How to Become a Big Data Engineer

Introduction
Who is a Big Data Engineer?
Big data Engineer Job description
Roles and Responsibilities of a Big Data Engineer
Big Data Engineer Salary Trends in India/US
Skills required to be a Big Data Engineer
A Day in the Life of a Big Data Engineer
How to Become a Big Data Engineer?
What are The Advantages of a Big Data Engineer Course?

Introduction

When we get into Information technology, we get to choose among various career options to pursue. One of those careers is Big data engineer. Big data is the core when it comes to Artificial Intelligence and some other IT fields. A big data engineer is an IT professional who deals with databases on a large scale. This guide on becoming a big data engineer helps you understand complete details about a big data engineer.

Big data is the fuel for today’s analytics applications. This article is an in-depth big data guide that explains how organizations can deploy it and what they have to try to utilize it efficiently.

The development of massive data technologies unlocked a treasure trove of data for businesses. Before big data was introduced, Business Intelligence and analytical applications were limited to structured data stored in relational databases and data warehouses transactions and financial records, for instance. Tons of probably valuable data that did not fit the relational mound was left unused. No more, though.

Who is a Big Data Engineer?

Big data engineer positions are extremely in-demand, and big data engineers have years of expertise and broad technical understanding. If you intend to be a big data engineer, you will be required to have a bachelor’s degree in computing, software engineering, mathematics, or some superior IT degree. Besides the degree, a big data engineer requirement holds a variety of technical skills and understanding to ensure that they will achieve success in their job. So, from SQL, Python, and a spread of cloud platforms, the proper knowledge can help an aspiring big data engineer succeed.

Once your degree is achieved, there are a variety of certifications that you can simply earn to continue your education. Before you decide to apply for the conventional big data engineer jobs, a consciousness of data Science can permit you to obtain the right certifications to fulfill the role. Check out these free courses to start with

You’ll build your big data engineer skills in many ways, and it starts with knowing which skills to hone.

Data Engineering is the management and processing of knowledge. This involves:
Development and construction of architecture systems.
Testing and maintenance of those systems.
Dealing with involving large-scale processing of info.
Data Engineers work to take care of these knowledge processing systems.

With the planet currently undergoing a digital revolution, data is now the fuel that drives forward the fashionable 21st century. Our lives spin around enormous data groups crosswise, changing grounds and trades. These range from everyday sectors like banking and education to e-commerce and even healthcare. This has led to an abrupt rise in the way we use and manage databases.

The data mentioned here refers to a group of qualitative or quantitative variables – be it structured or not, digital or analog, confidential or not. Once you break it down, datasets are made from individual data points that provide value.

The term Big Data doesn’t mean more data. It refers to data points that accumulate much faster than “normal” software can manage. The demarcation of massive data isn’t strictly defined – but large amounts of knowledge include:

A million sales transactions by a web retailer.
A million introductory phone calls by a communications provider.
A sensor that produces 50 megabytes of knowledge every two nanoseconds.

And so, if you’re employed with and/or manage Big Data, you’ll be mentioned as an enormous Data Engineer. Thanks to the augmented difficulty of enormous data, the engineer has grown to learn multiple Big Data frameworks & NoSQL databases.

Big data Engineer Job description

To understand the role of a big data engineer, you would like to know that big data is an in-depth collection of data that the normal software options aren’t equipped to handle. The normal data collection is often well structured, while big data comes in unstructured. Big data engineers are available by employing a range of their technical skills to urge the work done.

Big data engineers are skilled as software developers, and they need to be proficient in coding, a superb data scientist, and an engineer all at the equivalent time. this is often a multi-faceted role, and any big data engineer could find themselves performing a variety of tasks on any day of the week. Identifying, extracting, and delivering data in a usable format. This data is then given to others to gauge. While an enormous data engineer is functioning, they need to ensure that the information they extract is valid. On any given day, an enormous data engineer could affect cloud computing environments, assist in documenting any requirements, resolve ambiguities within the data, and more.

Roles and Responsibilities of a Big Data Engineer

As a big Data Engineer, you’re liable for the management of information. This includes utilizing the available data and technologies to form an information landscape for data scientists.

Your knowledge isn’t only limited to the info available within the company and its storage locations. Still, you’re also liable for data integration into central analysis infrastructure and determining which technologies are suitable for this.

The work of an information Engineer begins with understanding the technical requirements. They then move to plan and develop a strong and versatile big data infrastructure. They’re responsible for collecting, storing, processing, and analyzing the information systems. A big Data Engineer is taken into account as the master of knowledge supply. They create essential data easily accessible across the corporate and usable in multiple departments.

Big Data Engineer Responsibilities:

Gather and process data at scale.
Design and develop data applications using selected tools and frameworks.
Read, extract, transform, stage, and cargo data to choose tools and frameworks as needed and requested.
Perform tasks like writing scripts, web scraping, calling APIs, writing SQL queries, etc.
Work closely with the engineering team to integrate your work into our production systems.
Process unstructured data into a form suitable for analysis.
Analyze processed data.
Support business decisions with unplanned analysis as required.
Monitoring data performance and modifying infrastructure as required.
Define data retention policies.

Roles

They help in getting a 360-degree picture of users to assist optimize retailing, progressing sales, and upgrading customer assistance.
They help grow customer procurement and retention, which is facilitated by better knowledge of customer needs and choices.
They help in establishing fraud obstruction and cybersecurity protections by more meticulous identifying questionable activities and security threats.
They help promote business forecasts and methods, optimize merchandise pricing, and increase operational efficiency.
They also help in generating personalization and guidance methods for corporate websites, streaming services, and online promotion.
They assist in analyzing text, videos, images, and audio to support known customer opinion, spot patterns, and match content to advertising.
They enable preventive keeping attenuating material failures and downtime in production plants and other mechanical operations.
And they help in identifying and mitigating potential risks in financial management, supply chains, logistics operations, and loan and policy approvals.

Big Data Engineer Salary Trends in India/US

One of the recent Gartner reports predicted that 2021 goes to be the year of hyper-automation. That’s where the necessity for data engineering pros comes into the image. And this is often why the sector of knowledge engineering is rapidly growing. What does this mean for you? It means major proportions of organizations, small and large, new players within the market. Therefore the established ones, all are able to pay hefty data engineering salaries and growth opportunities.

However, there are several factors that affect the typical data engineer’s salaries, several of which include:

Skills
Country – and typically specific locations too
Experience
Industry
Type and size of the organization

Oftentimes education and training too.

Currently, in this article, we will discuss three aspects that affect the salary of a big data engineer.

Big Data Engineer Salary: Based on Company or organization

Here is some company that hires Big data Engineer.

Company	Salary
Amazon	$139,513/year
Facebook	$210,895/year
Cognizant	₹8,32,838/yr.
TCS	₹9,28,621/yr.

Big Data Engineer Salary: Based on Experience

Experience plays an important when it comes to employment and salary, so let’s see how much effect it does on a big data engineer’s salary.

Experience	Salary
0-1 years	₹456,000
1-4 Years	₹ 716,000
5-9 Years	₹10,00,000
10-19 Years	₹20,00,000

Big Data Engineer Salary: Based on Skills

Skills	Salary
SQL	₹ 813,909
Python	₹ 801,659
ETL(Extract, Transform, Load)	₹872,886
Apache spark	₹ 965,571
Big data Analytics	₹ 906,269

There are a few other skills that are required for a big data engineer, but there are the basics/ fundamental skills.

Skills required to be a Big Data Engineer

There are specific responsibilities that are expected of an enormous data engineer. Not only would you like to possess a Bachelor’s degree as mentioned earlier, but you’ll also have the proper knowledge of massive data technology, communicate these ideas within a team, and skills to affect commercial IT infrastructures. Expertise in multiprocessing Databases, also as scripting languages, is a requirement. Other responsibilities include:

Familiarity with NoSQL solutions also as Cassandra, HIVE, CouchDB, and HBase.
They must have familiarity with big data technology such as Hadoop, MapReduce, and Streaming.
They must have wrought access to commercial platforms in big data technologies, like IBM or Oracle.
They must have the working ability in Analytics, OLAP technologies, and some more tech stacks.
Experience in agile development methodologies may be a must.
Sound communication skills – both written and verbal – are necessary when handling a team of engineers.
The ability to multi-task with no supervision is preferred by several employers.

With the proper big data engineer skills listed above, finding a replacement role won’t be difficult. The foremost essential aspect of winning big data engineering jobs is comprehending you’re working hard to appeal to a variety of employers. Being correctly educated and authorized is important for such a varied role!.

A Day in the Life of a Big Data Engineer

There’s no typical day, but a number of the large blocks of activity include:

Data Loads – One-time loads for Adhoc projects or recurring extractions for normal reports. SQL, CRON jobs, and ETL tools to demand information from somewhere or a different system and settle them onto the sandbox for the information study.

Data Preparation – Side-by-side wrangling, coding, labeling, creating synthetic data, creating metadata to rework data into a usable form. SQL, Python.

Data Pipelines / Exposing Data – Creating dimensional tables to support BI cubes, reports, or analytical base tables for machine learning. Creating database views or regular materialization to update data that are employed by analysis. SQL.

Optimizing Queries – Monitor all data jobs hitting the servers and data loads. Cancel large-running queries or jobs that bag information volume. Find ways to shorten regular loads and transformations for efficiency. Linux, Ganglia, SQL.

Automating and Publishing Analysis – Take results of ad-hoc analysis and expose them as simple web apps and visualizations for easier interaction with clients. Take prototype scripts of machine learning and analytics jobs and produce them as in-database processes for normal runs. SQL, Python, D3.

How to Become a Big Data Engineer?

To become an enormous data engineer, you’ll get to get conversant in all of its concepts.

Data engineering consists of collecting, managing, and processing data. While data scientists are experts in Math and Statistics, Big data engineers are experts in computing and Programming.

However, you don’t necessarily get to have a computing background to enter this field. Like other data-related fields, you’ll find people from various backgrounds in this sector too.

To become a knowledge engineer, you ought to learn the subsequent things:

Algorithms

Algorithms are instructions for performing a series of actions during a specific order. Usually, algorithms are independent of the programing language.

This means you’ll use an algorithm regardless of your programming language.

In data structures, you’ll be using algorithms for the subsequent tasks:

Finding an item during a database
Inserting an item during a database
Sorting the things in a particular order
Deleting an item

It is a fundamental concept of knowledge engineering. So you ought to put considerable time into mastering it.

Data Structures

A data structure may be a way of organizing data for better management. While handling data, you’ve got to keep it in an efficient order, so you’ll access it easily.

Data structures (also referred to as databases) are of various types. you’ll need to get conversant in all of them.

Some of them are:

Array
Heap
Binary Tree
Graph
Queue
Matrix

Once you get conversant in basic data structures, you’ll advance to abstract data structures.

SQL

SQL stands for Structured Query Language). it’s been present within the market since the 70s and has become the primary choice for several developers, engineers, and analysts.

No matter what anyone says, SQL is here to remain. A knowledge engineer must know this language.

There were rumors that SQL is dying or losing popularity, but they’re all fake. SQL isn’t dying. it’s one of the foremost popular programming languages among data professionals.

Why is SQL essential, and why do numerous data professionals use it?

Well, SQL is the primary language one uses to get queries to the database from a client program. In other words, it allows your database servers to edit and store data on them.

Without SQL, you can’t perform those tasks.

Moreover, it’s used almost everywhere, so learning it’ll help make sure that you’ll work with any organization required.

Python and Java (or Scala)

Python is present everywhere. it’s a must-have for any data enthusiast. it’s widely popular due to its versatility and simple working.

You can find a Python library for any task you would like to perform. Java and Scala are equally crucial for you to find out.

That’s because most of the info storage tools are written in these languages, including Hadoop, HBase, Apache Spark, and Apache Kafka.

You can’t use these tools without learning these languages. it’ll assist you in understanding how these tools work and what you’ll do with them.

Each of those languages has its qualities. Scala is fast, Java is vast, and Python is flexible.

Big Data Tools

There are tools popular in this field. They include:

Apache Hadoop

Apache Spark

Apache Kafka

Try to study them for the maximum amount. Learning about these big data tools and technology is important because they create the task of knowledge storage and management more effortless.

For example, professionals use Hadoop for solving problems associated with vast amounts of knowledge and collection. it’s a gaggle of open-source software solutions and frameworks.

Similarly, Spark provides you with an interface for programming clusters.

Many companies require candidates to be conversant in these tools.

The tools we’ve mentioned above are the foremost popular ones within the big data industry. However, they aren’t the sole tools data engineers use for their tasks. you’ll get to study more tools as you get deeper into the topic.

Distributed Systems

Data is present in clusters, which function independently. An outsized cluster would have a better chance of developing problems than a smaller one, thanks to more member nodes.

To become a knowledge engineer, you’ll need to study data clusters and their systems.

You will even have to learn about the varied problems data clusters face and how to unravel them.

Data Pipelines

A data pipeline may be a software solution that makes a pathway for data flow and removes multiple manual steps from transferring knowledge from one point to another.

Although a knowledge pipeline can transfer data to data warehouses, the destination doesn’t always need to be that.

You can also use data pipelines to transfer chunks of knowledge to applications.

As a knowledge engineer, you’ll spend tons of time building and managing data pipelines. Data pipelines help in generating abundant sources of knowledge, storing the info within the cloud, and performing data analysis.

Top 40 Hadoop Interview Questions You Should Prepare for 2021

What are The Advantages of a Big Data Engineer Course?

When we become big data engineers, we get a lot of advantages as an individual. Some of them are mentioned below.

High salaries
Big Data is used in every industry
Skill enhancement
Provides competitive advantage
Better career opportunities
The exponential growth of the Big Data market.

FAQs:

Is it essential to have a college degree to become a Big data Engineer?

Ans: The answer is no. You just need skills to be a Big data engineer. Degrees just help us to get the 1^st job in an organization, and after that, your experience does everything.