WHAT WE DO

Data Engineering

Empowering seamless data integration, processing, and quality for smarter business insights.

Data Engineering services concentrate on building and maintaining infrastructure, pipelines and frameworks for data ingestion, collection, preparation, processing, storing, analyzing and estimating data quality, securing and exposing data to data consumers – applications and business users. The primary goal is to integrate and consolidate data from various sources, process data in the most efficient way and prepare data products of the highest quality for downstream consumers – business intelligence, advanced analytics, and machine learning applications.

Data infrastructure, pipelines, frameworks and standards might be grouped into (Intelligent) Data Platform. The component that enables individuals without deep technical skills to perform data wrangling and produce data products.

Core Data Engineering Services:

Data Ingestion

Establishing Data Lake folder organization, naming convention and standard datatypes catalogue

Building metadata driven framework to perform data ingestion.

Full data replication

Incremental data replication

Change Data Capture data replication – enabler for (near) real time data processing, reduce cloud costs and improve load performances.

Frameworks for parsing standard file types (.txt, fixed width files, xlsx ,xls, json, xml)

Framework for real time data replication

Data Quality mechanism and data reconciliation

Data ingestion pipeline orchestration

Data Pipeline Development

Metadata driven frameworks for the implementation of repetitive tasks as implementation of SCD2 dimensions.

Enable data lineage for most important products or wherever is needed

Business logic implementation

Enable real time processing

Enable processing of unstructured and semi structured data

Enable Big Data processing

Using modern industry standard tools like Airflow, Spark and Kafka

Data Integration

Consolidate data from desperate sources (DBs, APIs, IOT devices, third-party systems)

Building unified data products with business transformation

Building Data Products

Analyse data sources and company processes

Assess company data maturity.

Analyse company data requirements

Create initial data product characteristics

Design data schemas and models to optimize storage and analytics

Create relational and dimensional models tailored to business needs

Create Data product prototypes and improve them in an iterative process

Building frameworks to expose unified data model to Data Consumers via required technology (DB, APIs, Files, Event supporting technologies)

Data Quality, Observability, Monitoring and Governance

Data Quality, Observability...

Implement a data quality framework which enables data flaw detections and maintaining hight quality data products

Ensure compliance with governance standards and regulations

Data lineage

Data dictionary and data glossary

Build systems to monitor pipeline health, track data lineage and detects anomalies

Provide tools and dashboards for proactive issue resolution

Business meeting, people sitting at the table and talking

Companies usually own raw data stored in various source systems built in different technologies (DBs, files, on-prem and cloud, APIs, etc..) and one wish to become data driven – because leveraging data effectively allows them to make informed decisions, optimize operations, improve customer experiences, and gain competitive edge.

Data engineering helps companies to close huge gap from raw data to becoming data driven and build modern Data Systems that are scalable, cost optimized, easily maintainable and sustainable.