In this article, we shared a list of the top 10 data engineering tools used by data professionals
Data engineering is the most popular and in-demand job in the big data domain worldwide. Data engineering is the complex task of making raw data used by data scientists and groups within an organization. Data engineersneeds are divided into various sets of requirements to design and build pipelines that transform and transport data into an understandable format by the time it reaches the Data Scientists. These requirements are met using data engineering tools, which include a mix of programming languages and data warehouses but are not limited to data management, BI, processing, and analytics tools. Here are the top 10 data engineering tools most used by tech professionals.
Amazon Redshift: Redshift is a petabyte-scale data warehouse solution built and designed for data scientists, data analysts, data administrators, and software developers. Amazon Redshift is a fully managed cloud warehouse built by Amazon. These two communicate using the industry-standard JDBC and ODBC drivers for PostgreSQL.
Google BigQuery: BigQuery is a fully managed cloud data warehouse. It is commonly used in companies that are familiar with the Google Cloud Platform. It is a powerful solution to democratize insights, power business decisions, run analytics, and analyze petabytes scale SQL queries and it is built on top of Dremel technology and has a serverless architecture.
Tableau: It is the oldest data visualization solution, the main function is to gather and extract data that is stored in various places. It is a data visualization and BI tool that is used for business applications like data modeling, creating live dashboards, and assembling data reports to empower business teams to make data-driven decisions.
Apache Spark: It represents one such popular implementation of Stream Processing. It is another open-source data engineering and analytics tool. And it is easy to use and offers high-performance data processing in a variety of industries ranging from retail and finance to healthcare and media.
Airflow: It is one of the most trending engineering tools that makes managing, scheduling, and building data pipelines easier for data engineers. And it is an ideal tool for data engineering workflows. The main advantage is its ability to manage complex workflows.
Python: It has become the de-facto standard when it comes to data engineering. Data engineers use Python to code ETL frameworks, automation, API interactions, and data munging tasks such as aggregating, reshaping, joining disparate sources, etc.
SQL: It is one of the most important tools that help access, update, insert, manipulate, and modify data using queries, data transformation techniques, and more. The primary purpose of knowing SQL is to write ‘data integration scripts’ and run analytical queries to transform and use data for BI.
Snowflake: It is a cloud-based data analytics and storage service provider. It helps customers migrate to a cloud-based solution quickly, and Snowflake’s shared data architecture makes it an excellent tool for data science and data engineering.
Power BI: It supports hybrid deployment support, which is primarily used in gathering data from different sources to create reports that will power the next business decision. The data models created from Power BI can be used in several ways for organizations, including telling stories through charts and data visualization.
dbt: It is a command-line tool, that allows data engineers, analysts, and scientists to model and transform data into a warehouse using SQL. And it allows companies to easily write transformations and orchestrate them more efficiently.