Top 10 Data Engineering Tools
Want to find out about the role of data engineers and data engineering? What are the top data engineering tools these professionals use? Read on to explore more.
Data engineers create pipelines to facilitate an organization's data analytics by collecting, merging, and transforming data. They create an infrastructure for modern data analytics.
Data engineers' work can be categorized into various sets of requirements that they must fulfill in building the pipeline. These requirements include gathering and merging data from different sources, transforming the collected data into formats that other applications can use, and storing data in appropriate forms that can be accessed by users.
You could say that data engineers play the biggest role behind the screen in the data analytics industry. Their efforts are critical for the success of the data analytics efforts of an organization. And data engineering tools are the most critical factor in deciding data engineers’ success.
What Is Data Engineering?
The process of extracting, converting, and loading data into a data lake or warehouse is known as data engineering. Data scientists and engineers who are skilled at applying analytical tools to address problems with Big Data generally undertake data engineering.
Data engineers use various tools to fulfill different requirements in the data analytics pipeline. Data modeling tools, database management systems (DBMS), and data warehouses are some of those tools.
This article will discuss 10 tools that are essential for data engineers, starting with data modeling tools.
Data Modeling and the Top Data Modeling Tools
Data Modeling can be considered the first step in data engineering. A good data model is like a strong foundation for a skyscraper. Collecting, analyzing, arranging, and presenting data, their relationships, and other information visually in an organized way is called data modeling.
Often, data models are created using ERDs, or entity-relationship diagrams. This offers a blueprint for moving forward in the data analytics pipeline, improving the development's efficiency and effectiveness.
Picking the right data modeling tool is crucial in building a perfect data analytics solution. Our picks for the top data modeling tools are as follows:
Vertabelo is an online data modeling tool that supports the most popular DBMSs (SQL Server, MySQL, PostgreSQL, Oracle, Amazon Redshift, etc.). It’s one of the best ERD tools, and it supports you in modeling your data across the three conceptual, logical, and physical stages on any platform. You can choose from the standard data modeling notations of Crow's Foot, IDEF1X, and UML. Collaboration and data-sharing options enable you to easily work with large or distributed teams.
This tool provides various features to speed up the data modeling process, including automating the creation of the database's physical data model and the DDL script that builds the physical database.
Vertabelo also provides forward and reverse engineering features to generate SQL scripts to add/remove database elements or create a data model from an existing database.
Most data engineers like this online ERD tool because of its clean, modern, and responsive user interface (UI). It has more features like model validation, built-in version control, text notes, version control with GIT, and more.
Navicat is also among the most popular ERD tools. It is an offline data modeler for Windows, Linux, and Mac OS. Navicat also supports data modeling with UML, Crow's Foot, and IDEF1X. You can create logical and physical models automatically with this tool as well.
Navicat provides reverse and forward engineering features and supports many databases, including SQL Server, Microsoft Azure, Amazon Redshift, Oracle Cloud, Google Cloud, MySQL, MariaDB, and Oracle. Also, you can use Navicat's Export SQL functionality to create SQL scripts from your physical data model.
3. Visual Paradigm
You can create numerous diagrams by collaborating virtually with your coworkers using the online edition of Visual Paradigm. It supports many DBMSs, including MySQL, SQL Server, Oracle, and MariaDB. Also, this tool offers a wide range of standard notations for creating your data model.
Two fabulous features of this data modeler are the Table Record Editor and the Automated Model Transitor. Developers can upload test data in the Table Record Editor to understand the data types that should be used in the database. The Automated Model Transitor maintains traceability while creating logical and physical models from earlier versions.
Visual Paradigm also creates DDL files for your physical database based on your physical model. A SQL script for distributing patches that compares the physical database to its physical data model can be created with this tool. Reverse engineering is also offered.
What Are the Best Database Management Systems?
A database management system (DBMS) is software that assists you in creating and managing databases. It allows end users to create, protect, update, read, and delete the data in a database. This acts as an interface between the database and the users (or applications) that use information stored in the database, ensuring data is kept organized and accessible. Relational, network, and hierarchical are the main types of DBMSs.
The following DBMSs are popular among data engineers.
Oracle is one of the most popular relational DBMSs. It was developed by the Oracle Corporation as a multi-model system. A relational database management system (RDBMS) is its primary database model. Its secondary database models include graph DBMS, document store, RDF store, and spatial DBMS.
Many top companies – like UnitedHealth, JPMorgan Chase, and Citigroup – use Oracle.
Oracle is an object-oriented system; it implements object-oriented features like polymorphism, inheritance, and user-defined types and allows users to store complex business models in a relational database.
MySQL is another popular open-source RDBMS. Its primary database model is relational; its secondary models are spatial DBMS and document store. Originally, MySQL was developed by MySQL AB. It was acquired by Sun Microsystems and then by the Oracle Corporation.
Many of the world’s largest and fast-growing organizations – like Facebook, Adobe, and Google – use MySQL. Also, WordPress uses MySQL databases to store its blogs.
Like many other popular DBMSs, MySQL supports client-server architecture.
Many MySQL graphical user interface (GUI) solutions are available, including MySQL Workbench, dbForge Tools for MySQL, phpMyAdmin, and SQLite Online. These tools make working with a MySQL database easier and more convenient.
Data Integration and Data Integration Tools
Data integration synchronizes various digital tools and technologies into a unified and accessible platform. Integrated tools enable businesses to improve workflows, optimizing their operations internally and externally.
For instance, data integration syncs your business email account, social media network info, and customer records to make accessing full data smoother. Numerous data integration tools exist to facilitate these tasks. These tools can be categorized as on-premise, Cloud-based, proprietary, or open-source.
Let’s have a look at some popular data integration tools:
Zapier is a unique (if expensive) data integration tool. It’s a no-code solution to integrate and automate various apps. Thousands of app integrations are built into this platform. Additionally, Zapier’s friendly user interface makes setting up and automating typical workflows easy.
Talend handles each part of the data lifecycle separately. These parts include data integration, data governance, data integrity, and application and API integration.
Considering its open-source characteristics, Talend is one of the best tools for businesses targeting cost optimization. It’s also one of the most famous open-source data integration tools among data engineers.
What Are the Top Data Warehousing Tools?
A data warehouse (or enterprise data warehouse) is a data management system that we use for storing, reporting, and data analysis. These are central repositories where data from a single or several different sources are kept. They are used by analytical tools to support decision-making.
Data warehouses collect historical business and organizational data so that it can be evaluated and insights can be drawn from it. It is the primary component of business intelligence. This helps develop a single, uniform system of truth for the entire organization.
We will discuss a few top data warehousing tools in this section.
1. Amazon Redshift
The most effective, scalable, and powerful data warehousing solution available today is Amazon Redshift. It is quick, dependable, and simple to use.
You can evaluate all of your data from many sources in one location using Amazon Redshift. With parallel SQL queries that simultaneously process data on all nodes, you can query hundreds of billions of rows in a matter of seconds. Additionally, Redshift automatically manages your clusters, so you don't have to bother about backup and recovery.
What Are Data Governance Tools?
Data governance tools ensure the effective use of data in an organization. They establish processes and responsibilities to ensure the security and quality of business data used across the organization.
Let’s have a look at a top data governance tool.
Organizations can better protect and collaborate on their most precious content with the help of Egnyte, a multi-Cloud platform for content security and governance. Since its founding in 2008, Egnyte has helped more than 17,000 enterprises democratize Cloud content protection by enhancing data security, preserving compliance, preventing and detecting ransomware attacks, and increasing employee productivity.
What Are Your Top Data Engineering Tools?
Data engineering is the process of extracting, converting, and loading data into a data lake or warehouse. In this article, we discussed different stages in the data engineering pipeline and the tools used in those stages.
If you’re looking for data engineering solutions, these tools are a good place to start. You can also visit the Vertabelo blog for tips on data modeling, database design, and more!