Most Popular Data Engineering Tools to Learn in 2023

Data engineering is a collection of operations used to create mechanisms and interfaces that allow the flow of data and the way you access information. Dedicated specialists called data engineers help maintain data so it can be available easily.

In other words, data engineers play a pivotal role in setting up the infrastructure and maintaining it. This is to ensure that the requisitedata is always available for analysts to complete their work.

What is Data Engineering?

• Data engineering is the specialized discipline of designing and creating systems that allow people to gather and evaluate raw data from various sources and in multiple formats. 
• These structures enable people discover real-world applications of the data, which companies can use to make important decisions to aid their growth. 
• This is achieved through the use of industry-leading data engineering tools that allow for the organization and storage of large volumes of data, what is also knows as big data. 

Top Data Engineering tools 

• Amazon Redshift

  Amazon Redshift is a fully managed cloud warehouse. Amazon’s easily usable cloud warehouse powers many businesses. Redshift allows easy setup of a data warehouse, and scales as the size of the data grows.

• Big Query

  Big Query is another completely administered data warehouse on the cloud. Companies who are familiar with Google Cloud Platform use Big Query. It also has built-in, powerful machine learning capabilities.

• Tableau

  Tableau is one of the best data engineering tools. It gathers data and extracts it to store it in several places. Tableau also has a drag-drop interface for employing data across various departments. Data engineers create dashboards with this data.

• Looker

  It is BI software for the visualization of data. Looker is popular across engineering teams. Looker has incorporated a fabulous LookML layer that describes dimensions, calculations, aggregates, and data relationships, all in a SQL database. Spectacles tool allows teams to deploy their LookML layer with confidence. With the help of this data engineering tools, data engineers can ease the usage of this data for non-technical employees.

• Apache Spark

  It is an open-source unified analytics engine that aids the large-scale processing of data and is a commonly used in data engineering projects . Additionally, Apache Spark can quickly process large data sets and distribute these data processing assignments across various servers. This can be done by Apache Spark itself or in collaboration with other computing tools for distribution. This makes it a great data engineering tool for big data and machine learning, that can handle large amounts of data and consume low power.

• Apache Airflow

  Apache Airflow is another open-source management platform for authoring, scheduling, and monitoring workflows. It ensures that every task in a data orchestration pipeline is executed in the predetermined sequence and that each task receives adequate resources for said execution. This is very common data engineering tool.

• Apache Hive

  Apache Hive is a data warehousing software project. This data engineering tools is built over Apache Hadoop to provide data analysis and queries. Hive provides an interface similar to SQL. Hadoop helps in three ways – it summarizes data, analyses data, and performs data queries. The query language is called HiveQL, which Apache Hive itself constructed. HiveQL transforms SQL-like queries into MapReduce jobs for Hadoop deployment.

• H3: Python

  Python has become a go-to data engineering tools for data engineering tasks. Its extensive ecosystem of libraries, such as Pandas and NumPy, makes it ideal for data manipulation, transformation, and analysis. Read more about Full Stack Development course.

• H3: SQL

  Structured Query Language (SQL) is one of the data engineering tools used for managing and manipulating structured data in databases. It enables data engineers to perform various operations like querying, updating, and managing relational databases efficiently. Read about Data Visualization Tools.

• H3: MongoDB

  MongoDB is a popular NoSQL database that provides flexibility in data storage. This data engineering tools is schema-less, allowing for dynamic data models and easy scalability, making it suitable for handling unstructured or semi-structured data. Learn about Data Warehousing and Data Mining.

• H3: PostgreSQL

  PostgreSQL is robust open-source relational database management system (RDBMS). This data engineering tools offers advanced features like ACID compliance, support for complex queries, and extensibility, making it a preferred choice for data engineers working with structured data.

• H3: Dbt

  Dbt (Data build tool) is an open-source command-line data engineering tools designed specifically for data transformation and modeling.

• H3: Apache Hadoop

  Apache Hadoop is distributed data engineering tools framework allowing for scalable and reliable processing of large datasets across clusters of computers. Explore the topic Data Structures in Java.

• H3: Apache Kafka

  Apache Kafka is distributed streaming platform enabling high-throughput, fault-tolerant, and real-time data streaming. This data engineering tools provides durable message storage and facilitates the integration of various data sources and consumers in data engineering pipelines.

• H3: Apache Flink

  Apache Flink supports event-driven computations and provides fault-tolerance, low-latency processing, and support for large-scale data streaming and batch processing.

• H3: Google Cloud Platform (GCP) Data Engineering Tools

  This data engineering tools includes services like BigQuery for analytics, Cloud Dataflow for stream and batch processing, and Cloud Composer for managing data pipelines, providing a scalable and managed environment for data engineering tasks.

• H3: Microsoft Azure Data Engineering Tools

  Microsoft Azure provides a range of data engineering tools, including Azure Data Factory for data integration, Azure Databricks for big data analytics, and Azure Synapse Analytics for data warehousing. 

Principles and concepts of data engineering

So far, we have seen the most used data engineering tools, let’s look at the basic principles of data engineering

• Expecting that data will be of poor quality
  Data will always be in the rawest of forms. You will have to team up with data scientists to clean, process, and store it.
• Measuring the characteristics of the data
  The data that will be processed needs to be accurate, complete, and reliable. It also needs to be relevant at all times. After that, you can develop a system for that data.
• Maintain provenance of data
  The provenance of data is related to questions like why the data was produced and how it was produced, where it was produced, when it was produced, and by whom it was produced. All this information helps in understanding the source of data.
• Keep the data storage immutable
  The data storage will always be completely static and unspoiled for eternity. Immutable storage stores specific data in a form that does not tamper, unmodify or remove.
• Monitor information loss
  Data engineering platforms should also be able to inspect the loss of data during delivery. They should know what data is not there and where it was lost. This happens when data is processed in an input file. 
• Data is static
  Although extremely rare, it is easy to understand and manage static data. The solution for such data works forever. But it is just the ideal situation. In reality, data is never static. Assuming data to be static, data engineers build a system around it.  
• Data set is ELT and ETL 
  It is also known as the data pipeline. Maintaining a secure and reliable flow of data is what the data engineering job is all about. ETL is an extract-transform-load process. Here the data is extracted from the source, processed to get the necessary information, and then loaded into the storage system. ELT is an extract-load-transform process where the transformed dataset is loaded back to the database. Together they make up the data pipeline.  
• Data engineering is all about making predictions
  Data engineering uses the power of prediction in various fields. But data engineering is behind all these operations, which helps in the computation of these predictions.
• Data engineering focuses on Algorithms
  Data engineering consists of a bunch of techniques based on algorithms. It is all about training computers to carry out specific tasks. Moreover, it helps to develop data processing platforms that train computers or systems to retrieve information through logical reasoning systems.

Data analysis vs. Data science vs. Data engineering 

Let’s look at the difference between Data analysis vs. Data science vs. Data engineering 

Skills needed for a Data Engineer job

Apart from having knowledge on the data engineering tools, every data engineer must possess certain skills when working on any data engineering projects, which include: 

1. Coding
   As a Data Engineer, you are expected to know several coding languages. But which ones should you go for? There are many programming languages, but you are not expected to know all. Every programming language has a specific purpose. For data engineering, you are expected to know SQL, NoSQL, Python, Java, R, and Scala.
2. Cloud storage and data warehousing
   You should be aware of cloud storage and its capabilities. Cloud allows you to store big data. You should also know about AWS (Amazon web services) and Google cloud. 
3. Knowledge of OS
   You might know about Windows OS or macOS, but you also need to know about Linux operating systems as a data engineer. You should know all about the components of infrastructure and the architecture of your OS. Can you navigate various configurations on the local server? Do you know about Access control methods? Big Data Engineer jobs require a strong foundation in operating systems.
4. Database Systems
   As a data engineer, you must ensure that you know about SQL databases, NoSQL databases, relational databases, and cloud databases. You should also know how to store big data on storage servers. Proficient knowledge of data engineering tools is very crucial for data engineers.
5. Analysis of data
   A data engineer job also requires a thorough knowledge of various data engineering tools, such as Apache Spark, Power BI, Tableau, to name a few. 
6. Critical Thinking
   This is one of the primary requisites of becoming a data engineer. Without being able to think critically, the data engineer might not be able to chalk out a solution to a problem. 
7. Understanding the basics of Machine Learning
   A Data Engineer is also expected to know the foundational principles of machine learning. They need to understand these concepts to develop machine learning platforms. 
8. Communication skills
   For a data engineering job, you are required to work with several stakeholders, which warrants strong communication skills. You should be able to communicate your ideas effectively and clearly.

If big data interests you, you may consider data engineering as a career option. If you want to learn about data engineering online, you can enroll in the Certificate Program in Data Engineering from Hero Vired.

Conclusion

Data engineering tools and software engineering tools are essential for efficiently managing and processing data in today's data-driven world. By leveraging the right data engineering tools and software engineering tools, organizations can unlock the full potential of their data and drive informed decision-making.