In the ever-evolving field of data engineering, a myriad of tools, technologies and approaches continuously redefine the way the community handles data. This dynamic area stands at the intersection of software engineering and data science, requiring a unique blend of skills and knowledge. As data volumes grow exponentially and demands for insights increase, the role of data engineering has become more crucial than ever.
Below is an overview of the essential tools and technologies that form the backbone of modern data engineering, by highlighting those that are most in demand from our clients.
The foundation of data engineering – programming involves writing and maintaining the code necessary primarily for data extraction, transformation, loading and analysis.
In addition to basic data manipulation, programming in data engineering encompasses developing algorithms for data processing, automating data pipelines, and integrating various data sources and systems.
Cloud platforms provide virtualised computing resources, offering a suite of scalable services for data storage, processing and analytics.
These platforms enable the deployment of large-scale data infrastructure, support big data processing, and offer integrated services for analytics and machine learning.
Data Integration Tools
Data integration tools are software solutions used for combining data from different sources, providing a unified view. They play a crucial role in ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) processes, catering to the diverse needs of data warehousing, data lakes and analytics platforms.
These tools facilitate the extraction of data from various sources, its transformation to fit operational needs, and its loading into a target data store. They are essential for data consolidation, ensuring data quality, and enabling comprehensive data analysis and reporting.
Version control is the practice of tracking and managing changes to software code. It’s essential for any development process, including data engineering, allowing multiple contributors to work on the same codebase without conflict and providing a history of changes. Git is widely used for version control.
These systems facilitate collaborative development, help maintain the history of every modification to the code and allow for reverting to previous versions if needed. They are fundamental in managing the lifecycle of code in a controlled and systematic way.
Data warehouses are specialised systems for querying and analysing large volumes of historical data.
They provide a central repository for integrated data from one or more disparate sources, supporting business intelligence activities, reporting, and analysis.
Data lakes are vast storage repositories designed to store massive amounts of raw data in its native format.
Data lakes are ideal for storing diverse types of data (structured, semi-structured, unstructured) and are particularly beneficial for big data analytics, machine learning projects, and situations where data needs to be stored in its raw form for future use.
Data Lakehouses represent a paradigm that combines the best elements of data lakes and data warehouses, aiming to offer both the raw data storage capabilities of lakes and the structured query and transaction features of warehouses.
They facilitate diverse data analytics needs — from data science and machine learning to conventional business intelligence — in a single platform with improved data governance and performance.
Pipeline orchestrators are tools that help automate and manage complex data workflows, ensuring that various data processing tasks are executed in the correct order and efficiently.
They coordinate various stages of data pipelines, handle dependencies, and manage resource allocation, which is crucial for reliable data processing and reporting.
Containers and Orchestrators
Containers are lightweight, standalone, executable software packages that include everything needed to run an application. Orchestrators manage these containers in production environments.
They provide a consistent environment for application deployment, simplify scalability, and improve the efficiency of running applications in different environments (development, testing, production).
Stream Processors & Real-Time Messengers
Stream processors are frameworks designed for processing large streams of continuously flowing data. Real-time messaging systems facilitate the efficient and reliable movement of data between different systems and services instantly.
They handle tasks like data transformation, aggregation, and real-time analytics, enabling applications that require immediate insights from incoming data, such as fraud detection, recommendation systems and live dashboards. These systems are crucial for building real-time data pipelines, enabling scenarios like live data monitoring, instant data synchronisation, and real-time analytics.
Infrastructure as Code (IaC)
IaC is a crucial practice in DevOps, particularly relevant to data engineering, as it involves the management and provisioning of computing infrastructure through machine-readable definition files. This approach is critical for data engineering because it facilitates the efficient setup, configuration, and scaling of data infrastructures, which are essential for handling large-scale data operations.
Incorporating IaC practices in data engineering leads to more efficient and reliable data pipeline construction, facilitating the handling of complex data at scale while ensuring consistency and quality in data operations.
DataOps is a collaborative data management practice focused on improving the communication, integration, and automation of data flows between data managers and data consumers across an organisation. It applies the principles of DevOps (agile development, continuous integration, and continuous deployment) to data analytics.
DataOps aims to reduce the cycle time of data analytics, with a focus on process automation, data quality and security. It involves various practices and tools, including but not limited to version control, to streamline the data lifecycle from collection to reporting.
Data Build Tool
DBT is an open-source tool that enables data engineers and analysts to transform data in the warehouse more effectively. It is distinct for its ability to apply software engineering practices to the data transformation process in a data warehouse.
DBT’s unique combination of features, focusing on the transformation phase with a developer-friendly approach, sets it apart in the data engineering toolkit. Its growing popularity and community support reflect its effectiveness in bridging the gap between traditional data engineering and analytics functions.
The data engineering landscape is vast and can seem overwhelming, especially for those new to the field or looking to keep pace with its rapid evolution. This discipline, essential in today’s data-driven world, encompasses a wide array of tools and technologies, each serving specific roles in the processing, management and analysis of data.
My experience over the past five years as a specialist data engineering recruiter has given me insight into the changing dynamics of the field. The growing need for expertise in cloud platforms, data lakes, stream processing, and emerging areas like DataOps and DBT, underscores the industry’s evolving requirements. Understanding these tools and technologies is crucial, not just for managing data but for adapting to the technological shifts in the landscape.
Both aspiring data engineers and experienced professionals face the challenge of continuous learning and skill enhancement. For hiring managers and talent teams, comprehending these technologies’ complexities, the difficulties in acquiring skilled talent, and navigating associated salary costs can be daunting tasks.
Recognising these challenges, ADLIB are dedicated to providing support and guidance to candidates, hiring managers and internal talent teams navigating the nuances of data engineering roles. If you seek to understand the current tools in demand, the details of acquiring specific skills, or need insights into salary implications, please get in touch. – Scott Rogers – Principal Recruiter, Data Platform & Architecture
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