INTRODUCTION TO DATA ENGINEERING

This article is aimed at giving you a brief and straight to point introduction to Data Engineering within a short read.

Data Engineering involves gathering, cleaning, transforming, storing and maintaining big data sets. It also involves building the infrastructures required to process and analyze them. They play a major role in the data lifecycle making sure data is available in the most usable form for Data Scientists and Analysts.

Some of the Skills required by Data Engineers are various technologies and programming languages required for data processing and integration Examples are SQL, Python, Spark, Hadoop, ETL(Extraction, Transformation, Load) tools, Scala, Java, PHP etc

Who is a Data Engineer and What are some of the key responsibilities of Data Engineers

• Data Pipeline Development: this involves building scalable and efficient pipelines to collect, ingest and transform data
• Data Warehousing: Designing and maintaining data warehousing solutions to store and organize large datasets for easy access and retrieval.
• Data Transformation and integration: Applying data transformations, data cleaning, and data integration techniques to ensure data quality and consistency across different systems.
• Data Modeling: Designing and implementing data models that facilitate data analysis and reporting requirements.
• Manages and maintains the infrastructure, logging of process and error
• Documentation of the whole process
• Collaborates with cross-functional teams, including data scientists, analysts, and business stakeholders, to understand their data needs and provide them with the necessary infrastructure and tools.

Let take a brief look at some Data Engineering tools and terminologies

ETL PIPELINE

This is the most common architecture in data engineering meaning Extraction, Transform and Load

DATA WAREHOUSE

A data warehouse is centralized repository containing optimized relational database for reading, aggregating and querying large volume of data.

Among the many valuable things that Data Engineers do, one of the highly sought-after skills is the ability to design, build and maintain data warehouses.

NOTE: Modern DW can also support unstructured data like images, audio, pdf.

DATA MARTS

Data marts are usually a smaller sized data warehouse with sizes not more than 100GB. They become needed when the company and the amount of its data grows and it becomes tedious and cumbersome searching through Enterprise DW.

DATA LAKE

Data lakes is a system, repository or pool of data stored in the natural/raw and unprocessed formats. It can contained both structured and unstructured data.

Others tools and terminologies to name a few are Data Lakehouse, Hadoop, Apache Spark, Docker, Terraform, Kafka, Apache Hive, Apache Flink, OLAP and OLAP Cubes etc.

As earlier stated, this article was aimed at giving you a short introduction to Data Engineering. I will post more articles as a follow up, do well to research more on your own if you require more knowledge in the field of DE. Thanks for reading

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