Data Analyst and Data Engineer

Difference between a Data Analyst and a Data Engineer

Data is king! In today’s world, where everything runs digitally, Data is the life and blood of every business operation. The increase in the use and importance of the data has led to an increase in career opportunities in the data science field. Two of the most popular data science career options are in Data Analytics and Engineering. And thus, this article discusses the similarities and dissimilarities between a Data Analyst as well as a Data Engineer.

Who is a Data Analyst?

A Data Analyst in the field of Data Science analyses data to draw meaningful conclusions for making informed decisions. The work of a Data Analyst includes using automated mechanical techniques and algorithms to interpret and conclude data. By using these techniques, a business can find and utilise necessary information from a large pool of data. Big Data Analytics can help a business in:

  • Analysing and identifying which business operations are working effectively.
  • Reducing costs by making the processes more efficient and storing all the data.
  • Analysing customer trends such as their needs, preferences, and satisfaction level to make better and more relevant products and services.

Types of Data Analytics that Data Analysts work with

Data Analysts have to deal with different analytical tools, but there are also different kinds of analytics that they work on to fulfill a business’s data demands.

  • Descriptive Analytics – this type gives details about activities that take place over a specific period of time. It deals with finding out when and how many units of a product are sold, when was the sale highest, etc.
  • Predictive Analytics: This is the most commonly used kind of Big Data Analytics. It helps predict what will happen in the future and helps the business identify trends, causes, and correlations.
  • Diagnostic Analytics: This type deals with past activities and answers why a specific thing happened. Diagnostic Analytics uses techniques like drill-down, data mining, data discovery, and correlation to find out the causes of past activities.
  • Prescriptive Analytics: Prescriptive Analytics combines AI and Big Data to help businesses in choosing the best course of action. It allows businesses to look at different alternatives, analyse them and then select the one that works the best.

Data Analysts work with numbers and complicated computer applications for which they need a good hold of statistics, mathematics, and computer languages. 

Data Analysts concern themselves with interpreting, analysing, and finding patterns only. Then where does this Data come from? This is where Data Engineers come in.

Who is a Data Engineer?

A Data Engineer is the one who’s responsible for the collection of data that is later analysed for decision making. These engineers perform the primary function in the process of working with data. They apply computer science to practical and functional mechanisation to collect and validate data.

Just like other engineers, Data Engineers too are concerned with building things. The difference here is that they design and build pipelines to transmute and transport data to the users like Data Analysts.

The tasks of a Big Data Engineer include:

  • Creating and maintaining data architecture
  • Managing and setting up databases
  • Designing and building data infrastructure

How can Big Data Engineers be classified?

Data Engineering is not limited to creating pipelines only; it has a much wider purview. The roles of a Data Engineer can be divided into three parts based on the size and work of the organisation.

  • Generalist

The generalist usually works in a small organisation and is the only person whose work involves dealing with data. A generalist Data Engineerdoes everything from collecting, managing, and analysing data.

  • Pipeline Centric

Pipeline-centric Data Engineers work in mid-sized organisations where complex Data is used. These organisations have a separate team that works with data, and Big Data Engineers collect, organise and share data with data scientists. These engineers have a thorough knowledge of distributed systems and computer science.

  • Database Centric

A database-centric engineer works with databases, from setting up to populating analytics. This engineering role is broader. It involves pipelines, creating and managing databases, and table schemas. These engineers work at big organisations where a good network of people working with data exists. Some common tools used in database-centric engineering are ETL and data warehouses.

Data Engineers create databases. The work and skillset are somewhat similar to that of a software engineer as it too requires a background in programming.

What are the differences between a Data Analyst and Data Engineer?

Both Data Analytics and Engineering are emerging as the number one career option for many people who want to work with numbers and advanced technology. Often, people get these terms confused with each other, but there are some factors that distinguish the two. Let’s look at them.

  • Meaning

A Data Engineer collects data and prepares databases. Their tasks include developing, testing, and maintaining data architectures that data scientists later use. Whereas Data Analysts study and analyse data for patterns to help the managers make informed strategic decisions.

  • Skills required

Becoming a Data Analyst requires skills like processing and inspecting data, while a Data Engineer requires programming skills.

The technical skills used by the two are mentioned in the table below.

Data Analyst

Data Engineer

Data warehousing

Data warehousing and ETL

Statistical and scripting skills

Thorough knowledge of SQL databases.

Data visualisation tools

Big Data tools

Adobe and Google analytics

Hadoop based analytics

Spreadsheets knowledge

ML concept knowledge

  • Roles and responsibilities

Data Analysts are responsible for reporting, visualising, statistical analysis, and interpretation of data. They optimise statistical tools to make data more efficient and improve its quality. On the other hand, Data Engineers create, test, and manage database architecture. They transport data to the users through pipelines and usually work in the background.

  • Salary

On average, a Data Analyst earns $59000/year, while a Big Data Engineer earns $90,8390. Since the work for engineering requires more specialised knowledge, the pay for it is higher than for Data Analysts.

How are the two fields interlinked?

Even though many differences separate Big Data Analytics and Data Engineering, there is one main thing that interlinks the two disciplines: data. Both Data Analysts and Engineers are concerned with working with data and having a database that is accurate, readable, and can be analysed to help organisations in making informed decisions.

It can be said that Data Engineers lay down the groundwork for Data Analysts to work on. The data used in Big Data Analytics is collected and prepared by the Data Engineers. While creating databases, the engineers make sure that the data is framed in a way that facilitates the analysis and interpretation.

Another factor that is common between the two is that both Data Engineers and Data Analysts need to know Big Data, system operations, and program languages because working with data depends heavily on computer science. It’s just the extent of the knowledge that differs between the two.

Bottom line

If you are thinking of working in the data science field, then you must understand the difference between the various job roles it provides. If you are confused about whether you should be a Data Analyst or a Data Engineer, then you must focus on your skillset. If you’re better at programming than you’re at analysing trends, then you can consider becoming a Big Data Engineer. On the other hand, if you have a knack for analysing numbers, then Data Analytics can be best for you.

Leave a reply:

Your email address will not be published.

Site Footer