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The main tools used in Data Engineering in 2024

In 2024, the field of Data Engineering continues to evolve rapidly, with the emergence of new tools and technologies designed to effectively manage ever-growing volumes of data. Here's a look at the main tools that will dominate the data engineering landscape this year.

1. Apache Spark

Apache Spark remains a pillar of massive data processing. This distributed processing engine offers in-memory computing capabilities, accelerating big data processing tasks. It is widely used for applications such as machine learning, real-time streaming and interactive analysis.

2. Docker and Kubernetes

Containerisation has become essential for the deployment and management of data engineering applications. Docker makes it possible to create isolated environments for applications, ensuring their portability and consistency between different environments. Kubernetes orchestrates the deployment, scaling and management of containers, making it easier to manage complex infrastructures.

3. Terraform

Terraform is an Infrastructure as Code (IAS) tool that enables cloud infrastructures to be provisioned and managed in a declarative way. It is particularly appreciated for its ability to manage multi-cloud infrastructures, giving Dev Ops and Data Engineering teams greater flexibility.

4. Snowflake

Snowflake is a cloud-based data warehousing platform for storing and analysing large-scale data. It offers a unique architecture that separates storage and computation, enabling independent scaling and optimised performance for analytical queries.

5. DBT (Data Build Tool)

DBT is an analytical engineering tool that enables data to be transformed directly in the data warehouse. It facilitates the creation of models, the management of dependencies and the documentation of data transformations, thereby improving the quality and maintainability of data pipelines.

6. Apache Kafka

The role of data analyst requires both solid technical skills and people skills. Technically, an analyst needs to master data manipulation tools (SQL, Python), visualisation software (Tableau, Power BI) and statistical methods. At the same time, communication skills are essential to convey insights clearly and convincingly to non-technical teams.

7. Airflow

Airflow is an open-source workflow management tool for planning, monitoring and managing complex data pipelines. It uses directed acyclic graphs (DAGs) to represent workflows, offering the flexibility and extensibility that Data Engineering teams appreciate.

Conclusion

In 2024, data engineering professionals will have a panoply of powerful tools at their disposal to manage data effectively, from ingestion to transformation and analysis. Mastering these tools is essential for building robust and scalable data pipelines that meet the growing data needs of businesses.