The demand for big data professionals has been growing over the past few years. The position for Machine Learning Engineers, Data Scientists, Big Data Engineer, and Data / Business Analysts are in great demand these days, and Data Engineers are gradually picking up pace and have started gaining momentum. Though, both Data Scientists and Data Engineers work on Big Data, both have different roles and responsibilities. While Data Science is heavily math-oriented and technology-focused, focusing on statistical models, machine learning algorithms, python libraries, and visualization tools for making inferences, analyses, and predictions, deriving insights, and providing the management with inputs that aid in decision-making, by contrast, Data Engineers work primarily on the technical side building infrastructure and data pipelines to store, process and organize data. In other words, Data Engineers build systems that collect, manage, and transform raw data into cleansed data, that is consumed by Data Scientists and Data Analysts for analysis and decision-making. In short, Data Engineers deal with the movement of data (ie., through a data pipeline) and storage of data (ie., with a data warehouse).
Before getting into what data pipelines are, let’s understand what Data Cleansing is which is an important aspect of Data Science. Data Cleansing is a process of removing or updating information that is incorrect, incomplete, duplicated, irrelevant, or formatted improperly. It is very important to improve the quality of data which results in the accuracy and productivity of the processes. The resultant data after cleansing are stored in a huge pool (ie., data reservoirs or say repository) which is nothing but filtered data (information) that are built and managed by Data Engineers. After Data Cleansing, depending on the tasks, the data engineer constructs a data pipeline that runs to and from the huge pools (reservoirs), which can be ultimately extracted and consumed by Data Scientists and Data analysts for making analysis and garnering insights and predictions. For example, while constructing a house, we construct an overhead tank on top of the house to store water, it is connected by pipes (for both inflow and outflow of water), to the rooms in the house. If it is a 10-storey building, the complexity will only increase. Here, the role of a plumber is similar to that of a Data Engineer. Technically in Data Science, programming languages such as Python, C++, Java, or Scala are used to construct data pipelines, depending on the task and scale, by the Data. Data Engineers coordinate with Data warehouse Engineers, Data Infrastructure Engineers, Data Architect, Analytics Engineer, DevOps Engineers, etc., to perform various tasks.
Professional opportunities with Data Engineering are huge these days. The role of the data engineer is to help businesses scale and make the most of their data resources. The prospective data engineer should be fluent in programming languages and need to work on the various tools and understand what these tools are meant to accomplish, and gain an understanding of the concepts behind building a robust pipeline.
Consider the following key steps if you want to build a career as a data engineer:
- 1. Earn a bachelor’s degree and begin working on projects: You will require atleast a bachelor’s degree in computer science, software engineering, data science, applied mathematics, statistics, or a related field. You will also require a real-world project experience either through internship or Bootcamps such as eduJournal Bootcamp (edujournal.com), to even qualify for entry-level positions.
- Hone those big data and analytical skills: Employers are looking for candidates with unique skills and a strong command of software and programming languages. You will need to hone your SQL skills and make analysis with data using SQL engines like Apache Hive etc., For statistical analysis and modeling, a knowledge of languages like Python and R could be helpful. A mastery of Spark, Hadoop, and Kafka will also come in handy. Beyond mastery of language, other skills include using database architecture, understanding machine learning, finding data warehousing solutions, data mining, constructing data pipelines, utilizing cloud platforms like Azure and Amazon Web Services, etc.,
- Obtain an entry-level job even if IT job: This job will enable you to think creatively and find unusual ways to solve problems and gain invaluable insights on how to approach data, organize and restructure data, and make analyses and predictions. You gain an understanding of how your industry functions, and how data can be collected, analyzed, and utilized.
- Get certifications for further specialization to make yourself more competitive: To advance your career in data engineering, you need to pursue additional courses and certifications to boost your skill and knowledge since the technologies are constantly changing. You can opt for certifications from Oracle, IBM, and Microsoft among others. Do speak and consult your mentors before joining the certification courses. One certification you can obtain is Certified Data Management Professional (CDMP), developed by Data Management Association International (DAMA), which is an all-around credential for general database professionals, which are recognized by companies worldwide.
- Pursue a master’s in data engineering: Not all jobs require a master’s in data engineering. It demonstrates you have taken additional steps to further your knowledge. For eg., a M.Tech candidate will have more weight than a B.Tech candidate with all other parameters being the same. Some employers are willing to accept relevant work experience and proof of technical expertise instead of a higher degree.
Courses are OK for acquiring knowledge, but nothing beats real-world experience. This is where our boot camp conducted at eduJournal (www.eduJournal.com) comes in handy. To become a Data Engineer, you will have to sharpen your programming skills viz., construct data pipelines, develop an interest in data and finding patterns in data, ability to create complex systems to handle huge data ie., Big Data Projects, handle infrastructure, DBA skills, serving the need of the internal team viz Data Scientist & Data Analyst to provide cleansed data, familiarity with various tools such as Apache Hadoop, Apache Spark, Apache Hive, Apache Kafka, etc.,
Responsibilities of a Data Engineer:
- Develop ETL (Extract, Transform, Load) processes to help extract and manipulate data from multiple data sources into a single repository (ie., Data Warehouse). Common ETL tools include Xplenty, Stitch, Alooma, and Talend.
- Prepare raw data in Data Warehouses into a consumable dataset for both technical and non-technical stakeholders.
3 Maintain and optimize the data infrastructure required for accurate extraction, transformation, and loading of data from a wide variety of data sources.
- Data cleaning. Generally, the likelihood of errors in data increases with the number of data sources required by a company for its activities. As a result, it’s not surprising that data engineers spend most of their time cleaning data that is corrupted, incorrectly formatted, duplicated, or incomplete data.
- Automate data workflows such as data ingestion, aggregation, and ETL processing.
- Partner with data scientists and functional leaders in sales, marketing, and product to deploy machine learning models in production.
- Leverage data controls to maintain data privacy, security, compliance, and quality for allocated areas of ownership.
- Build, maintain, and deploy data products for analytics and data science teams on cloud platforms (e.g. AWS, Azure, GCP).
- Data monitoring. Between the conception phase and the production phase of a machine learning model, there is a long way full of potential obstacles. Data engineers are also tasked with the monitoring and optimization of the data architecture and data processing systems.
- Design, build and maintain batch or real-time data pipelines in production.
- Managing Big Data with tools such as Hadoop, Kafka, MongoDB, etc.,
Finally to conclude, Data Engineers are responsible for acquiring data for Data Scientists and Data analysts in a format that allows them to query, interpret and analyze with the tools available to them. The Data Engineer has to migrate it from where it lives (raw data), cleanse it, transform it in a manner that makes sense to everybody, and make it available to Data Scientists and Data Analysts to derive higher insights. In other words, Data scientists and Data Analysts often rely on the work of data engineers to obtain the data they need for making analyses and making predictions. Data Engineers do no work on the front end UI’s or applications like Data Analyst and Data Scientists, to get noticed by everybody, but instead, work deep in the system stack ie behind the scenes, but their job is incredibly complex (much more difficult than a software engineer’s job), which involves the use of complex technologies that involves a lot of skill like for building an ETL pipeline.
The right technology will eventually become the wrong technology over time. You will have to spend your time and effort to keep pace with new technologies. If you have the vision and drive to succeed, you will make a good Data Engineer with time….
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