Data Warehouse to Data Lake Migration
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Data Warehouse to Data Lake Migration

Unlocking the Value of Lake and Lakehouse Architectures

Traditional data warehouses were designed for structured data and predictable reporting needs. Today's enterprises operate in a very different world—one where data is generated continuously across cloud applications, IoT devices, and streaming sources. SaqSam's Data Warehouse to Data Lake Migration services help organizations move beyond the limitations of tightly coupled, high-cost warehouses and adopt cloud-native data lakes and lakehouse architectures. We deliver migrations that are secure, governed, high-performance, and aligned with your long-term data strategy.

Strategic Shift in Data Storage

Data lakes support broader data types, lower storage costs, and more powerful analytics pipelines. SaqSam helps clients:

Consolidate fragmented data into scalable cloud-native storage
Ingest structured and unstructured data without rigid schema constraints
Enable advanced analytics and data science at petabyte scale
Reduce storage and compute costs through optimized tiering
Support real-time and streaming data alongside historical records
Modern Architecture

Our Migration Service Areas

Platform Assessment & Migration Road Mapping

Comprehensive evaluation of existing warehouses to guide a phased, low-risk migration roadmap.

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Lake & Lakehouse Architecture Design

Designing architectures with multi-zone storage tiers and separation of storage and compute.

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Data Modeling & Schema Evolution

Re-engineering data models for flexible formats like JSON, Parquet, and Avro.

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ETL/ELT Migration & Orchestration

Re-platforming legacy ETL pipelines into scalable cloud-native ELT.

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Real-Time & Streaming Data Integration

Building streaming architectures using Kafka and Kinesis for low-latency AI inference.

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Performance Optimization & Cloud Cost Management

Fine-tuning query engines and storage tiering for sustained efficiency.

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Migration Methodology

01

01Analyze

Evaluate warehouse complexity and dependency maps.

02

02Architect

Design the target lake/lakehouse environment.

03

03Migrate

Execute phased data and workload movement.

04

04Reconcile

Validate data integrity and business logic alignment.

Migration Accelerators & Frameworks

Lakehouse Deployment Blueprint

Reference architectures for scalable, governed ecosystems

SaqSam Data Modernization Framework

End-to-end methodology for cloud-native data estates

ELT Migration Toolkit

Tools for converting legacy ETL into optimized ELT pipelines

Data Validation & Reconciliation Suite

Automated checks for accuracy and completeness

Streaming Architecture Playbook

Best practices for high-volume, real-time pipelines