Data engineering is a rapidly growing and important field within the tech industry. It involves the design, development, and maintenance of data management systems that enable the efficient storage, movement, and accessibility of data. This role is critical in a world increasingly reliant on the use of technology and data. As such, data engineers must have access to the best tools in order to carry out their duties successfully. In this post, we’ll discuss the various data engineering tools available to help data professionals work smarter and more efficiently. We’ll break down the different types of tools, explore their features, and discuss how they can be used to their full potential. So, whether you’re just starting out in data engineering or are an experienced professional, this post will provide you with valuable insight into the top data engineering tools available on the market.
5 Great Data Engineering Tools For 2021 – My Favorite Data Engineering Tools
What is data engineering?
The practice of developing, operating, and maintaining software systems that gather, store, and analyze data for an organization is known as data engineering. These programs employ a wide range of online resources, tools, languages, and software. Effective data engineering provides information to analysts and data scientists to monitor and enhance production, sales, distribution, and revenue methods.
21 data engineering tools
Here are 21 data engineering tools with an explanation of their functions and characteristics:
1. Python
A general-purpose programming language called Python is frequently used to create systems for data engineering. It provides a range of functions and resources for creating data pipelines and automating programs. Python programming is frequently used for data munging tasks like reshaping and aggregating in order to quickly and automatically perform data analysis.
2. Structured Query Language
Data engineers frequently use the Structured Query Language (SQL) to query databases. Tools for building reusable data structures, running complex queries, and modeling business logic are all included in SQL. SQL manages data within relational databases and data management systems.
3. PostgreSQL
PostgreSQL is an open-source relational database. High levels of customization, data security, and capacity are provided by its features. This tool can be used by data engineers to create workflows and manage massive amounts of data.
4. MongoDB
MongoDB is a NoSQL database that can handle large-scale data sets and stores both structured and unstructured data. The database provides flexibility for unstructured data and content by organizing data in a straightforward manner. Distributed key-value storing, document-oriented tools, and calculation features are some of MongoDB’s key features.
5. Apache Spark
Apache Spark focuses on stream processing for data. Stream processing aims to capture and display real-time data efficiently. With the aid of the tools provided by Apache Spark, data scientists can run and view queries on continuous data streams.
6. Apache Kafka
Apache Kafka is an open-source event streaming data platform. It has tools for real-time data streams, group messaging, and data synchronization. Apache Kafka is primarily used in data engineering as a tool for data collection and transportation.
7. Apache Airflow
Data engineers use Apache Airflow as a workflow management platform. It enables managers and engineers to design, alter, and carry out data pipeline tasks and schedules. Additionally, features assist with monitoring, visualizing, and troubleshooting while the data production process is underway.
8. Apache Hadoop
A group of open-source tools known as Apache Hadoop collaborate to manage and process large-scale data programs, such as data from computer networks. The sources enable the storage, organization, and output of crystal-clear, in-depth data analytics. High fault tolerance, a large data capacity, and real-time data tracking are the main features.
9. Apache Hive
As a data warehouse and management tool, Apache Hive is an addition to Apache Hadoop. It enables users to handle data requests and get analytics from the outcomes. With a basic understanding of the SQL language, Hive’s interface and structure are similar to those of the SQL language, making it simple to use.
10. Apache Kudu
Apache Kudu offers basic storage and organization of data. The main feature of Kudus enables users to quickly produce analytics and create column-oriented data storage. Additionally, it integrates with the Apache Hadoop library and offers the ability to manage massive data sets.
11. Apache Cassandra
The Apache Cassandra NoSQL database structure enables users to scale and process data from various sources at once. Understanding Cassandra’s data architecture and its capacity to build unique data infrastructures are prerequisites for using this tool. When performing scalable and effective data analysis, data engineers frequently use Apache Cassandra.
12. Snowflake
Snowflake is a cloud-based data warehouse program. It provides data storage, computing and cloning tools for engineers. In order to provide comprehensive and in-depth data reports, Snowflake also permits integration with third-party data tools.
13. Cloudera
A cloud-based tool for machine learning and data analysis, Cloudera It provides tools for data engineers as well as business analysts who evaluate the data outcomes. In addition to providing an easy-to-use user interface, Cloudera also provides tutorials and guides for entering and processing data.
14. Big Query
A fully managed, cloud-based data warehouse tool is called Big Query. It enables engineers and analysts to enter data, process it, and modify the scope and timeframe of operations to suit their evolving needs. Machine learning tools, business intelligence analysis, and real-time data reporting are some of Big Query’s key features.
15. Tableau
Tableau combines data engineering and business analysis functions. It focuses on organizing and creating visual data metrics. Data engineers can sort, collect, visualize, and organize data for each department using the drag-and-drop interface. To create and share dashboard data reports across an organization, Tableau provides helpful tools.
16. Looker
Looker offers detailed data visualization reports. The LookML tool provides visualization tools for an SQL database that show dimensions, aggregates, calculations, and data relationships. Engineers can effectively share information with analysts and coworkers by creating visuals and graphs for each data set.
17. Segment
Segment focuses on collecting and analyzing data from users. Data engineers can collect, translate, and store consumer and user data with the help of these tools and procedures. Additionally, new functions improve the effectiveness of data collection through automated data processing and machine learning.
18. DBT
Data engineers can use DBT, a command-line tool, to perform SQL transformations on data stored in their warehouse. The functions and tools facilitate the development of transformation programs that speed up data processing techniques. DBT does not offer tools for loading and extracting data points; it only focuses on the transformation of data.
19. Redash
Redash wants to function as an all-purpose data tool for people of all skill levels. Redash is a tool that data engineers can use to query, visualize, and share data from various sources. This system’s tools and interface enable data communication and comprehension across all levels and departments.
20. Presto
Presto is an open-source SQL query engine. Without transferring data to a different system, the tools built into Presto’s system can be used to access data stored in external sources. These tools can be used by data engineers to conduct continuous queries on external data and generate analyses quickly.
21. Microsoft Power BI
Interactive data visualization tools and business intelligence analytics are offered by Microsoft’s Power BI system. It aims to produce straightforward data reports for professionals and analysts of any skill level. Power BI can be used by data engineers and business analysts to build business dashboards and share data information within an organization.
Please note that Indeed is not affiliated with any of the businesses mentioned in this article.
FAQ
Do data engineers use SQL?
Data engineers prepare the data for data scientists by using tools like SQL and Python. Data engineering collaborates with data scientists to comprehend their unique employment requirements. They create data pipelines that gather the data and format it into the necessary structures for analysis.
What language is used in data engineering?
A deep understanding of data structures, familiarity with various data storage technologies, knowledge of distributed and cloud computing systems, etc. are all necessary for becoming a data engineer. In addition to all of these abilities, SQL and database expertise are essential to data engineering.
What data engineering involves?
In our company, SQL, Python, R, and Scala are frequently used These programming languages’ primary motivations are cost, efficiency, security, and cross-program collaboration
