Data Engineering Tools: Definition, Examples and FAQs

By Indeed Editorial Team

Published 4 May 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

A data engineering tool refers to software that allows data engineers to use program language to create data pipelines, extraction methods and automated analytical applications. Data engineers use these tools to create effective software for businesses to collect and review data. By reviewing some example tools, you can determine which one might be appropriate for your place of work. In this article, we define data engineering tools, list example tools for you to consider, provide tips for choosing one and share answers to some frequently asked questions.

What are data engineering tools?

Data engineering tools are typically part of a larger data software with varying functions. A data engineering tool is technically an aspect of an Extract, Transform and Load (ETL) platform with open-source availability. ETL tools allow businesses to extract data from multiple sources, load data into a digital warehouse and transform it into interpretable information. The data engineering aspect of an ETL tool usually provides features for creating applications with program language, such as Python and Structured Query Language (SQL).

It can be important to understand that a data engineer may work alongside a software engineer to create ETL tools, or the data engineer may work within an ETL tool to create complex data models. The applications that data engineers develop typically allow their users to automate data extraction methods through engineered data pipelines. These pipelines are a collaboration of codes and scripts that collect information from specific sources and transfer it into digital storage. Data engineers also use engineering tools to create complex data models. These models can automatically process information and identify relationships between data sets.

Related: What Is a Data Engineer? (With 4 Steps of Becoming One)

4 examples of data management tools

Below, you can find example data engineering software that you might choose:

1. Redash

Redash can be an excellent tool for businesses with minimal technical expertise in data technology. This tool provides many features for visualising, extracting, storing and loading data. Data engineers can use an SQL editor, which can allow them to create custom applications, such as pipelines and data models. Users, such as data scientists or business analysts, can use a drag-and-drop feature to manage and operate these custom applications.

2. Apache Kafka

This is an open-source data tool that provides several ETL services, such as visualisations and permanent storage. The reason Apache Kafka might be popular is that it's part of a range of Apache products, including Apache Hive and Apache Spark. This can allow the business to integrate software that assists most data functions. Apache Kafka focuses on offering reliable data streams, secure client libraries and an abundance of trusted data sources. The primary purpose of Apache Kafka is to provide its users with consistent data streams in real-time pipelines.

Related: How to Become a Software Development Manager (A Step Guide)

3. Snowflake

This data tool is highly flexible in its engineering capabilities and general data functions. Snowflake offers features for storing data in cloud-based warehouses and sharing custom dashboards. These features can be excellent for organisations with multiple departments that regularly collaborate on projects. One of the notable features that might make Snowflake a popular choice amongst data engineers is its compatibility with varying program languages. This means data engineers can create applications in multiple languages and transform them into visualisations using a single platform.

4. Looker

This is a platform that's specific to data engineering. The initial creation of applications in Looker can require extensive programming knowledge, but once an application is complete, it's usually easy to operate and manage. Looker's a relatively unique software because it uses a feature known as the LookML layer. This layer is technically its own language that's specific to an SQL database. The LookML layer can help engineers develop dimensions, calculations, formulas, aggregates and data relationships.

Tips for choosing a data engineering tool

Here, you can find several tips to consider when choosing a data engineering tool for the business:

Establish the data stream requirements

Many data engineering platforms can provide you with varying features for customising data pipelines and streaming functionality. Depending on the data requirements of the organisation, some tools may be more appropriate than others. For example, if the organisation only requires data pipelines, rather than storage and visualisation, you might choose software that focuses on creating pipelines.

Determine the budget

The amount of money you have to spend on a data engineering tool can be a limiting factor, depending on the size of the organisation's budget. Some engineering tools might involve a substantial cost if they provide many features. Some free data engineering platforms might be available, but they typically offer limited services for creating codes and custom applications.

Consider the existing IT infrastructure

If the organisation already uses an ETL tool, you may review the features of the tool to determine if it's suitable for data engineering purposes. You can also consider its source code and programming language. If the existing information technology (IT) infrastructure uses a language, such as Python, you might choose a data engineering tool that operates using Python. This is to ensure a smooth integration of data applications to existing infrastructure.

Identify the organisation's IT capabilities

Depending on the organisation's size, it may employ an IT department with specialists in data integration. If the organisation has access to professionals in data engineering, you might choose a complex and advanced tool because they typically allow more customisation opportunities. If the organisation is relatively small, you might choose software such as Redash because it provides a simple user interface and automated templates for pipelines, models and visualisations.

Determine scalability requirements

If the organisation expects to increase its data intake in the short term, you might choose a data engineering tool that allows for scalable pipelines and models. Most data engineering platforms provide features that automatically scale pipelines to suit additional capacity requirements. You may also review the overall data capacity of a data tool because some might have restricting capacities.

Frequently asks questions

Below, you can find answers to some frequently asked questions about data engineering and the tools involved:

Why is a data engineering tool important?

A data engineering tool is important because it's usually a necessity for developing custom data pipelines and data models. Some ETL tools have preset templates for pipelines and models, but these templates may have limitations depending on the business operation. Large corporations with extensive data ingestion needs may utilise a data engineering tool to create custom pipelines that collect specific data from chosen sources. They can also create complex data models for identifying accurate relationships between information sets.

Data engineering is essentially the creation of ETL applications. Without these applications, business operations may have a limited choice regarding data extraction methods and visualisation models. If an ETL tool is open-source, it means anyone can access the software's program language and create custom codes. This can significantly reduce development time and reduce the resources required for creating unique data models and pipelines.

Related: What Does a Software Engineer Do? (Plus How to Become One)

Is a data engineering tool the same as an ETL tool?

There may be some confusion between data engineering tools and ETL tools because occasionally they may be the same software. A data engineering tool is technically a programming language that allows engineers to utilise a source code for creating applications. An open-source ETL tool is a data tool that allows access to its source code, which is the software's program language. A data engineering tool differs from an ETL tool, though an engineering tool may sometimes be a feature of an ETL platform, rather than a standalone software.

A data engineer can utilise an open-source ETL platform as a data engineering tool or they may use specific data engineering software. For example, Singer is a data ingestion platform that includes features for visualising, organising and storing data, but it also allows for custom applications using its source code. DBT is a specific data engineering tool that has no extraction or loading features, though it provides extensive functionality for creating custom data streams using the SQL program language.

Related: What Does a Data Analyst Do? (With Skills and Career Steps)

What's the difference between data engineering and data science?

Data engineering and data science are complementary disciplines within analytical industries. Data engineers build data pipelines, models and visualisations that help data scientists review complex information. A data scientist analyses data to identify relationships, while a data engineer creates applications to collect that data. For example, a data engineer may develop pipelines that extract information from specific financial sources. These pipelines ensure the data collected is accurate, reliable and consistent. A data scientist may then review the financial data and identify relationships, such as business growth, security projections and potential investment opportunities.

Please note that none of the companies, institutions or organisations mentioned in this article are affiliated with Indeed.

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