What is Data Engineering?

Using data can help in many things, from personalized marketing campaigns to driving self-driving cars. Data processing specialists are responsible for analyzing the data and using it for various purposes.

However, they need good quality data to perform complex tasks such as predicting business trends. This is where data engineers come in.

Data engineering is the science of collecting and validating information (data) that data scientists can use what does an engineer do.

Role of Data Engineer: -

Data Engineers are the people who create the information infrastructure on which data science projects depend. These professionals are responsible for designing and managing data flows that combine information from various sources into a common pool (such as a data warehouse) from which it can be retrieved for analysis by data scientists and business intelligence analysts. Typically this involves implementing data pipelines based on some form of ETL model (extract, transform, and load) .

To create this information architecture, data engineers rely on a variety of programming and data management tools to implement ETL, manage relational and non-relational databases, and build data warehouses. Let's take a quick look at some of the most popular tools.

Data Engineering Tools: -

Pace Hadoop is the fundamental data engineering foundation for storing and analyzing large amounts of information in a distributed processing environment. Hadoop is not a single unit, but a set of open source tools such as HDFS (Hadoop Distributed File System) and MapReduce distributed processing engine.

Pace Spark is a Hadoop-compatible data processing platform that, unlike MapReduce, can be used for real-time stream processing as well as batch processing. He was almost in 100 times faster than the MapReduce, and seems to displace it in the Hadoop ecosystem. Spark offers APIs for Python, Java, Scala, and R, and can run as a stand-alone platform independent of Hadoop.

Pache Kafka is the most widely used data collection and food intake tool today. Kafka is an easy to configure and use high performance platform that can transfer large amounts of data very quickly to a target such as Hadoop.

Apache Cassandra is widely used to manage large amounts of data with lower latency for users and automatic replication to multiple sites for fault tolerance.

S QL and NoSQL (relational and non-relational databases) are fundamental tools for data engineering applications. Historically, relational databases like DB2 or Oracle have been the standard. But with today's applications increasingly processing huge amounts of unstructured, semi-structured, and even polymorphic data in real time, non-relational databases are now coming into their own.