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 2023 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.
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.
We’ve compiled a list of the top data engineering tools in 2023 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
In the era of big data, cloud-based data engineer 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
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.
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.
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.
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