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Data Engineering Solutions in Austria

A robust data foundation determines how effectively your organisation can extract value from its information assets. Our team constructs scalable infrastructure and streamlines data processes to unlock valuable insights that enhance productivity and drive measurable performance gains. Specialising in efficient enterprise data transfer, our cloud data engineers ensure swift and seamless migration. With deep market expertise and a proven track record across the DACH region, you can confidently entrust our senior professionals with your most complex data challenges.

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Scope

Data Engineering Services We Deliver

Data Architecture Development

We design adaptable, highly accessible data architecture frameworks that map the flow of data within your organisation, providing a clear route to achieving your business objectives.

Data Lake Deployment

Data lakes are essential for managing vast amounts of raw, unprocessed data ready for analytics applications. Dev House Austria delivers data lake solutions that boost productivity and enable scalable growth without operational strain.

Data Warehouse Implementation

We construct data warehouses that consolidate your company's information from disparate sources into a single analytical repository — separate from operational databases and optimised for valuable business insights.

Cloud Data Migration

Migrating data to the cloud is vital for modern businesses. Our cloud data engineers efficiently set up your data lake, enabling swift and cost-effective migration of enterprise data with minimal disruption.

Data Management and Compliance

Effective data governance and compliance are critical for ensuring data security and adherence to both business policies and regulatory requirements including GDPR. Our team ensures your data is protected to the highest standards.

Data Analytics and Visualisation

We provide tools that simplify the analysis of large datasets, presenting information in accessible formats. With Dev House Austria's data engineering technologies, your organisation gains enhanced access to the critical insights that drive improvement.

Data Engineering Consulting

A skilled engineering team is vital for successful data management. Our data engineers design and oversee your data systems, ensuring they are optimised for reporting and enable better decisions informed by reliable data.

DataOps Implementation

DataOps practices enhance communication, integration, and automation of data flows across your organisation. We optimise your DataOps processes, ensuring your business consistently delivers relevant, high-quality data to stakeholders.

Technological Stack Expertise

Our Data Engineering Technology Stack

Dev House Austria's data engineers are highly skilled professionals capable of tackling any data challenge. They excel in utilising advanced technologies and consistently deliver robust data engineering solutions. Our engineers are proficient with platforms including AWS, Google Cloud Platform, Azure, and Apache. Python is frequently employed for a wide range of data engineering tasks.

AWS

  • S3
  • Glue
  • EMR
  • Lambda
  • Athena
  • SQS
  • CloudWatch
  • EC2
  • Transfer Family
  • EFS
  • EBS
  • S3 Glacier
  • Kinesis
  • QuickSight
  • API Gateway

Microsoft Azure

  • Data Lake
  • Data Factory
  • DataBricks
  • Functions
  • Blob Storage
  • Data Explorer
  • Data Catalog
  • Data Share
  • Power BI

Google Cloud Platform

  • DataProc
  • DataFlow
  • Cloud Storage
  • FileStore
  • CloudFunctions
  • DataPrep
  • Pub/Sub
  • KMS
  • DataStore
  • Compute Engine

Apache

  • Airflow
  • Hadoop
  • Spark
  • Hive
  • Cassandra
  • Beam
  • Kafka
  • HBase
  • NiFi
  • Flink
  • Superset
  • Presto

BI tools

  • Power BI
  • Tableau
  • Google Data Studio
  • Looker
  • QuickSight
  • QlikView
  • Qlik Sense

Machine Learning

  • TensorFlow
  • Keras
  • PyTorch
  • Theano
  • SciPy
  • Caffe
  • SKlearn
  • OpenCV

Data Science

  • Pandas
  • NumPy
  • Matplotlib
  • Seaborn
  • Plotly

Other Tools

  • dbt
  • TimeXtender
  • Azkaban
  • Cloudera
  • Segment

Build Reliable Data Infrastructure

Partner with Dev House Austria for data engineering solutions that scale with your business across the DACH region.

Process

Our Data Engineering Process

Dev House Austria tailors our approach to each client's unique needs. We collaborate closely to identify the right technologies, infrastructure, and advanced tools that address specific business challenges while aligning with your architectural requirements.

  • 01

    Requirements Analysis

    In the initial phase, we meticulously assess the detailed needs and expectations for a new or updated data product. This analysis forms the foundation for all subsequent engineering activities.

  • 02

    Data Architecture Design

    We develop a comprehensive framework that defines data sources, transport mechanisms, security controls, and storage strategies. This architecture underpins your entire data strategy.

  • 03

    Data Ingestion

    We facilitate the transfer of data into storage or prepare it for immediate processing, ensuring it is readily available and correctly formatted.

  • 04

    Data Cleaning

    Before entering the data pipeline, all data undergoes a rigorous cleaning process to eliminate irrelevant, duplicate, or erroneous elements.

  • 05

    Data Lake Construction

    We establish data lakes to efficiently store raw, structured, and unstructured data in a single repository at minimal cost — using platforms like Hadoop, Google Cloud Storage, or Azure with complex data engineering in Python.

  • 06

    ETL/ELT Pipelines Implementation

    Once data is prepared and stored, our ETL engineers initiate processing operations. This crucial pipeline step transforms raw data into validated, analysis-ready insights.

  • 07

    Data Modelling

    At this stage, we explore and visualise data structures, representing relationships within the data and categorising it effectively for downstream consumption.

  • 08

    Quality Assurance

    Prior to further processing, data undergoes rigorous testing against our quality standards. Our experts create test cases to verify and validate every element of the data architecture.

  • 09

    Automation and Deployment

    This pivotal stage involves crafting a DevOps strategy that automates the data pipeline, significantly reducing the time, cost, and effort required for ongoing pipeline management.

IFAVH - Austria

IFAVH - Austria

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What Our Clients Say

"Our managers who interact with Dev House Austria are all in agreement that this is an outstanding company. They are meticulous, patient, and extremely capable."

Jim Murray

Operations Director at Prosperity.ie

"Dev House Austria has constantly under-promised and over-delivered. We couldn't be happier with their professionalism, confidentiality, and attention to detail."

Anonymous

Chief Executive Officer at SaaS Company

"There were no delays. They presented things quickly to me. They were very good and up-to-date with their technology."

Edel McDonnell

Owner at KingFisher Restaurant

"They always look for alternative ideas to enrich value. They are disciplined, keep meetings on track, and provide detailed updates."

Fintan Knight

Chief Executive Officer at Automotive Equity Management Ltd.

"What impressed us most was their commitment to delivering an excellent result. The commitment was extraordinary from the first day."

Bob Khanna

Office Manager at Aesthetic Clinic

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FAQs

  • Answer: Data engineering focuses on building and maintaining the infrastructure that makes data accessible and usable — pipelines, storage, and transformation layers. Data science analyses this prepared data and presents insights through models and visualisations. The two disciplines are complementary: data engineers provide the foundation that data scientists depend on for their analyses.

  • Answer: Data engineering is critical for any data-driven enterprise, enabling efficient utilisation and optimisation of large datasets. It is a cost-effective approach that improves data quality, boosts productivity, and significantly reduces the time required for data management and analysis.

  • Answer: A data pipeline consists of a sequence of automated processes that move and transform raw data from its source to its destination. These pipelines ensure data is prepared, validated, and ready for analysis or operational use by your teams.

  • Answer: Data is the foundation of every modern organisation. Data engineering ensures this data remains accessible, reliable, and available for timely analysis. A key advantage is the capacity to store and manage vast amounts of data with minimal limitations and maximum consistency.

  • Answer: DataOps is a methodology designed to improve communication, integration, and automation of data workflows between data teams and business stakeholders. This practice ensures that high-quality, relevant data is consistently delivered in a timely manner across the organisation.

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