Data Engineering

Foundation &
Architecture

Building the bedrock of your data platform. From ingestion strategies to scalable storage architectures.

Phase 1: Design & Build

Pipeline Architecture Studio

We don't just script ETL jobs; we design scalable Data Architectures. Select a pattern below to see how we structure data flow for different business needs.
Source
Batch Layer
Speed Layer
Serving Layer
Latency
Mixed
Complexity
High
Cost Profile
$$

Build Optimization: Compression

We use columnar formats like Parquet and Snappy compression to reduce storage costs by up to 90% compared to JSON/CSV.

Standard JSON
Text-based, Row-oriented
1.2 GB
RECOMMENDED
Apache Parquet + Snappy
Binary, Columnar, Compressed
130 MB
Infrastructure as Code
module
"data_pipeline" {
  source = "./modules/kinesis_firehose"
  format = "PARQUET"
  compression = "SNAPPY"
}

Ingestion Strategy

The first step in any data lifecycle. We select the right pattern based on your Throughput and Latency requirements.

Configuration

  • Source Connectivity: S3, JDBC, APIs.
  • Payload: Bulk CSV/Parquet.

Batch Ingestion

Scheduled & Event-Driven

Scheduled Windows

Cron-based extraction (e.g., hourly, daily) for predictable workloads.

Event Triggers

Ingestion starts immediately when a file lands in the data lake (Object Store).

Bulk Loading

High-performance parallel loading for migrating terabytes of legacy data.

Phase 2: Storage Strategy

Intelligent Data Store Selection

Choosing the right storage engine is critical for performance. We match your Access Patterns to the optimal technology.
Recommended Architecture

Columnar Warehouse

Redshift / Snowflake

Why this choice?

Columnar storage allows skipping irrelevant columns, perfect for aggregating millions of rows.

Format Optimization: Row vs. Columnar

For analytical workloads (OLAP), reading entire rows is inefficient. We use Columnar Storage to skip irrelevant data blocks.
Scanning entire rows (Inefficient)
Phase 3: Data Discovery & Governance

The Unified Data Catalog

Data is useless if you can't find it. We implement automated Data Catalogs that crawl your lakes and tag sensitive information (PII).

Catalog Explorer

raw_transactions
Bronze
PII Finance
dim_customers
Silver
PII Master Data
rpt_daily_sales
Gold
Report Public

Schema Evolution Simulator

Handling upstream changes

EVENT: Upstream API adds 'discount_code' column
❌ Pipeline Fails. Alert sent. (Prevents corruption)

Auto-Classification Crawler

PII Detection

invoice_001.pdf
CONFIDENTIAL

Orchestration & Governance

Stop writing brittle cron jobs. We build enterprise-grade Directed Acyclic Graphs (DAGs) that handle dependencies, retries, and backfills automatically.

Intelligent Orchestration

Airflow / Step Functions

We design pipelines that are Self-Healing. If a step fails due to a transient network error, the system automatically applies Exponential Backoff logic to retry.

Data Lineage & Traceability

Governance & Compliance

Know exactly where your data comes from. We implement lineage tracking so you can trace a metric in your CEO's dashboard all the way back to the raw source.

Raw Clean Report
Pipeline Orchestrator
Event Trigger
Ingestion Job
Transformation
Quality Gate
Load WH
Waiting for trigger...

Next Step: Processing