Hire Hadoop/Spark Engineers
Hadoop is a free and open-source software framework for storing and processing large amounts of data in a distributed computing environment utilizing commodity hardware clusters. It allows clusters to quickly examine big datasets by distributing computations over numerous processors. Hadoop has become the de facto standard for managing massive data systems used in a variety of Internet applications.
Using core programming methods, the Apache Hadoop software library offers a framework for sharing the processing of massive data volumes among clusters of devices. To put it another way, it’s an excellent tool for dealing with the massive amounts of data created by Big Data and developing viable plans and solutions based on it.
A Hadoop/Spark engineer job is the most sought-after and well-paid position in today’s IT industry. This High-Caliber profile requires a better skill set in order to handle massive volumes of data with exceptional accuracy. We’ll go through a Hadoop/Spark engineer’s duties. A Hadoop/Spark engineer is a skilled programmer who is familiar with Hadoop components and technologies. A Hadoop/Spark engineer is someone who designs, develops, and deploys Hadoop applications while thoroughly documenting them.
What does Hadoop/Spark development entail?
The worldwide Big data (Hadoop/Spark/Apache) industry is expected to reach $84.6 billion by 2021, according to Allied Market Research. Hadoop ranks fourth among the top 20 technical competencies for Data Scientists, indicating a severe shortage of experienced employees, resulting in a talent gap. What is causing such huge demand? Because businesses are realizing that delivering individualized customer service provides them a substantial competitive edge. Customers demand high-quality goods at affordable prices, but they also want to feel valued and as though their needs are being satisfied.
How can a business determine what its consumers want? Of course, you may do this via market research. As a consequence of marketing research, their digital marketing teams are inundated with reams of Big Data. What is the most effective way to analyze Big Data? Hadoop is the answer! A corporation may target consumers and give them with a tailored experience by translating data into actionable information. Businesses who can effectively follow this approach will ascend to the top of the heap. As a result, Hadoop/Spark engineer positions are and will remain in high demand. Businesses want someone who can go through all of that data and come up with amazing marketing, concepts, and techniques to attract customers.
What are the duties and obligations of a Hadoop/Spark engineer?
Various firms encounter various data difficulties. As a result, the roles and duties of developers must be modified so that they can react swiftly to a range of scenarios. Some of the most significant and general roles and obligations in a Hadoop remote job are as follows.
- 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.
- Analyzing 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?
One of the first things you should consider if you want to work as a Hadoop/Spark developer is how much education you’ll need. Even though the majority of Hadoop roles need a college degrees, getting one with just a high school certificate is difficult. When learning how to become a Hadoop/Spark engineer, choosing the correct major is crucial. We discovered that the most prevalent majors for remote Hadoop employment were mostly Bachelor’s or Master’s degrees. A diploma and an associate degree are two more degrees that we often find on Hadoop/Spark engineer resumes.|
Previous job experience may help you gain a career as a Hadoop/Spark engineer. Indeed, previous knowledge in a field such as Java Developer is required for many Hadoop/Spark engineer roles. Meanwhile, previous expertise as a Java/J2ee Developer or Senior Java Developer is required for many Hadoop/Spark engineer opportunities.
Qualifications for a Hadoop/Spark Engineer
Remote Hadoop/Spark engineer positions need a certain set of talents, although businesses and organizations may prioritize any of the skills indicated here. A list of Hadoop/Spark engineer skills is provided below. You do not, however, have to be an expert in all of them!
Fundamentals of HadoopWhen you’re ready to begin searching for a remote Hadoop/Spark engineer job, the first and most important step is to thoroughly comprehend Hadoop ideas. You must be familiar with Hadoop’s capabilities and uses, as well as the technology’s myriad benefits and drawbacks. The stronger your foundations, the simpler it will be to learn more complex technology. Tutorials, journals and research papers, seminars, and other online and offline resources may all assist you in learning more about a certain subject.
SQLYou’ll also require a solid understanding of Structured Query Language (SQL) (SQL). Working with other query languages, like as HiveQL, will assist you if you are comfortable with SQL. Brush up on database principles, distributed systems, and other relevant subjects to broaden your horizons.
Fundamentals of LinuxYou should also learn about Linux fundamentals since the great majority of Hadoop deployments are built on it. Meanwhile, while studying Linux Fundamentals, you should cover a variety of other topics such as concurrency, multithreading, and so on.
Hadoop ComponentsNow that you’ve learned about Hadoop ideas and the necessary technical abilities, it’s time to learn about the Hadoop ecosystem as a whole, including its components, modules, and other features. The Hadoop ecosystem is made up of four primary components: – Hadoop is a distributed file system that enables data mapping and reduction. – A new resource negotiator has been hired. Hadoop is frequently used.
Languages of InterestOnce you’ve studied the above-mentioned Hadoop components, you’ll need to understand the appropriate query and scripting languages, such as HiveQL, PigLatin, and others, to operate with Hadoop technology. HiveQL (Hive Query Language) is a query language used to deal with structured data that has been stored. HiveQL’s syntax is substantially identical to that of Structured Query Language. PigLatin, on the other hand, refers to the programming language used by Apache Pig to analyze Hadoop data. To operate in the Hadoop environment, you must be familiar with HiveQL and PigLatin.
ETLt’s time to go further into the realm of Hadoop development and get acquainted with a few key Hadoop technologies. ETL (Extraction, Transformation, and Loading) technologies such as Flume and Sqoop are necessary for data loading. Flume is a distributed program for gathering, compiling, and transporting massive volumes of data to HDFS or other central storage systems. In contrast, Sqoop is a Hadoop tool that links Hadoop to relational databases. You should also be familiar with statistical applications such as MATLAB, SAS, and others.
Spark SQLSpark SQL is a structured data processing Spark module. It includes DataFrames as a programming framework and can conduct distributed SQL queries. It is also nicely integrated with the rest of the Spark ecosystem (e.g., integrating SQL query processing with machine learning). To obtain remote Spark developer jobs, you must first master the skill.
Streaming using SparkSpark Streaming is a Spark API extension that enables data engineers and scientists to analyze real-time data from sources such as Kafka, Flume, and Amazon Kinesis. Data may be supplied to file systems, databases, and live dashboards once it has been reviewed.
Spark DataFrames and DatasetsSpark datasets are an extension of data frames. In essence, it earns two sorts of API characteristics: highly typed and untyped. Unlike data frames, datasets are always a collection of strongly typed JVM objects. It also takes advantage of Spark’s Catalyst optimizer.
GraphX libraryETL, exploratory analysis, and iterative graph computing are all combined in GraphX. The Pregel API allows you to monitor the same data in graphs and collections, instantly convert and combine graphs using RDDs, and develop custom iterative graph algorithms.
How can I get work as a remote Hadoop/Spark engineer?
You must develop an effective job-search strategy while gaining as much practical experience as feasible. Before you start searching for job, think about what you’re looking for and how you’ll utilize that information to focus your search. It’s all about getting your hands dirty and putting your abilities to use when it comes to showing to employers that you’re job-ready. As a consequence, it is critical to continue to learn and grow. You’ll have more to speak about in an interview if you work on a lot of open source, volunteer, or freelance projects.
Works has a number of remote Hadoop/Spark engineer opportunities available, all of which are tailored to your career objectives as a Hadoop/Spark engineer. Working with cutting-edge technology to address challenging technical and commercial challenges may aid in your rapid expansion. Join a network of the world’s greatest engineers to find a full-time, long-term remote Hadoop/Spark engineer position with greater salary and opportunities for promotion.
Responsibilities at work
- Create and develop Hadoop apps to analyze 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.