Apache HBase Experts in Germany
matched in minutes by AIHire experts who design distributed data stores, tune HBase clusters and connect Hadoop-based pipelines with Apache Phoenix or Spark. FRATCH matches you quickly and precisely with vetted, available freelancers.
Meet FRATCH Experts in Germany, who have recently used Apache HBase
Serge K.
Last position:
MLOps (machine learning operations) at REWE Digital GmbH
- It is like a startup within REWE, where we have to build a new forecasting system on Google Cloud Platform from the scratch. Although, officially my role is called MLOps, my actual tasks also include development of data processing pipelines (data engineering) and data scientists tasks such as feature engineering and model trainings.
- GCP: Terraform (tofu), Vertex AI (Kubeflow), Cloud Run, IAM, Google Cloud Storage, BigQuery, Artifact Registry
- Data engineering: Snowflake as the main data warehouse, Terraform, DBT for data model implementations
- CI/CD: GitLab. We have built a CI/CD pipeline that automates deployments of new releases up to production environment
Thorsten L.
Last position:
Senior Security Analyst
- Analysis and remediation of security vulnerabilities in a risk assessment system for energy trading
- System analysis
- Threat modeling
- Decision-making on vulnerability mitigation strategies
- Implementation of vulnerability detection mechanisms
- Security scans
- DevSecOps practices
- Technical environment: Visual Studio Code, JetBrains Suite, MS Threat Modeling Tool, DevSecOps, Git, AWS, DynamoDB, SQL Server, Endur, Snowflake, Orca
- Languages: C#, JavaScript, TypeScript, Python, PowerShell, Bash, Terraform, SQL
Nune I.
Last position:
Fractional CTO at OpsWorker
OpsWorker turns Kubernetes alerts into root-cause analyses, on top of the monitoring a team already runs. I lead the technical side: the agent architecture, the AWS infrastructure it runs on (fully inside EU regions), and the engineering decisions behind it, read-only in the cluster by default, human in the loop for judgment. The stack underneath: Amazon Bedrock and Bedrock AgentCore, agents built with the Strands Agents SDK, the Claude and OpenAI APIs, and the Kubernetes API.
Maurice K.
Last position:
Solution Architect – Cloud-Native Transformation of IoT Monitoring Platform at NDA / Wind Energy Sector
- Led the end-to-end architecture and migration of a legacy on- premise IoT monitoring system to a cloud-native Azure platform within a 17-member development team
- Designed and implemented a scalable microservices architecture (Java 21, Spring Boot, Kubernetes, Azure Services), decoupling IoT data streams and eliminating legacy system bottlenecks
- Defined technology stack, mentored developers, and orchestrated cross-functional teams in 4 countries to ensure high-quality delivery and alignment with architectural standards
- Drove requirements engineering and system redesign, removing years of technical debt and introducing event-driven processing and automated workflows
- Key Achievements
- Increased system stability and uptime by ~10x, eliminating need for 24/7 DevOps intervention
- Reduced hosting costs by ~80% (5x savings) through cloud optimization
- Improved performance and scalability, enabling stable handling of high-volume IoT data streams
- Delivered successful zero-disruption migration from on-prem to cloud, with strong user satisfaction and reliability from day one
Patrick V.
Last position:
Senior Director, Retail Media at EUROBAUSTOFF Handelsgesellschaft mbH & Co. KG
- Strategic consulting on marketing funds (WKZ) and retail media, focusing on monetization opportunities and data-driven business models
- Conducting a portfolio analysis of existing WKZ measures to assess the revenue and ROI impact of WKZ investments on supplier performance
- Potential analysis of digital WKZ products and initiatives to identify growth and efficiency levers
- Preparing and presenting the results to management and deriving a strategic move-forward plan
- Designing and facilitating several executive workshops to develop a holistic retail media vision and transformation roadmap
- Defining and prioritizing retail media business cases for data-driven evaluation of investment options
- Developing a technical target architecture considering heterogeneous ERP infrastructures and designing an integrated loyalty program
- Designing change management, including impact analysis on organizational structures and processes
- Creating and presenting C-level decision templates
- Establishing a clear retail media governance structure and technical foundation for data-driven marketing
- Developing a roadmap for implementation in 2026
Alona L.
Last position:
AI Architect
AI-powered platform for automated UX validation and designer support
- Designed and led technical implementation of an enterprise-wide AI solution for automated UX review that improved design quality and significantly reduced manual review processes in teams
- Developed an automated UX validation tool as a Figma plugin and web application that generates test cases based on internal guidelines and reliably checks current designs for consistency and standard compliance
- Implemented an interactive designer chat based on RAG that answers questions about the current design and the company's UX guidelines, and designed the deployment architecture using containerized services
- Python, Azure OpenAI, PostgreSQL, REST API, Docker, OpenShift, Helm, CI/CD, Figma MCP, LLM, RAG, Prompt Engineering, GenAI, XAI, AI Architecture, AI Strategy
Sridhar H.
Last position:
SAP CX Consultant at Anonymous
- Stack: SAP Commerce 2011 (B2C).
- Migrating the data from Stibo system to SAP Commerce Cloud.
- Mapping the data from Stibo system to SAP Commerce Cloud.
- Integrating hot folders to support multiple countries and languages.
- Integrating validation tools for migrated data and generating validation reports.
Anton K.
Last position:
Head of Overall Technical Integration NSC / Hadoop Cloud Development at IABG
Head of overall technical integration NSC (National Secure Cloud, project with approx. 60 employees).
Technical integration of all subprojects into one product, definition of interfaces and basic components of a cloud including hardware, technical architecture of the IABG platform.
Development of a Cloud Management Platform (CMP) capable of creating private/mixed clouds of any complexity based on a textual description with one click or interactively.
CMP also includes the complete hardware management lifecycle.
Kubernetes, OpenStack and Hadoop are used as the foundation.
The management layer includes Harbor, Gitea, Longhorn, Keycloak, Rancher and Jenkins, which are configured automatically.
Private cloud can run any customer workloads, including a full Hadoop layer with HDFS, Spark, MapReduce, Mesos, HBase and around 20 additional ML/DL technologies.
Hadoop worker clusters can also be installed automatically without Kubernetes on bare metal or commodity hardware.
OpenStack with Nova, Neutron, Ironic, Swift, Cinder, Ceph.
Development of a Java application Rudi: SOAP, REST, containers, DB.
Technologies: Kubernetes (K3s, Rke2, Minikube, Harbor, Gitea, Jenkins, Longhorn, Keycloak, Rancher), OpenStack (Nova, Neutron, Keystone, Swift, Ceph, Cinder, Sahara, Magnum, Kayobe, Kolla, Bigrost, Ironic), Hadoop (HDFS, Ambari, Solr, Livy, Ranger, YARN, Tez, HBase, Kafka, Hive, Zookeeper, MapReduce, Spark, Oozie, Flink), virtualization (Kubernetes (K3S), VMware, Oracle), scripting (Ansible, Puppet, Juju, Shell, Groovy, Gradle, Maven).
Stephan S.
Last position:
Senior Data/ML Consultant & Technical Lead at Jolin.io
Role: Software Engineer & Applied Mathematician (Mathematical optimization for scheduling; duration: 1 months; team setting: Team of 2, remote; technologies: JuMP, Julia, Pluto, Svelte, JavaScript, TypeScript, JetBrains Space, Terraform, Nomad)
Role: Software & Cloud & Web Engineer (Building scalable data science compute cluster from scratch; duration: 11 months; team setting: Team of 1, on-site; technologies: Terraform, Kubernetes, k8s ingress, k8s services, k8s RBAC, k8s networking, k3s, etcd, S3, DNS, certificates, Julia, Pluto, JavaScript, Tailwind, Astro, npm, Parcel, Preact, MUI, JWT, AWS SQS, AWS RDS, Python, GitLab, GitHub)
Role: AI & Web Engineer (Custom ChatGPT service; duration: 1 months; team setting: Team of 2, remote; technologies: Python, Poetry, LangChain, Tailwind, ChatGPT API, Flask, FastAPI)
Role: Architect & Data Engineer (Central datalake setup and ingestion; duration: 9 months; team setting: Team of 5, remote; technologies: Infrastructure-as-code, AWS CDK, Python, Boto3, PySpark, AWS Glue, IAM, S3, ECS, Fargate, Lambda, Apache Hudi, DeltaLake, Databricks, GitHub, Jira, Miro)
Role: Software Engineer (PoC Julia migration of scikit-decide; duration: 1 months; team setting: Team of 2, remote; technologies: Python, Julia, GitHub)
Tan P.
Last position:
DevOps Engineer in the DevOps Team at Rise-World
- Implementation of specified DevOps solutions to automate infrastructure (Terraform, Bicep, CloudFormation, Ansible) on-premises datacenter (Ovirt, Proxmox, Ceph Cluster, MinIO) and private cloud.
- Administration, configuration and implementation of CI/CD DevOps pipelines (GitLab, GitFlow) to support development process (Artifactory, Prometheus, Istio, service mesh, Helm Chart, OpenShift (Red Hat Enterprise) / Kubernetes cluster), Red Hat Satellite.
- Administration, setup, monitoring and patching of Linux infrastructure based on Red Hat Enterprise for Dev, Test and QA.
- Use of Scrum and Kanban methods.
- Administration, configuration and implementation of security standards for deploying on Dev, Test, QA and Prod stages of the new ePA applications.
- Development of new plugins and add-ons needed on current infrastructure.
- Database support.
- Data analytics support (Python, Spark, Pandas, Power BI, Splunk Enterprise).
- Implementation of best practices for DevSecOps and BizDevOps using GitOps (ArgoCD), Streamlit framework, Semaphore Ansible UI.
- Configuration and testing of iperf, uperf, sysbench using benchmark-operator for external source data and IoT/MDM devices, creating reports via ELK / OpenSearch.
- Building a new Databricks platform to collect and analyze big data from different sources and IoT devices into Hadoop framework (Python, Pandas, PySpark, Power BI, Apache Airflow).
- Building backend data aggregation and processing to automate configuration deployment between different OpenShift clusters and big data framework (Python, Pandas, PySpark, Apache Spark, PostgreSQL, Django 2, Ansible Automation, Jira JSM).
- Building a new ML pipeline platform using Kubeflow, TensorFlow, KServe.
- Data extraction, transformation and loading from different data sources including structured and unstructured data to analytic DWH / big data cluster using Python, Pandas, Polars, Power BI, Django backend and PostgreSQL.
- Setup of new DevOps Test and QA HashiCorp Vault cluster for PKI and IAM.
- Configuration and testing of automated patching based on CVSS score, SIEM-integrated CVEs.
- Use of Nexpose and InsightVM to scan vulnerability events in network, host, container and application.
- Design and implementation of secure and scalable AWS architectures including VPC, EC2, S3, RDS and Route53 and similar setups on Azure and GCP.
- Automated system provisioning and deployment using CloudFormation templates.
- Configuration of IAM roles, policies and permissions to ensure secure access control.
- Patch management, backup automation and disaster recovery setup on AWS infrastructure.
- Monitoring and optimization of system performance using AWS CloudWatch and AWS Trusted Advisor.
- Support of VMware services (vSphere, Aria, Horizon) and the virtual desktop environment.
- Development and maintenance of CI/CD pipelines using Jenkins, GitLab CI/CD and AWS CodePipeline with interface to Nutanix.
- Configuration of AWS CloudWatch to monitor application performance and system events.
- Planning and execution of migration of on-premises applications to AWS cloud platforms.
- Deployment of containerized applications using Docker and Kubernetes in AWS environments.
- Deployment of internal software packages between availability zones using AWS CodeDeploy.
- Building and deploying ML models using Scikit-learn, XGBoost and Spark MLlib including hyperparameter tuning, model evaluation and production deployment.
Maziyar K.
Last position:
Data Engineer at MSD Germany
- Lead Architect to design and implement the data lake and ETL Pipeline using AWS Stack
- Performance Optimization of Data Ingestion of ETL Pipeline
- Development of Data Validation using Great Expectations
- Leading of the data migration for two sources exchanges
- Data Modeling in AWS Redshift
MLOps
- Model inference implementation by mlflow and AWS SageMaker
- Feature Engineering for the running ML Models ( Recommender Engineer, Clustering )
- Implementatino of Model Registry and artifactory using mlflow
- Historization an Profiling of the Input Data Using AWS Glue Crawler and AWS Data Catalog
- Feature importance using mlflow
Tech. Stack: Python 3, AWS Glue, AWS Step Fucntion, AWS Lambda, AWS EventBridge, AWS IAM Role, AWS SageMaker, AWS EC2, AWS Glue Crawler, AWS CloudWatch, MLFlow, ETL, Data lake, GitHub Action, Terraform, Jenkins, Ansible playbooks (Infrastructure as Code), CI/CD, GitLab, SQL, PySparkSCRUM, Agile, Jira, BigData, VSCode, DBeaver, MSSQL, MySQL, grafana, Docker, Linux, Bash, MapReduce, Data Modeling (ORM), Pandas, YAML, SQL-Alchemy
Philipp B.
Last position:
Instructor at Spark Rockstars Academy
- Help developers with individual live coaching to become pro-level Apache Spark engineers
- Organize and host multi-day, tailored Apache Spark workshops for development teams
- Create educational technical content on a self-hosted blog, YouTube, and social media
Mykhaylo K.
Last position:
Lead Developer Backend, Frontend, BPMN / Administrator at Main Customs Office, Department for Digital Affairs
- Development of backend, frontend, and BPMN processes in the Atlas Centralised Clearance for Import project
- Definition and implementation of REST, JMS, and SOAP endpoints
- Implementation of database access with JPA
- Implementation of BPMN processes with Camunda
- Frontend implementation with React
- Configuration of JBoss via CLI
- Performing code reviews
- Participating in communities of practice for developers and architects
- Implementation of frontend-backend communication with WebSockets
Reinhard D.
Last position:
Continuing Education Data Analytics, Artificial Intelligence, Deep Learning, Machine Learning at Continuing Education
- Continuing education in Data Analytics, Artificial Intelligence, Deep Learning and Machine Learning
- Operating system: Windows
- Development environments: JetBrains PyCharm, Jupyter Notebook, Spyder
- Programming languages: Python 3.10
- Other technologies: Anaconda, Keras 2.10, NumPy, OpenCV, Pandas, Scikit-learn, Seaborn, TensorFlow 2.10, PyTorch
Christian R.
Last position:
Freelance Data Engineer at Ingenieurbüro Christian Richter – Data, Cloud & Container
- Contributed to over 20 successful projects
Discover over 15,000 top freelancers
Statistics of experts using Apache HBase
Aggregated from the professional profiles of matched freelancers.
Experience
21 years
Position duration
3.3 years
Positions per freelancer
17
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Banking and Finance, Professional Services
Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
89%
Master's degree or higher
67%
Doctorate
11%
Certifications per freelancer
4
Most common languages
German, English, Russian
Speak two or more languages
100%
Based on our profile pool as of 19 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Germany are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows how many freelancers charge within that range.
Average rates of experts in Germany using Apache HBase
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 19 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Apache HBase experts industry focus
See which industries our matched freelancers work in most often — every figure is calculated live from the freelancers on FRATCH.
- Information Technology (100%)
- Banking and Finance (53%)
- Professional Services (53%)
- Retail (47%)
- Automotive (42%)
- Healthcare (42%)
- Education (37%)
- Insurance (37%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What HBase does
Apache HBase is a distributed, column-oriented NoSQL database built for large tables and fast, random read and write access. It runs on top of the Hadoop Distributed File System and is designed for data that must remain available as it grows across many machines. Teams use it for time-series records, user profiles, event data, operational datasets and other sparse tables.
Core architecture
HBase stores data in tables made of rows, column families and versioned cells. Regions divide tables across servers, while the master coordinates assignments and cluster operations. ZooKeeper supports coordination, and HDFS provides durable storage. Strong specialists understand compaction, splits, replication, block caching, write-ahead logs and the effects of schema choices on performance.
Ecosystem and tooling
HBase commonly appears alongside Hadoop components and data-processing tools. Relevant expertise may include:
- Designing row keys, column families and access patterns
- Operating HBase with HDFS, YARN and ZooKeeper
- Connecting applications through the Java API, REST or Thrift
- Using Apache Phoenix for SQL access where appropriate
- Integrating Spark, Kafka or Flink for data pipelines
When expertise matters
Companies bring in freelance specialists when a cluster needs a careful migration, performance investigation or operational reset. They may also need support for schema design, capacity planning, backup strategy, security controls or integration with an existing Hadoop estate. In Germany, HBase can support data-intensive work across manufacturing, logistics, research and digital services.
Project deliverables
A professional may produce a schema and access-pattern review, a tested cluster configuration, migration runbooks or monitoring dashboards. Other deliverables include Java client integrations, Phoenix query design, disaster-recovery procedures and documented operating standards. The right scope depends on data volume, latency targets, availability needs and the skills already present in the team.
Choosing strong specialists
Look for evidence of production work with region management, compactions, failure recovery and uneven workloads. Strong specialists can explain why a row key fits the query model and when HBase is a poor match for the problem. They also work clearly with application teams, Hadoop operations and data specialists, whether collaboration is remote or on site and whether German-language communication is required.
Frequently asked questions
Need clarity? These are the questions we hear most often about Apache HBase.
Apache HBase is used for large, sparse datasets that need quick random access and horizontal scaling. Common examples include event histories, sensor records, customer profiles, messaging data and operational tables with changing columns.
Apache HBase favors distributed scale and key-based access over joins, fixed schemas and broad relational querying. A relational database is often better for transactional integrity across related tables, while HBase fits workloads that can be modeled around row keys and column families.
Apache HBase is a natural choice when a team already operates Hadoop and needs close integration with HDFS or Hadoop processing. Cassandra may be preferable for simpler multi-region write availability, while the right decision depends on access patterns, consistency needs, operations and the surrounding ecosystem.
A strong HBase specialist should understand HDFS, ZooKeeper, Hadoop security and JVM-based application work. Experience with Spark, Kafka, Phoenix, Kubernetes or cloud storage can also help when the database is part of a wider data platform.
The needed depth depends on the task. Apache HBase schema advice may be handled through a focused review, while migrations, unstable clusters and recovery design call for a specialist who has operated comparable production workloads and can validate changes safely.
Apache HBase work is often suitable for remote collaboration when secure access, observability and clear runbooks are available. On-site sessions can still help with datacenter changes, incident workshops or teams that need close coordination, and German or English communication can be agreed at the start.
Ask the HBase specialist to explain a row-key decision, a compaction issue and a failure-recovery approach using a realistic scenario. Look for clear trade-offs, measurable validation plans, careful change control and an ability to connect database behavior with application queries and infrastructure.
Apache HBase itself exposes a key-value and column-family model rather than a conventional relational SQL interface. Apache Phoenix can provide SQL access over HBase, but it does not remove the need to understand row keys, indexing, data locality and the limits of the underlying storage model.
The average hourly rate of freelancers in Germany who have used Apache HBase in their recent projects is 110 €, which corresponds to a daily rate of about 878 € based on an 8-hour working day.
Of the freelancers in Germany who have used Apache HBase in their recent projects, 89% hold at least a Bachelor's degree, 67% hold at least a Master's degree, and 11% hold a doctorate.
On average, freelancers in Germany who have used Apache HBase in their recent projects have 21 years of professional experience, with a single engagement typically lasting around 3.3 years.
The most common languages among freelancers in Germany who have used Apache HBase in their recent projects are German (100%), English (100%), and Russian (26%).
The most common industries among freelancers in Germany who have used Apache HBase in their recent projects are Information Technology (100%), Banking and Finance (53%), and Professional Services (53%).
The most common business areas among freelancers in Germany who have used Apache HBase in their recent projects are Information Technology (100%), Product Development (89%), and Business Intelligence (74%).
Main locations of FRATCH Experts, who have recently used Apache HBase
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
Berlin Hamburg Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
