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Top Data Engineering Tools for Each Stage of a Data Pipeline

Data engineering is an in-demand and well-renowned job in the significant data sector. Data engineers create pipelines that help businesses collect, merge, and transform data to ensure hassle-free analytics. They make the infrastructure design, which allows the latest data analytics.

┬áTo create a pipeline, data engineers’ needs are categorized into different need sets. Such requirements include data collection and data merging from other sources. Now, we are going to talk about the latest data engineering tools for every stage of the data pipeline:

Docker

It is regarded as a well-renowned containerized aws data pipeline platform, which is beneficial in data engineering for creating, shipping, and running different data apps and tools. It offers a constant, portable, lightweight option to package and deploy different apps and tools, making it an excellent choice for data engineers. This tool is used to develop and handle the containers for different data tools, like data processing frameworks, data warehouses, and data visualization tools.

Secoda

aws data engineer chose this data engineering tool to consolidate the data catalog, documentation, observability, monitoring, and lineage within a single platform. Such a platform offers visibility into the pipeline metadata like popularity, query volume, and cost. These tools track and optimize the health of the infrastructure, processes, and pipelines.

Terraform

It refers to the open source IaC of Infrastructure as Code tool, which provides the ideal scope for data engineers to deploy and define the data infrastructure. It helps to describe the state and the desired phase of the infrastructure instead of the steps essential to reach the specific state.

MongoDB

It is regarded as an open-source and accessible database, which makes it the ideal choice for creating and scaling apps within the cloud. This tool indexes and maps the data. It is designed with JSON documents. Hence, you can make the best use of it to query and store the data through the programming language of your choice. As it is swift, it allows you to create the applications without stressing about performance bottlenecks.

Apache Kafka

This data engineering with aws tool helps to create the data pipeline, which manages vast data. It offers the ideal scope to process and ingest messages in real-time. It helps to store the messages in the topics so they can be retrieved later. It provides different ready-made, high-availability features. Hence, the data will be readily available and cater to the needs.

BigQuery

It refers to the powerful tool that allows the potential user to analyze massive datasets without worrying about the underlying infrastructure. The scalability and speed of BigQuery make it the right option to use AI and machine learning to gain insights from the data. Thus, you will be able to use it to store and query the data in real-time. Hence, you can choose it for different real-time dashboards, apps, and processes.

Apache Hive

It refers to the open-source data warehouse tool, which offers SQL-like language to query the data. It allows the user to query the massive datasets stored within HDFS through SQL. Hence, it is used to analyze and query the data, starting from petabytes and gigabytes.

Apache Spark

It is an open-source unified analytics engine. This data processing framework is equipped with the capabilities to run different processing tasks on massive data sets. It is also beneficial in distributing the data processing tasks across different computers.

PostgreSQL

It is a powerful open-source relational database management system with a centralized repository to store, analyze, and manage the massive structured data volume from different sources. It provides different features, such as parallel query execution, indexing, and partitioning, which allow you to execute complicated queries and massive data sets effectively.

Luigi

This data engineer tool provides the ideal scope to create complicated data pipelines in long-running batch jobs. It is meant to handle different tasks, like data validation, data processing, and data aggregation. It helps to create sophisticated and straightforward data workloads. It provides the ideal scope for the potential user to create different pipelines to analyze and process massive data volume.

Power BI

It refers to business analytics, which offers business intelligence capabilities and interactive visualization, using the interface for the potential users to develop dashboards and reports.

Tableau

It is another worth mentioning data engineering tool, which aims to collect and extract the stored data in different places. It correctly uses the drag-and-drop interface to use the data across various departments. The goal of the data engineering manager is to develop the dashboards.

Choosing the right data engineering tool has become the need of the hour as it helps to maintain and create the data pipelines, to ensure the proper decision-making process.

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Corine W. Saad