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Data Engineering Tools: What Frameworks are Necessary for Successful Data Engineering in 2023

Correct and reliable data is an essential criterion for the success of any data solution project. The primary skill of the data engineer is knowledge of particular technologies for processing, storage, and management of data. Due to these technologies, companies can make additional profits or reduce costs. This article discusses what technologies data engineers use to solve their tasks.

What is a Data Engineering Tool?

This term refers to specialized up-to-date tools used by data engineers to facilitate their data processing, storage, and analysis. These tools help build data pipelines, enable seamless ETL/ELT operations and visualize data. Data engineers are much more productive with the right tools.

What Tools are Used in Data Engineering?

Massive big data arrays must be stored safely and processed to receive helpful information. Data engineer needs to know how to combine data of different formats from multiple sources. So what tools do data engineers use?

SQL is a standard language for queries to relational databases. It is one of the important tools that help access, update, and modify data with queries, data conversion methods, and others. SQL is used in any program and on any website with databases. It is universal and has a clearly defined structure due to established standards. Interaction with databases is quick, even if data amounts are large. Almost all relational databases use SQL: Presto, Athena, Microsoft SQL Server, PostgreSQL, Redshift, etc.

Apache Spark tool became the standard for big data processing. It allows processing large amounts of data in distributed mode and creating data marts and real-time applications.

Apache Kafka is a data streaming platform. It is capable of performing real-time analytics and highly productive data processing.

Apache Airflow is a tool for data pipelines organization and planning. This workflow automation and scheduling system manages all tasks in directed acyclic graphs (DAGs) supporting complex workloads.

Apache Hadoop is a basic data engineering tool for storing and analyzing large amounts of information in a distributed processing environment. Hadoop is not a single whole but a set of open-source tools such as HDFS (Hadoop Distributed File System) and the distributed MapReduce engine. Hadoop is great for customer analytics, enterprise projects, and data lakes. Hadoop cloud tools like Amazon EMR enables businesses to easily configure their infrastructure and achieve scalability.

Apache Hive is created to combine the scalability of one of the most popular big data platforms. It turns SQL queries into chains of MapReduce tasks. Hive can be integrated with Hadoop to analyze large data amounts.

Presto open source is a distributed parallel query engine optimized for low interactive query analysis. Presto efficiently performs queries and scales without downtime, even from gigabytes to petabytes. One Presto request can process data from multiple sources, such as HDFS, MySQL, Hive, and other data sources.

ElasticSearch is a distributed search engine based on Apache Lucene. It is also part of the so-called ELK stack consisting of ElasticSearch, Logstash, and Kibana.

AWS Athena is an SQL query service of Amazon. It allows developers to make SQL queries directly to data stored in the AWS Simple Storage Service.

AWS Glue is a fully managed data ETL service that simplifies data preparation and loading for analysis. AWS Glue can be used to classify, purge, extend, and securely move data among warehouses. This service significantly simplifies the ETL tasks creation, faster work and reduces costs. It is server-free, so it is not necessary to configure or manage the infrastructure.

AWS EMR provides many functions facilitating tasks of a data engineer. It is a web service providing a manageable environment for Apache Hadoop, Apache Spark, Presto in a simple and secure way. It is used for data analysis, web indexing, data storage, financial analysis, scientific modeling and so on.

Google Cloud BigQuery is a fast, cost-effective, and scalable warehouse for big data. This framework from Google allows users to query and analyze large amounts of read-only data. Using syntax similar to SQL, Big Query GCP queries billions of rows of data in seconds.

Amazon Redshift is a fast, fully managed petabyte storage solution. It simplifies and cost-effectively analyzes data using existing business intelligence tools. There are no initial costs, long-term liabilities, and pricing structure on demand.

Kubernetes is a software product for managing container virtualization. This framework allows the deployment of a microservice infrastructure where each service can operate in one or more containers located on different physical servers.

Periscope (acquired by Sisense) is quite an easy-to-use tool with limited functionality. The cloud platform covers all stages of the data pipeline and provides all the necessary functions for the data operation. It allows to:

  • create dashboards

  • implement BI

  • use the functions of built-in analytics

  • organize reporting.

One more helpful tool is Great Expectations. This platform is a library of data validation, documentation, profiling to support data quality and improvement of communication among data teams, helping them eliminate pipeline debt.

New Cloud Data Technologies are New Future

DBT (Data Build Tool) provides huge capabilities to work with data already loaded into the warehouses. The primary purpose of dbt is to take the code, compile it into SQL, and execute commands in the correct sequence in the warehouses.

Snowflake. Cloud data storage and analysis platform Snowflake allows collecting, managing, analyzing, sharing information, integrating data with different programming languages, and developing analytical applications.

Fivetran is an automated ETL platform for integration and analytics, which combines data from several sources in one service. It can analyze information that no longer exists in the source system. This tool does not store data but loads it into the storage selected.

Segment is the single hub to collect, manage and route customer analytics data.

It's not an ETL data engineering tool, but it includes connectivity to some SaaS data sources and data warehouse destinations.

Stitch is an ETL connector that integrates various data sources into a central data warehouse. This framework is a completely managed and scalable service.

The data engineering task is to prepare data shown to the customer in BI tools. The most popular for today are the following:

BI is a data and business intelligence platform that allows users to collect information and create interactive reports and dashboards with the key metrics you select.

Tableau is a visual analytics platform that collects and analyzes data using machine learning. With Tableau, software developers can create drag and drop visualizations and statistical modeling based on artificial intelligence.

Looker is a cloud-based data analysis and BI platform that collects and combines data from multiple sources into an automatically generated LookML model. It customizes alerts, dynamic dashboards, and visualization. The built-in code editor helps to modify automatically created models if necessary.

Power BI is a Microsoft BI platform and reporting tool that allows data engineers to collect and visualize, create personalized reports, and safely share data with colleagues.

Qlik Sense is a platform for data analysis based on artificial intelligence and an associative analytics mechanism helping to create reports and interactive dashboards. It supports a variety of environments, including cloud, streaming platforms, storage, and others.

Artificial intelligence(AI) is the ability of a computer to learn, make decisions, and perform actions inherent in human intelligence. In data engineering, it helps streamline and speed up software design, development, and implementation. AI-based tools work as assistants of project managers, business analysts, programmers, and test engineers. Developers can create and test parts of code faster and at a lower cost.

ChatGPT, as an AI language model, can assist data engineers by providing them with timely and accurate information, helping them to optimize their workflows and processes, and facilitating the development of effective data solutions. Its capabilities continually improve, and it can adapt to new challenges and technologies as users emerge.

Notebooks for working with data are an Interactive environment with a convenient interface for executing code, visualizing data, and evaluating results. Notebooks allow users to combine data and context into a single story that is easy to send to colleagues for revision or open on other devices. Popular interactive environments are the Jupiter notebooks, Apache Zeppelin and Datalore Platform.

These tools are important not only for data engineers but also for other data processing and analysis specialists, as they greatly simplify managing, storing, and analyzing data.

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This article provides an in-depth look at the essential tools and frameworks for data engineering in 2023, highlighting the importance of correct and reliable data for successful data solutions. Key tools include SQL, Apache Spark, Kafka, Airflow, Hadoop, and AWS services like Athena and Glue. It also covers modern tools like Snowflake, Fivetran, and dbt, which enhance data processing, storage, and analysis capabilities. The article emphasizes the significance of BI tools like Tableau and Power BI in data visualization.

For a deeper dive into essential data engineering concepts, check out this comprehensive guide on Data Engineering.

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