Migrate from Hadoop to Modern Cloud Data Platforms
Hadoop transformed big data processing when it launched, but most engineering teams today are actively planning their Hadoop migration. Self-managed HDFS clusters, aging MapReduce workloads, and expensive on-premise infrastructure no longer make sense when modern cloud-native alternatives deliver better performance, lower cost, and far less operational overhead.
BigData Boutique designs and executes Hadoop migrations to Databricks, AWS EMR, Apache Spark on Kubernetes, ClickHouse, and Apache Iceberg-based data lakehouses—with zero data loss, minimal downtime, and full workload validation before cutover.
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Trusted By
50%+
Average Cost Reduction
0
Data Loss Migrations
13+yrs
of Big Data Platform Expertise
Why Organizations Are Leaving Hadoop
Hadoop was designed for a world where commodity hardware was cheap and cloud computing didn't exist. Today, that calculus has reversed. HDFS requires dedicated, persistent clusters with high operational overhead. MapReduce is slow compared to Apache Spark. YARN resource management is complex and brittle. Upgrading Hadoop versions is notoriously painful.
Modern alternatives—Databricks, AWS EMR Serverless, Apache Spark on Kubernetes, ClickHouse, and Apache Iceberg—provide elastic compute, pay-per-use pricing, open table formats, and dramatically better developer experience. Organizations migrating from Hadoop consistently report 40–70% infrastructure cost reductions and significant improvements in processing speed and operational simplicity.
Hadoop Migration
Target Platforms
Hadoop to Databricks
Databricks is the most common Hadoop replacement for organizations that run complex Spark and ML workloads. We migrate your HDFS data to cloud object storage (S3, GCS, ADLS), convert your HDFS-based data formats to Delta Lake or Apache Iceberg, and translate Hive and MapReduce jobs to optimized Spark notebooks and jobs—with full lineage and governance through Unity Catalog.
Hadoop to AWS EMR
For organizations on AWS, EMR Serverless provides a natural Hadoop replacement with native support for Spark, Hive, and Presto/Trino. We migrate your cluster configurations, job definitions, and HDFS data to S3-based architectures while integrating AWS Glue Data Catalog for metadata management and governance.
HDFS to Apache Iceberg Data Lakehouse
Apache Iceberg has emerged as the open standard for analytical data storage, providing ACID transactions, schema evolution, time travel, and efficient partition pruning on top of cloud object storage. We migrate your HDFS data to Iceberg tables on S3 or GCS, enabling multi-engine access from Spark, Flink, Trino, and Athena without vendor lock-in.
MapReduce & Hive to Spark or Flink
Legacy MapReduce and Hive jobs are often the most challenging part of a Hadoop migration. We rewrite MapReduce jobs as Spark DataFrames or Flink pipelines, translating HiveQL to Spark SQL and optimizing execution plans for modern in-memory processing. The result is typically 10–50x faster job execution.
The BigData Boutique
Hadoop Migration Approach
Workload & Data Inventory
We catalog your entire Hadoop environment: HDFS datasets, Hive tables, MapReduce and Spark jobs, Oozie workflows, and data ingestion pipelines. We classify workloads by complexity and business criticality to build a phased migration roadmap.
Data Migration & Format Conversion
We migrate HDFS data to cloud object storage with format conversion from sequence files, ORC, and Avro to Parquet or Iceberg as appropriate. We validate data integrity at every stage and build parallel pipelines to keep target and source in sync during the migration period.
Workload Translation & Optimization
We rewrite your Hadoop jobs for the target platform, optimizing for cloud execution patterns. MapReduce becomes Spark or Flink. Hive queries become Spark SQL or Trino. Oozie workflows become Airflow DAGs or managed orchestration. Each translated workload is validated for functional correctness and performance.
Validated Cutover & Decommission
We run Hadoop and the target platform in parallel until all outputs are validated to match. Cutover is phased and reversible. Once all workloads are confirmed on the new platform, we assist with Hadoop cluster decommissioning and final infrastructure cost optimization.
Ready to Migrate from Hadoop?
Schedule a free Hadoop migration consultation. We'll assess your current Hadoop environment, identify the right target platform, estimate your cost savings and performance improvements, and outline a phased migration plan that minimizes risk and disruption.