Apache HBase Experts in Germany
in minutes from over 15,000 CVs with the power of AIHire experts who design HBase tables, tune region servers, and troubleshoot low-latency access on Hadoop stacks. Work with specialists who know Phoenix, ZooKeeper, and integration patterns for large-scale data systems, matched fast and precisely from vetted, available freelancers.
Meet FRATCH Experts in Germany, who have recently used Apache HBase
Nune Isabekyan
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.
Serge Kalinin
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
Tan Pham
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.
Thorsten Lenzen
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
Mykhaylo Khazanovych
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
- Conducting code reviews
- Participation in communities of practice for developers and architects
- Implementation of frontend-backend communication with WebSockets
Maurice Knopp
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 Von Der Gönna
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 Liuzniak
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 Huple
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.
Stephan Sahm
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)
Maziyar Khorrami
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 Brunenberg
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
Reinhard Duy
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
Anton Klonov
Last position:
Head of Technical Overall Integration NSC / Hadoop Cloud Development at IABG
Head of technical overall integration NSC (National Secure Cloud project with about 60 employees).
Technical integration of all subprojects into one product, definition of interfaces, basic components of a cloud including hardware, technical architecture of the IABG base.
Development of a Cloud Management Platform (CMP) that can create a private/mixed cloud of any complexity based on a textual description with one click or interactively.
CMP also includes the complete hardware management cycle.
As a foundation, it uses Kubernetes, OpenStack, and Hadoop.
The management layer includes Harbor, Gitea, Longhorn, Keycloak, Rancher and Jenkins, which are automatically configured.
The private cloud can run any customer workloads, including a full Hadoop stack with HDFS, Spark, MapReduce, Mesos, HBase and around 20 other ML/DL technologies.
Hadoop worker clusters can also be automatically installed on bare metal or commodity hardware without Kubernetes.
OpenStack with Nova, Neutron, Ironic, Swift, Cinder, Ceph.
Development of a Java application Rudi: SOAP, REST, containers, database.
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).
Christian Richter
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 30 Aug 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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
HBase at a glance
Apache HBase is a distributed, column-oriented NoSQL database built for very large tables and fast random reads and writes. It sits on Hadoop storage and fits workloads that need wide, sparse data models, durable storage, and predictable access at scale.
Where it fits
Teams use HBase when relational databases become too rigid or too slow for event-heavy systems.
- Time-series and telemetry stores
- User activity and profile data
- Feature stores and lookup layers
- Write-heavy operational data
- Large sparse datasets
Core ecosystem
Strong HBase work depends on the surrounding stack as much as the database itself. Common pieces include the Hadoop file system, ZooKeeper for coordination, Phoenix for SQL access, and tools for bulk loading, backups, and monitoring.
What specialists deliver
Experienced professionals model row keys carefully, choose column families with restraint, and plan for region distribution. They also handle cluster upgrades, compaction tuning, replication, data recovery, and client integration from Java or other JVM-based services.
When to bring in help
Bring in freelance expertise when a cluster is unstable, reads are slow, or schema design is already hurting performance. This is also common during migrations from Cassandra or relational stores, or when a team in Germany needs short-term support for a local data platform or a remote review in English.
What strong experts know
- HBase data modeling and access patterns
- Region splitting, balancing, and compaction behavior
- ZooKeeper coordination and cluster health
- Phoenix, MapReduce, Spark, and JVM integration
- Backup, restore, and replication planning
A strong professional can explain trade-offs clearly and show how design choices affect latency, storage, and maintenance over time.
Frequently asked questions
Need clarity? These are the questions we hear most often about Apache HBase.
Apache HBase is used for large, sparse, and write-heavy data stores that need low-latency access. It is a strong fit for event data, user state, telemetry, and lookup tables that do not map well to a relational schema. Teams often choose it when they need horizontal scaling and tight control over access patterns.
HBase is usually chosen for tight integration with the Hadoop ecosystem and for workloads that need strong consistency on a row level. Cassandra is often favored for multi-region, always-on writes with a different operational model, while relational databases are better when joins, constraints, and ad hoc querying matter more. The right choice depends on data shape, access pattern, and operations.
A strong HBase specialist should know row-key design, column-family strategy, region management, and compaction behavior. Helpful adjacent skills include ZooKeeper, Hadoop, Phoenix, Spark, and Java or other JVM services. Experience with monitoring and recovery matters as much as coding.
A good Apache HBase engagement needs access to the schema, the write and read paths, and the current bottlenecks. Without that context, it is hard to tell whether the issue is data modeling, cluster sizing, or client usage. The best specialists ask for logs, metrics, and a sample of real queries early on.
Most Apache HBase work can be done remotely if the team can provide access to logs, metrics, and a safe test environment. On-site sessions can help during incident response, architecture workshops, or when a German team prefers closer coordination with operations staff. Many projects use a mix of both.
HBase often pairs with Apache Phoenix when teams want SQL access on top of the same storage layer. Phoenix can make reporting and application queries easier without replacing HBase’s underlying data model. A specialist should know when Phoenix helps and when it adds unnecessary complexity.
Look for clear row-key reasoning, practical tuning decisions, and evidence that the person has handled real production incidents in HBase. Good experts explain trade-offs, not just commands. They should also show how they reduce latency, protect data, and keep clusters maintainable.
If your Apache HBase cluster has hot spots, long compactions, slow scans, or uneven region distribution, it is time to bring in help. Other signs are difficult migrations, uncertain backup and restore plans, or a schema that no longer matches the workload. Early intervention usually saves more time than late firefighting.
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 883 € 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
Get in touch with the FRATCH team and we will get back to you within 4 hours.
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Philipp Thomaschewski
FRATCH CEO
