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Ruhma Khawaja author
Ruhma Khawaja
| July 6

Data engineering tools are software applications or frameworks specifically designed to facilitate the process of managing, processing, and transforming large volumes of data.

These tools provide data engineers with the necessary capabilities to efficiently extract, transform, and load (ETL) data, build data pipelines, and prepare data for analysis and consumption by other applications.

Data engineering tools offer a range of features and functionalities, including data integration, data transformation, data quality management, workflow orchestration, and data visualization.

data engineering tools

Top 10 data engineering tools to watch out for in 2023

1. Snowflake:

Snowflake is a cloud-based data warehouse platform that provides high scalability, performance, and ease of use. It allows data engineers to store, manage, and analyze large datasets efficiently. Snowflake’s architecture separates storage and compute, enabling elastic scalability and cost-effective operations. It supports various data types and offers advanced features like data sharing and multi-cluster warehouses.

2. Amazon Redshift:

Amazon Redshift is a cloud-based data warehousing service provided by Amazon Web Services (AWS). It is known for its high performance and cost-effectiveness. Amazon Redshift allows data engineers to analyze large datasets quickly using massively parallel processing (MPP) architecture. It integrates seamlessly with other AWS services and supports various data integration and transformation workflows.

3. Google BigQuery:

Google BigQuery is a serverless, cloud-based data warehouse designed for big data analytics. It offers scalable storage and compute resources, enabling data engineers to process large datasets efficiently. BigQuery’s columnar storage and distributed computing capabilities facilitate fast query performance. It integrates well with other Google Cloud services and supports advanced analytics and machine learning features.

4. Apache Hadoop:

Apache Hadoop is an open-source framework for distributed storage and processing of large datasets. It provides a scalable and fault-tolerant ecosystem for big data processing. Hadoop consists of the Hadoop Distributed File System (HDFS) for distributed storage and the MapReduce programming model for parallel data processing. It supports batch processing and is widely used for data-intensive tasks.

5. Apache Spark:

Apache Spark is an open-source, unified analytics engine designed for big data processing. It provides high-speed, in-memory data processing capabilities and supports various programming languages like Scala, Java, Python, and R. Spark offers a rich set of libraries for data processing, machine learning, graph processing, and stream processing. It can handle both batch and real-time data processing tasks efficiently.

6. Airflow:

Apache Airflow is an open-source platform for orchestrating and scheduling data pipelines. It allows data engineers to define and manage complex workflows as directed acyclic graphs (DAGs). Airflow provides a rich set of operators for tasks like data extraction, transformation, and loading (ETL), and it supports dependency management, monitoring, and retries. It offers extensibility and integration with various data engineering tools.

7. dbt (Data Build Tool):

dbt is an open-source data transformation and modeling tool. It allows data engineers to build, test, and maintain data pipelines in a version-controlled manner. dbt focuses on transforming raw data into analytics-ready tables using SQL-based transformations. It enables data engineers to define data models, manage dependencies, and perform automated testing, making it easier to ensure data quality and consistency.

8. Fivetran:

Fivetran is a cloud-based data integration platform that simplifies the process of loading data from various sources into a data warehouse or data lake. It offers pre-built connectors for a wide range of data sources, enabling data engineers to set up data pipelines quickly and easily. Fivetran automates the data extraction, transformation, and loading processes, ensuring reliable and up-to-date data in the target storage.

9. Looker:

Looker is a business intelligence and data visualization platform. It allows data engineers to create interactive dashboards, reports, and visualizations from data stored in data warehouses or other sources. Looker provides a drag-and-drop interface and a flexible modeling layer that enables data engineers to define data relationships and metrics. It supports collaborative analytics and integrates with various data platforms.

10 Tableau:

Tableau is a widely used business intelligence and data visualization tool. It enables data engineers to create interactive and visually appealing dashboards and reports. Tableau connects to various data sources, including data warehouses, spreadsheets, and cloud services. It provides advanced data visualization capabilities, allowing data engineers to explore and analyze data in a user-friendly and intuitive manner. With Tableau, data engineers can drag and drop data elements to create visualizations, apply filters, and add interactivity to enhance data exploration.

Tool Description
Snowflake A cloud-based data warehouse that is known for its scalability, performance, and ease of use.
Amazon Redshift Another popular cloud-based data warehouse. Amazon Redshift is known for its high performance and cost-effectiveness.
Google BigQuery A cloud-based data warehouse that is known for its scalability and flexibility.
Apache Hadoop An open-source framework for distributed storage and processing of large datasets.
Apache Spark An open-source unified analytics engine for large-scale data processing.
Airflow An open-source platform for building and scheduling data pipelines.
dbt (Data Build Tool) An open-source tool for building and maintaining data pipelines.
Fivetran A cloud-based ETL tool that is used to move data from a variety of sources into a data warehouse or data lake.
Looker A business intelligence platform that is used to visualize and analyze data.
Tableau A business intelligence platform that is used to visualize and analyze data.

Benefits of Data Engineering Tools

  • Efficient Data Management: Extract, consolidate, and store large datasets with improved data quality and consistency.
  • Streamlined Data Transformation: Convert raw data into usable formats at scale, automate tasks, and apply business rules.
  • Workflow Orchestration: Schedule and manage data pipelines for smooth flow and automation.
  • Scalability and Performance: Handle large data volumes with optimized processing capabilities.
  • Seamless Data Integration: Connect and integrate data from diverse sources easily.
  • Data Governance and Security: Ensure compliance and protect sensitive data.
  • Collaborative Workflows: Enable team collaboration and maintain organized workflows.


 Wrapping up

In summary, data engineering tools play a crucial role in managing, processing, and transforming data effectively and efficiently. They provide the necessary functionalities and features to handle big data challenges, streamline data engineering workflows, and ensure the availability of high-quality, well-prepared data for analysis and decision-making.

Data Science Dojo
Saad Shaikh
| October 6

Data Science Dojo is offering SnowSQL for FREE on Azure Marketplace packaged with pre-configured CLI for data manipulation at Snowflake warehouse 


What is Snowflake? 

Snowflake is a cloud computing-based data cloud platform. It is a user-friendly data warehousing product that supports both ETL and ELT functionalities. It supports multiple data workloads from data warehouses and data lakes to enable data storage, data engineering, processing, and analytics. Further, using Snowflake can help you with better data warehouse jobs in the near future.

It is relatively new, flexible, easier and provides a pure cloud SQL based warehouse. It is not created upon any database tech and has a high affordability rate. 

Challenges for developers 

Execution issues while attempting to load and query information, shortcomings in dealing with a variety of data, absence of central source causing conflicting corrupt data, and unfortunate data sharing were a few big obstacles encountered by data engineers and developers to look forward to. 

Therefore, a data cloud warehouse capable of storing variable-length records at vast scale maybe having some fault tolerance or availability or fast processing can reduce the task overhead by a big margin. Not to forget, the data able to be transformed using any standard open-source language would suffice. 

Snowflake: SnowSQL 

Using data engineers’ one of the favorite languages, SQL, developers can now load, transform and unload data at their Snowflake cloud. SnowSQL is an interactive command line scripting-cum-query tool that allows users to perform DML and DDL operations at the CLI level. It is produced with increased protection standards and has strong integration with Snowflake core architecture.

With this tool, you can connect to your Snowflake account and configure your databases, schemas, and warehouses using simple SQL. Users can import existing databases of the data cloud into local machines and then perform various transform operations. It also allows you to unload or dump your transformed data back into the database containers. 

Snowflake architecture - SnowSQL
Snowflake architecture – Data Science Dojo

What we provide 

Snowflake: SnowSQL packaged by Data Science Dojo serves as a pre-installed CLI environment for the Snowflake data cloud so it provides an ease to the developers to perform SQL operations on the data from the warehouse without the burden of installation. Offer listed by Data Science Dojo, provides the following elements: 

  • A Command Line Interface (CLI) installed with SnowSQL which can be connected to your Snowflake account 
  • Support for standard SQL 
  • Robust integrations 
  • Ability load and unload data having CSV, XML, JSON, Avro etc formats 
  • Includes syntax highlighting and auto-complete 

Significant characteristics of SnowSQL 

  • Centralization and Democratization: Snowflake combines warehouses and data lakes into a focal data storage and democratizes it to enable clients for better processing and analytics 
  • Smart Data Handling: Snowflake is able to manage exponential volumes, variety, and velocity of data. Using SnowSQL we can perform ETL and ELT operations using ANSI SQL 
  • Secure and Fault Tolerant: SnowSQL secures connections to Snowflake using TLS with OCSP checks. Your data is available even if one or more nodes go down as source is fault-tolerant 
  • Recovery and Time Travel: The undrop table command is unique as it restores the table back again. The time travel feature allows the recovery of the original version of an object to reverse the updates 
  • High Elastic Performance: With SnowSQL you create multiple virtual warehouses of variable sizes which ensures your ETL process go smoothly 


SnowSQL leverages the power of Azure services and Snowflake Cloud to load and process large volumes of data with continuous availability, high scalability, and data distribution from the comfort of CLI. In this way, Azure increases the fault tolerance of Snowflake clusters.

The power of Azure ensures maximum performance and high throughput for Snowflake nodes by providing a low latency network. Since SnowSQL mirrors Snowflake which stores and computes data, the elastic nature of the cloud will allow it to load data faster or run a high volume of queries. 

Recovery of data has never been this easy. It also provides optimized query parsing and strong integration. Install the SnowSQL offer now from the Azure Marketplace by Data Science Dojo, your ideal companion in your journey to learn data science! 

Click on the button below to head over to the Azure Marketplace and deploy SnowSQL for FREE by clicking on “Get it now”. 


SnowSQL Package

Note: You’ll have to sign up to Azure, for free, if you do not have an existing account. 



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