Top Data Engineering Tools/Platforms in 2022

The phrase “data engineering tools” refers to a broad category of technologies that comprise the contemporary data stack. Modern data stacks require specialized technologies to speed up data integration engineering. These connectors are scalable to accommodate your expanding data needs, end-user-centric, and independent of the cloud. Data engineering tools typically assist with the following:

  • Building a pipeline for data.
  • Facilitating efficient ETL/ELT processes.
  • Creating reports using data visualization and business intelligence.
Let’s briefly go over them with a few examples and the Importance of data engineering.

Data Integration: Fully managed ETL tools are necessary to offer real-time or almost real-time data for business monitoring. Fivetran, Hevo Data, Xplenty, and many more are a few examples.

Data Destination: Cloud data warehouses are the next option on the list for two reasons: first, they offer an improvement over on-premise legacy databases. Second, an agile data warehousing solution’s on-the-go scalability and off-the-shelf deployability make it ideal for modern corporate operations. Examples include Google BigQuery, Snowflake, Amazon Redshift, and many more.

Data transformation: Effective data analytics are made possible by data transformation. Transforming typically entails converting data from one format to another. Adeptia, Hevo Data, Boomi, and many more are a few examples.

Data visualization and business intelligence: Business intelligence tools are vital to finding solutions. Businesses may reduce operational risk and maximize operational enablement efficiency by using BI tools to make data-informed choices. Power BI, Tableau, Looker, and many more are a few examples.

Top Tools for Data Engineering

Amazon Redshift

A fully managed cloud warehouse created by Amazon is called Redshift. In our interviews, we spoke with around 60% of the teams who utilize it. Another industry standard that fuels thousands of enterprises are Amazon’s user-friendly cloud warehouse. Anyone can simply build up their data warehouse using the application, which scales well as your business expands.

Big Query

BigQuery is a fully managed cloud data warehouse, much like Amazon Redshift. The Google Cloud Platform is commonly used by businesses familiar with it. It allows engineers and analysts to start utilizing it with modest data sets and build it up as their data sets grow. It also includes powerful built-in machine-learning capabilities.

Hevo Data

Finding trends and opportunities is simpler when you aren’t concerned about keeping the pipelines in good shape. You can duplicate data from more than 150 sources, including Snowflake, BigQuery, Redshift, Databricks, and Firebolt, in almost real-time with Hevo. Without authoring even one line of code. Therefore, maintenance is a less worrying thing when Hevo is used as your data pipeline platform.

Google BigQuery

BigQuery is a fully managed, serverless, enterprise-grade data warehouse for analytics. By assembling data from spreadsheets and object storage into a logical data warehouse and columnar storage, it enables today’s data analysts and scientists to examine data effectively. BigQuery ML, BigQuery GIS, BigQuery BI Engine, and linked sheets are some of their standout features.

BigQuery is an effective tool for running analytics, democratizing insights, and analyzing petabyte-scale SQL queries. Bigquery offers a serverless design and is based on Dremel technology. It provides distinct storage and processing clusters and decouples data locality.


A high-level, object-oriented programming language called Python is frequently used to create software and websites. Task automation, data analysis, and data visualization are some areas where Python is used. Python has been used by accountants, scientists, data professionals, and others for various activities, including arranging finances and objectifying 3D models of scientific ideas. Python is comparatively simple to use and master.


A “standardized programming language,” SQL (Structured Query Language), was developed in the early 1970s and is used to manage and extract data from relational databases. Today, understanding SQL is necessary for software engineers and database managers. Knowing SQL is primarily used to create “data integration scripts” and execute analytical queries that modify and utilize data for business information.


According to the poll, Tableau is the second most often used BI tool. The primary purpose of one of the earliest data visualization systems is to collect and retrieve data that is stored in numerous locations. Utilizing this data, the data engineer manager develops dashboards. To use data across many departments, Tableau offers a drag-and-drop interface.


Looker is BI software that aids in data visualization for employees. Looker is well-liked and frequently used by engineering teams. Looker has developed a superb LookML layer in contrast to conventional BI tools. This layer’s language describes a SQL database’s dimensions, aggregates, calculations, and data relationships. Spectacles is a recently released tool that enables teams to deploy their LookML layer with assurance. It lets teams manage their LookML layer. Data engineers can facilitate the usage of company data by non-technical personnel by updating and maintaining this layer.

Apache Spark

Apache Spark is an open-source unified analytics engine for analyzing enormous amounts of data. It is a data processing framework that can swiftly perform operations on big data sets and distribute functions across numerous machines when used alone or in conjunction with other distributed computing tools. The fields of machine learning and big data, which demand the mobilization of tremendous computing capacity to process vast data warehouses, depend on these two qualities.


An open-source workflow management system is called Apache Airflow. In October 2014, Airbnb used it as a way to handle the growingly complicated operations of the business. Airbnb was able to automatically author, plan, and track its processes using Airflow, thanks to creating the Airflow user interface. About 25% of the data teams we spoke with used it, making it the most popular workflow management option.

Apache Hive

To provide data query and analysis, Apache Hive is a data warehouse software project built on top of Apache Hadoop. Hive offers a SQL-like interface for querying data held in multiple Hadoop-integrated databases and storage systems. Data summarization, analysis, and querying are the critical functions for which Hive is used. HiveQL is the only query language supported by Hive. For use on Hadoop, this language converts SQL-like queries into MapReduce jobs.


Segment makes gathering and utilizing user data for your digital assets easy. Using Segment, you may gather, modify, transfer, and archive your customer data. Teams can work more efficiently since the technology makes it easier to collect data and connect it to new technologies while also saving time.


Snowflake is the perfect platform for data warehousing, data lakes, data engineering, data science, and creating data applications since its data workloads scale independently. Today’s enterprises need the performance, scale, elasticity, and concurrency that Snowflake’s distinctive shared data architecture offers. We found that a lot of the teams we spoke with were curious about Snowflake and its ability to store and process data, so we anticipate that more teams will make the switch in the upcoming years.


Data engineers and analysts can utilize DBT, a command-line tool, to use SQL to change data in their warehouse. The product was created by Fishtown Analytics, and data engineers are gushing about it. DBT does not provide extraction or load operations because it is the transformation layer of the stack. Companies may write it transforms quickly and more effectively because of this technology.


Redash is made to make it possible for everyone, regardless of technical proficiency, to harness the power of large and small data. Redash is used by SQL users to browse, query, view, and share data from any source. Everyone in their organization can use the data due to their efforts with little to no learning curve.


A robust ETL tool is Fivetran. The effective collection of client data from relevant servers, websites, and applications is made possible by Fivetran. To use other analytics, marketing, and warehousing tools, the obtained data is first relocated from its initial location to the data warehouse.

Great Expectations

A Python-based open-source library called great expectations is used to track, verify, and comprehend your data. It focuses on assisting data engineers in maintaining data quality and enhancing team communication. Great expectations introduce the same methods to data engineering teams as automated testing software has been used by software teams for some time to test and monitor their code.

Apache Kafka

Thousands of data sources continuously produce information known as streaming data, the majority of which transmit records simultaneously. Kafka is generally used to create real-time streaming data pipelines and applications that can react to those data streams. Kafka was initially developed at LinkedIn to build networks between people, where it helped analyze the connections between its millions of professional users.

Power BI

Microsoft offers a service for business analytics called Power BI. It seeks to provide business intelligence capabilities and interactive visualizations with a user interface that is straightforward enough for users to build their own reports and dashboards. Organizations can utilize the data models produced by Power BI in various ways, such as telling stories through charts and data visualizations and exploring “what if” scenarios in the data.


A cloud-first, open-source platform for transporting data quickly is called Stitch. Stitch is a straightforward, effective ETL solution that connects to all your data sources, including databases like MySQL and MongoDB and SaaS programs like Zendesk and Salesforce, then replicates that data to a location of your choice.

Periscope (Acquired by Sisense)

The business intelligence and analytics tool are called Periscope. Redash enables you to utilize SQL to display your data, and Periscope is comparable. You may combine data from many sources with the software, and you can also produce graphics that you can share with your colleagues.


A web-based analytics platform is called Mode Analytics. Mode offers staff members a user-friendly workstation with some external sharing options. The team’s main priority is creating reports, dashboards, and visualizations. In addition, Mode analytics SQL is a semantic layer that makes it easier for non-technical individuals to utilize the platform.


An open-source tool called Prefect checks that data pipelines function as planned. Prefect Core, a tool for data workflow engineers, and Prefect Cloud, a platform for workflow orchestration, are the company’s two products.


A networked, open-source SQL query engine is called Presto. Without the need to move data into another analytics system, Presto can query data right where it is kept. Most results rapidly return when a query is executed using a pure memory-based architecture.

Note: We tried our best to feature the best tools/platforms available, but if we missed anything, then please feel free to reach out at 
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Prathamesh Ingle is a Mechanical Engineer and works as a Data Analyst. He is also an AI practitioner and certified Data Scientist with an interest in applications of AI. He is enthusiastic about exploring new technologies and advancements with their real-life applications

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