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Apache Hadoop Experts in Frankfurt

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Hire experts who build and run HDFS storage, YARN-based cluster workloads, and Hadoop data pipelines with Hive, Pig, and Spark integrations. Get fast, precise matching with vetted, available freelancers.

Meet FRATCH Experts in Frankfurt, who have recently used Apache Hadoop

Verified expert

Prasad Tilloo

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Solution Architect / Senior Manager – DTC E-Commerce Platform

Frankfurt
Prasad Tilloo

Last position:

Solution Architect / Senior Manager – DTC E-Commerce Platform at BRITA

  • Led discovery phase and POC for Shopware to Shopify Plus migration across EMEA markets, evaluating platform suitability, technical architecture, and multi-brand/multi-country capabilities against business requirements.
  • Designed reference architecture for Shopify Plus implementation incorporating headless front-end patterns (Vue.js, Nuxt.js), CMS integration (Magnolia), and Azure middleware (APIM, Functions, Logic Apps, Service Bus) for 11 EMEA markets.
  • Defined migration strategy analyzing data mapping, cutover approach, and zero-downtime deployment patterns using Varnish caching, GitOps pipelines, and CI/CD orchestration across six vendor teams.
  • Architected multi-tenant Shopify Plus governance model with centralized admin, localized storefront customization, and compliance controls (GDPR, data residency).
  • Prototyped AI-driven search optimization (LLM.txt, JSON-LD) for product discoverability in Google AI results, demonstrating post-launch performance opportunities.
  • Defined EMEA expansion roadmap for 15+ markets through C-level strategic workshops, identifying phased rollout, market-specific configurations, and resource requirements.
  • Tech Stack: React, Nuxt.js, Vue.js, Magnolia CMS, Shopware, Shopify Plus, Azure (APIM, Functions, Logic Apps, Service Bus, Front Door), Varnish, SAP, MS Dynamics, Docker, Kubernetes, GitHub Actions, PostgreSQL, Kafka
Verified expert

Tan Pham

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DevOps & Fullstack Engineer

Hanau
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.
Verified expert

Ulm Paunel

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Freelance IT Specialist

Steinbach (Taunus)
Ulm Paunel

Last position:

DataStage ETL Expert at ING Bank

  • Datastage 11.7, dbt, Oracle 19, Python 3.12 / PySpark 3.5, Azure GitHub, Azure DevOps, Automic
  • Development of migration jobs to transfer data from the collection DWH to the new Risk Mart, as well as development of ETL pipelines to migrate historical data from the old Mart to the new Risk Mart.
  • Storage of the silver layer on Hadoop and the gold layer in Oracle.
  • Translation of DataStage jobs into dbt to publish reporting data in Google Cloud to a PostgreSQL database.
  • Creation and optimization of complex SQL queries for data extraction from a data vault, taking into account historical data in the point-in-time tables.
  • Creation of Oracle table definitions (DDL) and adjustment of existing stored procedures.
  • Versioning changes in GitHub and deployment via the CI/CD portal.
  • Refactoring long-running DataStage jobs into Python using PySpark to reduce server load.
  • Migration of SAS scripts to PL/SQL, including new development of distribution functions that have no direct equivalent in Oracle.
  • Development of Automic jobs to run DataStage pipelines and Python scripts (PySpark jobs) that control the population of the SME and institutional risk tables in the Risk Mart and perform business calculations.
  • Participation in the agile process, including creating user stories, estimations, and planning in Azure DevOps.
  • Handling Azure DevOps tickets and close collaboration with testers and business teams for error analysis and resolution.
Verified expert

Ashkan Zadeh

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Microsoft Azure Senior Data Engineer / Senior Data Scientist

Kelkheim (Taunus)
Ashkan Zadeh

Last position:

Microsoft Azure Senior Data Engineer / Senior Data Scientist at Vattenfall Europe

  • Advising on the use of analytics and BI tools and services in the Microsoft Azure stack (e.g. MS Fabric, Synapse Workspaces and dedicated SQL pools, SQL Database, PostgreSQL, Snowflake, Databricks, Data Factory, SSIS, Analysis Services, Function Apps, Power BI, ML)
  • Independently designing analytics solutions with Python, SQL, etc.
  • Designing and implementing ETLs and data pipelines
  • Creating and maintaining APIs
  • Independently applying CI/CD, testing, and version control
  • Data modeling
  • Model development and optimization
  • Anomaly detection with AI
  • Predictive analytics

Used technologies:

  • Snowflake
  • Fabric
  • Azure Synapse Analytics
  • Azure DataFactory
  • Azure Data Lake
  • Azure DevOps
  • Databricks
  • Spark
  • CI/CD
  • SQL Database
  • Python
  • Power Platform
Verified expert

Alona Liuzniak

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AI Architect

Frankfurt am Main
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
Verified expert

Yevgeniy Ă–sterle

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Business & Data Analyst

Bad Vilbel
Yevgeniy Ă–sterle

Last position:

Tester, Test & Data Analyst at NORD/LB

  • Analyze system requirements and mapping concepts (ETL requirements) for data flows and transformation logic in the bank's DWH
  • Analyze data in DB tables and views of the DWH using SQL (DB2)
  • Independently define, create, and execute test cases in JIRA Xray (SIT)
  • Write SQL queries in DB2 to verify data scenarios and mappings
  • Create test plans for SAP FSDP and concurrent projects
  • Conduct error and root cause analyses in coordination with business analysts, developers, test managers, and the infrastructure team
  • Thoroughly document test results in JIRA Xray
  • Coordinate between business analysis, development, DB infrastructure, business units, and external vendors
Verified expert

Ritika Solanki

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Services Solution Architect

Frankfurt
Ritika Solanki

Last position:

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  • Global marketing analytics for Hitachi Energy as part of a global data modernization initiative aiming to enhance data retention, historical data availability and provide Eloqua's 2-year retention for remote interaction reporting and analytics.

  • Analyzed Eloqua's default retention policy and identified risk of data loss for records older than two years.

  • Designed and implemented historical data preservation strategy by creating transformed tables in the target data platform to archive older data while ensuring data quality dashboards.

  • Collaborated with the Power BI team to re-point dashboards from raw Eloqua imports to the newly created archival layer.

  • Leveraged Jira to track and manage data engineering tasks, bugs, and feature requests across Agile sprints; coordinated backlog prioritization and task assignment to align data pipeline development with business needs.

  • Power BI dashboard optimization:

  • Worked closely with business stakeholders to assess and understand reporting needs for reverse customer data.

  • Designed and implemented incremental refresh in Power BI to ensure daily updates without full data reloads.

  • Collaborated with Azure data engineers to optimize data processing and publication pipelines.

  • Stakeholder communication & data modeling:

  • Acted as liaison between Group Data Office and Technology Office to align data modelling standards.

  • Gathered requirements from data engineering team and participated in weekly status meetings to provide implementation updates and resolve blockers across teams in Germany, Poland, and India.

  • Documentation & quality assurance:

  • Prepared end-to-end technical design documentation, data flow diagrams, and Power BI audit guides for future reference.

  • Participated in UAT sessions with business users to validate data outputs and report accuracy.

Verified expert

Ahsan Javed

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Data Analytics Developer

Frankfurt
Ahsan Javed

Last position:

Data Analytics Developer at Level Next Productions

  • Built Power BI dashboards and enabled data-driven strategies across digital platforms

Discover over 15,000 top freelancers

Statistics of experts using Apache Hadoop

Aggregated from the professional profiles of matched freelancers.

Experience

20 years (Germany: 18 years)

Position duration

1.6 years (Germany: 2.8 years)

Positions per freelancer

15 (Germany: 12)

Top business areas

Information Technology, Business Intelligence, Product Development

Top industries

Information Technology, Banking and Finance, Healthcare

Certification focus areas

Information Technology, Business Intelligence, Product Development

Bachelor's degree or higher

100% (Germany: 95%)

Master's degree or higher

67% (Germany: 64%)

Doctorate

11% (Germany: 8%)

Certifications per freelancer

6 (Germany: 4)

Most common languages

German, English, Italian

Speak two or more languages

100% (Germany: 98%)

Based on our profile pool as of 30 Aug 2026.

Daily rate distribution

0 2 4 6 8
<€480 €640-​720 €720-​800 €800+

The chart shows how the daily rates of freelancers in this technology in Frankfurt 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 Frankfurt using Apache Hadoop

Rates are based on recent contracts and do not include FRATCH margin.

1000
750
500
250
Rate comparison chart
Daily rate avg. 717 €
Germany avg. 788 €

The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.

1000
750
500
250
Rate comparison chart
Median rate 800 €
Germany median 800 €

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

Hadoop basics

Apache Hadoop is a distributed system for storing and processing very large data sets across clusters of commodity hardware. Companies use it for batch analytics, log processing, data lake foundations, and offline reporting. It often appears as Hadoop or Apache Hadoop in search.

Core stack

  • HDFS for distributed storage
  • YARN for cluster resource management
  • MapReduce for batch processing
  • Hive for SQL-style analysis
  • Spark or Oozie in mixed data stacks

Strong specialists understand how these pieces fit together and where each one should stop.

When companies hire

Teams bring in freelance expertise when a Hadoop cluster needs tuning, migration, repair, or documentation. That also includes security hardening, job optimization, and cleanup after years of patchwork changes. In Frankfurt, this often matters for firms that run large data or compliance-heavy environments.

What good work looks like

  • Clear cluster design and capacity planning
  • Stable ingestion and batch pipelines
  • Practical HDFS and YARN administration
  • Query layers that fit real business use
  • Careful handling of data locality and failures

Good professionals leave systems easier to operate, not just working on the day they deliver.

Related skills

Apache Hadoop rarely stands alone. Strong experts usually also know Linux, shell scripting, Java, SQL, Hive, Spark, Kafka, and basic cloud storage patterns. They should be comfortable with backups, monitoring, access control, and troubleshooting noisy distributed systems.

Frankfurt projects

Frankfurt projects often need on-site access for legacy environments, audits, or close work with internal data teams. Remote collaboration also works well for cluster review, pipeline changes, and documentation. The best specialists communicate clearly, explain trade-offs, and keep the stack understandable for the people who own it.

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Frequently asked questions

Key details about Apache Hadoop, drawn from the questions we get asked most.

Apache Hadoop is used to store and process large data sets in a distributed way. Companies use it for batch analytics, log processing, data lake foundations, and other workloads that do not need low-latency answers. It is common in older data stacks, but it still matters where large HDFS clusters already exist.

Hadoop is built around distributed storage and batch processing, while Spark focuses more on fast in-memory processing. Cloud data platforms can reduce the need to manage clusters, but many companies still run Hadoop for existing data pipelines, HDFS storage, or hybrid setups. The right choice depends on what is already in place and how much operational control the team wants.

A strong Apache Hadoop specialist usually knows Linux, shell scripting, SQL, Java, and distributed system troubleshooting. Hive, Spark, Kafka, and monitoring tools are also common companions. For larger environments, security, backup strategy, and cluster operations matter just as much as query work.

Companies usually look for Apache Hadoop expertise when a cluster is unstable, slow, or hard to maintain. It also helps during migrations, version upgrades, storage changes, or when internal teams need a clean handover after years of ad hoc fixes. A freelancer can step in for short, focused work without replacing the full internal setup.

Apache Hadoop can still be useful if you already rely on HDFS, have large batch workloads, or need to extend an existing cluster. For new builds, many teams compare it with Spark-first or cloud-native options because they may be simpler to run. A good specialist will judge the stack against your real data flow, not by habit.

Look for someone who can explain the cluster in plain language and show how they have improved reliability, query performance, or failure handling. Strong Hadoop experts leave behind readable jobs, clear configuration choices, and useful documentation. They should also be able to discuss trade-offs in HDFS layout, YARN resource use, and data access patterns.

Yes, much of Apache Hadoop work can be done remotely, especially reviews, pipeline changes, documentation, and troubleshooting with access to logs. On-site work in Frankfurt is useful when the environment is legacy-heavy, tightly controlled, or needs direct coordination with local teams. Many projects use a mix of both.

Ask which parts of Hadoop they have worked on: HDFS, YARN, Hive, MapReduce, cluster operations, or migration work. Then ask how they approach performance issues, access control, and long-term maintainability. A good answer is concrete, specific, and tied to real systems rather than general theory.

The average hourly rate of freelancers in Frankfurt, Germany who have used Apache Hadoop in their recent projects is 90 €, which corresponds to a daily rate of about 717 € based on an 8-hour working day.

Of the freelancers in Frankfurt, Germany who have used Apache Hadoop in their recent projects, 100% hold at least a Bachelor's degree, 67% hold at least a Master's degree, and 11% hold a doctorate.

On average, freelancers in Frankfurt, Germany who have used Apache Hadoop in their recent projects have 20 years of professional experience, with a single engagement typically lasting around 1.6 years.

The most common languages among freelancers in Frankfurt, Germany who have used Apache Hadoop in their recent projects are German (100%), English (100%), and Italian (22%).

The most common industries among freelancers in Frankfurt, Germany who have used Apache Hadoop in their recent projects are Information Technology (89%), Banking and Finance (67%), and Healthcare (67%).

The most common business areas among freelancers in Frankfurt, Germany who have used Apache Hadoop in their recent projects are Information Technology (100%), Business Intelligence (89%), and Product Development (67%).

Main locations of FRATCH Experts, who have recently used Apache Hadoop

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

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