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Data engineering is one of the fastest-growing fields. With data growing in volumes, forms, and varieties, the act of processing, storing, and analyzing it calls for new and emerging tools and technologies in greater demand. This is going to boost your capabilities phenomenally as a data engineer once you enter 2024. Here are the top 10 data engineering tools to master in 2024, with unique features and advantages.
Apache Spark is the open-source in-memory data processing engine for handling giant-sized datasets. It provides an interface to programmatically work with whole clusters through implicit data parallelism and built-in support for general-purpose computation. It is known to be fast and friendly to use, which is why it is used so widely by data engineers for batch processing, real-time data processing, and machine learning.
Apache Kafka is a distributed streaming application that can process real-time data feeds, that is, it is used for constructing real-time data pipelines and streaming applications. It can process and store streams of records in a fault-tolerant manner, making it one of the important tools for data engineers working with real-time data.
Apache Airflow is an open-source platform for programmatically authoring, scheduling, and monitoring workflows. Its extensibility allows even data engineers to build complex pipelines easily and since it handles dynamic pipeline generation along with robust scheduling capabilities, this must-have tool is truly a magic trick of data infrastructure.
dbt is an interface command-line utility that allows data analysts and engineers to transform data within a warehouse more efficiently. The facility allows users to write modular SQL queries, test data quality, and document data transformations. Due to its focus on transformation, it turns out to be a valuable tool in the hands of data engineers, coupled with its connection to modern data warehouses.
Snowflake is a cloud-based warehousing solution, which offers high performance, scalability, and concurrency. It supports lots of data types and comes with robust security features. The potential to carry huge amounts of data without much difficulty makes it one of the top options for data engineers when seamlessly integrated with other data tools.
It is a development platform that empowers the developer to automate the deployment application within light portable containers, hence, of very high importance for a data engineer, as it gives a constant environment for developing, testing, and also deploying the data applications. It allows isolating applications and managing dependencies on Docker.
Kubernetes is open-source software for automating deployment, scaling, and the management of containerized applications. It was designed to work well with Docker. Used to manage thousands of containers, its capabilities of managing container orchestration with scalability make it a significant tool for data engineers.
Terraform Terraform is an open-source IaC tool allowing users to define infrastructure in data centers using a high-level language that reads like configuration code. The tool is used for cloud services and infrastructure management. Data engineers consider Terraform a good tool to automate the management of infrastructure and the support it provides for multiple cloud providers.
Tableau can be used in data visualization, allowing data engineers and analysts to come up with an interactive as well as sharable dashboard. This tool connects to different data sources and accommodates a great variety of visualization options. The ease of use and its ability to accommodate large datasets make it the most sought-after application for data visualization.
Fivetran is a fully managed data pipeline tool that automates the integration of many sources into a data warehouse. It supports several data sources and automated schema management. Its strength in simplifying data integration and powerful data connectors make Fivetran a key tool for data engineers.
Mastering these top 10 data engineering tools for 2024 will equip you with the skills needed to work on complex data engineering tasks efficiently. Each tool offers unique features that are tailored toward the different aspects of data engineering-that is, toward better data processing and real-time streaming, data visualization, and infrastructure management
