What Is Data Engineering??
By: Tech In
Tags: LinkedIn, Java, Python, Big Data, Data Science

Source: ElderResearch

LinkedIn’s 2020 Emerging Jobs Report and Hired’s 2019 State of Software Engineers Report ranked Data Engineer jobs right up there with Data Scientist and Machine Learning Engineer.

What is Data Engineering?

Harnessing data can enable a lot of things, from personalizing marketing campaigns to powering self-driving cars. Data scientists are responsible for analyzing data and using it for various purposes.

However, they need good quality data to accomplish complex tasks, such as forecasting trends for business. That’s where data engineers come in.

Data engineering is the science of collecting and validating information (data) such that data scientists can use it.

The role of Data Engineer:-

Data engineers are the people who build the information infrastructure on which data science projects depend. These professionals are responsible for designing and managing data flows that integrate information from various sources into a common pool (a data warehouse, for example) from which it can be retrieved for analysis by data scientists and business intelligence analysts. This typically involves implementing data pipelines based on some form of the ETL (Extract, Transform, and Load) model.

In creating this information architecture, data engineers rely on a variety of programming and data management tools for implementing ETL, managing relational and non-relational databases, and building data warehouses. Let’s take a quick look at some of the most popular tools.

Data Engineering Tools:-

Apache Hadoop is a foundational data engineering framework for storing and analyzing massive amounts of information in a distributed processing environment. Rather than being a single entity, Hadoop is a collection of open-source tools such as HDFS (Hadoop Distributed File System) and the MapReduce distributed processing engine.

Apache Spark is a Hadoop-compatible data processing platform that, unlike MapReduce, can be used for real-time stream processing as well as batch processing. It is up to 100 times faster than MapReduce and seems to be in the process of displacing it in the Hadoop ecosystem. Spark features APIs for Python, Java, Scala, and R, and can run as a stand-alone platform independent of Hadoop.

Apache Kafka is today’s most widely used data collection and ingestion tool. Easy to set up and use, Kafka, is a high-performance platform that can stream large amounts of data into a target like Hadoop very quickly.

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

SQL and NoSQL (relational and non-relational databases)are foundational tools for data engineering applications. Historically, relational databases such as DB2 or Oracle have been the standard. But with modern applications increasingly handling massive amounts of unstructured, semi-structured, and even polymorphic data in real-time, non-relational databases are now coming into their own.

Programming Languages:-

Python is a very popular general-purpose language. Widely used for statistical analysis tasks, it could be called the lingua franca of data science. Fluency in Python (along with SQL) appears as a requirement in over two-thirds of data engineer job listings.

R is a unique language with features that other programming languages lack. This vector language is finding use cases across multiple data science categories, from financial applications to genetics and medicine.

Java, because of its high execution speeds, is the language of choice for building large-scale data systems. It is the foundation for the data engineering efforts of companies such as Facebook and Twitter. Hadoop is written mostly in Java.

Scala is an extension of Java that is particularly suited for use with Apache Spark. In fact, Spark is written in Scala. Although Scala runs on JVM (Java Virtual Machine), the Scala code is cleaner and more concise than the Java equivalent.