7 Data Engineering Tools for Beginners

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Data engineering is an often underrated yet highly lucrative field that forms the backbone of data analysis and machine learning. While many gravitate towards data analysis or machine learning, it is the data engineers who provide the essential infrastructure and data required for analysis and model training. With an average salary of $150K USD per year and the potential to earn up to $500K USD.

In order to begin working in this field, it is important 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 top-tier 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 simple Python API, making it easy for anyone to 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 lets you save results, deploy the workflow, automate the workflow, and receive notifications of run status.

 

2. PostgreSQL

 

PostgreSQL is a secure and high-performance open-source relational database. It focuses on data integrity, security, and performance, making it an excellent choice for beginners in need of a robust database solution. 

PostgreSQL is a popular and sometimes the only choice for all data-related tasks. You can use it as a vector database, data warehouse, and optimize it for use as a cache.

 

3. Apache Spark

 

Apache Spark is an open-source unified analytics engine designed for large-scale data processing. It supports in-memory processing, which significantly speeds up data processing tasks. Apache Spark features Resilient Distributed Datasets (RDDs), rich APIs for various programming languages, data processing across multiple nodes in a cluster, and seamless integration with other tools. It is highly scalable and fast, 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 a data warehouse. Fivetran's ease of use 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 Build Tool)

 

dbt is an open-source command-line tool and framework that empowers data engineers to efficiently transform data within their data warehouses using SQL. This SQL-first approach makes dbt particularly accessible for beginners, as it allows users to write modular SQL queries that are executed in the correct order. dbt supports all major data warehouses, including Redshift, BigQuery, Snowflake, and PostgreSQL, making it a versatile choice for various data environments. 

 

6. Tableau

 

Tableau is a powerful business intelligence tool that allows users to visualize data in their organization. It provides an intuitive drag-and-drop interface to create detailed reports and dashboards, making it accessible for beginners. Tableau's ability to connect to various data sources and its powerful visualization tools make it an excellent choice for analyzing and presenting data effectively for non-technical stakeholders.  

 

7. Apache Kafka

 

Apache Kafka is an open-source distributed streaming platform used for building real-time data pipelines and streaming applications. It is designed to handle high-throughput, low-latency data streams, making it ideal for real-time data processing. Kafka's robust 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 mix of data orchestration, transformation, warehousing, visualization, and real-time processing capabilities. By mastering these tools, beginners can take a step towards becoming professional data engineers and work with top-paying companies like Netflix and Amazon.
 
 

Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master's degree in technology management and a bachelor's degree in telecommunication engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.

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