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Guide to Become a Data Engineer

  • Amruta Bhaskar
  • Sep 30, 2020
  • 0 comment(s)

From helping cars drive themselves to help Facebook tag you in photos, data science has attracted a lot of buzz recently. Data scientists have become extremely sought after, and for good reason — a skilled data scientist can add incredible value to a business. But what about data engineers? Who are they, and what do they do?

A data scientist is only as good as the data they have access to. Most companies store their data in a variety of formats across databases and text files. This is where data engineers come in — they build pipelines that transform that data into formats that data scientists can use. Data engineers are just as important as data scientists but tend to be less visible because they tend to be further from the end product of the analysis.

A good analogy is a race car builder vs a race car driver. The driver gets the excitement of speeding along a track, and thrill of victory in front of a crowd. But the builder gets the joy of tuning engines, experimenting with different exhaust setups, and creating a powerful, robust, machine. If you’re the type of person that likes building and tweaking systems, data engineering might be right for you.

The data science field is incredibly broad, encompassing everything from cleaning data to deploying predictive models. However, it’s rare for any single data scientist to be working across the spectrum day today. Data scientists usually focus on a few areas and are complemented by a team of other scientists and analysts.

Data engineering is also a broad field, but any individual data engineer doesn’t need to know the whole spectrum of skills. In this section, we’ll sketch the broad outlines of data engineering, then walk through more specific descriptions that illustrate specific data engineering roles.

A data engineer transforms data into a useful format for analysis. Imagine that you’re a data engineer working on a simple competitor to Uber called Rebu. Your users have an app on their device through which they access your service. They request a ride to a destination through your app, which gets routed to a driver, who then picks them up and drops them off. After the ride, they’re charged and have the option to rate their driver.

Data engineers commonly deal with both structured and unstructured data sets -- as a result, they must be versed in different approaches to data architecture and applications. A variety of big data technologies, including an ever-growing assortment of open-source data ingestion and processing frameworks are also part of the data engineer's tool kit.

To carry out their duties, data engineers can be expected to have skills in such programming languages as C#, Java, Python, Ruby, Scala and SQL. They also need a good understanding of extract, transform and load tools and REST-oriented APIs for creating and managing data integration jobs, and providing data analysts and business users with simplified access to prepared data sets.

Hadoop data lakes that offload some of the processing and storage work of established enterprise data warehouses have been a chief area of application for the data engineer in support of big data analytics efforts. NoSQL databases and Apache Spark systems are also becoming increasingly common components of the data workflows set up by data engineers. Another area of focus is Lambda architecture, which supports unified data pipelines for both batch and real-time processing.

As the data engineer job has gained more definition, IBM, Hadoop vendor Cloudera Inc. and other organizations have begun offering certifications for data engineering professionals.

Data engineering is a highly strategic job with many responsibilities spanning from the construction of high-performance algorithms, predictive models, and proof of concepts, to developing data set processes needed for data modelling and mining.

Here is an overview of data engineer responsibilities:

Ensuring that data storage and collection systems meet business requirements and accepted industry standards.

  • Integrating new data management software into a company’s existing structures or research new opportunities for a business’ data acquisition. This could mean helping a company come up with a new way to efficiently bring in data from a brand-new client.
  • Creating custom software components using a wide range of languages and tools — like scripting languages — to merge different systems or develop a strong analytics infrastructure for measuring your data stored by a business.
  • Storing and processing data securely at all times. Data engineers remain on the frontlines of a company’s cyber defences, installing and updating disaster recovery protocols, in addition to recommending ways to improve data reliability and quality.

Becoming a data engineer can be an opportunity to collaborate with an interdisciplinary group of people, working closely with data architects, modellers, and IT specialists to achieve different project goals.

The data engineering field is one that is constantly evolving. Shifts in the industry like the evolution of Hadoop, which is increasingly being used as an enterprise data hub, advances in processing power for predictive analytics, and a general move toward the Cloud could make a data engineer’s life more complicated. But it also presents more job opportunities.

You can work as a data engineer, a senior cloud data engineer, a senior data engineer, and a big data engineer, among other roles.

It’s an exciting time to be a data “builder.” If you love playing with new tools and can think outside the relational database box, you’ll be in a prime position to help companies adapt to the demands of this industry.

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