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15+ Best Data Engineering Tools to Explore in 2024
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12th Jan, 2024
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The tremendous growth in data generation, then the rise in data engineer jobs - there’s no arguing the fact that the big data industry is at its best pace and you, as an aspiring data engineer, have a lot to learn and make out of it - including some tools!
Data engineers add meaning to the data for companies, be it by designing infrastructure or developing algorithms. The practice requires them to use a mix of various programming languages, data warehouses, and tools. While they go about it - enter big data data engineer tools.
These tools give data engineers a hand in meeting various requirements in the pipeline. Here, we'll take a look at the top data engineer tools in 2024 that are essential for data professionals to succeed in their roles. These tools include both open-source and commercial options, as well as offerings from major cloud providers like AWS, Azure, and Google Cloud.
What are Data Engineering Tools?
Data engineering tools are software applications that help data engineers manage and process large and complex data sets. Data engineering is a field that requires a range of technical skills, including database management, data modeling, and programming.
Data engineering tools can help automate many of these processes, allowing data engineers to focus on higher-level tasks like extracting insights and building data pipelines. If you want to learn more about data engineering tools, get guidance from Data Engineer courses online.
Here are some essential skills for data engineers when working with data engineering tools.
- Strong programming skills: Data engineers should have a good grasp of programming languages like Python, Java, or Scala, which are commonly used in data engineering.
- Database management: Data engineers should be proficient in storing and managing data and working with different databases, including relational and NoSQL databases.
- Data modeling: Data engineers should be able to design and develop data models that help represent complex data structures effectively.
- Data processing: Data engineers should know data processing frameworks like Apache Spark, Hadoop, or Kafka, which help process and analyze data at scale.
- Data integration: Data engineers should be able to integrate data from various sources like databases, APIs, or file systems, using tools like Apache NiFi, Fivetran, or Talend.
Data engineering tools can help data engineers streamline many of these tasks, allowing them to be more productive and effective in their work. By mastering these skills, data engineers can become proficient in using data engineering tools to extract insights and build robust data pipelines.
Top Data Engineering Tools
We've compiled a list of the top data engineering tools in 2024 that offer a range of functionalities, including data integration, processing, transformation, and visualization, to help data engineers extract actionable insights from data. Let’s take a look:
1. Power BI
Power BI is a cloud-based business analytics service that allows data engineers to visualize and analyze data from different sources. It provides a suite of tools for data preparation, modeling, and visualization, as well as collaboration and sharing. With Power BI, data engineers can easily create interactive reports and dashboards that can be accessed from anywhere, on any device.
Key features:
- Robust data visualization capabilities
- Seamless integration with Microsoft tools
- Easy-to-use interface
2. Looker
Looker is a business intelligence (BI) and data analytics platform that provides a unified view of data from different sources. It is one of the most liked data engineering tools of the present day. It allows data engineers to easily build and customize data models and create interactive dashboards and reports. Looker also provides an SQL-based interface for querying and analyzing data, which makes it easy for data engineers to integrate with existing tools and applications.
Key features:
- Interactive data exploration
- Real-time reporting
- Easy data modeling
3. MongoDB
MongoDB is a NoSQL document-oriented database that is widely used by data engineers for building scalable and flexible data-driven applications. It provides a flexible data model that can handle different types of data, including unstructured and semi-structured data.
Key features:
- Flexible data modeling
- High scalability
- Support for real-time analytics
4. Snowflake Data Warehouse
It allows data engineers to store both structured and semi-structured data, including JSON and XML. Snowflake also provides an SQL-based interface for querying and analyzing data, which makes it easy for data engineers to integrate with existing tools and applications.
Key features:
- Instant elasticity
- Support for semi-structured data
- Built-in data security
5. Segment
The segment is a customer data platform (CDP) that helps data engineers and marketers collect, unify, and route customer data to various marketing and analytics tools. It provides a unified API that allows businesses to collect customer data from various sources, such as websites, mobile apps, and servers.
Key features:
- Centralized customer data
- Real-time data streaming
- Support for data governance
6. Apache Spark
Apache Spark is a distributed computing platform that is open-source and used for processing massive amounts of data. It provides high-level APIs in Java, Scala, Python, and R, and an optimized engine for distributed data processing. Spark can be integrated with various data sources, including Hadoop Distributed File System (HDFS), Apache Cassandra, Apache HBase, and Amazon S3.
Key features:
- In-memory processing
- Support for real-time streaming
- Machine learning capabilities
7. Apache Kafka
Real-time data processing is supported by Apache Kafka, an open-source distributed activity streaming platform. It can handle high volumes of data, supports data integration with various systems, and provides low-latency processing.
Key features:
- Scalable data processing
- Supports fault-tolerant data replication
8. Apache Hadoop
Apache Hadoop is one of the open source data engineering tools used for distributed storage and processing of big data. It is widely used by data engineers for building scalable and reliable data processing systems. Hadoop provides tools for data storage, processing, and analysis, including Hadoop Distributed File System (HDFS) and MapReduce. It can add more processing power and storage as the data grows.
Key features:
- Scalable data storage
- Fault-tolerant
- Support for batch processing
9. Apache Airflow
Apache Airflow is an open-source platform used for orchestrating complex data pipelines. It provides an extensible architecture that allows data engineers to define, schedule, and monitor workflows. Airflow supports a wide range of data sources, including databases, cloud services, and APIs, and provides a rich set of built-in operators and plugins
Key features:
- Flexible task scheduling
- Support for multiple workflows
- Integration with various data sources
10. Fivetran
Fivetran is a popular cloud-based data integration platform that simplifies the process of data engineering by automating data pipeline creation, management, and maintenance. With FiveTran, data engineers can effortlessly extract data from multiple sources and load it into their preferred data warehouse or data lake.
Key features:
- Easy data integration
- provides pre-built connectors to over 150+ data sources
- near real-time data synchronization
11. Apache Hive
It is a Hadoop-based data management and storage tool that allows data analytics through an SQL-like framework. The highlight features of Apache Hive are workload management and better security. Through workload management, you can form resource pools and manage them to meet the demands of your research project. Furthermore, Hive controls the file system and the resources in your system’s memory to provide a secure warehouse environment.
12. Tableau
It is one of the most popular big data engineering tools that helps gather data from different resources with a simple drag-and-drop interface. Data engineers can use it to compile data reports and create live dashboards to help them analyze data. The highlight feature of the tableau is its ability to work with large datasets quickly without impacting the dashboard performance. Moreover, it is an easy-to-understand tool that is visually appealing and has top-notch features that make data analysis utterly simple and smooth.
13. Big Query
This tool is popular with companies that are familiar with cloud platforms. It has features similar to amazon redshift and lets you fully manage the cloud data warehouse. The data engineers use this platform to handle small data scales and change their tool options as their data grows. The highlight feature of Big Query is its in-built machine learning capabilities.
AWS Data Engineering Tools
In the era of big data, cloud-based data engineering tools have become increasingly popular due to their ease of use, scalability, and cost-effectiveness. Amazon Web Services (AWS) offers a wide range of data engineering tools that can be used to efficiently process and analyze large volumes of data.
Here, we will discuss two of the most popular AWS data engineering tools, Amazon Redshift and Amazon Athena. If you need more information, search for Data Sciences courses to know more about these tools.
1. Amazon Redshift
Amazon Redshift is a cloud-based data warehousing service that can be used to store and analyze large amounts of data. It provides a fast and cost-effective way to analyze data using SQL queries. Amazon Redshift offers features like columnar storage, compression, and advanced query optimization, which makes it an ideal choice for data warehousing.
Key features of Amazon Redshift:
- Columnar storage for efficient data storage and retrieval.
- Advanced compression techniques for reducing storage costs.
- Automatic optimization of queries for faster performance.
- Integration with AWS data lake services for easy data ingestion.
- Scalability and elasticity to handle growing data volumes.
2. Amazon Athena
A serverless, interactive visualization service called Amazon Athena makes it simple to use regular SQL to evaluate data in Amazon S3. It allows users to analyze large amounts of data without the need for a dedicated data warehouse or complex ETL processes. Amazon Athena supports a wide range of file formats and compression techniques, which makes it easy to work with data stored in Amazon S3.
Key features of Amazon Athena:
- Serverless architecture for cost-effectiveness and ease of use.
- Interactive queries using standard SQL.
- Integration with AWS Glue for data cataloging and ETL.
- Support for a wide range of file formats and compression techniques.
- Automatic scaling and management of underlying infrastructure.
Azure Data Engineering Tools
Microsoft Azure offers a suite of powerful data engineering tools that can help businesses to address their complex data processing needs. Here, we will discuss two of the most popular Azure data engineering tools, Azure Databricks and Azure Data Factory, where azure data engineer skills become effective.
1. Azure DataBricks
Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics platform that provides seamless integration with Microsoft Azure. It is a fully managed cloud service that allows businesses to build, train, and deploy machine learning models at scale. Some of the key features of Azure Databricks are:
- Provides an easy-to-use interface for data engineers, data scientists, and business analysts to collaborate on data processing tasks.
- Offers a scalable and secure cloud infrastructure for running large-scale data processing jobs.
- Supports various programming languages like Python, R, Scala, and SQL for data processing, machine learning, and analytics.
- Provides an extensive library of pre-built machine learning algorithms and data processing frameworks to help businesses get started quickly.
2. Azure Data Factory
Azure Data Factory is a cloud-based data integration service that allows businesses to create, schedule, and manage their data pipelines. It provides a code-free environment for data engineers to build data integration workflows that can move data from various sources to various destinations. Some of the key features of Azure Data Factory are:
- Offers a code-free environment for building, scheduling, and managing data integration workflows.
- Provides support for various data sources and destinations, including on-premises and cloud-based data stores.
- Allows businesses to transform their data using mapping data flows, which provides a code-free visual interface for data transformation tasks.
- Provides real-time monitoring and logging of data pipeline activities.
Azure Databricks and Azure Data Factory are two of the most popular data engineering tools offered by Microsoft Azure. While Azure Databricks is a fully managed Spark-based analytics platform that allows businesses to build, train, and deploy machine learning models at scale, Azure Data Factory is a cloud-based data integration service that provides a code-free environment for building data integration workflows.
Both tools offer a wide range of features that can help businesses to manage, process, and analyze their data efficiently.
GCP Data Engineering Tools
Google Cloud Platform (GCP) provides a range of data engineering tools that help data engineers to develop, deploy, and maintain their data processing pipelines in a reliable, scalable, and cost-effective way. In this section, we will discuss the key features and benefits of some of the top GCP data engineering tools that can help you to become a Google Cloud Certified Data Engineer.
1. Google BigQuery
Google BigQuery is a fully managed, serverless data warehouse service that lets you store and query massive amounts of data in a fast, secure, and cost-effective way. Some of its key features include:
- BigQuery can handle petabytes of data and scale to meet your business needs.
- BigQuery supports real-time streaming ingestion and allows you to query data in real time.
- BigQuery supports standard SQL and provides a familiar interface for data analysts and developers.
- BigQuery can integrate with other GCP services like Cloud Dataflow, Cloud Dataproc, and Cloud Composer to build end-to-end data processing pipelines.
2. Google Cloud Composer
Google Cloud Composer is a managed workflow orchestration service that helps you to author, schedule, and monitor your data processing pipelines. Some of its key features are mentioned here.
- It provides a visual interface for designing, authoring, and monitoring workflows.
- Cloud Composer can integrate with other GCP services like BigQuery, Cloud Storage, and Cloud Dataflow.
- It is based on the open-source Apache Airflow project, which provides a powerful and flexible platform for building complex workflows.
3. Google Cloud Dataflow
Building and running data processing pipelines are both possible with Google Cloud Dataflow, a fully-managed service. Some of its key features are mentioned here:
- Dataflow can handle any size of data and scale to meet your business needs.
- Dataflow supports multiple programming languages and provides a flexible programming model for building custom pipelines.
- Dataflow supports both real-time and batch processing of data.
We discussed some of the top GCP data engineering tools that can help you to become a Google Cloud Certified Data Engineer. Google BigQuery is a fully-managed, serverless data warehouse service.
If you want more access to these pieces of information, look for KnowledgeHut’s Data Engineer courses online. By using these tools, data engineers can develop, deploy, and maintain their data processing pipelines in a reliable, scalable, and cost-effective way.
Data Engineering Tools for Data Research and Students
Research projects are all about data analysis. What happened, when, and how can only be figured out through the data stored in different sets. For example, market research is possible only by analyzing consumer data. Similarly, in the education sector, data talks about the performance and details of a chemical reaction and many other things. Thus, it can be rightfully said that even research scientists or students can also use data engineering tools and technologies for efficient work.
MATLAB
It is a popular numeric computing platform that research scientists and millions of engineers use. It allows them to manage data, create models and use algorithms to study different patterns. The interface is easy to understand and has all the features you might need while working on academic data. After formal training, you can get well-versed with the tool and use it for your operations.
SAS
SAS stands for statistical analysis system. It is a programing language you can use for data extraction and mining in different industries. Using the tools under the SAS program, researchers can generate reports related to predictive analysis, multivariate analysis, and a lot more. In addition, you can pick the best-fit option from the suite of software tools to manage and analyze data sets of any size with perfection.
Zoho Corporation
It is the most reliable, unique, and powerful software suite that makes data analysis extremely simple and efficient for you. This suite can completely change the way you work through their unified cloud software. Using the data tools and techniques under Zoho corporation, the research efficiency improves by up to 80%. That is the reason it is gaining popularity with each passing day.
Microsoft Excel
It is an age-old, yet highly efficient tool used by millions of researchers to date. Microsoft Excel has various features that you can use to gather, store, analyze, and modify data. First, however, you would have to learn the formulas, which you can use to manipulate the data in an Excel file.
Python
Python is extensively used to gather data from different resources. For example, data engineers can use Python libraries to get fruitful data through web scraping. Moreover, they interact with the application programming interface of various companies and connect their databases to build efficient data pipelines.
All these tools and many others make the challenging data engineering work easy and efficient. So, learning them can save time and effort. Moreover, the results you get would be reliable.
How to Enhance Your Data Engineering Skills?
It is not a surprise anymore that data runs the world these days! Everything happens by studying the data patterns, be it scientific research or market analysis. As every business domain has started using its data for significant decision-making, the demand for data engineers has increased. According to a report by Datanami, the demand for data engineers has increased by 50% in the last few years.
Source: datanami
So, planning to enhance your data engineering skills is one of the best decisions you can make. Some of the things you can do to be an efficient data engineer are:
Start by getting better at the fundamentals. Learn all the basic concepts thoroughly. Once you get familiar with the domain, you can start learning the latest tools. You can join Knowledgehut’s complete data science course and learn all the tools and techniques of data engineering. This certification will be an added highlight in your resume and can help you fetch the best job opportunities.
Try finding jobs that give you live projects, as hands-on experience in the domain is the key to scale in your professional career. With these simple tips, you can enhance your data engineering skills. Moreover, owing to the demand in the job market, your expertise will definitely fetch you the opportunity that gives you job security and excellent monetary perks.
Choosing the Right Tools for Your Needs
To effectively manage and handle their data, organizations need strong tools. AWS, Azure, and GCP provide a range of data engineering tools that enable businesses to store, process, and analyze their data in the cloud.
Amazon Redshift and Athena from AWS, Azure Databricks and Data Factory from Azure, and Google BigQuery, Cloud Composer, and Cloud Dataflow from GCP are just a few examples of the tools available. Data engineer skills do matter for each of the tools mentioned in this blog.
Each tool has unique features that set it apart from others, making it essential for data engineers to be proficient in multiple tools to be able to choose the best tool for each use case. By mastering these cloud-based data engineering tools, professionals can become invaluable assets to their organizations and enhance their career prospects in this rapidly growing field.
Frequently Asked Questions (FAQs)
1. Do data engineers use ETL tools?
Yes, data engineers use ETL (Extract, Transform, Load) tools extensively to build and maintain data pipelines. These tools help to extract data from various sources, transform it into a desired format and load it into a target system. Some popular ETL tools used by data engineers are Apache NiFi, Talend, Informatica, and AWS Glue.
2. Is Python an ETL tool?
Python is not an ETL tool, but it is a popular programming language used by data engineers to build ETL pipelines. Python has many libraries, such as Pandas, Numpy, and PySpark, which make it easier to work with data. Data engineers can use Python to write custom ETL scripts, which can integrate with various data sources and systems.
3. Do data engineers need SQL?
Yes, data engineers need to be proficient in SQL (Structured Query Language) to work with relational databases, which are the backbone of many data systems. SQL is used to extract, manipulate, and analyze data stored in a relational database. Data engineers need to write SQL queries to extract data from databases, join tables, filter data and perform other data processing tasks.
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