How To Become A Big Data Engineer? | Big Data Engineer Roadmap | Edureka Rewind

Key Takeaways

a worldwide 2.5 quintillion bytes of data is produced every day a professional who can take all this enormous resources and provide the Frameworks for a business solution is indeed a hero hi all I'm a partner from edureka and in this module we are going to talk about how to become a big data engineer before we begin let's discuss the agenda for today so first of all we're going to talk about who is a big data engineer we're going to basically talk about this role or job description in specific then we're going to talk about what does a big data engineer do followed by a few rules and responsibilities then we're going to talk about the skills required to become a big data engineer and finally I'm going to take you through the learning path so without Much Ado let's get straight into the module so who's a big data engineer now every data driven business needs to have a framework in place for the data science and data analytics Pipeline and a data engineer is the one who's responsible for building and maintaining this framework now these Engineers must ensure that there is an uninterrupted flow of data between servers and applications so in simple words a data engineer builds tests maintains data structures and architectures for data ingestion processing and deployment of large-scale data intensive applications now data Engineers work in tandem with data architect data analysts and data scientists so they must all share these insights to other stakeholders in the company through data visualization and storytelling but what does a big data engineer do exactly now the most crucial part of a big data engineer is to design develop construct install test and maintain the complete data management and processing systems they are basically the ones who handle the complete end-to-end infrastructure for data management and processing they built a pipeline for data collection and storage and funnel the data to data analysts and scientists so basically what they do is they create the framework to make data consumable for data scientists and analysts so they can use the data to derive insights from it note that the data Engineers are the Builders of data systems and not those who mine for insights so the data engineer Works more behind the scenes and must be comfortable with other members of the team producing Business Solutions from this data now all their responsibilities revolve around this they need to take care of a lot of things while performing these activities hence one of the most sought after skills in data engineering is the ability to design and build data warehouses this is where all the raw data is collected stored and retrieved from without data warehouses all the tasks that a data scientist us will become obsolete it is either going to get too expensive or very very large to scale now data Engineers should always keep in mind that the system which he or she builds needs to be scalable robust and fault tolerant so that the system can be scaled up without increasing the number of data sources and can handle a huge amount of heterogeneous data without any failure now imagine a situation wherein the source of data is doubled or tripled but the system cannot scale up will it not cost a lot more time and resources to build the same system again which is suitable for this kind of intake exactly this is why the Big Data Engineers have a role here next he or she is the one that handles the extract transform and load process which is basically the blueprint for how the collected raw data is processed and transformed into Data ready for analysis now you're going to acquire a lot of data data from different sources how do you bring them together to one platform ETL is your answer apart from all this a data engineer should always aim at deriving insights by acquiring data from new sources some of the responsibilities of a data engineer also include improving data foundational procedures integrating new data management Technologies and the software into existing systems and building data collection pipelines and finally one of the major roles of a data engineer is to include performance tuning and make the whole system way more efficient which is pretty self-explanatory if you ask me now most of us have some idea about who a big data engineer is but there's still some confusion about their responsibilities now this ambiguity further increases when we gain more information about the role now let me help you debunk all your queries about it so let's talk about some big data engineer responsibilities first up we have data ingestion now this is associated with the task of getting data out of the source systems and ingesting it into a data Lake now a data engineer would need to know how to efficiently extract the data from a source including multiple approaches for both batch and real-time extraction as well as needing to know about the incremental data loading fitting within small Source windows and parallelization of data loading as well now another small subtask of data ingestion is data synchronization but because it's such a big issue in the Big Data world we are going to talk about it now since Hadoop and other big data platforms don't support incremental loading of data a data engineer would need to know how to deal with detecting changes in the data source merge and sync change data from sources into the Big Data environment next we have data transformation this is basically the T in the extract transform and load that we had discussed earlier it has been basically focused on integration and transformation of data for a specific use case now a major skill set here is the knowledge of SQL as it turns out not much has changed in terms of the type of data Transformations that people are doing now compared to purely relational environments now imagine all this data that you've acquired from various sources what would you have to do to make them all palatable in the same platform you need to transform that data and this is what a data engineer does here and finally we have performance optimization which is one of the tougher areas because anyone can build a slow performing system the challenge is to build data pipelines that are both scalable and efficient so the ability and understanding of how to optimize the performance of an individual data Pipeline and the overall systems are a higher level of data engineering skill now for example Big Data platforms continue to be challenging with regard to query performance and have added complexity to a data engineer's job in order to optimize performance of queries and creation of reports the data engineer needs to know how to denormalize partition and index data models he also needs to understand tools and Concepts regarding in-memory models and olap cubes now let's quickly move ahead and look at the required skills to fulfill these responsibilities now we'll be going through these skills in a clockwise order so starting with big data Frameworks now with the rise of big data in the early 21st century a new framework was born and that is Hadoop all thanks to Doug cutting for introducing this framework it not only stores big data in a distributed manner but also processes the data parallely there are several tools in the Hadoop ecosystem which cater differently for different purposes and Professionals for a big data engineer mastering Big Data tools is a must some of the tools which you will need to Master first of all you have hdfs which is the storage part of Hadoop being the foundation of Hadoop knowledge of hdfs is a must to start working with Hadoop framework next we have yarn which performs resource management by allocating resources to different applications and scheduling jobs now mapreduce is a parallel processing Paradigm which allows data to be processed parallely on top of the hdfs next we have begin Hive now Hive is a data warehousing tool on top of hdfs which caters to professional from an SQL background to perform analytics on top of hdfs whereas Apache pig is a high level platform which is used for data transformation on top of Hadoop now high was generally used by data analysts for creating reports whereas pig is used by researchers for programming both are pretty easy to learn if you're already familiar with SQL next we have Flume and scoop Flume is a tool which is used to import unstructured data to hdfs and scope is used to Import and Export structured data from rdbms now next we have zookeeper which acts as a coordinator among the distributed Services running in a Hadoop environment it basically helps to configure management and synchronize services and finally we have Uzi which is basically a scheduler which binds multiple logical jobs together and helps in accomplishing a complete task next up we have real-time processing Frameworks now real-time processing with quick actions is the need of r either it is a credit card fraud detection system or a recommendation system now imagine if you wanted a red dress today and Amazon decides to suggest it to you a month later now wouldn't that be completely useless for you in this case you need real-time processing it is very important for a data engineer to have knowledge of real-time processing Frameworks now Apache spark is one of the distributed real-time processing Frameworks which is used in the industry rigorously it can be easily integrated with Hadoop leveraging hdfs as well next we have dbms now a database management system stores organizes and manages a large amount of information within a single software application now data Engineers need to understand the database management system to manage data efficiently and allow users to perform multiple tasks with ease this will help data engineers in improved data sharing data security data access and better data integration with minimize data inconsistencies these are the fundamentals that data Engineers should know prior to building a scalable robust and fault tolerance system next we have SQL based Technologies now there are various relational databases that are used in the industry such as Oracle DB Microsoft SQL Server Etc now data Engineers must have at least the knowledge of one such database now knowing SQL is also a must this structured query language as SQL is also known as used to structure manipulate and manage data stored on relational databases as data Engineers work closely with rdbmss they need to have a strong command on SQL now next we have nosql Technologies as the requirements of organizations have grown Beyond structured data nosql databases have been introduced into this environment it can store large volumes of structured semi-structured or structured data with quick iteration and agile structure as per application requirements some of the most prominently used databases are hbase Cassandra and mongodb now hbase is a column oriented nosql database on top of hdfs which is great for scalable and distributed Big Data stores it is also great for applications with optimized read and range based scan and it provides consistency and partitioning out of cap now Cassandra is a highly scalable database with incremental scalability and the best part about Cassandra is the minimal Administration and no single point of failure it's good for applications with fast and random read and writes it provides available and partitioning out of cap and finally we have mongodb which is basically a document oriented nosql database which is a schema free database it gives full index support for high performance and replica station for fault tolerance it has a Master Slave sort of architecture and provides CP out of cap it is rigorously used by web applications and semi-structured data handling next we are going to discuss programming and scripting languages so various programming languages can serve for the same purpose so knowledge of one programming language is enough I'm saying this because the flavor of language may change but the logic Remains the Same if you're a beginner you can go ahead with python as it is an easy language to learn due to its syntax and good Community Support whereas R has a steep learning curve which is developed by statisticians and it is mostly used by analysts and data scientists the next skill we're going to discuss is an important one it is ETL or data warehousing now 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 extract transform and load now data warehousing is used for analytics and Reporting and is a very very crucial part of every business intelligence solution because this is the part which is going to take you most time now it is very important for a big data engineer to Master One data warehousing or ETL tool after mastering one it becomes pretty easy to learn new tools and as the fundamentals remain the same now Informatica click View and talent are very well known tools used in the industry Informatica and talent Open studio are data integration tools with ETL architecture the major benefit of Talon is its support from the Big Data Frameworks if you're new to data warehousing and ETL tools I would definitely recommend and you start with talent because after learning this any data warehousing tools will become a piece of cake and finally we have our operating systems now intimate knowledge of Unix Linux and Solaris is very helpful as many mathematical tools are going to be based off of these systems due to their unique demands for root access to hardware and operating system functionality above and beyond that of Microsoft's Windows or Mac OS now some level of understanding of how to act upon this data is also very valuable for data Engineers for this reason some knowledge of statistical analysis and the basics of data modeling are also hugely valuable knowledge of machine learning in Cloud also will serve as a big plus while machine learning is technically something relegated to a data scientist knowledge in this area is helpful to construct Solutions usable by your cohorts now this knowledge has the added benefit of making you extremely marketable in this space as being able to put on both hats in which case makes you a really formidable tool now let's discuss a little bit about the learning path to becoming a big data engineer now you will need a bachelor's degree in computer science or software engineering applied math physics statistics or a related field and a lot of Real World skills to qualify for most entry-level positions you may also consider a master's degree in computer engineering or computer science to fine-tune your skills and expand your knowledge if you're starting as a fresher you can first start with the programming language and my recommendation would be python because of its clear and readable syntax versatility and widely available resources and a very supportive Community next you need to master at least one operating system try and Master on the Linux or the Unix operating systems rhel is again a very popular OS adopted by the industry which you can Master next you need to enhance your dbms skills and get hands-on experience on at least one relational data database preferably MySQL or Oracle DB you should be thorough with database administrator skills like capacity planning installation configuration database design migration performance monitoring security troubleshooting as well as backup and data recovery nosql databases should be your next skill to focus this will basically help you understand how to handle semi and unstructured data next up you have your ETL and data warehousing tools where you'll have to understand on how to extract data from various sources transform and clean your data according to the use case and then load your data into a data warehouse this is a very important skill which a data engineer should possess now sixty percent of data engineer tasks will basically revolve around this now we are at an age of data Revolution where data is the fuel of the 21st century various data sources and numerous technologies have evolved over the last two decades and the major ones are all no SQL databases and Big Data Frameworks so with the Advent of big data in data management system the data engineer now has to handle and manage big data and their role has been upgraded to a big data engineer now due to Big Data the whole data management system is becoming more and more complex but that does not mean it's becoming less fun so now a big data engineer has to learn multiple Big Data Frameworks to create and design the processing systems this should be your next skill to focus on next you should be concentrating on learning real-time processing Frameworks such as Apache spark which is basically an open source cluster Computing framework for real-time processing and when it comes to real-time data analytics spark stands as the go-to tool across all solutions today's spark is being adopted by Major players like Amazon eBay and Yahoo many organizations run spark on clusters with thousands and thousands of nodes and this calls for a huge opportunity next in your career path you should definitely learn Cloud which will serve as a big plus a good understanding of cloud technology will provide the option of storing significant amounts of data and allowing big data to be further available scalable and fault tolerant now the part to being a big data engineer may be long and strenuous but with some structure and guidance you can definitely make an entrance into it with that I conclude my session thank you and have a great day ahead