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Data Engineering Tech stack for aspiring data engineers
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Data Engineering: Tech stack for aspiring data engineers

In the last two years, if there is any word resonating continually in the IT world besides COVID-19, it is data engineering. With data becoming one of the most valued assets in companies, the investment and importance around building a robust data infrastructure have also grown leaps and bounds. This infrastructure is, however, complex to build and operate. Organizations need the right people and technology to ensure that the data is enriched for analysis. In this article, we list some technologies that are essential to the tech stack for data engineers.

And that is what makes the job of a data engineer the most sought-after these days. A data engineer must build, assemble complex data sets, prepare them for analysis, build data pipelines and ensure optimal data delivery. For this, they should have expertise in software, programming languages, ETL, SQL, among many other data engineering frameworks.

Some of the recommended frameworks & tech stack for data engineers

SQL: Structured Query Language (SQL) is an industry primary programming language used for managing data in relational database management systems (RDBMS). SQL is one of the critical tools to perform various operations, including creating, manipulating, and querying databases in RDBMS. It is most useful in databases where data sources and destinations are the same types.

Python: Python is a high-level programming language for web applications. One of the most significant advantages of Python is that it has a vast community and libraries. In most of the job descriptions for the data engineer, Python is listed. It is known for its widespread libraries. In addition, many open-source data application frameworks are based on Python.

Apache Hadoop: Apache Hadoop is a series of open-source software libraries that enables distributed processing of large data sets across thousands of servers and computers. Based on simple programming models, Hadoop can scale up from single to clusters of devices, depending on the data and operational mode. It supports many programming languages, including Java, Python, Scala, and R.

Azure: Microsoft Azure is a cloud technology that supports building large-scale data analytics structures. Azure provides software as a service (SaaS), Platform as a service (PaaS), and infrastructure as a service (IaaS). It supports different programming languages, tools, and frameworks. One of its most significant advantages is that it allows servers to automate with packaged analytics systems that are easily deployable.

Machine Learning: Machine learning is an essential tool for data engineers to learn. The ML algorithms, also known as models, help data scientists to make predictions on data. Learning ML allows engineers to sort and process a high volume of data in a short time. Data engineers may have only the basic knowledge of ML, but it always helps them understand the data scientists’ requirements better and therefore build data pipelines more accurately.

Essential tech stack of data engineers

 

As companies try to find more ways to optimize the value of their data for predicting sales, customer preferences, stock management, market demand, and even to build new products, the role of data engineers continues to grow in importance. Technologies for data processing are complex and are constantly evolving. As a result, companies are looking for experts who bring a combination of data technologies skill sets.

 

Know more about real time data processing vs batch processing. Read more article on data processing 

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