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- Data Engineer with a Ph.D. degree in Measurement methods, Master of industrial automation - 16+ years experience with data-driven projects - Strong background in statistics, machine learning, AI, and predictive modeling of big data sets. - AWS Certified Data Analytics. AWS Certified Cloud Practitioner. - Experience in ETL operations and data curation - PostgreSQL, SQL, Microsoft SQL, MySQL, Snowflake - Big Data Fundamentals via PySpark, Google Cloud, AWS. - Python, Scala, C#, C++ - Skills and knowledge to design and build analytics reports, from data preparation to visualization in BI systems.

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Last Updated: 4 Jul 2023

- 8+ year experience in building data engineering and analytics products (Big data, BI, and Cloud products) - Expertise in building Artificial intelligence and Machine learning applications. - Extensive design and development experience in AZURE, Google, and AWS Clouds. - Extensive experience in loading and analyzing large datasets with Hadoop framework (Map Reduce, HDFS, PIG and HIVE, Flume, Sqoop, SPARK, Impala), No SQL databases like Cassandra. - Extensive experience in migrating on-premise infrastructure to AWS and GCP clouds. - Intermediate English - Available ASAP

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Mykola V., Data Architect

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Oliver O., DevOps Engineer/ Data Architect

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- 4+ years of experience in IT- Versatile Business Intelligence professional with 3+ years of experience in the telecommunications industry- Experience with data warehousing platform to a Big Data Hadoop Platform - Native English- Available ASAP

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Alex K., Data Engineer

Oradea, Romania
Last Updated: 13 Nov 2023

- Senior Data Engineer with a strong technology core background in companies focused on data collection, management, and analysis. - Proficient in SQL, NoSQL, Python, Pyspark, Oracle PL/SQL, Microsoft T-SQL, and Perl/Bash. - Experienced in working with AWS stack (Redshift, Aurora, PostgreSQL, Lambda, S3, Glue, Terraform, CodePipeline) and GCP stack (BigQuery, Dataflow, Dataproc, Pub/Sub, Data Studio, Terraform, Cloud Build). - Skilled in working with RDBMS such as Oracle, MySQL, PostgreSQL, MsSQL, and DB2. - Familiar with Big Data technologies like AWS Redshift, GCP BigQuery, MongoDB, Apache Hadoop, AWS DynamoDB, and Neo4j. - Proficient in ETL tools such as Talend Data Integration, Informatica, Oracle Data Integrator (ODI), IBM Datastage, and Apache Airflow. - Experienced in using Git, Bitbucket, SVN, and Terraform for version control and infrastructure management. - Holds a Master's degree in Environmental Engineering and has several years of experience in the field. - Has worked on various projects as a data engineer, including operational data warehousing, data integration for crypto wallets/De-Fi, cloud data hub architecture, data lake migration, GDPR reporting, CRM migration, and legacy data warehouse migration. - Strong expertise in designing and developing ETL processes, performance tuning, troubleshooting, and providing technical consulting to business users. - Familiar with agile methodologies and has experience working in agile environments. - Has experience with Oracle, Microsoft SQL Server, and MongoDB databases. - Has worked in various industries including financial services, automotive, marketing, and gaming. - Advanced English - Available in 4 weeks after approval for the project

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Table of Contents

Soft skills of a Apache Hadoop Developer

Soft skills are essential for Apache Hadoop Developers to effectively collaborate and communicate with team members and stakeholders. These skills play a crucial role in the success of Hadoop projects and contribute to overall productivity and efficiency.


  • Effective Communication: Ability to clearly convey technical concepts and ideas to team members and stakeholders.
  • Problem Solving: Aptitude for identifying and resolving issues that arise during Hadoop development.
  • Collaboration: Willingness to work in a team environment and contribute to collective goals.
  • Adaptability: Capacity to quickly adapt to changing requirements and technologies in the Hadoop ecosystem.
  • Time Management: Skill in managing time and prioritizing tasks effectively to meet project deadlines.


  • Leadership: Capability to lead a small team of developers and provide guidance and mentorship.
  • Analytical Thinking: Ability to analyze data and draw insights to optimize Hadoop infrastructure and applications.
  • Presentation Skills: Proficiency in presenting complex technical information to both technical and non-technical audiences.
  • Conflict Resolution: Skill in resolving conflicts and addressing challenges that arise within the development team.
  • Attention to Detail: Thoroughness in ensuring the accuracy and reliability of Hadoop solutions.
  • Client Management: Ability to understand client requirements and effectively manage client expectations.
  • Continuous Learning: Commitment to staying updated with the latest advancements in Hadoop technologies.


  • Strategic Thinking: Capacity to align Hadoop solutions with overall business objectives and provide strategic insights.
  • Project Management: Proficiency in managing large-scale Hadoop projects and coordinating with multiple stakeholders.
  • Team Building: Skill in building and nurturing high-performing development teams.
  • Negotiation Skills: Ability to negotiate contracts, agreements, and partnerships related to Hadoop projects.
  • Innovation: Aptitude for identifying and implementing innovative solutions to enhance Hadoop infrastructure and applications.
  • Mentorship: Willingness to mentor and guide junior developers to foster their professional growth.
  • Business Acumen: Understanding of business processes and the ability to align Hadoop solutions with business needs.
  • Conflict Management: Proficiency in managing conflicts and fostering a positive work environment.

Expert/Team Lead

  • Strategic Leadership: Ability to provide strategic direction to the development team and align Hadoop solutions with organizational goals.
  • Decision Making: Skill in making informed decisions that impact the overall success of Hadoop projects.
  • Risk Management: Proficiency in identifying and mitigating risks associated with Hadoop development and implementation.
  • Thought Leadership: Recognition as an industry expert and the ability to influence the Hadoop community.
  • Vendor Management: Experience in managing relationships with Hadoop vendors and evaluating their products and services.
  • Collaborative Partnerships: Skill in building collaborative partnerships with other teams and departments within the organization.
  • Strategic Planning: Proficiency in developing long-term plans and roadmaps for Hadoop infrastructure and applications.
  • Change Management: Ability to effectively manage and lead teams through organizational changes related to Hadoop.
  • Technical Expertise: In-depth knowledge and expertise in Apache Hadoop and related technologies.
  • Thoughtful Innovation: Capacity to drive innovative initiatives that push the boundaries of Hadoop capabilities.
  • Business Strategy: Understanding of business strategy and the ability to align Hadoop solutions with organizational objectives.

Pros & cons of Apache Hadoop

6 Pros of Apache Hadoop

  • Scalability: Apache Hadoop can handle massive amounts of data by distributing it across multiple nodes in a cluster. This allows for easy scalability as the amount of data grows.
  • Cost-effectiveness: Hadoop runs on commodity hardware, which is much more cost-effective compared to traditional storage solutions. It enables organizations to store and process large volumes of data without significant upfront investments.
  • Flexibility: Hadoop is designed to handle structured, semi-structured, and unstructured data, making it suitable for a wide range of use cases. It can process various data formats like text, images, videos, and more.
  • Fault tolerance: Hadoop provides fault tolerance by replicating data across multiple nodes in a cluster. In case of node failures, data can be easily recovered, ensuring high availability and reliability.
  • Data processing capabilities: Hadoop has a powerful processing framework called MapReduce, which allows for distributed data processing. It can efficiently perform complex computations on large datasets by dividing the work into smaller tasks and executing them in parallel.
  • Data storage: Hadoop Distributed File System (HDFS) provides a scalable and reliable storage solution for big data. It allows for the storage of large files across multiple machines and ensures data durability.

6 Cons of Apache Hadoop

  • Complexity: Setting up and managing a Hadoop cluster can be complex and require specialized knowledge. It involves configuring various components, optimizing performance, and ensuring proper security measures.
  • Processing overhead: Hadoop’s MapReduce framework introduces some processing overhead due to the need to distribute and parallelize tasks. This can result in slower processing times compared to traditional data processing methods for certain types of workloads.
  • Real-time processing limitations: Hadoop is primarily designed for batch processing of large datasets. It may not be the best choice for applications that require real-time or near-real-time data processing and analysis.
  • High storage requirements: Hadoop’s fault tolerance mechanism, which involves data replication, can lead to higher storage requirements. Storing multiple copies of data across different nodes increases the overall storage footprint.
  • Skill requirements: Successfully utilizing Hadoop requires skilled personnel who understand the intricacies of the platform and can effectively optimize and tune the system for specific use cases.
  • Security concerns: Hadoop’s distributed nature introduces security challenges, such as data privacy, authentication, and authorization. Organizations must implement proper security measures to protect sensitive data stored and processed in Hadoop clusters.

TOP 10 Apache Hadoop Related Technologies

  • Java

    Java is the most widely used programming language for Apache Hadoop development. Its robustness, scalability, and extensive libraries make it a perfect fit for handling big data processing.

  • Hadoop Distributed File System (HDFS)

    HDFS is a distributed file system designed to store and process large datasets across clusters of commodity hardware. It provides high fault tolerance and enables data throughput at a scalable level.

  • MapReduce

    MapReduce is a programming model and software framework for processing large amounts of data in parallel across a Hadoop cluster. It simplifies complex computations by breaking them down into map and reduce tasks.

  • Apache Spark

    Apache Spark is an open-source distributed computing system that provides high-speed data processing capabilities. It can seamlessly integrate with Hadoop and offers advanced analytics and machine learning libraries.

  • Pig

    Pig is a high-level scripting language for data analysis and manipulation in Hadoop. It provides a simplified way to write complex MapReduce tasks and enables users to focus on the data processing logic rather than low-level coding.

  • Hive

    Hive is a data warehouse infrastructure built on top of Hadoop that provides a SQL-like query language called HiveQL. It allows users to query and analyze data stored in Hadoop using familiar SQL syntax.

  • Apache Kafka

    Apache Kafka is a distributed streaming platform that can be integrated with Hadoop for real-time data processing. It provides high-throughput, fault-tolerant messaging capabilities and is widely used for building data pipelines.

Let’s consider Difference between Junior, Middle, Senior, Expert/Team Lead developer roles.

Seniority NameYears of experienceResponsibilities and activitiesAverage salary (USD/year)
Junior0-2 yearsAssisting with basic coding tasks, bug fixing, and testing. Learning and acquiring new skills, technologies, and processes. Working under the supervision of more experienced developers.$50,000 – $70,000
Middle2-5 yearsDeveloping software components, modules, or features. Participating in code reviews and providing feedback. Collaborating with team members to meet project requirements. Assisting junior developers and sharing knowledge and best practices.$70,000 – $90,000
Senior5-10 yearsDesigning and implementing complex software solutions. Leading development projects and making architectural decisions. Mentoring and coaching junior and middle developers. Collaborating with cross-functional teams to deliver high-quality software.$90,000 – $120,000
Expert/Team Lead10+ yearsLeading and managing development teams. Setting technical direction and making strategic decisions. Providing technical expertise and guidance to the team. Ensuring high performance, quality, and adherence to coding standards. Building and maintaining strong relationships with stakeholders.$120,000 – $150,000+

Cases when Apache Hadoop does not work

  1. Insufficient hardware resources: Apache Hadoop is a resource-intensive framework that requires a cluster of machines to work efficiently. If the hardware resources, such as CPU, memory, and storage, are not sufficient, it can negatively impact the performance and stability of Hadoop.
  2. Inadequate network bandwidth: Hadoop relies heavily on data distribution across a cluster of machines. If the network bandwidth between the nodes is limited or congested, it can lead to slow data transfer and hamper the overall performance of Hadoop.
  3. Unoptimized data storage format: Hadoop works best with data stored in a specific format, such as Hadoop Distributed File System (HDFS) or columnar formats like Parquet and ORC. If the data is stored in an incompatible format or not optimized for Hadoop, it can result in reduced query performance and inefficient data processing.
  4. Improper cluster configuration: Hadoop requires proper configuration of its various components, such as NameNode, DataNode, ResourceManager, and NodeManager, to function correctly. If the cluster is not configured optimally or misconfigured, it can lead to instability, data loss, and performance issues.
  5. Insufficient data replication: Hadoop ensures data reliability and fault tolerance through data replication across multiple nodes. If the replication factor is set too low or there are frequent failures leading to insufficient data replication, it can increase the risk of data loss and impact the reliability of Hadoop.
  6. Unsupported workloads: While Hadoop is well-suited for batch processing and large-scale data analytics, it may not be the ideal choice for all types of workloads. Real-time processing, low-latency requirements, and certain complex analytics scenarios may be better served by other technologies or frameworks.
  7. Security vulnerabilities: Hadoop has built-in security mechanisms, such as Kerberos authentication and Access Control Lists (ACLs), but it can still be susceptible to security vulnerabilities if not properly configured or patched. Failure to address security vulnerabilities can expose sensitive data and compromise the overall security of the Hadoop cluster.
  8. Lack of expertise and support: Successfully deploying and managing a Hadoop cluster requires specialized skills and knowledge. If an organization lacks the necessary expertise or fails to get adequate support, it can lead to operational challenges, inefficient resource utilization, and failure to derive value from Hadoop.

TOP 13 Facts about Apache Hadoop

  • Apache Hadoop is an open-source framework for distributed storage and processing of large datasets.
  • It was initially developed by Doug Cutting and Mike Cafarella in 2005, inspired by Google’s MapReduce and Google File System papers.
  • Hadoop is designed to handle big data, which refers to extremely large and complex datasets that cannot be easily managed using traditional data processing applications.
  • The core components of Hadoop include the Hadoop Distributed File System (HDFS) for storing data and the Hadoop MapReduce programming model for processing data in parallel across a cluster of computers.
  • Hadoop utilizes a master-slave architecture, where one or more master nodes coordinate the overall operations, while multiple worker nodes perform the actual data processing tasks.
  • The Hadoop ecosystem consists of various complementary tools and frameworks, such as Apache Hive for data warehousing, Apache Pig for data analysis, and Apache Spark for in-memory processing.
  • Apache Hadoop is highly scalable and can handle massive amounts of data by distributing it across multiple nodes in a cluster.
  • It provides fault tolerance by replicating data across multiple nodes, ensuring data availability even in the event of node failures.
  • Hadoop’s distributed processing model allows for parallel processing of data, enabling faster data analysis and insights.
  • Hadoop is widely used in industries such as finance, healthcare, e-commerce, and social media, where large volumes of data need to be processed and analyzed.
  • Companies like Yahoo, Facebook, Netflix, and Twitter have adopted Hadoop as part of their data processing and analytics pipelines.
  • Hadoop has become a de facto standard for big data processing and is supported by a large community of developers and contributors.
  • Apache Hadoop is a key technology driving the growth of the big data industry, enabling organizations to extract valuable insights from vast amounts of data.

What are top Apache Hadoop instruments and tools?

  • Apache Hadoop: Apache Hadoop is an open-source framework that allows for the distributed processing of large data sets across clusters of computers. It was initially created in 2005 by Doug Cutting and Mike Cafarella and is now maintained by the Apache Software Foundation. Hadoop has become a popular tool for big data processing and is used by numerous organizations, including Yahoo, Facebook, and Twitter.
  • Apache Hive: Apache Hive is a data warehouse infrastructure built on top of Hadoop that provides a query language called HiveQL for querying and analyzing large datasets stored in Hadoop’s distributed file system. Hive was developed by Facebook and became an Apache project in 2008. It has gained popularity for its ability to enable SQL-like queries on Hadoop data, making it more accessible to users familiar with SQL.
  • Apache Pig: Apache Pig is a high-level platform for creating and executing data analysis programs on Hadoop. It provides a scripting language called Pig Latin, which abstracts the complexities of writing MapReduce jobs and allows users to express their data transformations in a more intuitive way. Pig was developed at Yahoo and became an Apache project in 2007.
  • Apache Spark: Apache Spark is an open-source distributed computing system that provides in-memory processing capabilities for big data. Spark was initially developed at the University of California, Berkeley, in 2009 and later became an Apache project. It offers a wide range of libraries and APIs for various data processing tasks, including batch processing, streaming, machine learning, and graph processing. Spark has gained significant popularity due to its speed and ease of use.
  • Apache HBase: Apache HBase is a distributed, scalable, and consistent NoSQL database built on top of Hadoop. It provides random, real-time read/write access to large amounts of data. HBase was initially developed by Powerset (later acquired by Microsoft) and was contributed to the Apache Software Foundation in 2008. It has been widely used for applications requiring low-latency access to massive amounts of data.
  • Apache Kafka: Apache Kafka is a distributed streaming platform that enables the building of real-time data pipelines and streaming applications. Kafka was initially developed at LinkedIn and later became an Apache project in 2011. It is known for its high-throughput, fault-tolerant, and scalable messaging system, making it suitable for handling large volumes of data streams.
  • Apache Sqoop: Apache Sqoop is a tool designed for efficiently transferring bulk data between Apache Hadoop and structured data stores such as relational databases. Sqoop supports various database systems, including MySQL, Oracle, PostgreSQL, and more. It was initially developed by Cloudera in 2009 and later became an Apache project. Sqoop simplifies the process of importing and exporting data to and from Hadoop, enabling seamless integration with existing data infrastructure.

How and where is Apache Hadoop used?

Utilization CaseDescription
1. Big Data AnalyticsApache Hadoop is widely used for big data analytics. It enables businesses to process and analyze massive amounts of data quickly and efficiently. With Hadoop’s distributed computing capabilities, organizations can perform complex analytical tasks such as machine learning, predictive modeling, and data mining. Hadoop’s MapReduce framework allows parallel processing of large datasets, enabling faster data analysis and insights.
2. Log ProcessingHadoop is a popular choice for log processing applications. It can efficiently handle large volumes of log data generated by various systems, such as web servers, applications, and network devices. By leveraging Hadoop’s scalability and fault-tolerance, organizations can collect, process, and analyze log data in near real-time. This helps in identifying patterns, troubleshooting issues, and monitoring system performance.
3. ETL (Extract, Transform, Load)Hadoop is often used as a data integration platform for ETL processes. It allows organizations to extract data from various sources, transform and clean the data, and load it into a target system or data warehouse. Hadoop’s distributed file system (HDFS) and parallel processing capabilities enable efficient data ingestion and processing, making it an ideal choice for handling large-scale ETL workloads.
4. Recommendation SystemsHadoop is utilized in building recommendation systems for personalized user experiences. By analyzing large datasets, Hadoop can identify patterns and make recommendations based on user preferences, behavior, and historical data. Recommendation systems powered by Hadoop are commonly used in e-commerce, content streaming platforms, and social media networks to enhance user engagement and drive personalized recommendations.
5. Fraud DetectionHadoop is effective in detecting and preventing fraudulent activities. By processing vast amounts of data from various sources, including transaction logs, user behavior patterns, and external data feeds, Hadoop can identify anomalies and suspicious activities in real-time. This enables organizations to detect fraud patterns, mitigate risks, and take proactive measures to prevent financial losses.
6. Data WarehousingHadoop can be used as a cost-effective alternative to traditional data warehousing solutions. It allows organizations to store and process large volumes of structured and unstructured data in a distributed and scalable manner. With Hadoop’s ability to handle diverse data types and its cost-efficiency, businesses can build data lakes and data warehouses to store, organize, and analyze their data for business intelligence and reporting purposes.
7. Genomic Data AnalysisHadoop is extensively used in genomic research and bioinformatics. Genomic data analysis requires processing and analyzing large-scale genomic datasets, which can be efficiently handled by Hadoop’s distributed computing capabilities. By leveraging Hadoop, researchers can analyze DNA sequences, identify genetic variations, and gain insights into diseases and their treatments, leading to advancements in personalized medicine and genomics research.

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