Get Some Featured Skill Sets to be a Data Engineer

Get Some Featured Skill Sets to be a Data Engineer

data engineer is basically an IT professional whose primary job is to prepare data for analytical or operational uses. These software engineers or computer operators are typically responsible for constructing data pipelines in order to collect the data from different source systems.

They integrate, unite, and cleanse data and organize it for use in analytics applications. Their goal is to make data easily accessible and to optimize their organization’s big data environment. Data engineers work in union with data science teams, enhancing data transparency and enabling businesses to make more trustworthy and reliable business decisions.

The Major Skills – That A Data Engineer Must Be Get

Generally, a data engineer is expected to learn how to;

  • Build and maintain database systems,
  • Be articulate in programming languages such as SQL, Python, etc.
  • Be adept at finding warehousing solutions,
  • Be an expert in using ETL (Extract, Transfer, Load) tools,
  • And get knowledge of essential machine learning and algorithms.

A data engineer’s skill set must consist of soft skills, including communication and collaboration. Data science is a highly collaborative and cooperative field, while data engineers work with a range of stakeholders, from data analysts to CTOs.

Now get a thorough knowledge of the featured skill sets of a data engineer.

1. Programming Languages

Programming languages pave the way to communicate with machines. Do you want to become the best in programming? Not at all. But you will have to be comfortable with it. You will be required to code the ETL process and construct data pipelines throughout the process.

The most popular and renowned programming languages include.

JavaScript

Python

C++

Scala

Swift

Haskell

R

2. SQL Databases

If you are aspiring to become a data engineer, you cannot quit learning about databases. In fact, you have to become intimately acquainted with how to handle databases, how to quickly execute queries, etc., as professionals because there’s just no way around it!

SQL databases are relational databases that store data in various relevant tables SQL is an absolutely essential skill for every data professional. Regardless of whether you are a data engineer, a Business Intelligence Professional, or a data scientist, you have to need Structured Query Language (SQL) in your day-to-day work. For this, you must have to know about how to;

  • Insert, update, and delete records from your database
  • Make reports and perform basic analysis using SQL’s aggregate functions
  • Perform proficient joins to bring your data from multiple tables

3. NoSQL Databases

In order to deal with the huge amount of data, we need a more advanced database system that can run multiple nodes and can store as well as query a huge amount of data. Like, you are generating data at an unparalleled pace and scale right now as text, images, logos, videos, etc.

There are various sorts of NoSQL databases; among those, some are assumed to be highly available and while some are highly consistent. Some of them are column-based, some are document-based, and some are graph-based databases.

Being a data engineer, you must have knowledge of how to choose the appropriate database for your use case and how to write optimized queries for these databases.

4. Apache Airflow, Apache Spark, and Apache Kafka

4.1 Apache Airflow

In each industrial sector, automation of work has great significance because it is assumed as one of the fastest and quickest ways to get functional efficiency.

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Apache Airflow is an essential and fundamental tool in order to automate some tasks. With this, you do not have to end up manually doing the same things repeatedly.

Generally, data engineers need to handle multiple workflows, such as; collecting data from multiple databases, pre-processing it, and uploading it. And Apache airflow will be very helpful for you in this regard.

4.2 Apache Spark

Here is another highly effective data processing framework in enterprises of the modern age. Apache Spark got a huge hype in the data science world. Although, its cost is relatively higher than other data processors and needs enough memory in your PC.

So, if you want to pursue a career as a data engineer, Spark will prove to be the best companion for you on this journey. Because it grants you support in various languages, i.e., Java, Scala, Python, and R. Moreover, it provides a whole framework in order to process streaming data, structured data, and graph data as well. And more interestingly, you can also mentor machine learning models too on big data and make ML pipelines.

4.3 Apache Kafka

In the current scenario, handling streaming data sets is becoming one of the most crucial and sought skills for scientists and data engineers. The main reason behind this is that today every business from mediocre to high level needs to track, analyze, and process real-time data.

To tackle all of these situations, Apache Kafka is assumed as the most-needed skill in all industries. So, if you want to be a data engineer, be a master of this soft skill to land your next-level role.

5. Hadoop Ecosystem

In order to handle big data, Hadoop Ecosystem grants a proper framework.

In this fast-paced world, big data is continually generated in all sectors of life on a daily basis. And to manage this massive amount of data via old traditional modes is not so easy for now. So, having various operational frameworks and processing models, Hadoop will be a helping hand to tackle big data. Thus, this open-source project must be learned by a data engineer.

6. ELK Stack

In the world of data science, the ELK stack got much hype because world-renowned companies are using ELK stack to a great extent.

It’s an astounding combo consisting of three open source products.

  • Elasticsearch: A particular form of NoSQL database that permits storing, searching, and analyzing a big volume of data.
  • Logstash: A data collection pipeline tool that can collect every sort of data from any source and ensure it for further use.
  • Kibana: To visualize data of any form, especially elasticsearch documents with various forms of charts, tables, and maps.

Wrapping up

This guide will play a key role in your journey of becoming a data engineer because all the major skills have been discussed here that will assist you in finding your role.

Last but not least, in this digital world, there is a huge requirement for skilled and educated people in the market. So, do something to revolutionize the world more. And Data Science is one of the biggest revolutions of this time.

Author Bio: Steve Parkar has extensive experience with inbound marketing for various industries like eCom, Mfg, Real-estate, Education and advertising. Having worked with a Digital Marketing Agency , he has gained expertise in digital content creation, SME acquisition, and white hat linking.

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