How do you handle schema changes when upstream sources change their data format?

We implement automated schema evolution detection. When a source schema changes, our pipelines flag the drift, log the change, and either adapt automatically or alert the team, preventing silent data corruption.

What happens if a pipeline fails mid-run?

Every pipeline we build includes idempotent checkpointing. If a job fails, it restarts from the last successful checkpoint rather than reprocessing everything from scratch, protecting both data integrity and compute cost.

Can this handle both batch and streaming data?

Yes. We design hybrid Lambda or Kappa architectures that serve both batch historical data and real-time event streams from a unified platform, giving your analysts consistent data regardless of ingestion method.

How do you keep pipeline costs under control at scale?

We instrument every pipeline with cost attribution tagging and build auto-scaling logic that right-sizes compute resources. Most clients see 30-40% infrastructure savings within 90 days of optimization.

Data & Analytics

Scalable Data Engineering Pipelines Built for Real-Time Scale

Building high-performance ETL/ELT pipelines that ingest, clean, and unify data from hundreds of sources, turning raw streams into clean, actionable intelligence delivered in real time rather than overnight batches.

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Architecture for Insight

Data is useless if it's trapped in silos or riddled with errors. We build industrial-strength ETL/ELT pipelines that ingest, clean, and unify your data from hundreds of sources in real time, at enterprise scale. Every record is validated, deduplicated, and lineage-tracked on the way in, so the analytics layer your teams depend on is fast, trustworthy, and ready the moment a decision needs to be made.

The Old Way

Data Chaos

The Intelegencia Way

Engineered Data

Siloed Information

Unified Data Lake

Inconsistent Formats

Real-Time Streaming

Slow Batch Processing

Automated Cleansing

Poor Data Quality

High Observability

High Failure Rates

Reliable Up-times

Real-Time Streaming Pipelines

We use Kafka and Spark to process data the second it's generated, allowing your teams to react to market shifts and user behaviors instantly, not hours later when it's too late. Streaming windows, exactly-once delivery, and automatic schema evolution keep the flow accurate even as upstream sources change shape underneath you.

Kafka / Flink Streaming

Spark / Databricks Processing

Automated Schema Evolution

Data Lineage Tracking

Error-Recovery Workflows

ETL / ELT Pipeline Design

We architect ingestion frameworks that handle hundreds of data sources: SaaS tools, operational databases, event streams, and third-party APIs. We deliver clean, consistent records to your analytics layer without manual intervention. Incremental loads, schema-drift detection, and a library of transformation rules mean new sources slot in within days, not quarters, and a single failure never silently corrupts the data downstream.

500+ Source Connectors

Incremental Load Logic

Schema Drift Detection

Transformation Rule Libraries

Automated Alerting on Failures

Pipeline Observability & Reliability

A pipeline that runs unmonitored is a liability. We instrument every workflow with real-time health checks, SLA monitoring, and automated recovery so your data team spends time on insights, not firefighting. Lineage-driven root-cause analysis pinpoints exactly which upstream change broke a report, and auto-restart logic recovers failed jobs from the last checkpoint before anyone is even paged.

Real-Time Pipeline Dashboards

SLA Breach Alerting

Lineage-Driven Root Cause Analysis

Auto-Restart on Failure

Cost Optimization by Job

The Pipeline Stack

Engineering Units

Ingestion, validation, lineage, and orchestration tools that keep data flowing reliably from source to warehouse at enterprise scale, with zero manual intervention.

Ingestion Bot

Connecting to 500+ SaaS and database sources with zero custom code.

Quality Guard

Automated validation and profiling of every incoming record.

Lineage Mapper

Knowing exactly where every byte came from and where it flows.

Workflow Orchestrator

Managing complex dependencies between pipeline tasks at scale.

The Pipeline

Our Data Flow

Five steps that unify scattered data sources into a real-time, production-ready pipeline: from source audit to observability and scale-testing that handles 10x traffic spikes.

Source Audit

Identifying every data island in your organization and mapping dependencies.

Pipeline Design

Mapping the path from raw source to clean data lake or warehouse.

ETL Build

Coding the transformations, validation rules, and error-handling logic.

Observability

Setting up real-time monitoring, alerting, and lineage dashboards.

Scale-Up

Stress-testing to ensure the pipeline handles 10x traffic spikes without degradation.

Measured Performance. Proven Growth.

Data Freshness

Pipeline Uptime

Ingestion Sources

Error Reduction

Frequently Asked Questions
About Scalable Data Engineering Pipelines

Here you will find answers to questions we get asked the most about our offerings.

We choose based on your scale and tooling. For massive cloud-native workloads, we typically recommend ELT using Snowflake or BigQuery. Loading raw data first gives you maximum flexibility for downstream transformation.