DATA ENGINEERING & ETL PIPELINES

Modern, Scalable Data Engineering & ETL Pipelines Built for Real-Time Decision-Making

ValDatum builds enterprise-grade data pipelines using Spark, Airflow, Databricks, and modern cloud architectures. We centralize your financial, operational, CRM, HR, and product data into governed data lakes and warehouses — enabling accurate reporting, BI dashboards, machine learning insights, and scalable analytics.

Included in Data Engineering Services

Spark ETL pipeline development
Airflow workflow orchestration
Data lakes & warehouses
Real-time & batch processing
Schema design & data modeling
Data quality, validation & governance

Why Data Engineering & ETL Matter

Most organizations operate on fragmented data scattered across ERPs, CRMs, billing systems, HRIS, web apps, spreadsheets, and databases. Without proper data engineering:

Dashboards show inconsistent or outdated information
Financial reporting becomes slow and error-prone
Data teams spend hours cleaning and joining data manually
Machine learning models cannot run reliably
Executives lack real-time visibility into performance

ValDatum solves this by building scalable data pipelines that automate extraction, transformation, validation, and loading (ETL), enabling a true single source of truth.

Our Data Engineering Services

1. Spark-Based ETL Pipelines

High-performance, distributed ETL pipelines designed to process large volumes of financial, operational, and transactional data with speed and accuracy.

Batch & streaming data pipelines
Data extraction from APIs, DBs, & flat files
Data cleaning, transformation & validation
Optimized Spark jobs using PySpark

2. Airflow Orchestration

Automated data workflows that orchestrate ETL pipelines with scheduling, retries, alerts, and monitoring.

DAG design & dependency management
Daily/Hourly pipeline scheduling
Error handling & alerts
Workflow observability dashboards

3. Data Lakes & Lakehouse Architecture

Build secure, scalable lakes for structured, semi-structured, and unstructured data.

Delta Lake
Parquet & ORC optimization
Versioned data with ACID transactions
Multi-zone storage (Raw → Refined → Curated)

4. Data Warehouses & Semantic Models

Enterprise data warehouses designed for BI, forecasting, and analytics.

Schema design (Star, Snowflake)
Semantic layers & KPI definitions
Fact & dimension modeling
Performance tuning & indexing

5. Data Quality, Observability & Governance

Systems that ensure the accuracy, completeness, and consistency of your data.

Data validation frameworks
Automated anomaly detection
Data lineage & documentation
Role-based access & security governance

6. Source System Integration

We integrate all your tools into a central data platform.

QuickBooks, Xero, NetSuite
HubSpot, Salesforce, Zoho CRM
Stripe, Chargebee, Razorpay
HRIS, ATS, ERP & SQL databases

Typical Data Engineering Architecture

A modern architecture built by ValDatum often follows this high-level structure:

  Source Systems
  ├── ERP (QuickBooks / NetSuite)
  ├── CRM (Salesforce / HubSpot)
  ├── Billing (Stripe / Chargebee)
  ├── HRIS / Payroll
  ├── Product DB / APIs
  │
  ▼
  Ingestion Layer (APIs, JDBC, SFTP)
  │
  ▼
  Spark ETL (PySpark Jobs)
  ├── Cleaning
  ├── Joining
  ├── Validation
  ├── Transformations
  │
  ▼
  Airflow Orchestration
  ├── Scheduling
  ├── Monitoring
  ├── Retries
  ├── Alerts
  │
  ▼
  Data Lake (Delta / Parquet)
  ├── Raw
  ├── Refined
  ├── Curated
  │
  ▼
  Data Warehouse (SQL / Databricks SQL)
  ├── Star Schema
  ├── Semantic Models
  │
  ▼
  BI Layer (Power BI / Tableau / ValDatum BI)

Deliverables — What You Receive

ETL Pipelines

Fully automated ETL with Spark + Airflow.

Data Lake Setup

Delta Lake storage with versioned layers.

Data Warehouse

Curated models for BI & forecasting.

Documentation

Pipeline docs, lineage, SOPs, data catalog.

Case Studies

SaaS Company — Built Complete Data Lake in 6 Weeks

The company had inconsistent revenue data across CRM, billing & ERP. ValDatum built an automated pipeline into Delta Lake and unified all KPIs.

Data refresh rate improved from weekly → hourly
Forecast accuracy improved by 28%
Zero manual consolidation needed

PE Portfolio Company — Airflow + Spark Modernization

Legacy pipelines failed frequently and caused reporting delays. ValDatum rebuilt all pipelines with Spark + Airflow.

Pipeline failure rate dropped to 0%
ETL runtime reduced from 4 hours → 18 minutes
Audit-ready data lineage implemented

Pricing Models

ETL Build Project

One-time pipeline development & deployment.

Data Platform Build-Out

Data lake + warehouse + BI pipeline setup.

Managed Data Engineering

Ongoing support, monitoring & optimization.

Frequently Asked Questions

What data sources can you integrate?

Any ERP, CRM, HRIS, SQL DB, billing system, APIs, flat files, or cloud platforms.

Do you support large datasets?

Yes — Spark enables scalable processing for massive data volumes.

Can you integrate with BI dashboards?

Yes — we design BI models that sit on top of the warehouse.

Ready to Build Your Data Platform?

Speak with ValDatum’s data engineering team to design ETL pipelines, data lakes, and data warehouses that power real-time analytics and insights.

Email Us