Analytics & Reporting
Data Engineering & ETL
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.
Apache Spark
Apache Airflow
Databricks
Delta Lake
Microsoft Fabric
Power BIScope
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 it matters
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, teams hit:
Dashboards show inconsistent or outdated information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.
Services
Our data engineering services
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
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
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)
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
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
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
Architecture
A typical data engineering architecture we build
A modern architecture built by ValDatum often follows this high-level structure, layer by layer:
Source systems
Ingestion layer
Spark ETL (PySpark jobs)
Airflow orchestration
Data lake (Delta / Parquet)
Data warehouse (SQL / Databricks SQL)
BI layer
Stack
Tools & technologies we use
Parquet
TableauDeliverables
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.
Proof
Case studies
SaaS company — built a 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
Engagement
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.
FAQ
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.