We live in the transition phase. A phase wherein everything rapidly changes from the real world to the virtual world. Many activities accomplished manually with a lot of effort earlier are now completed digitally on a computer sitting at homes or offices. Digital transformation is a new reality, particularly in times of pandemics. While some organizations are willing to transform their businesses into a virtual world to enhance their customer base, others are compelled to do so. Companies embracing transformation and working on innovative techniques are at a competitive advantage. Companies need extensive amounts of data to adapt to digital transformation for an unmatched end-user experience.
It is not an exaggeration to say that the contemporary era belongs to data. Everything around us, every tool, every technology uses extensive amounts of data. Better the tracking dev performance, the better for the companies. In each department and every level of the organization, employees use data. Data helps in everything from bigger research and development projects to smaller day-to-day activities. As such, companies have now started to offer data engineering Bootcamp to train their employees on handling data. More data is beneficial for organizations; however, it is quite challenging to effectively manage the vast unstructured information commonly referred to as big data.
In simple terms, BigData is a high volume of data collected from various sources and in multiple formats. Big Data is beneficial if transformed into a more readable form to identify trends and patterns. A team of data experts collects, cleans, stores, retrieves, analyzes, models, and visualizes big data to obtain meaningful insights. Without the help of a data expert collecting vast amounts of data does not achieve the desired outcome. Data experts are professionals skilled in handling extensive data and are further categorized based on their work to accomplish the data transformation. Two of the most promising critical roles for organizations over the past few years are data engineer and data scientist. As both work closely, it is sometimes difficult to clearly understand the difference for beginners.
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The current article tries to build an understanding of what data engineers and data scientists refer to and the key differences between them.
What is a Data Scientist?
Data scientist is a research-oriented role wherein one collects data relevant to the issue, processes it, and models it to arrive at the desired outcome. Data scientists work closely with other data experts, namely data engineers and data analysts. Data scientists work on data exploration for innovative solutions for each unique business problem. Data scientists must work on advanced machine learning tools and data techniques such as neural networks, regression analysis, data clustering, decision trees, statistics, etc., to identify trends and patterns.
Some of the responsibilities handled by data scientists include:
- Research on data gathering against defined business problems.
- Work with data engineers for Merging, cleaning, processing, and visualizing data to unearth hidden insights from data.
- Developing algorithms and mathematical models for problem-solving or product enhancements.
- Testing, validating, and optimizing existing models and implementing them on larger data sets.
- Summarising and communicating findings to stakeholders for support in decision makings.
What is a Data Engineer?
A data engineer needs to ensure that the required data is available in the correct format to other team members for further analysis. To ensure data availability, data engineers work on planning, developing, and maintaining data architecture and pipeline. In other words, data engineers focus on data pipelines to collect, clean, and transform raw data into a usable format. Developing a pipeline may require writing complex scripts and designing databases.
Some of the responsibilities of a data engineer include:
- Working on data cleaning, concatenation, and conversion from different data sources and storing it in a data warehouse.
- Developing pipelines that transform raw data into usable formats for further processing.
- Creating and maintaining the infrastructure, namely servers, databases, and processing systems.
- Supporting in writing complex queries for transforming data.
- Optimizing the resources and working on improving the reliability and efficiency of the system.
- Ensuring access to the data to all team members, all the time.
- Risk analysis and mitigations in case of unexpected failures.
Data Engineer vs. Data Scientist
Tools Proficiency Requirement:
Data scientists are required to use scripting languages such as Python, Java. Sometimes they use analytical and visualization tools such as Tableau, PoweBI, Rapidminer, Splunk. Data Scientists work with machine and deep learning libraries extensively, including TensorFlow, PyTorch, Theano, Caffe, Keras, and Scikit-Learn.
Data engineers require extensive command of programming languages such as Python, Java, R, or Scala. They should be proficient in multiple programming languages. Data engineers need to work on databases such as MongoDB, Cassandra, SAP, Oracle, PostgreSQL, Apache Hive. They need to work on distributed systems, data pipelines tools such as Apache Kafka, IBM InfoSphere DataStage, Pentaho, Talend, Apache Hadoop, and Apache Spark.
Areas of Focus:
The data scientists concentrate on finding solutions for critical business problems such as improving the product, reducing product costs, optimizing operations, enhancing customer base, bringing greater customer satisfaction, preventing future losses, etc. They work on the data provided to design mathematical models, interpret data, make predictions and deliver outcomes to improve business decisions. While data engineers gather, clean, integrate, optimize and store data to warehouses for further work on the data. It requires a focus on writing complex queries, designing data pipelines and data flow to facilitate the transformation of raw data.
Both data scientists and data engineers are high-paying and lucrative job roles. Many prominent recruiters are constantly looking for good profiles, including Amazon, TCS, Infosys, IBM, Accenture, Capgemini, Ernst & Young, Microsoft, Facebook, General Electric, and Apple. According to PayScale, here are their average annual salary:
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- Data Scientist
- India – INR 824,359
- The US, US$ 96,589
- Data Engineers
- India – INR 833,384
- The US – US$ 92,664.
Data scientists should be proficient in scripting, data wrangling, visualization, advanced mathematics (algebra, differentiation, integration), probability, statistics, numerical methods, machine, and deep learning principles, along with good communication and logical skills. Data engineers require proficiency in multiple programming languages and a stronghold in distributed systems, data pipelines, and data flow architecture—in-depth understanding of database design, configuration and query languages, sensor configuration
After reading this article, It will be easier for anyone to distinguish between data engineer and data scientist roles clearly. Both roles are high-paying, stable, and highly valued in all big organizations. It is entirely up to a professional’s interest and career aspiration to choose between both complementary roles. Big tech firms are constantly looking for skilled individuals for both job roles. One should take the opportunity at hand in soaring demands. One should start gaining the essential skills by enrolling in an excellent online training program. Ample reputed data engineering and data science courses are available to boost one’s knowledge and make one more employable. So, don’t wait; explore all options and dive into the data world today.