Data Engineering for Beginners: A Step-by-Step Guide
Published: 2023-11-09 13:07:09

There has been an increase in popularity for data engineering, yet it remains a vague field. What is this data engineering? Data engineering is the process of designing, building, and maintaining data pipelines that collect, transform, and deliver data for various purposes, such as analysis, visualisation, machine learning, and reporting. Data engineering is a crucial component of data science, as it enables data scientists to access and manipulate high-quality and reliable data.

In this article, we will introduce you to the basics of data engineering, including the skills, tools, and concepts that you need to know to become a successful data engineer. By the end of this article, you will have a clear understanding of what data engineering is, why it is important, and how you can get started in this exciting and rewarding field.

Skills for Data Engineering
Data engineering is a multidisciplinary field that requires a combination of technical, analytical, and communication skills. Some of the most important skills that a data engineer needs to have are:

• Programming: Data engineers need to be proficient in at least one programming language, such as Python, Java, Scala, or R, that can be used to write scripts, applications, and algorithms for data processing and analysis. Programming skills also include the ability to use various libraries, frameworks, and APIs that can facilitate data engineering tasks, such as NumPy, Pandas, Spark, TensorFlow, and more.

• Database: Data engineers need to be familiar with various types of databases, such as relational, non-relational, distributed, and cloud-based, that can store and manage large volumes of structured and unstructured data. Database skills also include the ability to use query languages, such as SQL, NoSQL, and Hive, that can retrieve and manipulate data from different sources and formats.

• Data Pipeline: Data engineers need to be able to design, build, and maintain data pipelines that can collect, transform, and deliver data for various purposes, such as analysis, visualisation, machine learning, and reporting. Data pipeline skills also include the ability to use various tools and platforms, such as Airflow, Luigi, Kafka, AWS, and Azure, that can automate, orchestrate, and monitor data flows and workflows.

• Data Quality: Data engineers need to be able to ensure the quality and reliability of data by applying various techniques and methods, such as data validation, data cleaning, data integration, data deduplication, and data governance. Data quality skills also include the ability to use various tools and frameworks, such as Great Expectations, Databricks, and Data Quality Services, that can help data engineers assess, improve, and maintain data quality.

• Data Analysis: Data engineers need to be able to perform basic data analysis and exploration using various tools and methods, such as descriptive statistics, data visualisation, and hypothesis testing. Data analysis skills also include the ability to use various tools and libraries, such as Matplotlib, Seaborn, and Plotly, that can help data engineers create and present insightful and interactive data visualisations.

• Communication: Data engineers need to be able to communicate effectively with various stakeholders, such as data scientists, business analysts, and managers, who have different needs and expectations from data. Communication skills also include the ability to use various tools and formats, such as Jupyter notebooks, Markdown, and PowerPoint, that can help data engineers document, explain, and showcase their data engineering projects and results.

These are some of the key skills that a data engineer needs to have. Of course, there are many more skills that can be useful and beneficial for data engineering, depending on the specific domain, industry, and project. However, these skills can provide a solid foundation and a good starting point for anyone who wants to learn and practise data engineering.

Tools for Data Engineering
Data engineering involves working with various types of data, such as structured, unstructured, streaming, and batch, that can come from different sources and formats, such as web, mobile, social media, sensors, and more. To handle the complexity and diversity of data, data engineers need to use various tools and platforms that can help them collect, store, process, analyse, and deliver data efficiently and effectively. Some of the most popular and widely used tools and platforms for data engineering are:

• Apache Hadoop: Apache Hadoop is an open-source framework that allows data engineers to store and process large-scale data sets across clusters of computers using simple programming models. Hadoop consists of four main components: Hadoop Distributed File System (HDFS), which is a distributed file system that provides high-throughput access to data; MapReduce, which is a programming model that enables parallel processing of data; YARN, which is a resource manager that allocates and manages resources for applications; and Hadoop Common, which is a set of utilities that support the other components. Hadoop also supports a variety of projects and tools that extend its functionality, such as Hive, Pig, Spark, HBase, and more.

• Apache Spark: Apache Spark is an open-source framework that provides a unified platform for data engineering, data science, and machine learning. Spark supports various types of data processing, such as batch, streaming, interactive, and graph, using a high-level API that supports multiple languages, such as Python, Scala, Java, and R. Spark also offers various libraries and modules that enable data engineers to perform various tasks, such as Spark SQL, which is a module that supports structured and semi-structured data processing; Spark Streaming, which is a module that supports real-time data processing; Spark MLlib, which is a library that supports machine learning algorithms; and Spark GraphX, which is a library that supports graph processing.

• Apache Kafka: Apache Kafka is an open-source platform that provides a distributed and scalable messaging system for data engineering. Kafka enables data engineers to publish and subscribe to streams of data, such as events, logs, and transactions, that can be processed in real-time or later. Kafka consists of three main components: Kafka Producer, which is an application that sends data to Kafka; Kafka Broker, which is a server that stores and manages data; and Kafka Consumer, which is an application that receives data from Kafka. Kafka also supports various tools and connectors that integrate with other systems and platforms, such as Hadoop, Spark, Storm, and more.

• Amazon Web Services (AWS): Amazon Web Services (AWS) is a cloud computing platform that provides a variety of services and solutions for data engineering. AWS enables data engineers to store, process, analyse, and deliver data using various tools and technologies, such as Amazon S3, which is a service that provides scalable and durable object storage; Amazon EMR, which is a service that provides managed clusters of Hadoop, Spark, and other frameworks; Amazon Redshift, which is a service that provides a fast and scalable data warehouse; Amazon Kinesis, which is a service that provides real-time data streaming and processing; and more.
* Other cloud services include Google Cloud Platform, Microsoft Azure, Confluent, etc

• Snowflake: Snowflake is a cloud-based data platform that provides a data warehouse as a service. Snowflake enables data engineers to store and query structured and semi-structured data using standard SQL without the need to manage any infrastructure or scale. Snowflake also supports various features and integrations that enhance data engineering, such as data sharing, data lakes, data pipelines, data governance, and more.

• dbt: dbt is an open-source tool that enables data engineers to transform data in their warehouse using SQL. dbt allows data engineers to write modular, reusable, and testable SQL code that can be executed and orchestrated using various platforms, such as Airflow, Dagster, Prefect, and more. dbt also supports various features and integrations that improve data engineering, such as documentation, version control, data quality, and more.

• Fivetran: Fivetran is a cloud-based data integration platform that provides a fully managed and automated service for data engineering. Fivetran enables data engineers to connect and sync data from various sources, such as databases, applications, files, and events, to their destination, such as a data warehouse or a data lake, without the need to write any code or maintain any infrastructure. Fivetran also supports various features and integrations that simplify data engineering, such as schema management, data transformation, data monitoring, and more.

These are some of the most common and useful tools and platforms that a data engineer needs to use. Of course, there are many more tools and platforms that can be helpful and relevant for data engineering, depending on the specific domain, industry, and project. However, these tools and platforms can provide a good overview and a good starting point for anyone who wants to learn and practise data engineering.