PADION
data lakehouse · Service Platform
AI · Data · Innovation · ON
Replacing Hadoop
for the next generation
Handle big data at the same scale — without complex HDFS · YARN cluster operations. Keep your existing Hive Metastore as-is for gradual migration.
Hadoop → Lakehouse
NameNode
YARN
MapReduce
HDFS
Container
Object Store
Trino
Hive Meta ✓
Eliminate operational burden, preserve your assets.
Why leave Hadoop now?
Hadoop opened the era of big data, but operational costs compound year after year. NameNode SPOF mitigation, YARN resource management, MapReduce tuning, cluster scaling and incident response — teams end up spending more time tending infrastructure than creating data value.
PADION data lakehouse replaces this burden with a lightweight, container-based lakehouse layer. Object storage (or compatible) + Trino query engine + Hive Metastore compatibility — modernize the infrastructure while keeping your data team's daily tools intact.
The key advantage: a gradual migration that does not require redefining existing Hive tables or schemas. A safe path of PoC → workload-by-workload migration → full cutover.
6 Key Features
No Hadoop ops burden
No NameNode, YARN, or MapReduce clusters. A lightweight, container-based lakehouse layer lifts weight off your ops team.
Unified ingestion (RDBMS · Hive · File)
Unify disparate sources into a single analytics layer. Federated query without moving data.
Hive Metastore compatible
Reuse existing Hive metadata as-is. Gradual migration without schema redefinition minimizes risk.
Docker Swarm released · K8s in progress
Stable operation on Docker Swarm today; Kubernetes support is in progress. Start small and scale only what you need.
Object-storage foundation
Eliminates the HDFS NameNode SPOF burden. Storage and compute scale independently.
Trino interactive queries
Replace MapReduce batch latency with interactive SQL. Analysts explore lakehouse data directly.
Hadoop → Lakehouse component map
How each component of the legacy Hadoop stack is replaced or carried over in lakehouse.
Before
Hadoop Stack
-
HDFS
NameNode SPOF · DataNode cluster operations
-
YARN
Resource scheduler · queue policy management
-
MapReduce
Batch processing · minute-scale query latency
-
Hive Metastore
Table & partition metadata catalog
After
PADION data lakehouse
-
Object Storage
Storage and compute separated · no SPOF.
-
Container Orchestration
Docker Swarm · simplified queue policies.
-
Trino Engine
Second/minute-scale interactive SQL.
-
Hive Metastore (compatible as-is)
Existing metadata assets carried over 100%.
Migration Path
3-step gradual migration
PoC
Validate identical queries on lakehouse via existing Hive Metastore connectivity alone.
Migrate selected workloads
Run lakehouse for specific domains / analytics workloads in parallel with Hadoop.
Full cutover
After validation, shut down the legacy cluster. Reallocate ops cost and staff.
Tech Spec
| Container orchestration | Released Docker Swarm In progress Kubernetes |
|---|---|
| Metadata catalog | Hive Metastore (compatible) |
| Query engine | Trino |
| Data sources | RDBMS · Hive · File (unified structured / unstructured ingest) |
| Storage | Object storage (separated storage & compute) |
| PADION integration | gatekeeper(Auth) · datakeeper(Authz) · EoH(ETL) · analyze(Analytics) |
| Recommended environment | Sizing by scale / workload determined during deployment consulting |
Position in the data flow
data lakehouse sits at step 07 — Storage in the PADION data lifecycle. Every stage runs as containers on the Operations Platform.
PADION Flow · You are here
06 / data lakehouse
Ops Platform → dnsd → gatekeeper → datakeeper → EoH → data lakehouse → analyze
It's time to graduate from Hadoop —
PADION is with you.
PoC, Hive Metastore integration validation, phased migration consulting — start with a single meeting.
Weekdays 10:00–19:00 KST · info@bcore.co.kr