The role of the Data Engineer is to transform the data into a format suitable for analysis. Its main task is the preparation of data.
Like all “engineers”, the Data Engineer designs and manufactures. In this case, its role is to design and build pipelines to transform data into a format that can be used by Data Scientists and transmit it to them. This data comes from multiple sources, and is aggregated into a single Data Warehouse.
What are the Data Engineer’s responsibilities?
In detail, the responsibilities entrusted to the Data Engineer can vary greatly from one organization to another. Often, he or she must develop data pipelines to assemble data from multiple systems.
He must also integrate, consolidate and cleanse data and structure them so that they can be used in individual analytical applications. He is responsible for building and maintaining the data infrastructure on which the company’s IT systems and applications are based. He must design, develop and install the data systems that feed the Machine Learning and AI analyses.
The data engineer usually works as part of an analytical team. Their role is to provide ready-to-use data to Data Scientists so that they can perform queries for predictive analysis, Machine Learning or Data Mining.
Indeed, to analyze and exploit the data, Data Scientists need high quality data.. The role of engineers is to collect and prepare data for use.
In many companies, the Data Engineer is also working on with the different departments of the company. It provides aggregated data to executives, business analysts and other end-users. They can then rely on simple data analysis to support their operations.
What are the Data Engineer’s skills?
A data engineer must master Big Data technologies, ingestion frameworks, etc. and open source data processing, and the different approaches to data architecture. It will deal with structured and unstructured data sets.
The Data Engineer is generally proficient in specialized programming languages such as C#, Java, Python, Ruby, Julia, Scala, Tensorflow and SQL. At a minimum, he or she must be expert in SQL, Python and R languages.
The SQL allows it to set up, query, and managing database systems. Python is used to create data pipelines, write ETL scripts, and to set up statistical models and perform analysis.
I mean, come on, the R is used to analyze dataThe project will include the development of statistical models, dashboards and visual displays. This language is particularly useful for data analysis and Machine Learning applications.
In addition, the data engineer must also know how to work with a wide variety of platforms. The SQL relational database systems such as MySQL, PostgreSQL and Microsoft SQL Server are particularly important for setting up and configuring database systems. However, NoSQL databases such as MongoDB, Cassandra and Couchbase are also important.
The Data Engineer must also handle ETL tools. These tools allow you to extract, transform and load data into Data Warehouses. They are also used to transform and migrate data from one storage system to another.
Once extracted from the systems, the data must be prepared and integrated into a Data Warehouse system. This integration is crucial for performing queries and gaining insights. The engineer must also know how to configure a Cloud-based Data Warehouse.
However, Data Warehouses only allow you to work with structured data. The engineer must therefore handle Data Lakes, allowing to work with any kind of data.
Data pipelines allow to connect information systems. A Data Engineer must therefore be able to work with REST, SOAP, FTP, HTTP and ODBC and understand the strategies for connecting one system to another as efficiently as possible.
The Data Engineer may also acquire skills usually associated with Data Scientists. They can learn to produce interactive dashboards using Business Intelligence platforms, or to deploy Machine Learning algorithms.
The Data Engineer must have a wide variety of skills related to programming languages, databases and operating systems. However, the acquisition of skills and knowledge continues throughout one’s career.
The History of Data Engineering
It is difficult to date the appearance of Data Engineering accurately. It is in the 1980s that the term “information engineering” is coined to describe the design of databases and to include software engineering in data analysis.
After the rise of the Internet in the 1990s and 2000s, and the emergence of Big Data, database administrators, SQL developers and other IT professionals related to this field were only not yet considered “Data Engineers.” .
This term was popularized in 2011by data-driven companies like Facebook and AirBnB. The software engineers at these data-mining companies needed to create tools to exploit this information.
The role of the Data Engineer was at the time to use traditional ETL tools. However, it has evolved to develop its own tools to handle the ever-increasing volumes of data.
From now on, following the flight of the Big DataData Engineering is a category of software engineering that focuses on data through infrastructure, data warehousing, data mining, data modeling and metadata management.
Data Engineer vs. Data Scientist: What’s the difference?
The Data Scientists use statistical modeling, Machine Learning and various tools to analyze the data. They work closely with decision-makers to implement a data strategy.
They are also insight leaders. They develop data visualizations and graphs to enable decision-makers and company employees to access essential information.
The role of the Data Engineer, on the other hand, is to develop the infrastructure required for the analysisand prepare the data. He works with Data Scientists to provide them with high quality data, through pipelines that he will also create and maintain.
These data pipelines allow to connect data from different systems. The engineer must also transform the data into a suitable format to allow the scientist to analyze it.
In summary, Data Engineers work in the shadow of Data Scientists, but are just as important…. Metaphorically, Data Scientists can be compared to train drivers while Data Engineers build the rail network that allows trains to move.
The Data Scientist interacts with data by writing queries, creating dashboards and working with decision makers to understand and meet their needs. The Data Engineer develops and maintains the data infrastructure that connects the company’s data ecosystems and makes the work of Data Scientist possible.
Why are Data Engineers so sought after?
The Data Engineers are more and more sought-after in companies. For good reason, companies have understood that it is not enough to let a Data Scientist analyze the data to fully exploit it.
According to Gartner, more than 80% of Big Data projects fail. For good reason. data must be of high qualitysecure and available for Data Scientists to do their work. Otherwise, data modeling tasks cannot be performed properly.
The upstream intervention of the Data Engineer is therefore essential to the success of a Big Data initiative, a fortiori in the face of the emergence of technologies such as IoT and AI. This explains the sharp increase in job offers for data engineers.