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Services / Data Engineering

Service · Data Engineering

From many sources to one truth. Always up to date.

We design, build, and run the platform where your data lives: replicated in real time, clean, modeled for the business, and governed by person, by department, and by client.

4 → 1

systems that didn't talk to each other, now a single source of truth: the numbers for all their clients, up to date and on one dashboard. Multi-client contact center.

The problem

The situation today.

Every system reports a different sales figure, and no one knows which one is right.

Data arrives late and by hand: every morning, someone exports it, pastes it, and forwards it.

Knowledge of the data rests with one or two people, and every business question has to go through them.

Capabilities

What we do well.

Building your lakehouse

A central platform where all your data lives, organized in quality layers: from raw data to decision-ready numbers.

  • Data lakes with a medallion architecture: bronze (raw), silver (clean), and gold (business-ready)
  • A data warehouse modeled for the business, on Databricks, Snowflake, BigQuery, Redshift, or Athena
  • Open formats (Parquet, Delta, and Iceberg), so you're not tied to a single vendor

Ingestion, replication, and migration

We bring in data from every system you already use, without stopping operations and, when needed, in real time.

  • Real-time replication (CDC) from PostgreSQL, SQL Server, and SAP HANA
  • NoSQL databases: MongoDB, DocumentDB, and DynamoDB
  • APIs, SharePoint, files, and streaming
  • Full database migrations with DMS

Automated data processing

Data is processed automatically every day, with checks so bad data doesn't make it into a report.

  • ETLs and jobs in Spark, Glue, EMR, and dbt
  • Orchestrated with Airflow, Step Functions, or Composer
  • Automatic retries and quarantine when something fails

Modeled for the business

Tables are organized the way the business thinks (sales, customers, products), not the way each system stores them.

  • Fact and dimension tables
  • Relational models and a final, dashboard-ready layer
  • Fast, low-cost queries: partitions, compaction, and views

Quality and governance

Every figure can be traced back to its source, and each person sees only what they're meant to see.

  • Automated quality rules on every table
  • Catalog and lineage for every metric
  • Permissions by department, client, row, and column

AI-ready data

Your data, prepared so AI models and agents work with reliable information.

  • Data preparation for models
  • Data lakes connected to SageMaker and Vertex AI
  • Vector databases and the data your agents use
How it works

Here's a complete data lake, up and running.

From scattered sources to a single source of truth: replication from every system, quality layers (bronze, silver, and gold), a model designed for the business, and governance that defines who can see each piece of data.

Systems we've built

What we built and what it achieved.

Real cases, described by industry. Client names are kept confidential.

Multi-client contact centerDMS · Glue · Iceberg · QuickSight

From four disconnected databases to a single source of truth, always up to date.

They had no shared place to analyze their four databases. Today, all four are copied in real time to a single platform that refreshes every day and sets aside questionable data before it reaches a report. It's documented so another team can run it.

4 → 1separate databases, now one up-to-date platform
Global pharmaceutical company · our team's experienceDatabricks · EMR · Airflow · Redshift

Clinical data organized to an international standard, term by term.

The team built and migrated the platforms where clinical data is processed, with automated processes that take it from raw to analysis-ready. It also aligned a 41-table model with an international clinical standard.

4,023clinical terms aligned with the standard, across 53 catalogs
B2B e-commerceDMS · AppFlow · Glue · Lake Formation

From reports built in Excel to data consolidated in one place.

Their reports were built in spreadsheets. Today, their databases and applications feed a single platform, with a catalog of what it holds and permissions that define what each person sees.

99.9%data platform availability
Lending fintechGlue Data Quality · Lake Formation

Data that checks itself, and the right access for each person.

Finding an inconsistency in their data took time. Today, automated rules check the quality of every table, and access to each piece of data is controlled.

−80%in the time it takes to find an inconsistency, compared with the previous process
Multi-client contact centerDocumentDB (MongoDB) · DMS · Glue · Athena

Their NoSQL data, replicated to SQL and analyzed alongside everything else.

Part of their operation lived in a NoSQL database (MongoDB) that couldn't be cross-referenced with the rest of their data. We replicated it in real time to SQL tables, alongside their relational databases, and today it flows into their reports and dashboards.

NoSQL → SQLreal-time replication, ready for reports and dashboards
Technologies

With the tools you already use.

Platforms
DatabricksSnowflakeBigQueryRedshiftAthena
Processing and orchestration
SparkAirflowGlueEMRdbtPython
Ingestion, formats, and databases
DMSKinesisIcebergDeltaPostgreSQLMongoDBSQL ServerSAP
Next step

Start with an assessment.

Within weeks, you know where the value is. From month one, you see it.

Book your assessment Explore our services in more detail →