Hire Hadoop/Spark Engineers
Hadoop is an open-source software framework that enables the storage and processing of large amounts of data in a distributed computing environment, utilising commodity hardware clusters. It is capable of quickly analysing vast datasets by distributing the computations across numerous processors, making it the go-to solution for managing large-scale data systems used in many different Internet applications.
The Apache Hadoop software library provides a framework for utilising core programming techniques to process large data sets across distributed clusters of devices. This makes it an invaluable tool for managing the significant quantities of data produced by Big Data and forming meaningful strategies based on it.
As one of the most sought-after and well-paid positions in the IT industry today, a Hadoop/Spark engineer is a highly valued professional with an impressive skill set for handling massive volumes of data with exceptional accuracy. As such, it is important to understand the responsibilities of this position. A Hadoop/Spark engineer is a skilled programmer who is knowledgeable in the Hadoop components and technologies. The engineer is responsible for designing, developing, and deploying Hadoop applications while providing comprehensive documentation of the process.
What does Hadoop/Spark development entail?
It is projected that the global Big Data (Hadoop/Spark/Apache) market will reach a staggering $84.6 billion by 2021, according to Allied Market Research. In the top 20 technical skills required of Data Scientists, Hadoop ranks fourth, indicating a severe lack of experienced professionals and a talent gap. What is driving such an immense demand? Businesses have come to understand that offering personalised customer service grants them a decisive competitive advantage. Customers are not only looking for high-quality products at reasonable prices, but also for an experience that makes them feel appreciated and their needs are being met.
Determining what consumers want is essential for businesses to stay competitive. To do this, many businesses turn to market research for insights. This produces an abundance of data, often referred to as Big Data, and analysing it in an effective way is key. Hadoop is a powerful technology for doing this and can help businesses to develop tailored experiences for their customers. Consequently, Hadoop/Spark engineers are in high demand as businesses seek individuals with the skills to turn data into actionable information and create memorable customer experiences. These professionals will be essential going forward as businesses look to maximise their marketing efforts and reach the top of their respective industry.
What are the duties and obligations of a Hadoop/Spark engineer?
As businesses strive to effectively manage large datasets, developers must be able to adapt their roles and responsibilities to meet the needs of a variety of data challenges. One of the most significant and common roles and duties associated with a remote job utilising Hadoop technology is as follows: writing and developing code to create data pipelines, creating and deploying data models, optimising data storage, performing data analysis and visualisation, and designing and coding applications. Additionally, developers must possess the necessary technical skills in order to quickly and accurately respond to changing data conditions.
- Creating Hadoop and putting it into action in the most efficient way possible Performance.
- Data may be obtained from a variety of sources.
- Create a Hadoop system, then install, configure, and maintain it.
- The ability to translate difficult technical requirements into a finished design.
- Analysing big data sets might help you come up with new ideas.
- Maintain your data’s privacy and security.
- Create scalable and high-performance data tracking web services.
- Data is being queried at an increasing pace.
- HBase data loading, deployment, and management.
- Defining task flows using schedulers such as Zookeeper Cluster Coordination services provided by Zookeeper.
How can I get a job as a Hadoop/Spark engineer?
When considering a career as a Hadoop/Spark developer, the amount of education and training necessary is a critical factor. While a college degree is commonly required for most Hadoop roles, it can be difficult to obtain such a degree with just a high school certificate. Therefore, it is important to ensure that the chosen major is suitable for the desired position. Our research has revealed that the most common degrees for remote Hadoop employment are Bachelor’s and Master’s degrees. Additionally, diplomas and associate degrees are also accepted qualifications for many Hadoop/Spark engineering roles.
Having a background in a related field such as Java Development can be extremely beneficial for those interested in pursuing a career as a Hadoop/Spark engineer. In fact, many roles for Hadoop/Spark engineer positions require previous experience as a Java/J2ee Developer or Senior Java Developer. This experience can be invaluable in preparing for a successful career in this field.
Qualifications for a Hadoop/Spark Engineer
As a remote Hadoop/Spark engineer, certain aptitudes and abilities are essential to success in the role. While businesses and organisations may have varied preferences when it comes to these skills, the following is a comprehensive list of abilities that are typically required for such a position. It is important to note that there is no need to possess expertise in each of these areas; rather, a working knowledge and proficiency in the majority of them should be enough for most people.
Fundamentals of HadoopWhen you have determined to begin looking for a position as a remote Hadoop/Spark engineer, it is essential to gain an understanding of Hadoop concepts. Being familiar with the features, applications, advantages and disadvantages of Hadoop will be immensely beneficial in the process of mastering more complex technologies. To gain a better understanding of the subject, reading tutorials, journal articles, research papers, attending seminars, and exploring other online and offline resources can be incredibly helpful.
SQLTo ensure success in your role, it is essential that you have a comprehensive understanding of Structured Query Language (SQL). Supplementing this knowledge with familiarity in other query languages, such as HiveQL, will further enhance your abilities. To further develop your skillset, it is recommended that you review and refresh your understanding of database principles, distributed systems, and any other related topics.
Fundamentals of LinuxIt is highly recommended that you acquire a thorough understanding of Linux fundamentals, as the vast majority of Hadoop projects are based on this platform. Additionally, during your studies, be sure to cover related topics like concurrency, multithreading, and similar topics.
Hadoop ComponentsNow that you have gained an understanding of the fundamental concepts of Hadoop and acquired the necessary technical skills, it is time to examine the Hadoop ecosystem in its entirety. The Hadoop ecosystem is comprised of four main components: the Hadoop Distributed File System (HDFS), MapReduce, a Resource Negotiator, and YARN. HDFS is a distributed file system which facilitates data mapping and reduction. A Resource Negotiator has been appointed to ensure that Hadoop is being utilised to its fullest potential. YARN is the resource manager for the ecosystem, which enables and manages applications and resources on the cluster.
Languages of InterestOnce you have gained an understanding of the core components of Hadoop, it is essential to become familiar with the appropriate query and scripting languages available, such as HiveQL and PigLatin, to effectively operate within the Hadoop environment. HiveQL (Hive Query Language) is a query language that is used to process structured data that has been stored. The syntax of HiveQL is closely related to Structured Query Language. PigLatin, on the other hand, is a programming language used by Apache Pig to analyse data stored on the Hadoop platform. In order to effectively work with Hadoop, it is important to be knowledgeable in both HiveQL and PigLatin.
ETLIt is time to delve deeper into the world of Hadoop development and become acquainted with some of the key Hadoop technologies. Extract-Transform-Load (ETL) technologies such as Flume and Sqoop are essential for loading data. Flume is a distributed program used to aggregate, collect and transport large volumes of data to HDFS or other central storage systems. On the other hand, Sqoop is a Hadoop tool used to connect Hadoop to relational databases. Furthermore, familiarity with statistical applications such as MATLAB, SAS and other similar programs is also important.
Spark SQLSpark SQL is a module of Spark which is used for structured data processing. It provides a programming framework known as DataFrames and can also handle distributed SQL queries. Moreover, it is well-integrated with the other components of the Spark ecosystem, enabling the combination of SQL query processing with machine learning tasks. To be able to compete for remote Spark developer jobs, you must develop a mastery of this skill.
Streaming using SparkSpark Streaming is an extension of the Spark API that allows data engineers and scientists to monitor and analyse real-time data from sources like Kafka, Flume, and Amazon Kinesis. After examining this data, they can then write it to file systems, databases, and even create live dashboards to display the results.
Spark DataFrames and DatasetsSpark Datasets are an evolved version of Data Frames and offer two distinct types of Application Programming Interface (API) characteristics: strongly typed and untyped. As opposed to Data Frames, Spark Datasets are composed entirely of strongly typed Java Virtual Machine (JVM) objects and leverage the capabilities of Spark’s Catalyst Optimizer for enhanced performance.
GraphX libraryGraphX is an integrated platform which facilitates the combination of Extract-Transform-Load (ETL), exploratory analysis and iterative graph computing. Its Pregel Application Programming Interface (API) enables users to keep track of the same data in graphs and collections, rapidly exchange and combine graphs by using Resilient Distributed Datasets (RDDs), and craft custom iterative graph algorithms.
How can I get work as a remote Hadoop/Spark engineer?
It is essential to develop a comprehensive job-search strategy that incorporates both gaining practical experience and learning new skills. Before beginning to look for a job, it is important to consider what you are looking for and how to focus your search in order to make the best use of the resources available. Demonstrating to employers that you are capable and knowledgeable is key, so it is essential to take advantage of any opportunities to gain hands-on experience. This could include working on open source, volunteer, or freelance projects, as these will enable you to expand your skill set and provide you with more to discuss in the interview.
We have a range of remote Hadoop/Spark engineering opportunities available, designed to help you reach your career goals as a Hadoop/Spark engineer. By utilising the latest technologies to tackle complex technical and commercial problems, you can help accelerate your growth and development. Join a network of the world’s most talented engineers and find a full-time, long-term remote Hadoop/Spark engineering role with increased earning potential and the possibility of career advancement.
Responsibilities at work
- Create and develop Hadoop apps to analyse data sets.
- Create frameworks for data processing.
- Create and improve Apache Spark ETL pipelines.
- Provide customers with scalable, cost-effective, and adaptable solutions.
- Participate in end-to-end application development that is iterative.
- Ensure on-time and high-quality product delivery.
- Perform feasibility study and provide functional and design requirements for suggested new features.
- Take the lead in diagnosing complicated difficulties that arise in client situations.
- Bachelor’s/degree Master’s in engineering, computer science, or information technology (or equivalent experience)
- 3+ years of Hadoop/Spark engineering expertise (rare exceptions for highly skilled developers)
- Extensive knowledge with Apache Spark development.
- Knowledge of the Hadoop ecosystem, its components, and the Big Data architecture.
- Hive, HBase, HDFS, and Pig are all well-known databases.
- Expertise in well-known programming languages such as Python, Java, Scala, and others.
- Expertise with Apache Spark and other Spark Frameworks/Cloud Services.
- Excellent knowledge of data loading techniques such as Sqoop and Flume.
- A thorough understanding of quality procedures and estimating approaches.
- To communicate successfully, you must be fluent in English.
- Work full-time (40 hours a week) with a 4-hour overlap with US time zones.
- SDLC and Agile techniques are well-understood.
- Knowledge of the UNIX/Linux operating system and development environment is required.
- Knowledge of performance engineering.
- Excellent technical, analytical, and problem-solving abilities.
- Excellent logical reasoning and collaboration abilities.