Big Data Engineer Skills

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Big Data Architect Masters Program (10 Blogs) Become a Certified Professional Big Data Engineer Skills: Skills Required To Become A Big Data Engineer Last updated on May 22,2019 11.2K Views Shubham Sinha Shubham Sinha is a Big Data and Hadoop expert working as a... Shubham Sinha is a Big Data and Hadoop expert working as a Research Analyst at Edureka. He is keen to work with Big Data... Bookmark Become a Certified Professional Most of us have an idea about who a data engineer is, but we are confused about the roles responsibilities of Big Data Engineer. This ambiguity increases once we start mapping those roles responsibilities with apt skill sets and finding the most effective and efficient learning path.But, dont worry, you have landed at the right place. This Big Data Engineer Skills blog will help you understand the different responsibilities of a data engineer. Henceforward, I will map those responsibilities with proper skill s et will guide you through the apt learning path.Lets start by understanding who is a Data Engineer.Who is a Data Engineer?In simple words, Data Engineers are the ones who develops, constructs, tests maintains the complete architecture of the large-scale processing system.Next, lets further drill down the job role of a Data Engineer.What does a Data Engineer do?The crucial tasks included in Data Engineers job role are:Designing, developing, constructing, installing, testing and maintaining the complete data management processing systems.Building highly scalable, robust fault-tolerant systems.Taking care of the complete ETL(Extract, Transform Load) process.Ensuring architecture is planned in such a way that it meets all the business requirements.Discovering various opportunities for data acquisitions and exploring new ways of using existing data.Proposing ways to improve data quality, reliability efficiency of the whole system.Creating a complete solution by integrating a variet y of programming languages tools together.Creating data models to reduce system complexity and hence increase efficiency reduce cost.Deploying Disaster Recovery TechniquesIntroducing new data management tools technologies into the existing system to make it more efficient.Next, I would like to address a very common confusion i.e., the difference between the data big data engineer.Difference Between Data Engineer Big Data EngineerWe are in the age of data revolution, where data is the fuel of the 21st century. Various data sources numerous technologieshave evolved over the last two decades, the major ones are NoSQL databases Big Data frameworks.With the advent of Big Data in data management system, the Data Engineer now has to handle manage Big Data, and theirrole has been upgraded to Big Data Engineer.Due to Big Data, the whole data management system is becoming more more complex. So, now Big Data Engineer has to learn multiple Big Data frameworks NoSQL databases, to crea te, design manage the processing systems.Advancing in this Big Data Engineer Skills blog, lets us know the responsibilities of a Big Data Engineer. This would help us to map the Data Engineer responsibilities with the required skill sets.Data Engineer ResponsibilitiesData ingestionData ingestion means taking the data from the various sources then ingesting it into the data lake. There are a variety of data sources with different formats structure of data.Data Engineerneeds skills to efficiently extract the data from a source, which can include different data ingestion approaches like batch real-time extraction. There are various other skills which could make the data ingestion more efficient like incremental load, loading the data parallelly, etc.When it comes to Big Data World, Data ingestion becomes more complex as the amount of data starts accelerating, the data is also present in different formats. Data Engineer also needs to know data mining different data ingestion APIs to capture inject more data into data lake.Data TransformationThe data is always present in raw format which cannot be used directly. It needs to be converted from one format to other, or from one structure to another based on the use-case. Data transformation can be a simple or complex process depending on the variety of data sources, formats of data the required output. This may include various tools custom script in different languages depending on the complexity, structure, format volume of the data.Performance OptimizationBuilding a system which is both scalable efficient is a challenging work. Data Engineer needs to understand how to improve the performance of individual data pipeline optimize the overall system.Again when we are dealing with Big Data platforms the performance becomes a major factor. Big Data engineer needs to make sure that the complete process, from the query execution to visualizing the data through report interactive dashboards should be optimized. This needs various concepts like partitioning, indexing, de-normalization, etc.Apart from these, a variety of responsibilities can be found in Data Engineer job based on the tools technologies which the industry is using.Summarizing the responsibilities of a Big Data Engineer:Design, create, build maintain data pipelinesAggregate Transform raw data coming from a variety of data sources to fulfill the functional non-functional business needsPerformance optimization: Automating processes, optimizing data delivery re-designing the complete architecture to improve performance.Handling, transforming managing Big Data using Big Data Frameworks NoSQL databases.Building complete infrastructure to ingest, transform store data for further analysis business requirement.If youll look compare different Big Data Data Engineer job descriptions, youll find most of the job description are based on modern tools technologies. Moving ahead in this Big Data Engineer skills blog, lets look at the required skills that will get you hired as a Big Data Engineer.Big Data Engineer Skills: Required Skills To Become A Big Data EngineerBig Data Frameworks/Hadoop-based technologies:With the rise of Big Data in the early 21st century, a new framework was born. That is Hadoop! All thanks to Doug Cutting, for introducing a framework which not only stores Big Data in a distributed manner but also processes the data parallelly.There are a number of tools in the Hadoop Ecosystem which caters different purposes professionals belonging to different backgrounds.For a Big Data Engineer, mastering Big Data tools is a must. Some of the tools which you need to master are:HDFS(Hadoop Distributed File System): As the name suggests, it is the storage part of Hadoop, which stores the data in a distributed cluster. Being the base of Hadoop, knowledge of HDFS is a must to start working with Hadoop framework.YARN: YARN performs resource management by allocating resources to different applications a nd scheduling jobs. YARN was introduced in Hadoop 2.x. With the introduction of YARN, Hadoop became more flexible, efficient scalable.MapReduce: MapReduce is a parallel processing paradigm which allows data to be processed parallelly on top of Distributed Hadoop Storage i.e. HDFS.PIG HIVE: Hive is a data warehousing tool on top of HDFS. Hive caters professionals from SQL background to perform analytics. Whereas Apache Pig is a high-level scripting language which is used for data transformation on top of Hadoop. Hive is generally used by the data analyst for creating reports whereas Pig is used by researchers for programming. Both are easy to learn if you are familiar with SQL.Flume Sqoop: Flume is a tool which is used to import unstructured data to HDFS whereas Sqoop is used to import export structured data from RDBMS to HDFS.ZooKeeper: Zookeeper acts as a coordinator among the distributed services running in Hadoop environment. It helps in configuration management and synchroni zing services.Oozie: Oozie is a scheduler which binds multiple logical jobs together and helps in accomplishing a complete task.Real-time processing Framework (Apache Spark): Real-time processing with quick actions is the need of the hour. Either it is acredit card fraud detection system or it is a recommendation system, each and every one of them needs real-time processing.It is very important for a Data Engineer to have knowledge of real-time processing framework. Apache Spark is a distributed real-time processing framework. It can be easily integrated with Hadoop leveraging HDFS.You can refer to Edurekas Hadoop Spark videos to gain comprehensiveknowledge.Database architectures:One of the most prominent data sources are databases. It is critically important for a Data Engineer to understand database design database architecture like 1-tier, 2-tier, 3-tier and n-tier. Data Models Data Schema are also amongst the key skills which a Data Engineer should possess.SQL-based technolog ies (e.g. MySQL): Structured Query Language is used to structure, manipulate manage data stored in databases. As Data Engineers work closely with the relational databases, they need to have a strong command on SQL. PL/SQL is also prominently used in the industry. PL/SQL provides procedural programming features on top of SQL.NoSQL technologies (e.g. Cassandra and MongoDB):As the requirements of organizations has grown beyond structured data, so NoSQL databases were introduced. It can store large volumes of structured, semi-structured unstructured data with quick iteration and agile structure as per application requirements.Some of the most prominently used databases are:HBase is column-oriented NoSQL database on top of HDFS which is good for scalable distributed big data store. It is good for applications with optimized read range based scan. It provides CP(Consistency Partitioning) out of CAP.Cassandra is a highly scalable database with incremental scalability. The best part of Cassandra is minimal administration and no single point of failure. It good for applications with fast random, read writes. It provides AP(Available Partitioning) out of CAP.MongoDB is a document-oriented NoSQL database which is schema-free, i.e. your schema can evolve as the application grows. It also gives full index support for high performance replication for fault tolerance. It has a master-slave architecture provides CP out of CAP. It is rigorously used by the web application semi-structured data handling.You can check out this video to know the difference between the three.Python/R:Various programming languages can serve the same purpose. Knowledge of one programming language is enough, as the flavor changes but the logic remain the same. If you are a beginner, you can go ahead with Python as it is easy to learn due to its easy syntax and good community support. Whereas R has a steep learning curve which is developed by statisticians. R is mostly used by analyst data scientist to perform data analytics.ETL/Data warehousing solutions(Talend, Informatica): Data Warehousing is very important when it comes to managing a huge amount of data coming in from heterogeneous sources where you need to apply ETL(Extract Transform Load). Data Warehouse is used for data analytics reporting, and is a very crucial part of Business Intelligence. It is very important for a Big Data Engineer to master a Data Warehousing or ETL tool. After mastering one, it becomes easy to learn new tools as the fundamental remains the same.Informatica Talend are the two well-known tools used in the industry. Informatica Talend Open Studio are Data Integration tools with ETL architecture. The major benefit of Talend is its support for the Big Data frameworks. I would recommend you to start with Talend because after this learning any DW tool will become a piece of cake for you.UNIX, Linux, Solaris orMS Windows Industry-wide various operating systems are used. Unix Linux are some of the prominently used operating systems Big Data Engineer needs to master one of them at least.Apart from the understanding of complete data flow business model, one of the motivations behind becoming a Data Engineer is the salary.Big Data Engineer Jobs SalariesThe average salary for Big Data Engineer ranges from $94,944 to $126,138 as per indeed. Whereas according to Glassdoor, the national average salary for a Senior Data Engineer is $181,773 in the United States.As of Nov 2019, the total number of jobs listed in renowned job portals are:LinkedIn(IN) 2,746 jobsLinkedIn(US) 39,647 jobsIndeed -127,091 jobsGlassdoor(US) 143,304 jobsI hope this Big Data Engineer Skills blog has helped you in figuring out the right skill sets that you need to become a Big Data Engineer. In the next Big Data Resume blog, we will be focusing on how to make an attractive Big Data Engineer Resume which will get you hired. Start your Big Data Engineer Journey with Edureka Learn Now If you are will ing to upgrade your career start your Big Data Engineers journey, check out the Edureka Masters program.Recommended blogs for you Function Overloading in C++ : All you Need to Know Read Article Your Guide to the Top 10 IT Certifications for 2020 Read Article How To Implement Static Variable In C? 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