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Data Engineering Services

Architecting the
Flow of Intelligence

Data is only as valuable as your ability to move and process it. We build robust, production-grade pipelines that turn raw data into high-fidelity business assets.

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The Data Friction

Is Your Data Trapped in
Disconnected Silos?

Poor data flow is the primary cause of failed AI initiatives. If you can't trust your data engineering, you can't trust your insights.

Brittle ETL Pipelines

Legacy scripts that break frequently, causing data downtime and eroding trust in business dashboards.

High Storage Costs

Storing unoptimized, redundant data across multiple cloud accounts leads to massive cloud bills.

Slow Analytics Response

Querying raw datasets takes minutes or hours instead of seconds, stalling critical executive decisions.

Governance Violations

Lack of data lineage and access control makes you vulnerable to privacy breaches and audit failures.

Dirty Data Ingestion

"Garbage in, garbage out." Raw data without validation destroys the integrity of your AI and ML models.

Siloed Data Lakes

Marketing, Sales, and Product data living in isolation, preventing a 360-degree view of your business metrics.

Our Capabilities

Data Engineering
Solutions

We architect modern data ecosystems that are fast, reliable, and fundamentally scalable.

Cloud Data Warehousing

Centralize your data with modern cloud warehouses. We specialize in Snowflake, BigQuery, and Redshift optimization.

Automated ETL/ELT

Build resilient pipelines using Airflow, dbt, and Fivetran. Automate the cleaning and transformation of raw data.

Real-time Data Streaming

Process millions of events per second with Kafka and Spark. Ideal for real-time analytics and event-driven apps.

Lakehouse Architecture

Combine warehouse structure with data lake scale. Expert implementation of Databricks and Delta Lake.

Data Quality & Observability

Proactive monitoring of data health. We use tools like Monte Carlo and Great Expectations to prevent breaking changes.

Compliance Engineering

Automate PII masking and access control. Built-in lineage for effortless GDPR, HIPAA, and SOC2 audits.

Our Data Ecosystem

We leverage a diverse and powerful ecosystem of cloud platforms, processing engines, and governance tools.

Cloud Warehouses

  • Snowflake
  • Google BigQuery
  • Amazon Redshift
  • Azure Synapse

Pipelines & ETL

  • Apache Airflow
  • dbt (Data Build Tool)
  • Fivetran / Stitch
  • Prefect / Dagster

Big Data Engines

  • Apache Spark
  • Databricks
  • Presto / Trino
  • Delta Lake

Streaming

  • Apache Kafka
  • Confluent
  • Amazon Kinesis
  • Google Pub/Sub

Storage Layers

  • Amazon S3
  • ADLS Gen2
  • Apache Iceberg
  • GCS

NoSQL & Cache

  • MongoDB
  • Redis
  • Cassandra
  • DynamoDB

Analytics & BI

  • Tableau
  • Power BI
  • Looker
  • Superset

Governance

  • Collibra
  • Amundsen
  • Immuta
  • Great Expectations

Why Trust Constelly for
Data Engineering?

We don't just move data; we architect reliability. Our solutions are built to withstand enterprise volume while maintaining 100% data integrity and compliance.

Production Reliability

99.9% uptime on pipelines with automated retry logic and proactive alerting.

Compliance First

Built-in PII detection and masking ensures HIPAA, GDPR, and PCI compliance by default.

Cost-Optimized Architecture

Smart partitioning and optimized formats (Parquet/Delta) reduce cloud storage bills by up to 40%.

10PB+

Data Managed

0

Data Loss Incidents

300+

Pipelines Built

24/7

Ops Support

Frequently Asked Questions

Everything you need to know about our data engineering processes.

ETL (Extract, Transform, Load) transforms data before loading it into the warehouse. ELT (Extract, Load, Transform) loads raw data first and then uses the warehouse's compute power for transformation. ELT is the modern standard for cloud-native ecosystems like Snowflake and BigQuery.
Data scientists focus on models and insights, while data engineers build the plumbing. Without engineers, scientists spend 80% of their time cleaning data. We ensure your scientists get clean, reliable data ready for modeling.
We optimize compute by implementing incremental refreshes, efficient file partitioning, and data compression. Most clients see a 30-50% reduction in their Snowflake or BigQuery bills after our optimization phase.
Yes. We use streaming architectures (Kafka, Kinesis, Spark Streaming) to process sub-second latency data from IoT devices, providing you with real-time operational visibility.
We primarily use Apache Airflow, dbt, and Prefect for complex DAG orchestration, ensuring data dependencies are managed with full observability and retry logic.
We implement automated testing using tools like Great Expectations. Every batch is validated for schema compliance, null values, and business logic before entering the production warehouse.
Absolutely. We are experts in building Medallion Architectures (Bronze, Silver, Gold) using Databricks and Delta Lake to give you the performance of a warehouse with the scale of a lake.
Compliance is built-in. We automate PII masking at the ingestion layer and implement robust RBAC (Role-Based Access Control) to ensure sensitive data is only visible to authorized personnel.
Yes, we specialize in high-availability migrations. We use CDC (Change Data Capture) to sync your on-prem databases to the cloud with near-zero downtime for your users.
A foundational data warehouse setup typically takes 4-8 weeks. Once the foundation is ready, additional data sources and complex transformations can be added iteratively every 2-3 weeks.

Build a Solid Data Foundation

Architect scalable data pipelines that power your analytics and AI initiatives. Ensure data quality, reliability, and accessibility across your organization.

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