Data Engineering: Skills in Demand

The foundation of data engineering – programming involves writing and maintaining the code necessary primarily for data extraction, transformation, loading and analysis.

Key technologies:

• Python, known for its simplicity and the extensive libraries available.
• SQL, essential for data manipulation in relational databases.
• Scala, used with Apache Spark for big data processing

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.

Key technologies:

• AWS, including services like S3, Redshift and EMR
• Azure, including services like Azure Data Lake and Azure Databricks.
• GCP, including services like BigQuery, Dataflow and Pub/Sub.

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 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.

Key technologies:

• Azure Data Factory
• AWS Glue
• Airbyte
• Talend
• Fivetran

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.

Key technologies:

• Github
• Bitbucket
• GitLab

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.

Key technologies:

• Synapse, Azure’s limitless analytics service
• Redshift, AWS’s data warehousing service
• BigQuery, GCP’s serverless, highly scalable data warehouse
• Snowflake, a cloud-native data warehousing solution
• Databricks, a managed Spark service which can be used as a data warehouse

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.

Key technologies:

• AWS S3
• Azure Data Lake Storage
• Google Cloud Storage
• Hadoop Distributed File System (HDFS)

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.

Key Technologies:

• Databricks
• Snowflake
• Azure Synapse

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.

Key technologies:

• Apache Airflow, for defining, scheduling and monitoring of workflows.
• Dagster, focuses on building and maintaining data pipelines.
• Prefect, for building, observing, and reacting to workflows.
• AWS Glue, a ETL tool that packs all the features required to build the pipeline into a single service.
• Google Cloud Composer, a fully managed workflow orchestration service built on Airflow.

They coordinate various stages of data pipelines, handle dependencies, and manage resource allocation, which is crucial for reliable data processing and reporting.

Containers are lightweight, standalone, executable software packages that include everything needed to run an application. Orchestrators manage these containers in production environments.

Key technologies:

• Docker, for creating and managing containers.
• Kubernetes, for automating deployment, scaling and management of containerised applications.
• GKE, ECS & AKS (managed services for Kubernetes from major cloud providers)
• Apache Mesos, to manage computer clusters.

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 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.

Key technologies:

• Apache Spark, an engine for large-scale data processing, known for speed and ease of use.
• Apache Flink, a framework for stateful computations over data streams.
• Amazon EMR (Elastic MapReduce), a cloud-native big data platform that provides a managed framework for stream processing as well as big data analytics.
• Apache Kafka, a distributed streaming platform for high-throughput, low-latency messaging).
• Apache Pulsar, known for its messaging and streaming capabilities, addressing some of the limitations of Kafka.
• AWS Kinesis, Azure Event Hubs & Google Pub/Sub, cloud-based services for real-time data.

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.

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.

Key technologies:

• Terraform, enables users to define and provision a data centre infrastructure using a high-level configuration language.
• AWS CloudFormation, for creating and managing a collection of related AWS resources, provisioning and updating them in an orderly and predictable fashion.
• Ansible, used for configuration management, application deployment, and automating repetitive tasks.

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.

Key Concepts:

• Continuous integration/continuous deployment (CI/CD) for data pipelines
• Automated testing
• Monitoring for data quality.
• Accuracy and automatically generates documentation of the data models and transformations

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.

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.

Key Features:

• Code-First Approach: DBT allows users to write transformations as code, primarily SQL, making it accessible to analysts who might not be familiar with more complex programming languages.
• Version Control Integration: Integrates with systems like Git for improved collaboration and change tracking.
• Data Modelling: Aids in creating complex data models and maintaining consistency in transformations.
• Testing and Documentation: Offers robust capabilities for data testing and generating documentation.

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.