Data Lake Modernization

Powered by Apache Hadoop (HDFS) or Ceph S3 for the storage backend, using Iceberg lakehouse tables with Spark, Kafka, Trino, Airflow, Superset, Spark Operator, plus ML tooling (MLflow, JupyterHub, KServe) and HBase on S3 where required.

Modernize proprietary Hadoop platforms into an open, scalable data lakehouse on upstream Apache and S3-based storage – backed by XaasIO’s SLA-driven, around-the-clock enterprise support. We help you migrate workloads, redesign storage and governance, and run the platform in production with predictable upgrades, observability, and operational runbooks.

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Open Data Lakehouse on HDFS or S3

A modern lakehouse foundation on Hadoop (HDFS) or Ceph S3, with Iceberg table formats to support governed analytics and ML at scale.

Modern Compute + Interactive SQL

Unified batch processing and fast interactive analytics using Spark and Trino, with consistent governance patterns across teams.

End-to-End Pipelines + ML Enablement

Operational pipelines with Airflow, self-service BI via Superset, and ML workflows using JupyterHub + MLflow + KServe.

Why Modernize

Reduce dependency on proprietary Hadoop distributions and licensing constraints
Move from HDFS-only designs to S3 lakehouse patterns where needed
Improve scalability, flexibility, and cost/performance predictability
Standardize governance, security, and operational visibility
Enable faster analytics delivery and AI/ML readiness on open platforms

Target Platform Capabilities

Storage & Lakehouse

Hadoop (HDFS) or Ceph S3 as the storage backend
Apache Iceberg for lakehouse tables (schema evolution, reliability, governance-friendly layouts)
HBase on S3 patterns when key-value/operational access is required

Processing & Streaming

Apache Spark for ETL and large-scale processing
Spark Operator for Kubernetes-native Spark scheduling and lifecycle management
Apache Kafka for streaming ingestion, event pipelines, and real-time processing

SQL, BI & Exploration

Trino for interactive SQL across Iceberg tables and external sources
Superset for self-service BI dashboards and governed reporting

Orchestration & DataOps

Apache Airflow for pipeline orchestration (DAGs, scheduling, dependency management)
DataOps practices: environment promotion, testing, and operational runbooks

ML Enablement

JupyterHub for notebooks and team workspaces
MLflow for experiment tracking and model lifecycle patterns
KServe for model serving patterns aligned to Kubernetes

Production Operations (Managed)

Observability integration with your standard stack (dashboards, alerting, log analytics)
Upgrade strategy, patch cadence, reliability improvements, and capacity governance

Reference Architecture
Open Data Lakehouse on HDFS or Ceph S3 Popular Topics

XaasIO delivers a layered architecture that separates storage, compute, query, orchestration, and ML so each layer scales independently while maintaining governance and operational control.

Architecture layers:

Modernization & Migration Approach (Proprietary Hadoop → Upstream Apache / HDFS or S3)

Assessment & Blueprint
(2 – 4 weeks)

Current platform inventory (clusters, workloads, SLAs, data flows)
Data governance and security requirements
Target architecture and migration waves
HDFS vs S3 backend strategy (or hybrid) and sizing guidance
Cutover strategy, success criteria, and risk plan

Foundation Build (4 – 8 weeks)

Deploy core services: Spark, Spark Operator, Kafka, Trino, Airflow, Iceberg, Superset
Define data zones, table layouts, retention and lifecycle policies
Implement operational guardrails and observability baselines
Integrate IAM patterns and environment structure (dev/test/prod)

Workload Migration (Iterative Waves)

Prioritize workloads: ETL, SQL queries, streaming pipelines, ML workflows
Migrate pipelines and datasets wave-by-wave
Validate performance, reliability, and governance against SLAs
Reduce and decommission proprietary dependencies progressively

Production Hardening & Operations

Upgrade strategy, patch cadence, runbooks, and incident response model
Performance and capacity governance across storage/compute/query layers
Training/KT and handover – or transition to XaasIO managed operations

Managed Data Lakehouse Operations
by XaasIO

XaasIO can operate the data platform with SLAs, upgrades, incident response, and continuous reliability improvement – so your internal teams focus on data products and outcomes.

Managed scope (high-level)

SLA-backed support (16×5 or 24×7 options)
Upgrade and patch cycles for platform components
Incident response, RCA, and problem management
Dashboards, alert tuning, and operational runbooks
Capacity planning and performance optimization

Use Cases

Downloads

Modernize legacy ETL pipelines to Spark
Build an Iceberg lakehouse on HDFS or Ceph S3
Streaming ingestion and real-time processing with Kafka
Interactive SQL and self-service analytics using Trino
BI dashboards and governed reporting with Superset
ML enablement: JupyterHub + MLflow + KServe
Operational data access with HBase on S3 (where needed)

Modernize Your Data Platform

Request a Modernization Assessment to validate target architecture, migration waves, and a practical path from proprietary Hadoop to an open lakehouse on Hadoop (HDFS) or Ceph S3 — with SLA-backed managed operations from XaasIO.

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