Data engineering is a process of extracting, transforming, cleaning, transforming, and loading data to the database. It includes collecting data from multiple sources, integrating them with a data warehouse, and then loading them into the data warehouse. Data engineering is performed by a data engineer. What is Data Engineering Team? The data engineering team is responsible for collecting data from multiple sources, integrating them with data warehouse, and then loading them into the data warehouse. The data engineering team is responsible for data integration, data warehousing, data quality, and data preparation. They write the data transformation scripts using tools like Pig, Python, Hive and Spark. Before data is loaded into data warehouse, it is processed by data engineers. Data is transformed from multiple sources like websites, log files, third-party databases, social media and then loaded into data warehouse. Data engineers are skilled professionals who understand data and how it is connected. They are capable of answering questions like What data is required? How do we get the data from multiple sources? How do we transform the data? How do we load data into the data warehouse?
What Tools are used in Data Engineering?
There are various tools used by data engineers. Data engineers need to know how to use them. Below are some of the tools used.
Hadoop – Data engineers use Hadoop for doing data processing and data integration. It is a free open-source software allows to store, process and analyze large data sets in a distributed computing environment. It provides a distributed file system, resource management, and computational services.
Pig – It is a high-level data-flow language for processing large data sets. It is a procedural language. Data engineers use Pig to create the data flow.
Hive – It is a data warehouse infrastructure built on top of Hadoop. A data warehouse is a data repository that is designed for data analysis. Data engineers use Hive to query data warehouses.
Spark – It is a fast and general engine for large-scale data processing. It is an open-source cluster computing framework. Spark is written in Scala and runs on top of Hadoop. It has a faster speed than Hadoop and other data processing tools.
Python – It is a general-purpose language. Data engineers write the data transformation scripts using Python.