Custom Data Architecture for Unique Requirements

Data engineering services for companies with custom data needs. ETL/ELT pipelines, data warehouses, real-time processing, and API integrations.

$25K-$100Kinvestment range
10-16 weeksdeployment timeline
Real-timeor batch processing
Customarchitecture for unique needs
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Technologies We Use

Modern cloud-native stack for scalable, reliable, and maintainable data infrastructure.

Cloud Platforms: AWS (S3, Redshift, Glue, Lambda, Kinesis), Azure (Data Lake, Synapse, Data Factory), Google Cloud (BigQuery, Dataflow)
ETL/ELT Tools: Apache Airflow (orchestration), DBT (transformation), Fivetran, Stitch, Custom Python/Spark
Databases: PostgreSQL, MySQL, SQL Server (OLTP), Snowflake, BigQuery, Redshift (OLAP), MongoDB, Cassandra (NoSQL)
Programming: Python (pandas, PySpark), SQL (transformations), Scala (Spark jobs)
WHAT WE BUILD

Data Engineering Capabilities

ETL/ELT Pipelines

Extract data from any source, transform with business logic, load to warehouse. Batch or real-time processing with incremental loads.

Data Warehouse Architecture

Medallion architecture (Bronze, Silver, Gold layers). Staging, integration, and presentation layers optimized for analytics and reporting.

Real-Time Streaming

Sub-second to few-second latency. Kafka, Spark Streaming, AWS Kinesis, Azure Event Hubs for IoT, clickstream, and live data processing.

API Integrations

Custom connectors for proprietary systems, legacy databases, and third-party APIs. Handle authentication, rate limiting, and error recovery.

Data Quality & Governance

Validation rules, duplicate detection, anomaly alerts, data lineage tracking, catalog documentation, and compliance (GDPR, HIPAA).

Cloud-Native Architecture

AWS, Azure, or Google Cloud deployment. Scalable, high-availability, disaster recovery, auto-scaling, and multi-region support.

PACKAGES

Data Engineering Packages

Pipeline Starter

$25,000 - $40,000
Timeline: 6-10 weeks
3-5 data sources • Moderate complexity

Best For: Standard ETL needs, batch processing

  • Data warehouse design
  • ETL pipeline development (3-5 sources)
  • Batch processing (daily/hourly refresh)
  • Basic data quality rules
  • Error handling and logging
  • Documentation
  • 30-day support
Get Started
Most Popular

Pipeline Professional

$50,000 - $80,000
Timeline: 10-14 weeks
5-10 data sources • High complexity

Best For: Complex transformations, multiple sources

  • Everything in Starter, plus:
  • Data warehouse (5-10 sources)
  • Complex transformations and business logic
  • Incremental data loading
  • Data quality framework
  • Real-time streaming (if needed, 1-2 sources)
  • API integration development
  • Disaster recovery and backup
  • Data governance setup
  • 60-day support
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Pipeline Enterprise

$100,000 - $250,000+
Timeline: 16-24 weeks
10+ data sources • Very high complexity

Best For: Enterprise data platform, real-time needs

  • Everything in Professional, plus:
  • Unlimited data sources
  • Real-time streaming architecture
  • Advanced data quality and governance
  • Machine learning pipeline integration
  • High availability and auto-scaling
  • Multi-region deployment
  • Advanced security and compliance
  • Dedicated data engineer (3-6 months)
  • DevOps and CI/CD pipelines
  • 90-day support + quarterly optimization
Get Started
CASE STUDY

LogiChain Distribution

Wholesale distribution, ₹280Cr revenue, 12 warehouses

Challenge: Inventory data from 12 warehouses across 4 different WMS systems. No real-time unified view, 5-day old reports, lost sales from stock visibility issues.
Investment: $72,000 | Timeline: 14 weeks
5 min
inventory visibility (from 5 days old)
-50%
order fulfillment time reduction
₹4.07Cr
total annual benefit
56x
ROI in first year

""We had inventory data in 4 different systems and no way to see the full picture. Maxwize built custom data pipelines that sync every 5 minutes. Now I pull up my dashboard and see exactly how many units of every SKU we have, in which warehouse, in which zone. Real-time. Optimized ₹8.2 crore in inventory allocation in the first 6 months. This system paid for itself in week 3.""

— VP Operations, LogiChain Distribution

Choose Custom Data Engineering If:

  • Unique data sources (proprietary systems, no pre-built connectors)
  • Complex transformations (custom business logic)
  • Real-time requirements (sub-minute updates)
  • High volume (millions of rows, TBs of data)
  • Compliance needs (HIPAA, GDPR, SOC 2)
  • Custom architecture (specific requirements platforms can't meet)

Use Platform Tools If:

  • Standard data sources (CRM, accounting, e-commerce - pre-built connectors exist)
  • Simple transformations (basic calculations, aggregations)
  • Batch processing OK (daily/hourly updates sufficient)
  • Moderate volume (under 10M rows)
  • Budget-conscious (platforms cheaper than custom)

Ready for Custom Data Engineering?

Schedule a data engineering consultation. We'll discuss your data sources, transformation requirements, and architecture approach.

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