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Data Engineering That Actually Works in Production

Pipelines that run on time, data that matches the source, and dashboards that people trust. We build and fix the plumbing that makes everything else in your data stack possible.

Your Data Pipeline Is Either an Asset or a Liability. There Is No In-Between.

Here is what we see in almost every company we talk to: a tangle of cron jobs, half-documented ETL scripts, and a data warehouse that nobody fully trusts. The analysts have learned to add 10% to the numbers because they know the pipeline drops records sometimes. The engineering team spends more time firefighting failed jobs than building new features.

Good data engineering is not glamorous. Nobody puts it on the conference stage. But it is the difference between a company that can answer a question in 5 minutes and one that needs 2 weeks and 3 meetings to get the same answer.

We build pipelines with Talend, Azure Data Factory, dbt, and custom Python - depending on what makes sense for your stack. Not every company needs the same tools. A Talend shop that is working well does not need to rip everything out and move to dbt just because it is trendy. A company loading into Snowflake probably does need dbt. We figure out what fits and build it right the first time.

The goal is always the same: data that arrives on time, matches the source, passes quality checks, and is documented well enough that the next engineer can understand it without a 2-hour walkthrough.

What We Build

Data Engineering Services

ETL & ELT Pipelines

Building data pipelines that extract from your sources, transform reliably, and load into your warehouse. We design for idempotency, incremental loads, and failure recovery - because every pipeline fails eventually.

Talend Development

Talend jobs that are performant, maintainable, and documented. We handle dynamic schemas, bulk loading, memory tuning, and all the edge cases that make Talend tricky at scale. Five articles on exactly these problems below.

Azure Data Factory

ADF pipelines for data orchestration within the Azure ecosystem. Metadata-driven designs, parameterized datasets, linked services, and the integration runtime configuration that actually works in production.

dbt & Transformation

Setting up dbt as your SQL-based transformation layer. Models, tests, documentation, incremental materializations, and CI/CD. We prefer dbt for Snowflake and BigQuery projects where ELT is the right pattern.

Data Quality & Governance

Building data quality checks directly into pipelines. Schema validation, null checks, row count reconciliation, freshness monitoring, and access control policies that keep sensitive data locked down.

Cloud Migration

Moving your data warehouse and pipelines from on-prem to the cloud. Assessment, tool selection, pipeline rebuilds, parallel validation, and cutover. We have done SQL Server to Snowflake, Oracle to BigQuery, and everything in between.

Deep Dives

Our Data Engineering Guides

Talend

Dynamic MySQL ETL in Talend: Step-by-Step Tutorial

Building dynamic ETL jobs that handle schema changes without breaking. Connection pooling, context variables, and parameterized extractions.

Performance Tuning in Talend: Optimizing ETL Jobs

Making slow Talend jobs fast. Parallel execution, bulk loading, connection optimization, and the JVM settings that actually matter.

Solving Heap Memory Issues in Talend

When your Talend job crashes with OutOfMemoryError. JVM heap tuning, garbage collection settings, and patterns for processing large datasets without running out of memory.

Null Pointer Exception in Talend: Causes and Solutions

Debugging the most common Talend error. Where NullPointerExceptions actually come from and how to fix them systematically.

tDBOutput vs tDBOutputBulk vs tDBBulkExec Comparison

When to use each Talend output component. Performance benchmarks, use cases, and the trade-offs between row-by-row and bulk loading.
Azure Data Factory

How to Create a Data Pipeline in Azure Data Factory

Building your first ADF pipeline from scratch. Linked services, datasets, activities, triggers, and the debugging workflow that saves hours.

REST API to Snowflake: Metadata-Driven ADF Pipeline

Building a metadata-driven pipeline that pulls from REST APIs and loads into Snowflake. Parameterized design, pagination handling, and error recovery.
Architecture & Best Practices

Managing Compute Workloads: ETL vs Analytics

Separating ETL and analytics workloads so they do not compete for resources. Warehouse sizing, scheduling strategies, and resource isolation patterns.

Data Access Control Strategies in Analytical Platforms

Implementing row-level security, column masking, and role-based access in your data warehouse. The governance layer that keeps auditors happy.

Cloud Migration Guide: Strategy, Best Practices & Steps

The full playbook for moving your data infrastructure to the cloud. Assessment frameworks, tool selection, migration patterns, and validation strategies.

Getting Started with dbt and Snowflake: Complete ELT Guide

Setting up dbt with Snowflake from scratch. Project structure, models, tests, incremental materializations, and CI/CD deployment.

Data Engineering Questions
We Hear Every Week

If you are loading into a modern cloud warehouse like Snowflake or BigQuery, ELT is almost always the right choice. You load raw data first, then transform it inside the warehouse where compute is cheap and scalable. ETL still makes sense when you need to clean or mask data before it lands anywhere - which is common in healthcare and finance.

For certain workloads, yes. Talend is strong for complex multi-source ETL jobs, especially when you have on-prem databases, legacy systems, or need heavy data quality rules. If your stack is purely cloud-native, you might be better served by dbt plus a managed ingestion tool. We help teams figure out which approach fits their actual situation.

We build data quality checks directly into the pipeline, not as an afterthought. That means schema validation on ingestion, null and duplicate checks at transformation, row count reconciliation between source and target, and anomaly detection on key metrics. In dbt, we use built-in tests. In Talend, we use tAssert components and custom validation jobs.

Yes, and we have done it multiple times. The typical path is: assess what you have, pick the right cloud target (Snowflake, Azure Synapse, BigQuery), rebuild pipelines with modern tools, run both systems in parallel for validation, then cut over. Most migrations take 8 to 16 weeks depending on complexity. We have a full cloud migration guide that covers the process.

ADF is cloud-native and great for orchestrating data movement within the Azure ecosystem. It is a managed service, so you do not run any infrastructure. Talend is more of a general-purpose ETL tool that runs anywhere and has stronger data quality features built in. We use ADF when the client is all-in on Azure. We use Talend when they need to connect to legacy on-prem systems or need complex transformation logic.

Something else on your mind?

Pipelines Breaking? Data Not Matching? Let's Fix It.

Tell us what your data stack looks like today and where it is falling short. We will tell you what is fixable, what needs rebuilding, and what it would take.

Talk to a Data Engineer