Apache Spark Experts in Nuremberg
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Meet FRATCH Experts in Nuremberg, who have recently used Apache Spark
Pawan S.
Last position:
CAPTCHA Recognition using CRNN
- Built a CRNN model with VGG16 and BiLSTM backbone for text-based CAPTCHA recognition
- Achieved 9.37% character error rate and 68.36% sequence accuracy on validation data
- Expanded data augmentation pipeline with distortions, noise injection, and clutter to improve robustness
- Conducted detailed error analysis on confusable characters (O, Q, D) and proposed error-specific augmentation
- Tech Stack: Python, TensorFlow/Keras, OpenCV, NumPy, Matplotlib
Ioan D.
Last position:
Senior Software Developer at ING
- Tribe Home, Product Area 4 - Customer in Life, Squad Cybertron.
- Analysis, design, development, and testing of new requirements for Optimmo, construction financing software.
- Analysis, design, development, and testing of the JEE application MWS kredit-baufi.
- Analysis, design, development, and testing of the Wicket Optimmo application.
- Analysis, design, development, and testing of kredit-baufi batch programs.
- Migration of kredit-baufi batches to RHEL9.
- CiL SCS Consumer Pacts with Finagle.
- CiL ScS Touch Point Architecture Integration.
- Team size: 10.
- Tools / Frameworks: OpenJDK Java 17, Kotlin 1.6.0, Azure, Jenkins, Stash, JEE, Gitlab, JIRA, Confluence, git, Spring 6.0, Sping Cloud, Spring Data, Hibernate, JPA, JMS, Kafka, Oracle, PL/SQL, Red Hat Enterprise Linux (RHEL), Maven, REST, JBoss, Intellij IDEA, Wicket 9 and 10, Istio.
Arun Sai T.
Last position:
AI-Backend Developer Intern at Calvergy UA
- Integrated complex AI-based energy system models into the frontend framework, enabling the visualization of insights for 6+ key clients and maximizing energy utilization.
- Maximized energy efficiency and utilization by architecting the seamless data flow between AI models and the user interface for rapid, actionable reporting.
Guino N.
Last position:
Senior Data Engineer at Infomotion
- Building a Data Analytics platform for Karl Storz
- Generic development of all ETL processes
- Preparing and providing data in Databricks Delta tables for reuse in Databricks Machine Learning
- Technologies & Tools: Azure Data Factory, CI/CD Pipeline with GitHub DevOps, Python, Azure Databricks (Unity Catalog), T-SQL
Andreas V.
Last position:
Senior Software Developer, Architect at ifm Solutions GmbH
- IoT platform to connect, transform and get actionable results
- Built a microservice observability solution to improve insights for developers and operations teams
- Made advanced performance optimizations in .NET code, databases and time series
- Provided architectural advice to developer teams on performance, scalability, reliability and code quality
- Evaluated and built PoCs for new technologies, including time series databases and messaging platforms
- Technologies: C#/F#/Rust, InfluxDB, ClickHouse, Prometheus, Grafana, k6, RabbitMQ, EMQX, Docker, Kubernetes, Azure AKS
Uddipan B.
Last position:
Research Team Member at Munich Music Labs, TUM
- Focused on exploring the intersection of Music and AI.
Vasuraj B.
Last position:
Cloud Data Analyst at Bhatia Reply
- Analyzed 50K+ customer records using SQL and Python in a cloud services firm, identifying trends
- Designed interactive Tableau dashboards for sales and marketing stakeholders, reducing report
- Developed ARIMA and AutoARIMA time series models to forecast AWS resource utilization, cutting
- Automated ETL pipelines with Python, improving workflow efficiency by 20% for scalable data
- Collaborated with DevOps teams to deploy 3 machine learning models in production using Docker
Ekaansh K.
Last position:
Master thesis - LLM powered RAG System at Friedrich-Alexander-Universität Erlangen-Nürnberg
- Developed a RAG system to automate student queries with 96% accuracy, built using FastAPI and LangChain and deployed on the university server with Docker.
- Evaluated performance using RAGAS, comparing LLMs (Llama3.3, Llama3.1, GPT-4o-mini), vector embeddings, and various retrieval techniques within the RAG pipeline.
- Technical Skills: Python, FastAPI, Docker, AWS, LangChain, LangSmith, NLP, HTML, CSS
Discover over 15,000 top freelancers
Statistics of experts using Apache Spark
Aggregated from the professional profiles of matched freelancers.
Experience
14 years
Position duration
1.6 years (Germany: 2.7 years)
Positions per freelancer
9 (Germany: 10)
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Information Technology, Automotive, Banking and Finance
Bachelor's degree or higher
100% (Germany: 97%)
Master's degree or higher
75% (Germany: 70%)
Certifications per freelancer
1 (Germany: 3)
Most common languages
German, English, Hindi
Speak two or more languages
100% (Germany: 97%)
Based on our profile pool as of 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Nuremberg are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows the share of experts charging within that range.
Average rates of experts in Nuremberg using Apache Spark
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 29 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Apache Spark 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%)
- Automotive (75%)
- Banking and Finance (63%)
- Education (50%)
- Manufacturing (50%)
- Energy (38%)
- Healthcare (38%)
- Professional Services (38%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Distributed Processing
Apache Spark is an open-source engine for processing large datasets across clusters. Its APIs support batch workloads, streaming, machine learning and graph processing, with interfaces for Scala, Java, Python and R. The core abstraction is resilient distributed data processing, while DataFrame and Dataset APIs provide structured work with optimized execution.
Data Pipelines
Companies use Spark to ingest, transform and enrich data from databases, object storage, event streams and business applications. It can power lakehouse pipelines, analytical data preparation, feature generation and near-real-time processing. Strong solutions address data quality, schema changes, retries and repeatable deployments rather than only writing transformations.
Ecosystem And Tools
Spark commonly works with Delta Lake, Apache Iceberg, Apache Hudi, Kafka, Hive, Parquet and cloud storage. Professionals also use Spark SQL, Structured Streaming, MLlib, notebooks, workflow orchestration and observability tools. Depending on the environment, workloads run through Kubernetes, Databricks or managed cloud services.
- Design batch and streaming pipelines
- Tune joins, partitions, caching and shuffle behavior
- Connect Spark with Kafka, warehouses and lakehouse storage
- Package, test and monitor production jobs
When Expertise Helps
Freelance expertise is useful when a team is moving from single-node scripts to distributed processing, replacing fragile ETL jobs or preparing a lakehouse migration. It also helps when cloud compute costs rise, streaming results lag, or scheduled jobs fail under production volumes. In Nuremberg, remote collaboration can support local industrial, logistics and software teams without limiting access to specialists.
Skills To Assess
Look for professionals who can explain execution plans, partitioning, serialization, memory pressure and data skew in practical terms. They should be comfortable with SQL, Python or Scala and able to connect pipeline design with security, testing and deployment. Experience with the relevant storage format, cloud environment and orchestration layer matters as much as Spark syntax.
Reliable Delivery
A strong Spark implementation has clear data contracts, reproducible environments and measurable pipeline behavior. Quality professionals profile workloads before tuning them, handle late or duplicate events deliberately and document operational trade-offs. They leave behind tests, alerts and runbooks so internal teams can maintain the system after the engagement ends.
Frequently asked questions
Quick answers to the questions that come up most around Apache Spark.
Apache Spark is used for distributed batch processing, streaming analytics, data preparation and machine learning workflows. It is suited to workloads that need to process data across a cluster rather than on one machine.
Apache Spark is widely used for unified batch and streaming pipelines, SQL workloads and lakehouse processing. Apache Flink is often preferred when continuous, low-latency stream processing and event-time behavior are the main priorities; the right choice depends on workload and operating model.
A strong Apache Spark specialist usually brings SQL, Python or Scala, distributed-systems knowledge and experience with Kafka or cloud storage. Familiarity with Delta Lake, Apache Iceberg, Kubernetes, Databricks and workflow orchestration can also be important.
The required background depends on the risk and scale of the work. A simple pipeline may need focused Spark knowledge, while a production platform requires experience with partitioning, data skew, streaming semantics, deployment, monitoring and failure recovery.
Apache Spark projects are often well suited to remote collaboration because code, cloud environments and data contracts can be reviewed online. Teams in Nuremberg should agree on working language, access controls, meeting overlap and clear ownership of production changes.
Bring in a Spark professional when pipelines are slow, unreliable, expensive or difficult to scale. External expertise can also help with migrations from legacy ETL, lakehouse adoption, streaming architecture and production readiness.
Ask the specialist to explain a real performance investigation, including the execution plan, partition strategy and trade-offs made. Look for evidence of testing, observability, data-quality controls and clear documentation, not only familiarity with API calls.
Apache Spark includes MLlib for distributed machine-learning workflows and can prepare features from large datasets. It is useful when data preparation and model training need to sit close to a distributed data platform, though specialized tools may be better for some advanced models.
The average hourly rate of freelancers in Nuremberg, Germany who have used Apache Spark in their recent projects is 88 €, which corresponds to a daily rate of about 708 € based on an 8-hour working day.
Of the freelancers in Nuremberg, Germany who have used Apache Spark in their recent projects, 100% hold at least a Bachelor's degree and 75% hold at least a Master's degree.
On average, freelancers in Nuremberg, Germany who have used Apache Spark in their recent projects have 14 years of professional experience, with a single engagement typically lasting around 1.6 years.
The most common languages among freelancers in Nuremberg, Germany who have used Apache Spark in their recent projects are German (100%), English (100%), and Hindi (38%).
The most common industries among freelancers in Nuremberg, Germany who have used Apache Spark in their recent projects are Information Technology (100%), Automotive (75%), and Banking and Finance (63%).
The most common business areas among freelancers in Nuremberg, Germany who have used Apache Spark in their recent projects are Information Technology (100%), Business Intelligence (88%), and Product Development (88%).
Main locations of FRATCH Experts, who have recently used Apache Spark
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.
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Hamburg 
Munich 
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Frankfurt 
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