Data Engineering Toolbox
Published: 2022-09-16 15:31:10

## **Data engineering toolbox**
 
A data engineer is an IT professional whose responsibility is to ensure that data is available in the right place, secure, and in the required form for analysis. They are referred to as data engineers because their work revolves around designing systems and processes that to collect data from diverse sources and lead them to a central storage i.e data warehouse or a data lake. To do this a data engineer needs several tools and technologies. The tools and the skills vary depending on the amount of data to be handled or processed. A data engineer must work in conjunction with other departments in the company they work for to better understand the requirements of the data they need to work on. Mostly they work with executives, data analysts and data scientists. after understanding the kind of data the company needs the engineer will advise the company on the technology they need to deploy. In this post, I will discuss the various tools an engineer can have in their toolbox and their use cases and options.
**Data engineering skill sets**
To be able to design systems and solutions for data engineers must be equipped with software development technologies, having a development mindset enables them to use a wide variety of programming languages and even learn new ones easily to build data pipelines through which data passes, in this pipelines the data is transformed and put in the required form before being deposited in the data warehouse or data lake since they have a background. It is impossible to master all the available languages but a solid foundation is enough to learn a new technology on the go. Below is a listing of the technologies in data engineering in job listings in the year 2020 by Jeff Hale.
 
**SQL**
Structured query language(SQL) is a language used when communicating with relational databases. 
Relational databases are databases that store related data i.e data organized in preset conditions and relationships where data is fed in tables with rows and columns. Some people have argued that SQL is not one language and they have divided it into data definition language(DDL) – This one deals with creating or modifying the database like creating and altering tables e.g using CREATE and ALTER commands, data manipulation language(DML) – this enables users to query data using commands like SELECT, UPDATE, DELETE, etc and data control language(DCL)- this enables access controls and security using commands like GRAND and REVOKE.
SQL has several that have some features different but advanced from the standard SQL, some of these dialects include ;
PL/SQL – procedural language/SQL
Transact- SQL 
PostgreSQL 
MySQL
This is a must-have tool for all professionals working with the data.

**Python** 
This is the most used language in the field of data it is a general purpose, high-level, interpreted programming language. Being a higher-level language it is easier to learn since beginners will not have to learn or understand what happens under the hood when a program is run.
Being a general-purpose language it can be used for quite a variety of domains like web applications development, automation, data engineering, data science, AI, machine learning, software development, mobile applications and so much more.
Being an interpreted language it doesn’t need a compiler and uses an interpreter that reads the source code line by line while it is executing. The main reason why python has become the de facto language for data is because of its simple syntax and its rich third-party libraries that have been developed for data applications.
**NOSQL**
NoSQL refers to an approach to storing and accessing data that is unstructured unlike in relational databases, NoSQL data is modeled in other forms other than the tabular forms in relational databases, NoSQL is preferred where high scalability and availability are required especially in big data where data is continuously growing. Examples of NoSQL  databases are MongoDB and Cassandra.
**Cloud platforms**
Due to the huge amount of data being generated each day data engineers must be well of the different cloud technologies available to store data in the cloud there are many cloud platform providers and mastering and or more is an invaluable tool for a data engineer. Amazon Web services(AWS), Microsoft Azure, and Google cloud platforms are the leading in the industry.

**Open frameworks **
There are several data engineering frameworks used to work on big data mastering the following will keep you equipped for data engineering roles;
1.	Apache Spark
2.	Hadoop
3.	Kafka
4.	MapReduce
5.	Perhaps Hive
6.	Apache Airflow
7.	Apache Storm
8.	Apache SAMOA(Scalable Advanced Massive Online Analysis).

In conclusion, data engineering is an ever-growing and evolving field and new tools are being invented daily to be efficient one has to keep learning to remain upto date. The goal is to develop efficient systems that are stable and reliable in collecting and maintaining data. A solid foundation of the above technologies will keep you ahead and you can always learn other tools on the go.