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Ihor K, Big Data & Data Science Engineer with BI & DevOps skills

Last Updated: 5 Mar 2024
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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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Amit, Expert Data Engineer

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

Last Updated: 4 Jul 2023

- Skillful Data architect with strong expertise in the Hadoop ecosystem (Clouder/Hortonworks Data Platforms), AWS Data services, and more than 15 years of experience delivering software solutions. - Intermediate English - Available ASAP

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Andrey L., Data Engineer

Sao Paulo, Brazil
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- 9+ years of experience as a development and architecture of Big Data solutions. - Advanced English - Available ASAP

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Oleg B., ML Engineer/Big Data Architect

United Arab Emirates
Last Updated: 5 Aug 2023

- Over 15 years experience in leading the design, developing, and delivery of complex IT projects and high-performance solutions, +10 years in business intelligence and in the data analytics field - Advanced hands-on experience in reactive, microservices-based, distributed system design and development including stream application platforms for advanced analytics including machine learning and data science - Proficient Data Engineer-researcher focused on the immediate benefits for the business using Big Data tools (AWS Glue, AWS Greengrass, AWS EMR, AWS Data Lake) with advanced analytical and visualization APIs (graph DB – Titan, Neo4J, Tinkerpop, software development – Scala, Python) with CI/CD pipelines – Jenkins, Circle CI, GitLab actions - Generative AI - Q&A with multiple choices, pre-trained models (Hugging Faces ecosystem, T5, BERT, GPT), ChatBot for online gambling platform (LangChain, Pinecone, Cohere, Faiss, Hugging Face Hub) - Generative AI in NLP - information retrieval for 1) generate personalized recommendations for products or services based on a user's preferences and past behavior 2) summarize legal documents and contracts, making it easier for lawyers and legal professionals to review and analyze large volumes of legal documents. 3) create content such as product descriptions, blog posts, and social media posts - Recommendations platforms - mobile games platform (generate game recommendations based on player history, promo-offers, AWS Personalize ), self-learning algorithms for data-based risk management in agriculture (Monte-Carlo tree and Markov chains) - Upper-intermediate English. - Availability starting from ASAP

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Table of Contents

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

Seniority NameYears of experienceResponsibilities and activitiesAverage salary (USD/year)
Junior Developer0-2 yearsAssisting senior developers in coding and testing, bug fixing, following coding standards and best practices, learning new technologies and frameworks50,000 – 70,000
Middle Developer2-5 yearsWorking independently on coding and testing, designing and implementing software modules, participating in code reviews, mentoring junior developers, collaborating with cross-functional teams70,000 – 90,000
Senior Developer5-8 yearsLeading software development projects, designing and architecting complex software systems, providing technical guidance and mentorship, resolving technical challenges, collaborating with stakeholders90,000 – 120,000
Expert/Team Lead Developer8+ yearsLeading a team of developers, managing project timelines and deliverables, making technical decisions, driving innovation and process improvements, collaborating with other teams and departments120,000 – 150,000+

How and where is Apache Hive used?

Case NameCase Description
Data WarehousingHive is commonly used for data warehousing, where it helps in processing and analyzing large volumes of structured and semi-structured data. It provides an SQL-like interface for querying and managing data stored in Apache Hadoop. Hive’s ability to handle massive datasets makes it suitable for data warehousing tasks.
Log AnalysisWith its ability to handle large-scale data processing, Hive is often used for log analysis. It can efficiently process log files generated by various systems, such as web servers, applications, and network devices. Hive’s query capabilities enable analysts to extract valuable insights from log data, such as identifying patterns, detecting anomalies, and optimizing system performance.
Business IntelligenceHive is frequently utilized in business intelligence (BI) applications. It allows organizations to perform complex data analytics and generate insightful reports and visualizations. By leveraging Hive’s querying capabilities and integration with popular BI tools, businesses can gain valuable insights into their operations, customer behavior, market trends, and more.
Recommendation SystemsHive can be employed in building recommendation systems that provide personalized recommendations to users based on their preferences and behavior. By analyzing large datasets, including user interactions and historical data, Hive enables businesses to develop effective recommendation algorithms that enhance user experiences and drive customer engagement.
Data IntegrationApache Hive plays a crucial role in data integration projects. It provides a unified platform for integrating data from diverse sources, including structured databases, log files, social media data, and more. Hive’s ability to process different data formats and perform transformations simplifies the process of combining and harmonizing data from multiple sources.
ETL (Extract, Transform, Load)Hive is widely used in ETL processes, where it facilitates the extraction, transformation, and loading of data from various sources into a target data warehouse or data lake. Its SQL-like interface and support for complex transformations make it an ideal tool for handling large-scale data integration and consolidation tasks.
Data ExplorationHive enables data scientists and analysts to explore and investigate large datasets efficiently. Its interactive query capabilities allow users to quickly extract subsets of data, apply filters, aggregate results, and perform exploratory data analysis. Hive’s integration with data visualization tools further enhances the data exploration process.
Real-Time AnalyticsWhile Hive is primarily designed for batch processing, it can also be utilized for real-time analytics by integrating with other frameworks like Apache Storm or Apache Kafka. This allows organizations to analyze streaming data and make timely decisions based on up-to-date information. Hive’s scalability and fault-tolerance make it suitable for handling real-time analytics workloads.

Cases when Apache Hive does not work

  1. Large-scale real-time processing: Apache Hive is primarily designed for batch processing rather than real-time processing. It may not be the best choice for use cases that require low-latency processing or real-time analytics. In such scenarios, alternatives like Apache Spark or Apache Flink might be more suitable.
  2. Small data: Hive is optimized for processing large volumes of data. If you have relatively small datasets, the overhead associated with Hive’s distributed processing architecture may outweigh the benefits. In such cases, traditional RDBMS or in-memory processing frameworks like Apache Impala may offer better performance.
  3. Complex OLTP workloads: Apache Hive is not well-suited for online transaction processing (OLTP) workloads that involve frequent read and write operations on individual records. Hive’s strength lies in its ability to perform complex analytical queries on large datasets rather than handling high-throughput transactional workloads. For OLTP use cases, traditional RDBMS systems like MySQL or PostgreSQL are typically more appropriate.
  4. Highly dynamic queries: Hive uses a schema-on-read approach, which means it infers the structure of data at the time of reading rather than enforcing strict schemas upfront. While this flexibility is beneficial for handling unstructured or semi-structured data, it can result in slower query execution speeds compared to systems with rigid schemas. If your use case involves highly dynamic queries that require frequent schema changes, a schema-on-write system like Apache HBase or Apache Cassandra might be more suitable.
  5. Real-time data ingestion: Hive’s strength lies in processing data stored in Hadoop Distributed File System (HDFS) or other compatible file systems. If you have a use case that requires real-time data ingestion from streaming sources like Apache Kafka or Apache Pulsar, Hive may not be the best choice. Specialized stream processing frameworks like Apache Storm or Apache Flink are better suited for these scenarios.

What are top Apache Hive instruments and tools?

  • Apache Hive: Apache Hive is a data warehouse infrastructure built on top of Apache Hadoop. It provides a high-level query language called HiveQL that allows users to analyze and query large datasets stored in Hadoop Distributed File System (HDFS). Hive was initially developed by Facebook in 2007 and later became an Apache project in 2008. It is widely used in big data processing and analytics applications.
  • Beeline: Beeline is a command-line interface and a replacement for the traditional Hive CLI (Command Line Interface). It provides a more modern and user-friendly way to interact with Hive. Beeline supports multiple authentication mechanisms, secure connections, and improved performance over the older CLI. It is commonly used by Hive users for running HiveQL queries and managing Hive sessions.
  • Apache Tez: Apache Tez is a framework for executing complex data processing tasks on top of Hadoop. It is designed to optimize the performance of Hive queries by providing a more efficient execution engine. Tez enables Hive to execute queries in parallel, resulting in faster query execution times. It was first released in 2013 and has since become an integral part of the Hive ecosystem.
  • Hue: Hue (Hadoop User Experience) is a web-based interface for interacting with Apache Hadoop and its related tools, including Hive. It provides a graphical user interface (GUI) that simplifies the process of creating and executing HiveQL queries. Hue offers features like query editor, result visualization, and job monitoring. It was initially developed by Cloudera and is widely used by developers and analysts working with Hive.
  • Apache Ranger: Apache Ranger is a comprehensive security framework for managing fine-grained access control policies across various Hadoop components, including Hive. It allows administrators to define and enforce access control policies based on user roles and privileges. Ranger provides centralized authorization and auditing capabilities, ensuring data security in Hive deployments. It was introduced in 2014 and has gained popularity for its robust security features.
  • Presto: Presto is an open-source distributed SQL query engine that can be integrated with Hive. It allows users to query data stored in Hive using standard SQL syntax. Presto is known for its high performance and low-latency query execution. It was initially developed by Facebook and is now maintained by the Presto Software Foundation. Many organizations use Presto alongside Hive to accelerate their analytical workloads.

TOP 10 Apache Hive Related Technologies

  • Languages

    Apache Hive primarily supports SQL-like queries, making it accessible to developers who are familiar with SQL. It also provides a command-line interface for interactive use and supports scripting languages like Python and Scala.

  • Hadoop

    Hive is built on top of Apache Hadoop, a widely used open-source framework for distributed storage and processing of large datasets. Hadoop provides the underlying infrastructure for Hive and enables it to handle big data workloads efficiently.

  • Apache Spark

    Hive can integrate with Apache Spark, a fast and general-purpose cluster computing system. This integration allows developers to leverage the power of Spark for data processing and analytics while using Hive’s SQL-like interface.

  • Apache Tez

    Hive can take advantage of Apache Tez, an extensible framework for building high-performance batch and interactive data processing applications. Tez improves the performance of Hive queries by optimizing execution plans and reducing data movement.

  • Apache Kafka

    Hive can be integrated with Apache Kafka, a distributed streaming platform. This integration enables developers to ingest real-time data from Kafka into Hive for further analysis and processing.

  • Apache NiFi

    Hive can work with Apache NiFi, a powerful data integration and dataflow management tool. NiFi allows developers to easily collect, process, and distribute data from various sources to Hive, making data ingestion and transformation workflows more streamlined.

  • Apache Ranger

    Hive can be integrated with Apache Ranger, a comprehensive security framework for Hadoop. Ranger provides fine-grained access control and data protection capabilities for Hive, ensuring the security of sensitive data stored in Hive tables.

Soft skills of a Apache Hive Developer

Soft skills are essential for an Apache Hive Developer to excel in their role. These skills complement their technical expertise and contribute to their overall effectiveness in the workplace.


  • Effective Communication: Ability to convey information clearly and concisely, actively listen to others, and ask relevant questions.
  • Collaboration: Willingness to work as part of a team, share knowledge, and contribute to a positive and productive work environment.
  • Adaptability: Ability to quickly learn new technologies, adapt to changes in project requirements, and handle multiple tasks simultaneously.
  • Problem Solving: Strong analytical skills to identify and resolve issues, troubleshoot errors, and improve query performance.
  • Time Management: Efficiently manage tasks, prioritize work, and meet deadlines to ensure timely delivery of projects.


  • Leadership: Take ownership of assigned tasks, guide junior team members, and provide mentorship to help them enhance their skills.
  • Conflict Resolution: Ability to handle disagreements and conflicts professionally, finding mutually beneficial solutions.
  • Attention to Detail: Paying close attention to the accuracy and quality of code, ensuring optimal performance and minimizing errors.
  • Documentation: Documenting processes, procedures, and troubleshooting steps to facilitate knowledge sharing and future reference.
  • Customer Focus: Understanding customer requirements and delivering solutions that meet their needs and expectations.
  • Continuous Learning: Keeping up-to-date with the latest advancements in Apache Hive and related technologies to enhance expertise.
  • Project Management: Capable of managing projects, coordinating with stakeholders, and ensuring successful project delivery.


  • Strategic Thinking: Ability to analyze complex business requirements, propose innovative solutions, and align technical strategies with organizational goals.
  • Empathy: Understanding and empathizing with the challenges and perspectives of team members, clients, and stakeholders.
  • Negotiation Skills: Effectively negotiate project timelines, resources, and scope with stakeholders to achieve mutually agreeable outcomes.
  • Presentation Skills: Clearly and confidently present technical concepts, project updates, and recommendations to various audiences.
  • Risk Management: Identify potential risks, develop mitigation strategies, and proactively address issues that may impact project success.
  • Influence and Persuasion: Ability to influence and persuade others, build consensus, and drive adoption of best practices and standards.
  • Team Building: Foster a collaborative and inclusive team environment, nurturing talent, and promoting professional growth.
  • Critical Thinking: Apply logical and analytical thinking to evaluate situations, make informed decisions, and solve complex problems.

Expert/Team Lead

  • Strategic Leadership: Provide strategic direction, set goals, and align the team’s efforts with the organization’s long-term vision.
  • Change Management: Effectively manage and lead teams through organizational and technological changes.
  • Innovation: Encourage innovation and creativity, exploring new approaches to enhance efficiency and deliver value-added solutions.
  • Conflict Management: Expertly handle conflicts, mediate disputes, and foster a harmonious work environment.
  • Business Acumen: Understand the business context, identify opportunities for process improvement, and make data-driven decisions.
  • Client Relationship Management: Build and maintain strong relationships with clients, understanding their needs, and exceeding expectations.
  • Thought Leadership: Contribute to the broader technical community through publications, speaking engagements, and knowledge sharing.
  • Strategic Partnerships: Collaborate with other teams, departments, or external vendors to achieve shared goals and mutual success.
  • Performance Management: Provide feedback, evaluate performance, and develop career growth plans for team members.
  • Conflict Resolution: Expertly handle conflicts and disagreements, finding win-win solutions that foster positive relationships.
  • Technical Expertise: Deep understanding of Apache Hive and related technologies, with the ability to provide guidance and mentorship.

TOP 10 Facts about Apache Hive

  • Apache Hive is an open-source data warehouse infrastructure built on top of Apache Hadoop, designed for querying and analyzing large datasets in a distributed computing environment.
  • Hive provides a SQL-like language called HiveQL, which allows users to write queries and perform data analysis using familiar SQL syntax.
  • It was initially developed by Facebook to handle their massive amounts of data and was later donated to the Apache Software Foundation.
  • Hive supports partitioning, which allows data to be divided into logical partitions based on specific columns. This feature enables faster query execution by reducing the amount of data that needs to be scanned.
  • Apache Hive integrates with other Apache projects, such as Apache Spark, Apache Tez, and Apache HBase, to provide a comprehensive ecosystem for big data processing and analytics.
  • Hive supports various file formats, including Apache Parquet, Apache ORC, and Avro, which provide efficient storage and query performance.
  • It offers an extensible architecture, allowing users to write custom user-defined functions (UDFs) and user-defined aggregates (UDAs) to perform complex data transformations and calculations.
  • Hive provides a built-in optimization framework that analyzes queries and automatically generates optimized execution plans, improving query performance.
  • It offers support for ACID (Atomicity, Consistency, Isolation, Durability) transactions, allowing users to perform updates, inserts, and deletes on data stored in Hive tables.
  • Apache Hive is widely used in various industries, including e-commerce, social media, finance, healthcare, and telecommunications, to process and analyze large volumes of data, enabling data-driven decision-making.

Pros & cons of Apache Hive

6 Pros of Apache Hive

  • Efficient data processing: Apache Hive allows for efficient data processing, especially for large datasets. It can handle petabytes of data and execute queries in parallel, making it suitable for big data analytics.
  • SQL-like interface: Hive uses a SQL-like language called HiveQL, which makes it easy for users familiar with SQL to write queries. This reduces the learning curve for new users and enables seamless integration with existing SQL-based systems.
  • Data warehouse capabilities: Hive provides data warehousing capabilities, allowing users to store, manage, and analyze structured and semi-structured data in a centralized repository. It supports partitioning, indexing, and compression techniques to optimize data storage and retrieval.
  • Integration with Hadoop ecosystem: Hive seamlessly integrates with other components of the Hadoop ecosystem, such as Hadoop Distributed File System (HDFS) and Apache Hadoop MapReduce. This enables efficient data processing and analysis across the entire Hadoop infrastructure.
  • Extensibility: Hive is highly extensible and supports user-defined functions (UDFs), custom data formats, and plug-ins. This allows users to customize Hive to meet their specific data processing needs and integrate with external tools and libraries.
  • Community support: Apache Hive has a large and active community of developers and users, providing extensive documentation, tutorials, and forums. This ensures ongoing support and continuous improvement of the platform.

6 Cons of Apache Hive

  • Higher latency: Hive is designed for batch processing rather than real-time analytics. As a result, it may have higher latency compared to other data processing engines, making it less suitable for interactive or time-sensitive queries.
  • Complex setup and configuration: Setting up and configuring Hive can be complex, especially for users who are new to the Hadoop ecosystem. It requires knowledge of Hadoop infrastructure and may involve manual configuration of various parameters.
  • Limited support for transactional processing: Hive has limited support for transactional processing, which can be a drawback for applications that require strong ACID (Atomicity, Consistency, Isolation, Durability) properties. However, recent versions of Hive have introduced some transactional capabilities.
  • Suboptimal performance for small datasets: Hive’s performance may not be optimal for small datasets, as the overhead of setting up and running MapReduce jobs can outweigh the benefits of distributed processing. Other data processing engines may provide better performance for smaller datasets.
  • Steep learning curve for complex queries: While HiveQL is SQL-like, complex queries involving multiple joins or transformations can be challenging to write and optimize in Hive. Users may need to have a deep understanding of Hive’s query execution model to achieve optimal performance.
  • Limited support for real-time analytics: Although Hive has made improvements in recent versions to support near-real-time analytics, it is still primarily designed for batch processing. Applications that require low-latency, real-time analytics may need to consider other data processing engines.

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