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Data engineering is an often undervalued but highly lucrative field that forms the basis of data analytics and machine learning. While many people gravitate toward data analytics or machine learning, data engineers provide the necessary infrastructure and data required to analyze and train models. With an average salary of PLN 150,000. USD per year and earnings potential of up to PLN 500,000. USD.
To start working in this area, it is vital to learn tools for data orchestration, database management, batch processing, ETL (Extract, Transform, Load), data transformation, data visualization and data streaming. Each tool mentioned in the blog is popular in its category and used by leading companies.
1. Prefect
Prefect is a data orchestration tool that enables data engineers to automate and monitor their data pipeline. It provides an intuitive dashboard and a uncomplicated Python API so anyone can easily create and run workflows without hassle. Prefect allows users to efficiently create, schedule, and monitor workflows, making it a great choice for beginners. It also allows you to save results, implement workflows, automate workflows, and receive workflow status notifications.
2. PostgreSQL
PostgreSQL is a secure and competent open source relational database. Its focus on data integrity, security, and performance makes it a great choice for beginners needing a solid database solution.
PostgreSQL is the popular, and sometimes only, choice for all data-related tasks. It can be used as a vector database, data warehouse and optimized for exploit as a cache.
3. Apache Spark
Apache Spark is a unified, open-source analytics engine designed for large-scale data processing. It supports in-memory processing, which greatly speeds up data processing tasks. Apache Spark offers resilient distributed datasets (RDD), prosperous APIs for various programming languages, data processing across multiple nodes in the cluster, and seamless integration with other tools. It is highly scalable and quick, making it ideal for batch processing in data engineering tasks.
4. Fivetran
Fivetran is a cloud-based automated ETL (Extract, Transform, Load) platform that simplifies data integration. It automates data extraction from various sources, transformation and loading into the data warehouse. Fivetran’s ease of exploit and automation capabilities make it an excellent tool for beginners who need to set up reliable data pipelines without extensive manual intervention.
5. dbt (data creation tool)
dbt is an open source command-line tool and platform that enables data engineers to efficiently transform data in their data warehouses using SQL. This SQL-based approach makes dbt especially accessible to beginners because it allows users to write modular SQL queries that execute in the correct order. dbt supports all major data warehouses, including Redshift, BigQuery, Snowflake, and PostgreSQL, making it a versatile choice for a variety of data environments.
6. Image
Tableau is a powerful business intelligence tool that allows users to visualize data in their organization. It provides an intuitive drag-and-drop interface for creating detailed reports and dashboards, making it accessible to beginners. Tableau’s ability to connect to a variety of data sources and its powerful visualization tools make it an excellent choice for effectively analyzing and presenting data for non-technical stakeholders.
7. Kafka’s Apache
Apache Kafka is an open-source distributed streaming platform used to create real-time data pipelines and streaming applications. It is designed to handle high-bandwidth, low-latency data streams, making it ideal for real-time data processing. Kafka’s tough ecosystem and scalability make it a valuable tool for beginners interested in real-time data engineering.
Final thoughts
These seven tools provide a solid foundation for beginners in data engineering, offering a combination of data orchestration, transformation, storage, visualization, and real-time processing capabilities. By mastering these tools, beginners can take a step toward becoming professional data engineers and work with top-paying companies like Netflix and Amazon.
Abid Ali Awan (@1abidaliawan) is a certified data science professional who loves building machine learning models. Currently, he focuses on creating content and writing technical blogs about machine learning and data science technologies. Abid holds a Master’s degree in Technology Management and a Bachelor’s degree in Telecommunications Engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.
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