Data Engineers’ Top 10 Tools
The demand for data engineers has skyrocketed due to the sudden increase in operations data in the analytics stack. These professionals build data pipelines that facilitate smooth analytics, which requires various data engineering tools. These tools include programming languages and data warehouses, among others, for managing, processing, and analyzing data.
Data engineers are responsible for building, maintaining, and optimizing data infrastructure for acquisition, storage, processing, and access. They create pipelines that transform raw data into a format that other professionals, such as data scientists, can use.
Data engineering teams will likely focus on defining and analyzing data after solving issues with data warehousing, ETL, and data quality. The future of data engineering remains uncertain, but it is expected to continue evolving to meet the changing needs of businesses and organizations.
Who is a data engineer?
A data engineer is an individual who creates, preserves, and optimizes data infrastructure for data acquisition, storage, processing, and access. Data engineers create pipelines that transform raw data into structures that data scientists and other professionals are able to leverage.
Recommended data engineering tools to explore in 2023
In 2023, there will be several top data engineering tools that data engineers should explore. These include Apache Spark, Amazon Redshift, Snowflake, Tableau, Looker, Hevo Data, Power BI, Segment, Python, and SQL. While these are just a few available options, they are essential for building efficient and robust data infrastructure.
Apache Spark
Apache Spark tool is an open-source data analytics engine with a custom ground of over 52K corps, including top companies like Apple, Microsoft, IBM, etc.
Amazon Redshift
Amazon Redshift is a fully managed cloud warehouse built by Amazon. Amazon’s easy-to-use cloud warehouse is another industry staple that powers thousands of businesses.
Snowflake
Snowflake is a one-of-a-kind cloud-based data storage and analytics service provider. Snowflake is a warehouse-as-a-solution designed to cater to today’s enterprises’ needs.
Tableau
Tableau is one of the big data industry’s oldest and most popular data engineering tools. Tableau assembles data from multiple sources using a drag-and-drop interface and allows data engineers to build dashboards for visualization.
Looker
Looker is BI software that helps employees visualize data. Looker is widespread and commonly adopted across engineering teams.
Hevo Data
Hevo lets you replicate data in near real-time from 150+ sources to the goal of your choice, including Snowflake, Redshift, Databricks, BigQuery, and Firebolt, without writing a single line of code.
Power BI
With around 40% BI market share since 2021, Microsoft Power BI is definitely one of the top business intelligence and data visualization tools.
Segment
Segment makes it simple to collect and use data from the users of the digital properties. With Segment, you can manage, transform, send, and archive your customer data.
Python
Python is a well-known, object-oriented, high-level programming language that is frequently used to develop software and websites. Python is also commonly used for task automation, data analysis, and data visualization.
SQL
SQL (Structured Query Language) was a standardized programming language created in the early 1970s. Its primary function is to manage and extract information from relational databases. Knowing SQL is now considered a prerequisite not only for database administrators but also for software developers.
While the list contains the top 10 data engineering tools, data engineers have many options available to them today. Nevertheless, these 10 data engineering tools are precious for data engineers looking to build an efficient and robust data infrastructure.
What’s next in Data Engineering?
It’s hard to speculate what’s next in data engineering. Based on our research, the primary focus of data engineering teams after solving the data warehousing, ETL, and quality problems is to start defining and analyzing the data.
