Data Engineering

Transformation &
Processing

Turning raw data into valuable insights through robust ETL pipelines, advanced modeling, and automation.

Transformation & Processing

Turning raw chaos into refined insights. We architect robust ETL/ELT Pipelines that clean, validate, and enrich your data at scale.

Extract

RUNNING

Reading raw JSON/CSV from Data Lake.

Cleanse

Removing nulls, handling schema drift.

Mask PII

Hashing emails for compliance.

Enrich

Joining with dimension tables.

Aggregate

Calculating daily KPIs.

Transformation Logic

PySpark / SQL

# Extract

df = spark.read.format("json").load("s3://raw-zone/")

# Cleanse

df.filter(col("id").isNotNull()).dropDuplicates()

# Mask PII

df.withColumn("email", sha2(col("email"), 256))

# Enrich

df.join(customers, "customer_id", "left")

# Aggregate

df.groupBy("date").agg(sum("amount"))

Data Quality Guardrails

Bad data shouldn't crash your pipeline. We implement "Dead Letter Queues" (DLQ) to isolate malformed records for manual inspection.

Performance Tuning

We optimize query performance using Partitioning. Instead of scanning the entire dataset, we target specific slices.

Scanning 100GB (Cost: $$)

Phase 4: Lifecycle & Cost Intelligence

Automated FinOps & Retention

Data isn't static. Its value changes over time. We implement automated Lifecycle Policies that move data to the most cost-effective storage tier without sacrificing compliance or durability.

Intelligent Tiering Simulator

Cost optimization based on data age

Data Age10 Days

Creation1 Year+

HOT

Standard (SSD)

WARM

Infrequent Access

COLD

Glacier / Archive

FROZEN

Deep Archive

Storage Cost

$0.023

per GB/mo

Retrieval

Savings

Compliance Retention Vault

WORM (Write Once Read Many) Protection

Rule: Financial RecordsRetain 7 Years

LOCKED

Data is cryptographically locked. Even root users cannot delete these objects until the retention period expires.

Resiliency Mode

Region A (Primary)

REPLICATION

Region B (DR)

Durability: 99.999999999% (11 9s)

Phase 5: Advanced Data Modeling & Optimization

Designing for Speed, Scale, and Evolution

Data structures aren't static. We design flexible schemas that evolve with your business while maintaining sub-second query performance through advanced indexing and partitioning strategies.

Data Modeling Studio

FACT Sales

DIM Time

DIM Product

DIM Store

DIM Customer

Query Optimization Sandbox

Simulate the impact of physical data layout on query performance. Toggle features to see metrics change.

Partitioning

Z-Order Indexing

Compression (ZSTD)

Data Scanned

1000 GB

Query Time

60 sec

Phase 6: Data Ops & Automation Ecosystem

Automated Processing & Reliability

Manual processing is the enemy of scale. We build Event-Driven Architectures where data arrival triggers serverless processing functions automatically.

Data Ops Console

Python SDK

import { DataClient } from '@react-digi/sdk';

async function triggerProcessing(fileId: string) {
  const client = new DataClient({ region: 'ap-east-1' });
  
  // Programmatic Job Execution
  const job = await client.startJob({
    name: 'transform-sales-data',
    args: { input: fileId, mode: 'overwrite' }
  });

  console.log(`Job started: ${job.id}`);
  return job.waitForCompletion();
}

Scripting Support

We support Python, Node.js, and SQL for custom transformations. No proprietary lock-in.

Auto-Diagnostic

AI-driven root cause analysis for failed jobs (e.g., "Memory Limit Exceeded").

Event-Driven Processing Architecture

Event: New File Uploaded (S3/Blob)

Lambda / Function: Validates & Triggers ETL Job

Outcome: Data Loaded to Warehouse

Transformation &
Processing