How to Become a Data Engineer? | Data Engineering Training | Edureka

Key Takeaways

hey everyone thank you so much for joining in and welcome to edureka webinars so team in today's session we'll be learning as how to become a data engineer so that's the agent of today's session as we proceed in today's session we will look into the concept of who exactly is a data engineer that is a big data engineer what does a big data engineer is going to do the rules and responsibilities of a data engineer the skill set that is required in order to become a data engineer and what is the Big Data engineering learning path okay so let's get started and now let's understand who exactly is a data engineer now I know that most of us have an idea about a data engineer is but in some scenarios we will be confused about what will be the rules and responsibilities of this big data engineer and this would actually this confusion actually increases once we start mapping those roles to the responsibilities with relevant skill set and finding the most effective and the efficient learning path so here the first question that we have got is who exactly is a data engineer so in simple words I can say that data Engineers are the one who develops constructs tests and maintains the complete architecture of the large-scale processing system so here when we talk about the role of a data engineer they are the one who develops as I mentioned they will construct and even they test and maintain the complete architecture of a large scale processing system here they are going to develop you can clearly observe right now okay and when we talk about the data Engineers so what exactly are they going to do well when we talk about the data Engineers they actually take up some of the crucial tasks in in their roles so this would involve designing developing constructing installing testing even maintenance of the complete data management and the processing system they are responsible for building the highly scalable robust and fault tolerant systems and data Engineers are the one who is responsible for taking care of complete end-to-end that is the edl pipeline or the ETL process which is nothing but the extract transform and load process and data Engineers they generally and they always ensure that the architecture is correctly planned in such a way that it is going to meet all the business requirement and not just that the data Engineers are responsible for discovering the opportunities for the data acquisition and exploring the new ways of using the existing data set and this one also includes the pro prop proposing the ways to improve they also I would say like they'll improve they'll propose the ways to in order to improve the data quality reliability and the efficiency of the entire system and data Engineers are the one who's responsible for creating the complete solution by integrating a variety of programming languages and the tools all together and they not only do that they'll also create the data models in order to reduce the system complexity and as a result this would increase the efficiency and reduce the cost and they are someone who is responsible for deploying the disaster recovery techniques and these data Engineers if I want to add one more point they are the one who introduces new data management tools and techniques into the existing system so that it can make it more efficient okay now that you have understood about the data engineer let me just give you a brief about the Big Data Engineers so here when we talk about the Big Data engineer so first of all let me just highlight the difference between data engineer and a big data engineer so as you know we are currently in the range in the age of I would say data Revolution now here data is actually the fuel of the current century that we have got even the chat GPT has been trained on huge volumes of the data set and that's the reason it is able to answer almost all the queries so data is the fuel for any application and there are various data sources that are available various technologies have coming to picture right now I would say in the last few uh few years itself and here I can say that the major ones are nosql database at the Big Data framework now with this big data in the data management system the data engineer they now have to handle and manage the big data and the role has been upgraded to the Big Data engineer now due to this big data the entire data management system is becoming more and more complex so the Big Data engineer uh has to learn some of the important Frameworks and the nosql databases to create design and manage the processing system now when we talk about the responsibilities so I'll say the first responsibility is data ingestion so data ingestion means taking the data from the various data sources and then ingesting it to it into the data Lake so here when I say injection it's about acquiring the data from various sources and ingesting into the data like that means in this case okay so we want to get the data from various sources and place it in a folder or a source okay place it in a source which can be accessed by the entire business so that is what we call it as a data ingestion technique now in order to do that the data engineer requires the skills to efficiently extract the data from the source which can include the various data ingestion approaches like batch real-time extraction and there are various other skills that would actually be required or the expected for the data ingestion to make it more efficient like other skill set in the data ingestion would include incremental load loading the data parallely and so on so when it comes to the Big Data world the data injection becomes more complex because the amount of data it actually starts access accelerating and the data is present in various formats so as a data engineer so you have to know about the data mining and the various data ingestion apis in order to capture and inject more data into the data Lake okay now here the next step or the next responsibility that we would do as part of data engineer is data transformation here at this step so the data is always present in a raw format that means once you inject the data it will be in a raw format or even during the extraction as well now you cannot use the raw data directly over there so you have to convert it from one format to the another format so depending on the use case you will do the changes in the data set and this is called as a data transformation now here this data transformation can be a simple or a complex process depending on the data sources that you are working with like or it could be the data formats and the required output so as a result of this you would include various tools and custom scripts in various languages depending on the complexity and the structure and format and the volume of the data set and the other thing that you will do is a performance optimization so here as a data engineer you are responsible for building a system which is both scalable and an efficient one okay it's scalable and an efficient one and then okay as a data engineer you need to understand as how to improve the performance of the individual data Pipeline and as a result optimize the overall system now again in this case when I'm dealing with a big data platform the performance becomes a major factor the Big Data engineer you have to ensure that the complete process from the query execution to visualizing the data through the report and the interactive dashboards should be optimized so in doing the optimization as a big data engineer you have to know about various Concepts like partitioning indexing denormalization and so on now apart from this the other responsibilities that you would generally find while working with or while going through the job portals would include like uh like you'll be responsible for uh like like you will you'll be responsible for I would say creating or building the data pipeline and that's a common uh responsibility which will be mentioned in various job portal Aggregate and transformation of raw data coming from various data sources to fulfill the functional and non-functional business need and performance optimization where you will be expected to automate the process optimizing the data delivery and redesigning the complete architecture is in our in order to enhance the performance like handling transforming and managing the big data using the big Frameworks Big Data Frameworks and the nosql databases and building the complete infrastructure to ingest transform and store the data for further analysis and the business requirement okay so those are the common rules and responsibilities that is expected by a big data engineer now in order to fulfill those roles and responsibilities in an effective manner you need to be aware of the important skills so let's have a look into the Big Data engineer skills so here when we talk about the Big Data engineer skills so first and foremost you need to be aware of the Big Data Frameworks and the Hadoop based Technologies so with the rise of big data in early 20s 21st century that we see right now uh like in this case Okay so a new framework has actually gone so that is actually the Hadoop framework now with this framework not only we are able to store the big data in a bit more distributed manner but also we can process these data set in a parallel manner now there are various tools that we have got in the Hadoop ecosystem which caters the various purpose depending on the people from various background we have got mapreduce we've got Hive and drill we've got mahot and Spark ml lib so we've got Pig we have got hbase if I think about it we've got uh so we've got something called as Apache sparky on hdfs so these are the various tools that you need to master so if I talk about one by one okay so here yeah I think this slide talks about the various skills or the Frameworks that you need to be mastering so one is hdfs as you can see over here so in case of hdfs which is nothing but the Hadoop distributed file system as the name suggests it's a storage part of the Hadoop which will help us to store the data okay it would help us install the data in a distributed cluster and this is a base of Hadoop and the knowledge of htms I would say it's very important in order to start working with the Hadoop framework and then you have got yarn so yarn performs the resource management by allocating the resources to various applications and scheduling the jobs and Yan was actually introduced in the version of hadoops 2.x okay so with this yard so it actually has made the Hadoop a much more flexible and an efficient one and we have got the map reduce as you can see here mapreduce is a parallel processing Paradigm which will allow the data to be processed parallely on top of the distributed Hadoop storage that is the hdfs and we have something called as Pig and Hive so Hive is a data warehousing tool on top of this hdfs so the hive caters the professionals from SQL background in order to perform the analytics where Apache pick is a high level scripting language which is generally used for data transformation on top of the Hadoop And Hive is I would say generally used by data analyst for creating the report whereas pig is used by researchers for programming so both are very easy to learn if you are familiar with SQL then we have something called as flu month scoop so Flume is actually a tool which is used to import the unstructured data into the hdfs where scoop over here is used to Import and Export structured data from rdbms to the hdfs and we have zookeeper so zookeeper generally acts as a coordinator among the distributed Services which are running in the Hadoop environment It generally helps in configuration management and the synchronization of the services and there is something called as Uzi so Uzi is actually a scheduler which binds multiple logical jobs together and helps in accomplishing a complete task okay and we have got Apache spark so here the real time processing it's a real-time processing framework so which has got the quick actions okay and so here this Apache spark if you talk about it it's a real-time processing framework whether it's a credit card flaw detection system that you're trying to build or if it's a recommendation system that you are trying to build each and every one of those application requires a real-time processing so as a data engineer it is very important that you have a knowledge of real-time processing framework and that is where the Apache Spar comes into picture this Apache spark is a distributed real-time processing framework now this can be easily integrated with Hadoop leveraging the Hadoop distributed file system and that's one of the key benefit of this Apache spark and you need to be aware of the database architectures so one of the prominent data sources or obviously the databases so as an engineer that is data and engineer you need to have a critical understanding about the database designed database architecture like one tier two tier three tier and nto along with that it is important that you have a knowledge about the data models and the data schema so which are the key skills for any data engineer and the knowledge of SQL based Technologies like the MySQL so like the knowledge of SQL Technologies like MySQL so that is also very important I would say like which is uh like structured query language which you generally use it to structure and manipulate and manage the data stored in the databases so as a data engineer you work closely with the relational databases so you need to have a strong command on SQL and pl SQL is also prominently used in some Industries so it also provides the processional programming features on top of the SQL and not just that you also need to know about the nosql Technologies like Cassandra mongodb Apache headspace so as a requirement of organization has grown beyond the storage data so that is where the nosql database came into picture it can help us to store the large volumes of structured semi-structured and even the unstructured data with quick iteration and agile structure as per the application requirement so the commonly used databases are hbase which is a column oriented database which is a nosql database which is on top of the htfs which is good for scalable and distributed Big Data store and this is generally good for applications with optimized read and range based scan scan so this generally provides CP that is consistency and partitioning out of cap and talking about Cassandra over here it's a highly scalable database with incremental scalability now one of the best part about this Cassandra is its minimal Administration and no single point of failure now this Cassandra I would say it's really good for application with fast and random read and writes so it provides the AP that is availability and partitioning out of the cap and mongodb it's a document oriented nosql database which is a schema free that means your schema can actually evolve as your application grows so this also gives us the full index support for high performance and replication for fault tolerance it has got a massive slave architecture and provides CP out of cap and this is rigorously used by especially by the web application and this semi-structured data handling now apart from that you need to be aware of the programming languages like there are programming languages which are available for us so knowledge of at least one programming language is mandatory okay if you're a beginner in programming then you can go ahead with Python programming language because it's easier to learn because of its easy syntax and good Community Support now when we talk about our programming language it has got a steep learning curve which is developed by statisticians and generally this are programming language is used mostly by analyst and the data scientist now apart from that yes you need to be aware of the detail and data warehousing Solutions now this would include uh Talent click you or Microsoft SQL server and so on so these data warehousing I can say it's very important especially when it comes to managing the huge amount of data which is coming in from various heterogeneous data sources where you have to apply the ETL that is extract transform load so this data warehouse is generally used for data analytics and the reporting purpose and I would say this data warehouse is one of the crucial part of business intelligence it's very important for the data engineer that is a big data engineer to master a data warehousing or an ETL tool now after mastering one it actually becomes easier for you to learn the new tools because basically the fundamentals would remain the same okay now under this ETL tools we've got Informatica and talent these are the two well-known tools that is currently used in the industry so Informatica and talent Open studio so they are actually the data integration tools with ETL architecture now the major benefit of this Talent is its support of Big Data Frameworks and apart from that yeah you need to be aware of the operating systems like Unix Linux Solaris or Ms windows so these are some of the industry-wide various operating systems okay I can say that out of all this operating system Unix and Linux are some of the prominently used operating system and the Big Data engineer needs to master one at least at least you need to master either Linux or Unix Unix okay so this is about the key skill sets that you need to remember that you need to learn in order to master in your career of this big data engineer now in order for in order for you to become to master your career at edureka we actually follow a learning approach that's called as a structural learning approach now with this structured learning approach here you'll start by a very basic so that you will progress in a sequential way which will enable you to learn about each and every Concepts in a sequential manner in a better way okay so in the first module you'll be learning about the big data and the Hadoop the second module is about the Hadoop architecture okay and the third module is Hadoop map reduce framework and food module is Advanced Hadoop mapreduce okay and fifth module is about the Apache Feb big sixth module is Apache Hive Summit module is about the advanced Apache Hive and the headspace and eighth module is about the advanced Apache headspace okay and 9th and 10th module is about processing the distributed data with Apache spark and Uzi and the Hadoop project as you can clearly see the modules have been structured in such a way that you will be able to progress in a sequential way with relevant Hands-On so that you not only understand it but also learn it in a practical manner with the relevant Hands-On with the help of the industry expert okay now moving on yeah let's look into the Big Data engineering learning path so this is how the learning path of a big data engineer would look like so recommendation is to start with the basics of by programming language it can be anyone followed by the knowledge of dbms and SQL and the nosql databases ETL and data warehousing Big Data Frameworks and learning about the real-time Frameworks and the knowledge of cloud so this is how you should plan your learning path for the Big Data engineering okay so with this C so we come to the end of this session as how you can become a big data engineer and what are the key skills that you need to focus on in order to become job ready do check out the course by edureka thanks a lot everyone