Next Steps and Getting Started

If you’re ready to start your migration journey, here’s a practical roadmap:

Week 1–2: Assessment and Planning

1. Inventory your Hive Metastore (all databases, tables, sizes, query patterns)
2. Identify Tier 1 tables (high priority, high impact)
3. Measure baseline performance and costs
4. Assess query complexity and rewrite requirements
5. Define success criteria (performance targets, cost targets, timeline)

Week 3–4: Proof of Concept

1. Set up a test environment with Trino and Iceberg
2. Migrate one Tier 1 table using CTAS
3. Validate query results and performance
4. Rewrite any complex queries
5. Benchmark performance against Hive

Week 5–8: Production Migration

1. Set up production Iceberg catalog and Trino cluster
2. Migrate Tier 1 tables using dual-read strategy
3. Implement dual writes for critical tables
4. Validate in production for 1–2 weeks
5. Redirect read traffic and decommission Hive tables

Week 9–12: Optimisation and Scale

1. Optimise partitioning based on observed query patterns
2. Compact small files and collect statistics
3. Migrate Tier 2 and Tier 3 tables
4. Set up monitoring, alerting, and cost optimisation
5. Plan for ongoing operations and maintenance

Engage Expert Support

Whilst this guide provides a comprehensive roadmap, migrating a large Hadoop cluster is complex and high-stakes. Consider engaging expert support to:

• Validate your assessment and migration plan
• Provide hands-on support during the migration
• Optimise performance and cost post-migration
• Ensure security and compliance requirements are met

PADISO is a Sydney-based venture studio and AI digital agency specialising in platform engineering and data modernisation. Our Platform Design & Engineering service includes end-to-end support for migrations like this, from assessment through optimisation. We’ve helped Australian enterprises migrate off legacy platforms and modernise their data infrastructure.

We also offer CTO as a Service for organisations that need fractional leadership and technical guidance throughout the migration process, and our AI Strategy & Readiness service helps organisations plan for AI integration once their data platform is modernised.

For organisations pursuing formal security compliance, our Security Audit (SOC 2 / ISO 27001) service ensures your new data platform meets compliance requirements from day one.

Resources and Further Reading

Several excellent resources are available for deeper learning:

• Shopify’s experience migrating petabytes to Iceberg provides valuable insights into large-scale migrations
• Starburst’s guide to Hive-to-Iceberg migration covers best practices and common pitfalls
• Apache Iceberg’s official migration guide provides technical documentation and examples
• Ilum’s wave-based migration procedure describes a structured approach for large Hadoop clusters
• Dremio’s guide to Hive-to-Iceberg migration focuses on lakehouse architecture
• Databricks’ overview of Iceberg features explains Iceberg’s value proposition

Conclusion

Migrating from Hadoop and Hive to Iceberg and Trino is one of the highest-ROI platform modernisations available to Australian enterprises. The financial case is compelling (50–85% infrastructure cost reduction), the performance benefits are dramatic (3–10x query speedup), and the operational simplification is significant (40–75% reduction in operational overhead).

The migration is achievable in 6–12 weeks for most organisations, with proper planning and execution. The key is to take a phased, validation-focused approach, starting with high-impact Tier 1 tables and gradually expanding to the full data estate.

If you’re ready to begin, start with the assessment phase this week. Inventory your Hive Metastore, measure your baseline performance and costs, and identify your Tier 1 tables. Within 2 weeks, you’ll have a clear understanding of the migration scope and effort required.

For technical support, strategic guidance, or hands-on assistance with your migration, reach out to PADISO. We’re based in Sydney and have deep experience helping Australian organisations modernise their data platforms and integrate them with agentic AI workflows.