More organizations want interoperable data solutions to avoid vendor lock-in and lower costs. They need single-source data accessible by multiple engines (e.g., Flink, Spark, Snowflake) through REST APIs, reducing unnecessary movement and duplication. Using open table formats and universal catalogs like Polaris, Nessie, or Lakekeeper ensures consistent sharing and governance across platforms. Legacy Hadoop users should modernize with cloud-native architectures for better performance and efficiency. NetApp helps users efficiently migrate from HDFS-based Hive tables to Apache Iceberg on object storage managed with OpenShift.
Technical Challenges in Hadoop Modernization:
Modernizing legacy Hadoop environments to cloud-native data platforms presents a series of strategic and operational hurdles:
- Strategic Data Migration: Orchestrating the movement of petabytes of data from HDFS-based Hive tables to open-format tables such as Apache Iceberg on object storage requires meticulous planning to prevent downtime and maintain data integrity. Selecting migration frameworks that support business continuity is essential.
- Architectural Transformation: Transitioning from monolithic, managed Hadoop systems to containerized, Kubernetes-native workflows (e.g., OpenShift) demand a clear vision for integration, automation, and orchestration. The focus should be on building scalable architectures that are resilient and future ready.
- Storage and Compute Scalability: Decoupling storage and compute resources—leveraging object storage alongside engines like Spark, Trino, and Airflow—enables flexible scaling and cost optimization. Evaluating solutions for seamless interoperability across analytics platforms is a key consideration.
- Workload Modernization: Migrating analytics workloads from Hadoop MapReduce to modern engines such as Apache Spark, Apache Flink, and SQL platforms like Trino, orchestrated by Airflow, is a complex but necessary evolution. Embracing technologies that deliver improved performance and support advanced analytics is critical.
- Performance Assurance: The new architecture must meet or exceed HDFS performance benchmarks, especially for interactive and large-scale analytics. Establishing clear metrics and validation processes will guide successful modernization.
- Unified Data Access: Facilitating simultaneous access to shared datasets for multiple teams and applications—without duplication or silos—requires robust data governance and cataloging strategies. Adopting open standards and universal catalog solutions maximizes data utility.
- Resilient Disaster Recovery: Continuous data synchronization across geographically distributed data centers is vital for business continuity. Investing in resilient replication and failover solutions safeguards critical assets.
- Infrastructure Modernization: Upgrading from legacy SATA-based storage to high-performance, low-latency all-Flash ONTAP and storageGRID are essential for supporting intensive analytics workloads. Infrastructure investments should align with long-term data strategy.
- Resource Contention Management: Addressing “noisy neighbor” issues in shared environments is crucial for maintaining quality of service for mission-critical applications. Implementing robust resource governance and monitoring frameworks ensures predictable performance.
Comparing Hive and Iceberg for Modern Data Management
Challenges of Traditional Hive
Traditional Hive introduces several challenges when it comes to modern data management requirements. One of the primary obstacles is the need for manual partition management, which often necessitates the use of numerous ALTER statements to modify and adjust partitions. This approach can be both cumbersome and prone to human error. Additionally, Hive does not support ACID (Atomicity, Consistency, Isolation, Durability) transactions, making it difficult to guarantee data consistency across operations.
Schema evolution in Hive typically requires planned downtime, which can disrupt ongoing business operations and result in reduced productivity. The system also faces performance issues, particularly when handling large quantities of small files, as it is not optimized for such workloads. Another limitation is the absence of time travel capabilities, preventing users from accessing or analyzing historical versions of the data. Furthermore, Hive depends heavily on manual maintenance and compaction tasks to maintain data optimization and accessibility.
Benefits of Apache Iceberg
Apache Iceberg addresses many of the limitations found in traditional Hive. It offers automatic partition discovery and management, eliminating the need for manual intervention and reducing the potential for errors. Iceberg provides full ACID transaction support, ensuring data consistency and reliability. Schema changes can be performed without incurring downtime, allowing for seamless evolution as business requirements change.
Iceberg automatically optimizes file layouts, enhancing read and write efficiency. It also introduces time travel and rollback capabilities, enabling users to access previous versions of their data for historical analysis or recovery. Built-in maintenance operations further reduce the administrative burden and ensure data remains optimized and accessible.
Why Iceberg Delivers Superior Performance
Iceberg achieves faster performance through several technical advancements. Metadata pruning minimizes unnecessary file reads by leveraging efficient metadata structures. Optimized file layouts reduce the number of input/output operations required, while predicate pushdown to the file level enhances query speed by filtering data early in the process. Additionally, support for vectorized operations further accelerates data processing tasks.
Hive vs. Iceberg: Performance Comparison
In a performance test conducted using a small dataset consisting of 15 partitions and 75 records, the following results were observed:
|
Query Type |
Hive (seconds) |
Iceberg (seconds) |
Improvement |
|
COUNT(*) operations |
4.2 |
1.8 |
+57% faster |
|
Aggregation queries |
8.5 |
3.2 |
+62% faster |
|
Partition pruning |
2.1 |
0.8 |
+62% faster |
|
Join operations |
12.3 |
5.7 |
+54% faster |
|
Schema reads |
1.5 |
0.3 |
+80% faster |
These results highlight Iceberg’s significant performance improvements over Hive, with faster query execution times across a variety of common operations.
Key NetApp Technologies Powering Data Platform Modernization
To address these multifaceted needs, the solution leverages key NetApp technologies:
- Accelerated Data Migration with XCP: The NetApp XCP Tool simplifies migrating data from HDFS files, MapRFS, or any HCFS files and directories to S3-compatible Iceberg storage. It maintains metadata integrity and performs thorough data checks, minimizing downtime and risk when moving to modern data platforms. XCP can migrate data using either production data lake worker nodes or by leveraging non-production environments, such as standalone Linux or Windows servers, to perform the migration.If the production cluster is heavily used by datalake applications that require significant CPU resources, it is advisable to run XCP in a non-production setting. Alternatively, customers may use "hadoop distcp" if the production Lakehouse cluster has sufficient CPU capacity and applications can tolerate waiting until migration is complete.
- Multi-Application Data Access with FlexClone & FlexCache: FlexClone allows simultaneous workflows like Spark and Trino queries on shared data without extra storage overhead. FlexCache delivers distributed caching for faster, low latency reads near compute clusters, reducing WAN traffic and scaling read performance across multiple cache sites.
- Enterprise-Grade Resiliency : SnapMirror delivers asynchronous replication designed for disaster recovery, while MetroCluster enables synchronous replication and automated failover to maintain data availability and resilience across two locations. Both SnapMirror and MetroCluster replicate and safeguard essential the underlying data lakehouse storage, supporting uninterrupted operations and preparedness for disaster recovery.
- All-Flash Storage Upgrade: NetApp ONTAP AFF and StorageGRID platforms supplant traditional SATA drives, delivering high input/output operations per second (IOPS) and reduced latency to effectively support intensive Spark and Trino analytics queries, thereby significantly decreasing processing durations.
- Tenant isolation with ONTAP storage virtual machines and StorageGRID: By logically partitioning resources, multi-tenancy is supported, which helps maintain data governance and enforces security boundaries across environments with multiple users and applications.
- Performance Assessment Leveraging StorageGRID and ONTAP: This integrated architecture enables support for Iceberg table formats on StorageGRID, facilitating scalable object storage, in conjunction with ONTAP to address high-performance workload requirements. Benchmark results indicate notable latency and throughput enhancements compared to HDFS, thereby substantiating the advantages of this architectural approach.
- StorageGRID Branch Bucket feature benefits to iceberg table:
- Snapshot views: Reproduce data at specific times for audits or queries.
- Isolated testing: Use branches for compaction, schema, or engine tests safely.
- Read-only sharing: Give immutable access to auditors/teams with Object Lock and policies.
- Fast recovery: Branch from pre-issue states to restore workloads quickly.
- Clone test environments: Create short-lived branches for CI and benchmarks.
- Multi-version checks: Test upgrades and changes on branches before production.
- DR/protection: Use replication and policies to meet compliance and resilience needs.
- Central management: Simplify branch operations via Tenant Manager.
NetApp Auto Support (ASUP) customer reports reveal that the Iceberg open-format table is primarily utilized by banks, trading firms, stock brokerages, and research institutions.
For enterprise customers, NetApp solutions deliver advanced analytics, operational simplicity, and robust resilience, helping organizations move beyond legacy Hadoop systems.
To decide if these solutions are right for your organization, review your current data infrastructure, workload needs, and compliance requirements. If your business values advanced analytics, simplicity, and resilience—like many financial and research institutions—NetApp’s solution for Iceberg open-format table may be an ideal fit. Evaluate your environment, pinpoint areas for improvement, and apply NetApp’s recommendations for better performance and protection. Use this analysis as a practical guide for your modernization journey. Look out for future blogs with deeper insights and actionable tips to help you make the most of these technologies.
