As an international, dynamic, and top performant service company, we assist our customers in the realization of IT projects with next-generation technologies.

Our services range from small data science applications to enterprise-grade, scalable end-to-end solutions. We offer specialist and generalist data consulting, use-case analysis, tailor-made software products, conception, implementation, testing, production, and deployment of solutions. The standard information cycle includes Data Migration, Data Lake Formation, Data Warehousing and ETL, Analytics, and Machine Learning implementation for Predictive Analytics.

Products

Data Migration

Data Lake

Machine Learning & AI

Analytics

DevOps

Cost estimation and tuning with maximized ROI

Migration at scale from multiple data sources to your Cloud Data Lake

Dynamic Cloud Migration with standard use-case based architecture

ETL pipelines secure reliable data migration from existing data sources

Services

Big Data Services

Cloud DevOps Services

Data Analytics Service

We effectively guide our customers through the risky and costly journey of enterprise data hub implementation.
Every phase of this journey requires different technologies, skills, and specialists.
Qimia is your reliable technology partner with well-founded expertise, that will lead your project to great success.
Qimia’s big data service model is designed to provide expertise and solutions needed for maximizing your ROI at any phase of a project.

Qimia Enterprise Data HubOff-the-shelf Big Data Platform:
Off-the-shelf Hadoop cluster and cloud data lake deployment.
Hadoop Data Hub: Apache Hadoop, Apache Spark, Apache Hive, HBase, Zeppelin.
Cloud Data Lake:Azure, AWS and GCP infrastructure-based data lake.

Big Data Consulting ServicesBest Practices: We have an in-depth overview and understand the details involved with deploying big data platforms. We help define your strategy and advise you on solution designs based on your business use-cases. Our experts design the architecture and help to establish the processes needed for the successful implementation of these.

Qimia Data Hub Operational ServiceQimia’s Data Hub Operational Service is a subscription service that manages customers’ enterprise data hub by our team of expert engineers. Monitoring and reporting, on-site architecture review, troubleshooting and incident management, capacity planning, multi-tenant and access management.

Standard Data SolutionsData processing solutions and off-the-shelf standard implementations for standard use-cases. Production-ready, tested and verified streaming and ETL pipelines secure reliable data migration from existing data sources (databases).

Qimia professional cloud DevOps services help customers of all sizes to design, architect, build, migrate, and manage their workloads and applications on public clouds (Azure, AWS, GCP, and IBM Cloud) via standard DevOps implementations. Industrialized agile enterprise software solution development and operations lifecycle management are provided on cloud infrastructure using DevOps tools and practices.
To remain as agile as possible, we utilize and integrate the latest automation tools and techniques, such as infrastructure as code (IaC). Qimia standard cloud services provide off-the-shelf cloud IaC solutions, including user management and access control, monitoring, storage, relational databases, NoSQL databases, and cloud network deployment.

Cloud AutomationEfficient cloud orchestration, provisioning, and management of client cloud-based IT environment.CI/CD (continuous integration and continuous delivery) practices automate the software build, deploy, and release process. Streamlined CI/CD deployment using Jenkins, Git, Ansible, and Terraform.

Cloud SecurityCloud security implementation based on industry standards. Continuous support via operations support subscription model. Monitor and address security issues in real-time, with preemptive protection of known vulnerabilities and new threat identification.

Cloud Migration and Cost ManagementMigrate to cloud with dynamic, standard use-case-based architecture. Use-case and requirement analysis for cloud architecture tailored to client business needs. Cost estimation and tuning to maximize ROI.

Cloud DevOpsPublic cloud services and state of the art DevOps tools and practices enable business adaptability. Optimize business value for cloud migration with Qimia DevOps solutions, providing infrastructure as code (IaC) templates and off-the-shelf deployments. Standard automated cloud DevOps solutions via Ansible, Terraform, Docker, Kubernetes, Jenkins, Git, ELK, Nagios, and Ganglia.

Standard solutions using statistics and probability to design, train, execute, and evaluate predictive machine learning models. Use-case-based feature engineering, design, implementation, and evaluation building blocks for customized solutions.
Big data ML pipelines on cloud and Hadoop using Spark.

Data Analytics ComponentsData warehouse models, Spark, cloud, Hadoop and Kubernetes deployments.

Data VisualizationTableau, Qlik, PowerBI and Superset.

Standard Model ImplementationSpark generics classification implementation and Spark generics recommendation implementation. Spark generics time-series implementation, standard feature engineering, ETL building blocks, standard hyperparameter tuning, cross-validation, and grid search in Spark ML.

Secure Notebook DeploymentDynamic privileges and secure containerization allows for robust deployment of containers in Zeppelin and Jupyter.

Company Data

With decades of experience, you will receive innovative, creative, and efficient execution.

5 Worldwide
Offices

Over 15 years of project experience

Certified Scrum
Masters

300+ Company Partners

Cloudera, Hadoop, AWS, Azure partner

No outsourcing to freelancers or subcontractors

80+ permanent Data Engineers and Data Scientists

Internal training & certification of employees

Realization of a variety ofcustomized software solutions

Machine Intelligence, Big Data & AI consulting company

Project References

AWS Data Lake Project

Data Engineering

Industry: Consulting

Subject: AWS Cloud Data Lake Development; Cloud Big Data Engineering

Description: Developing and maintaining data lakes on AWS. Data migration from RDBMS and file sources, loading data into S3, Redshift, and RDS.
Designing and developing big data batch solutions using AWS Data Pipeline and AWS Glue and EMR. Developing a massive data warehouse using Redshift and Redshift Spectrum.

Project Task Summary

ETL workflows in Data Pipeline, monitoring and management of ETL pipelines
Batch RDBMS data migration using AWS DMS
Batch processing in EMR and Glue using Scala Spark
Designing and developing data warehouse on Redshift
DWH data model and table design
Accessing and processing big data on S3 via SQL using Redshift Spectrum
Python ML implementation with Pandas, scikit-learn using Jupyter on AWS
CI/CD development using Gitlab and Ansible

Technologies

AWS CLI
AWS Console
IAM
S3
AWS ECS
AWS Batch
AWS CLI
AWS Console
IAM
S3
AWS ECS
AWS Batch

AWS CLI
AWS Console
IAM
S3
AWS ECS
AWS Batch
AWS CLI
AWS Console
IAM
S3
AWS ECS
AWS Batch

AWS Data Lake Project

DevOps

Industry: Consulting

Subject: Cloud Data Lake DevOps; AWS DevOps

Description: Provisioning and deployment of big data solutions on AWS. Operationalize cloud data solutions, implementing infrastructure as code (IaC), using CloudFormation templates for resource management. Provisioning and deploying on-demand Redshift cluster and RDS instances using CloudFormation. Development, management, and deployment of Docker images and containers.

Project Task Summary

Provisioning resources using CloudFormation templates
Provisioning of Redshift, Data Pipeline, and Glue ETL pipelines
User account and access management in IAM
Develop Docker images for batch processing applications and Python, ML models, using AWS Container Registry (AWS ECR)
Docker container deployment using AWS ECS
CI/CD implementation using GitLab

Technologies

AWS CLI
AWS CloudWatch
AWS CloudFormation
AWS Glue
IAM
AWS Data
Pipeline
AWS ECS
Redshift
Python
SQL

AWS ECS
Redshift
Python
SQL
Docker
Dockerfile
CI/CD
Git
Gitlab
Scrum
Slack

Azure Data Lake Project

Data Engineering

Industry: Consulting

Subject: Azure Cloud Data Lake Development; Azure Big Data Engineering

Description: Data lake development on Microsoft Azure. Data migration from RDBMS and file sources, data loading into Azure Blob storage and Azure SQL. Design and development of big data batch solutions using Data Factory and Databricks. Massive data warehouse development using Azure SQL Data Warehouse.

Project Task Summary

Create ETL workflows in Data Factory with data factory ETL pipeline monitoring and management
Batch processing in Azure Databricks using Scala Spark
Data warehouse design and development using SQL Data Warehouse
DWH data model design, featuring index and partitioning table design
Accessing and processing big data in Blob storage via Transact-SQL using Polybase
CI/CD development using SBT and Gitlab

Technologies

Azure CLI
Azure Portal
Data Factory
Polybase
Azure SQL DWH
Databricks
Spark
Spark SQL

Scala
Python
SQL
Transact-SQL
GitLab
Scrum
SBT

Azure Data Lake Project

DevOps

Industry: Consulting

Subject: Cloud Data Lake DevOps; Azure DevOps

Description: Data Factory and Databricks provisioning and deployment. Operationalization of cloud data solutions and infrastructure as code (IaC) implementation using ARM templates and Azure Python SDK for resource management. Azure SQL data warehouse provisioning and deployment. CI/CD implementation using Azure DevOps tools. Development, management, and deployment of Docker images and containers.

Project Task Summary

Azure resources (VM and storage account, SQL DB and network) provisioning using Azure Python SDK and ARM template
SQL data warehouse provisioning with Databricks and Data Factory integration, using Python scripts and ARM templates, with Azure Key Vault for deployment
User account and role-based (RBAC) access management in Azure Active Directory
Docker image development for batch processing applications and ML model APIs, using Azure Container Registry for build, storage, and management of images
Azure container deployment on ACI (Azure Container Instances)
CI/CD implementation via Azure Repos, Azure Artifacts, Azure Pipelines, and Azure Test Plans

Technologies

AWS CLI
Azure Python SDK
Azure Portal
Azure Active Directory
Data Factory
Databricks
Python
SQL
ARM
Docker

Dockerfile
Azure Container Registry
CI/CD
Azure Repos
Azure Artifacts
Azure Pipelines
Azure Test Plans
Git
SBT

ML/AI Projects

NLP Project

Industry: AI/Automatization

Subject: Implementation of supervised Machine Learning Algorithm for automatic keyphrase extraction.

Description: Implementation of automated Context Tagger for a B2B Marketing automated AI solution. Text classification models are implemented in Python using Python Text Mining, NLP and other ML and data analysis libraries (Python Data Science and ML stack). Text mining, data processing, and feature engineering of a massive dataset in Spark.

Project Task Summary

Design and implementation of a very fast multi-threaded AKKA-based stream (SAX/Stax) processing of XML data for transforming huge XML data to CSV format.
Preprocessing of the data by filtering normalizing text content and applying Spacy and NLTK
Text mining and data preprocessing in Spark SQL Scala on Hadoop and S3
Training of embedding and language models using fastText, Gensim, and GPT-2
Modeling term to tag relations in massive graph networks in Tigergraph
Keyphrase extraction (automatic tagging) using N-Grams, Word2vec scoring and PageRank algorithm on massive graphs of tag-to-tag relations
FP-Growth association rules learning
Distributed CNN training in Docker containers on AWS using GPU instances

Technologies

Python
Scala
Apache Spark
Spark SQL
Tigergraph
Apache Hadoop
Apache Hadoop
Spacy
XML
AKKA
Word2vec
GPT-2

Pandas
NumPy
SciPy
Scikit-learn
Keras
PyTorch
CNN
Docker
Kubernetes
FastText
Gensim

Classification
NLTK
Jupyter
Parquet
Apache Arrow
IntelliJ
SBT
Nvidia-docker
Linux
Git
GitLab

Customers

Ready to get started?

Germany

contact@qimia.de

+49 221 5796 7940

USA

contact@qimia.io

+1 (858) 286 61 10

Turkey

contact@qimia.io

+90 542 412 02 31

Qimia specializes in custom use-case implementation

Products

Services

Company Data

Project References

AWS Data Lake Project

Project Task Summary

Technologies

AWS Data Lake Project

Project Task Summary

Technologies

Azure Data Lake Project

Project Task Summary

Technologies

Azure Data Lake Project

Project Task Summary

Technologies

ML/AI Projects

Project Task Summary

Technologies

Customers

Ready to get started?

Germany

USA

Turkey