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Natig, Data Engineer

Last Updated: 14 Jul 2023

- 12+ years experience working in the IT industry; - 12+ years experience in Data Engineering with Oracle Databases, Data Warehouse, Big Data, and Batch/Real time streaming systems; - Good skills working with Microsoft Azure, AWS, and GCP; - Deep abilities working with Big Data/Cloudera/Hadoop, Ecosystem/Data Warehouse, ETL, CI/CD; - Good experience working with Power BI, and Tableau; - 4+ years experience working with Python; - Strong skills with SQL, NoSQL, Spark SQL; - Good abilities working with Snowflake and DBT; - Strong abilities with Apache Kafka, Apache Spark/PySpark, and Apache Airflow; - Upper-Intermediate English.

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Apache Spark

Apache Spark


Python   4 yr.

Microsoft Azure

Microsoft Azure   5 yr.

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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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Apache Spark

Apache Spark

AWS big data services

AWS big data services

AWS Quicksight

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Apache Kafka

Apache Kafka

Data Pipelines (ETL)

Data Pipelines (ETL)

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Henry A., Software Engineer with Python and Data Analytical Skills

Last Updated: 23 Apr 2024
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- 8+ years experience working with Python; - 5 years of experience as a BI and 4 years of experience with Tableau; - 8 years of experience with various data sets (ETL, Data Engineer, Data Quality Engineer); - 3 years of experience with Amazon Web Services (AWS), Google Cloud Platform (GCP); - Data Analytics/Engineering with Cloud Service Providers (AWS, GCP) - Experience working with MySQL, SQL, and PostgreSQL; - Deep abilities working with Kubernetes (K8s); - Hands-on scripting experience with Python; Microsoft Power BI, Tableau, Sisense, CI/CD principles, Data Validation, Data QA, SQL, Pipelines, ETL, and Automated web scraping. - Pet web3 projects (solidity, wallet integration) - Upper-intermediate English

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Apache Spark

Apache Spark


Python   8.5 yr.

Data Analysis

Data Analysis   6 yr.

Google Cloud Platform (GCP)

Google Cloud Platform (GCP)   4 yr.


Tableau   4 yr.

Microsoft Power BI

Microsoft Power BI   4 yr.

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Last Updated: 25 Oct 2023

- 10+ years experience working in the IT industry; - 8+ years experience working with Python; - Strong skills with SQL; - Good abilities working with R and C++; - Deep knowledge of AWS; - Experience working with Kubernetes (K8s), and Grafana; - Strong abilities with Apache Kafka, Apache Spark/PySpark, and Apache Airflow; - Experience working with Amazon S3, Athena, EMR, Redshift; - Specialised in Data Science and Data Analysis; - Work experience as a team leader; - Upper-Intermediate English.

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Apache Spark

Apache Spark


Python   8 yr.

Amazon Web Services (AWS)

Amazon Web Services (AWS)

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Rostyslav, Rust Engineer

Czech Republic
Last Updated: 21 Jul 2023

- 8 + years experience in IT; - 5+ years experience working with Rust; - Good skills in creating smart contracts for Solana and NEAR Blockchains; - Experience in building a bridge to Casper Network; - Experience working with Filecoin, Zero Knowledge modules, and Fuel Blockchain; - Deep abilities with MySQL, PostgreSQL, MongoDB; - Experience working with Python, Java, PHP, Scala, and Spring; - Good knowledge of AWS ElasticSearch; - Experience working with Docker and Kubernetes (K8s); - Experience working with DeFi and DEX projects; - Deep skills with Apache Cassandra and Apache Spark; - English: Upper-Intermediate.

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Apache Spark

Apache Spark





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Our journey starts with a 30-minute discovery call to explore your project challenges, technical needs, and team diversity. Meet Carefully Matched Apache Spark Talents. Within 1-3 days, we’ll share profiles and connect you with the right talents for your project. Schedule a call to meet engineers in person. Validate Your Choice. Bring a new Apache Spark developer on board with a trial period to confirm that you’ve hired the right one. There are no termination fees or hidden costs.

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Upstaff Managers conduct an introductory round with potential candidates to assess their soft skills. Additionally, the talent’s hard skills are evaluated through testing or verification by a qualified developer during a technical interview. The Upstaff Staffing Platform stores data on past and present Apache Spark candidates. Upstaff managers also assess talent and facilitate rapid work and scalability, offering clients valuable insights into their talent pipeline. Additionally, we have a matching system within the platform that operates in real-time, facilitating efficient pairing of candidates with suitable positions.

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Table of Contents

TOP 10 Apache Spark Related Technologies

  • 1. Scala

    Scala is the most popular programming language for Apache Spark development. It is a statically typed language that seamlessly integrates with Spark, allowing developers to write concise and expressive code. Scala’s functional programming capabilities make it an excellent choice for distributed computing tasks.

  • 2. Java

    Java is another widely used language for Apache Spark development. It has a large developer community and extensive libraries, making it a solid choice for building Spark applications. Java provides a more object-oriented approach compared to Scala, which can be beneficial for certain use cases.

  • 3. Python

    Python is a versatile language that has gained popularity in the Spark ecosystem. It offers an easy-to-learn syntax and a rich set of libraries, making it accessible to both beginners and experienced developers. Python’s simplicity and readability make it an excellent choice for data exploration and prototyping.

  • 4. Apache Spark SQL

    Spark SQL is a module in Apache Spark that provides a programming interface for working with structured and semi-structured data. It allows developers to perform SQL-like queries on Spark data structures, making it easier to integrate Spark with existing data processing workflows.

  • 5. Apache Spark Streaming

    Spark Streaming is a powerful real-time processing engine in Apache Spark. It enables developers to ingest and process data streams in real-time, making it ideal for applications that require near-instantaneous insights from streaming data sources.

  • 6. Apache Spark MLlib

    MLlib is Spark’s machine learning library, which provides a rich set of algorithms and tools for building scalable machine learning models. It supports both batch and streaming data processing, making it a versatile choice for machine learning tasks on large datasets.

  • 7. Apache Kafka

    Apache Kafka is a distributed messaging system that integrates seamlessly with Apache Spark. It provides high-throughput, fault-tolerant messaging capabilities, making it an excellent choice for building scalable and reliable data pipelines in Spark applications.

TOP 12 Facts about Apache Spark

  • Apache Spark is an open-source, distributed computing system designed for big data processing and analytics.
  • Spark was originally developed at the University of California, Berkeley’s AMPLab in 2009 and later open-sourced in 2010.
  • Spark provides a unified framework for processing and analyzing large-scale data across various data sources, including Hadoop Distributed File System (HDFS), Apache Cassandra, Apache HBase, and more.
  • One of the key features of Spark is its in-memory processing capability, which allows it to cache data in memory, resulting in faster data processing and reduced disk I/O.
  • Spark supports various programming languages, including Scala, Java, Python, and R, making it accessible to a wide range of developers.
  • Spark offers a high-level API, called Spark SQL, which allows developers to perform SQL-like queries on structured data, enabling seamless integration with existing SQL-based tools and platforms.
  • With its resilient distributed datasets (RDDs) abstraction, Spark provides fault-tolerance and efficient distributed data processing, enabling reliable and scalable data analytics.
  • Spark’s machine learning library, known as MLlib, provides a rich set of algorithms and tools for building and deploying scalable machine learning models.
  • Spark Streaming allows developers to process real-time streaming data and perform near-real-time analytics on the data stream.
  • Spark’s graph processing library, GraphX, enables efficient processing and analysis of graph-structured data, making it suitable for tasks such as social network analysis and recommendation systems.
  • Apache Spark has a vibrant and active community, with frequent updates and contributions from various organizations and individuals worldwide.
  • Spark is widely adopted in industry and used by many renowned companies, including Netflix, Alibaba, Adobe, and IBM, among others.

Pros & cons of Apache Spark

6 Pros of Apache Spark

  • High Speed: Apache Spark is designed to process large-scale data quickly and efficiently. It achieves this by leveraging in-memory processing, which allows it to perform data operations up to 100 times faster than traditional disk-based systems.
  • Scalability: Spark can scale horizontally across clusters of machines, making it suitable for handling big data workloads. It can seamlessly distribute data and computations across multiple nodes, ensuring high availability and fault tolerance.
  • Flexibility: Apache Spark provides a wide range of APIs, allowing developers to write applications in multiple languages such as Scala, Java, Python, and R. This flexibility enables teams to use their preferred programming language and integrate Spark into their existing workflows.
  • Real-time Stream Processing: Spark Streaming module enables real-time processing of streaming data. It can handle large volumes of data in real-time, making it suitable for applications such as fraud detection, log analysis, and sensor data processing.
  • Advanced Analytics: Spark provides a rich set of libraries for machine learning (MLlib), graph processing (GraphX), and SQL queries (Spark SQL). These libraries make it easier for data scientists and analysts to perform complex analytics tasks without having to rely on separate tools.
  • Integration: Apache Spark integrates well with other popular big data technologies such as Hadoop, Hive, and HBase. It can read data from various data sources, including HDFS, Apache Cassandra, and Amazon S3, making it highly versatile for different use cases.

6 Cons of Apache Spark

  • Learning Curve: Apache Spark has a steeper learning curve compared to traditional big data tools. It requires knowledge of distributed systems and programming concepts, which can be challenging for beginners or teams without prior experience in distributed computing.
  • Memory Requirements: Spark’s in-memory processing relies heavily on RAM, and large datasets may require substantial memory resources. It is crucial to carefully allocate memory and optimize data storage to avoid out-of-memory errors.
  • Complexity: Spark introduces additional complexity in terms of its architecture, configuration, and deployment. Setting up and managing a Spark cluster requires expertise and proper infrastructure planning to ensure optimal performance and resource utilization.
  • Data Serialization: Spark uses its own data serialization mechanism, which may not be compatible with other tools. This can lead to challenges when integrating Spark with existing data pipelines or sharing data with systems that use different serialization formats.
  • Debugging and Monitoring: Debugging Spark applications can be more challenging compared to single-node applications. Identifying and resolving issues in distributed systems requires specialized tools and expertise. Additionally, monitoring the performance of Spark clusters and optimizing resource usage can be complex.
  • Cost: Spark clusters can be resource-intensive and require significant computational power, memory, and storage capacity. This can result in higher infrastructure costs compared to traditional batch processing systems.

Cases when Apache Spark does not work

  1. Insufficient hardware resources: Apache Spark requires a significant amount of memory and processing power to efficiently handle large-scale data processing tasks. If a system does not meet the minimum hardware requirements, Spark may fail to function properly or perform poorly. It is recommended to have a cluster with sufficient CPU cores, memory, and storage to ensure smooth operation.
  2. Incompatible versions: Apache Spark is a rapidly evolving technology, and different versions may introduce changes that are not backward compatible. If you try to run Spark code on an incompatible version, it may result in errors or unexpected behavior. It is crucial to ensure that the Spark version you are using is compatible with your code and other dependencies.
  3. Network connectivity issues: Spark relies on network communication between its components, such as the driver and executors. If there are network connectivity problems within the Spark cluster, it can lead to failures or delays in job execution. It is essential to have a stable and reliable network infrastructure in place to avoid such issues.
  4. Insufficient disk space: Spark performs various disk-based operations, such as shuffling data during processing. If the disk space available on the system running Spark is limited, it can lead to failures or performance degradation. Sufficient disk space should be allocated to accommodate the data processing needs of Spark.
  5. Unsupported data formats: Although Spark supports a wide range of data formats, there may be certain formats that are not compatible with Spark’s data processing operations. If you attempt to process data in an unsupported format, Spark may not be able to handle it correctly. It is important to ensure that the data you are working with is in a format supported by Spark.
  6. Insufficient data partitioning: Spark operates on data partitions, and the performance of Spark jobs heavily depends on how the data is partitioned. If the data is not properly partitioned, it can lead to uneven workload distribution among the Spark executors and result in performance issues. Adequate attention should be given to data partitioning strategies for optimal Spark performance.
  7. Improper configuration: Spark provides a wide range of configuration options that allow users to fine-tune its behavior according to their specific needs. If the Spark configuration parameters are not set appropriately, it can lead to suboptimal performance or even failure of Spark jobs. It is important to understand the various configuration options and adjust them based on the requirements of your workload.

What are top Apache Spark instruments and tools?

  • Apache Spark: Apache Spark is an open-source distributed computing system designed for big data processing and analytics. It was first released in 2010 and has gained significant popularity due to its speed and ability to handle large-scale data processing. Spark supports various programming languages and offers a wide range of libraries for data manipulation, machine learning, and graph processing. It is widely used by companies such as Netflix, Uber, and Airbnb for their data-intensive workloads.
  • Hadoop: Hadoop is an open-source framework that provides distributed storage and processing of large datasets. It includes the Hadoop Distributed File System (HDFS) for data storage and the MapReduce programming model for data processing. Apache Spark can be integrated with Hadoop, allowing users to leverage the benefits of both systems. Spark can read data from HDFS and perform advanced analytics on it, making it a powerful tool in the Hadoop ecosystem.
  • Apache Kafka: Apache Kafka is a distributed streaming platform that allows for the ingestion and processing of high-volume, real-time data streams. Spark Streaming, a component of Apache Spark, can be integrated with Kafka to process and analyze streaming data in real-time. This combination is commonly used in use cases such as real-time analytics, fraud detection, and monitoring systems.
  • Apache Cassandra: Apache Cassandra is a highly scalable and distributed NoSQL database designed for handling large amounts of data across multiple commodity servers. It provides a fault-tolerant and highly available data storage solution. Spark can be used to interact with Cassandra, allowing users to perform analytics and machine learning tasks on the data stored in Cassandra clusters.
  • Apache Flink: Apache Flink is an open-source stream processing and batch processing framework. It provides low-latency processing of real-time data streams and supports event time processing, state management, and fault tolerance. Flink can be used as an alternative to Spark Streaming for certain use cases that require strict event time processing and low latency.
  • Apache Zeppelin: Apache Zeppelin is a web-based notebook that provides an interactive and collaborative environment for data exploration, visualization, and analysis. It supports multiple programming languages, including Scala, Python, and SQL, and allows users to create and share interactive notebooks. Zeppelin can be integrated with Spark, enabling users to write and execute Spark code within the notebook environment.
  • Apache Parquet: Apache Parquet is a columnar storage file format designed for efficient and optimized data processing. It is compatible with various data processing frameworks, including Spark. Parquet provides benefits such as column pruning, predicate pushdown, and efficient compression, making it an ideal choice for big data analytics workloads.
  • Apache Arrow: Apache Arrow is a cross-language development platform for in-memory data. It provides a standardized format for efficient data interchange between different systems and programming languages. Spark leverages Apache Arrow for efficient data transfer and interoperability between Spark and other data processing tools.

How and where is Apache Spark used?

Case NameCase Description
Real-Time AnalyticsApache Spark enables real-time analytics by processing data in near real-time, allowing organizations to gain valuable insights and make informed decisions quickly. It can handle large volumes of data and perform complex computations in memory, resulting in faster processing times. This case is particularly useful in industries such as finance, e-commerce, and telecommunications, where real-time insights are crucial for optimizing business operations, detecting fraud, and improving customer experience.
Machine LearningApache Spark provides a powerful platform for building and deploying machine learning models at scale. It offers a rich set of libraries and algorithms, such as MLlib, that can be utilized for tasks like classification, regression, clustering, and recommendation systems. With its distributed computing capabilities, Spark can handle large datasets and perform iterative computations efficiently, making it ideal for training and deploying machine learning models in production environments.
Stream ProcessingApache Spark Streaming allows organizations to process and analyze streaming data in real-time. It supports various data sources, including Kafka, Flume, and HDFS, and provides high-level APIs for handling streaming data. This case is valuable in scenarios where continuous data ingestion and real-time analytics are required, such as monitoring social media feeds, analyzing sensor data from IoT devices, or detecting anomalies in network traffic.
Graph ProcessingApache Spark’s GraphX library enables efficient and scalable graph processing. It provides a unified API for performing graph computations and offers a range of graph algorithms, such as PageRank and connected components. This case is beneficial in applications like social network analysis, recommendation systems, fraud detection, and network optimization. Spark’s ability to distribute graph computations across a cluster of machines allows for faster processing of large-scale graph data.
Data IntegrationApache Spark facilitates seamless data integration by providing connectors for various data sources, including relational databases, Hadoop Distributed File System (HDFS), Amazon S3, and more. It supports reading and writing data in different formats, such as CSV, JSON, Parquet, and Avro. Spark’s ability to handle diverse data sources and formats makes it a versatile tool for data integration tasks like data ingestion, data transformation, and data loading into target systems.
Batch ProcessingApache Spark excels in batch processing scenarios, where large volumes of data need to be processed in parallel. It offers a distributed computing framework that leverages in-memory processing to accelerate batch jobs. Spark’s ability to cache data in memory and perform operations like filtering, aggregating, and transforming data efficiently enables faster batch processing times. This case is useful for various use cases, including data cleansing, data preparation, and running complex data transformations.
Data VisualizationApache Spark integrates with popular data visualization tools like Apache Zeppelin and Jupyter Notebook, allowing users to create interactive visualizations and reports. It provides APIs for generating visualizations from processed data, enabling data analysts and data scientists to gain insights from their data easily. This case is valuable for presenting data-driven insights, sharing reports, and conducting exploratory data analysis.

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

Seniority NameYears of experienceResponsibilities and activitiesAverage salary (USD/year)
Junior0-2Assisting in the development of software applications, bug fixing, writing and executing test cases, learning and implementing new technologies, collaborating with senior developers.50,000-70,000
Middle2-5Designing and implementing software features, debugging complex issues, participating in code reviews, mentoring junior developers, collaborating with cross-functional teams, contributing to architectural decisions.70,000-90,000
Senior5-8Leading the development of complex software modules, providing technical guidance and mentorship to the team, conducting code reviews, optimizing performance and scalability, collaborating with product managers and stakeholders.90,000-120,000
Expert/Team Lead8+Leading a team of developers, setting technical direction and strategy, overseeing project timelines and deliverables, resolving technical challenges, representing the team in cross-functional meetings, driving innovation and process improvements.120,000+

Soft skills of a Apache Spark Developer

Soft skills are essential for an Apache Spark Developer to effectively collaborate, communicate, and contribute to the success of a project. These skills enable developers to work efficiently in a team, adapt to changes, and deliver high-quality solutions.


  • Strong problem-solving skills: Ability to analyze and troubleshoot issues, identify root causes, and propose effective solutions.
  • Effective communication: Clear and concise communication to understand requirements, work collaboratively, and provide updates to the team.
  • Attention to detail: Paying close attention to details in code, data, and documentation to ensure accuracy and quality.
  • Curiosity and eagerness to learn: Willingness to explore new technologies, learn from experienced team members, and continuously improve skills.
  • Team player: Ability to work well in a team, actively participate in discussions, and contribute to a positive and collaborative work environment.


  • Leadership skills: Ability to take ownership of tasks, guide junior developers, and mentor them to enhance their skills.
  • Time management: Efficiently manage tasks, prioritize work, and meet project deadlines.
  • Adaptability: Flexibility to adapt to changing requirements, technologies, and project dynamics.
  • Problem-solving mindset: Approach challenges with a structured and analytical mindset, leveraging past experiences to find optimal solutions.
  • Collaboration: Work effectively with cross-functional teams, build strong relationships, and promote teamwork.
  • Effective documentation: Proficient in documenting code, design decisions, and project information for knowledge sharing and future reference.
  • Attention to performance: Optimize code and query performance, identify bottlenecks, and propose improvements.


  • Strategic thinking: Ability to think beyond immediate tasks and contribute to long-term project planning and architecture.
  • Mentorship: Demonstrate expertise by mentoring team members, sharing best practices, and guiding them in their career growth.
  • Stakeholder management: Effectively communicate with stakeholders, understand their needs, and manage expectations.
  • Conflict resolution: Skillfully resolve conflicts within the team, facilitate constructive discussions, and promote collaboration.
  • Technical leadership: Lead technical discussions, provide guidance on design decisions, and drive technical excellence within the team.
  • Continuous improvement: Advocate for process improvements, identify areas for optimization, and implement best practices.
  • Strong decision-making: Make informed decisions based on data, experience, and business requirements.
  • Project management: Ability to plan, coordinate, and manage complex projects, ensuring successful delivery.

Expert/Team Lead

  • Strategic vision: Ability to envision long-term goals, align them with business objectives, and drive innovation.
  • Team management: Effectively manage a team, delegate tasks, provide feedback, and foster a culture of growth.
  • Influence and negotiation: Skillfully influence stakeholders, negotiate contracts, and resolve conflicts at a higher level.
  • Enterprise-level thinking: Understand the impact of decisions on the organization as a whole, considering scalability, security, and compliance.
  • Thought leadership: Contribute to the Spark community through research, publications, conference presentations, and open-source contributions.
  • Business acumen: Understand the business domain, identify opportunities for value creation, and align technical solutions with business goals.
  • Strategic partnerships: Build and maintain strategic partnerships with vendors, clients, and other industry leaders.
  • Risk management: Proactively identify and mitigate risks, develop contingency plans, and ensure project success.
  • Quality assurance: Drive a culture of quality by implementing robust testing practices, code reviews, and quality standards.
  • Resource management: Optimize resource allocation, manage budgets, and ensure efficient utilization of team members.
  • Executive communication: Effectively communicate technical concepts to non-technical stakeholders, ensuring alignment and support.

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