Data Engineering 101: Introduction to Data Engineering.

Companies operate based on the insights drawn from their data. This data can be customer feedback, stock price performance, Sales data, product reviews. This data is available in raw format and needs to be processed for business to find practical application from the data.

Data engineers, data scientists, data analyst

Data engineers mine through the data for insights and convert unstructured data into a more usable form for data analysts. They write queries, maintain the architecture and design of data and create data warehouses for large databases.
Data scientists, on the other hand, mine and clean unstructured data, create models to work on big data and analysis of big data.
While data analysts process data and provide summary reports. Gather information out of a database by writing queries. Use basic algorithms and have knowledge of statistics, data visualization and analysis.
A Data Engineer can be critically termed as the first member of the Data Science team. He/she works with huge amounts of data to maintain the analytics infrastructure and making it suitable for Data Scientists to work on

Tools in Data Engineering

Data Science projects largely depend on the information infrastructure structured by Data Engineers. They typically implement their pipelines based on the ETL (extract, transform, and load) model. To get started with data engineering projects, you will need to have a good understanding of the following tools

1. Python: It is the most popular general-purpose language used for statistical analysis. A majority of Data Engineer job descriptions mention ‘fluency in Python’ as a mandatory requirement.
2. Relational and non-relational databases: SQL and NoSQL act as the basic tools for executing Data Engineering applications. They are known for handling enormous amounts of real-time unstructured and polymorphic data.
3. Apache Spark: It is used for stream processing and batch processing. It is quicker than MapReduce and is estimated to replace MapReduce in the Hadoop Ecosystem soon.
4. Apache Hadoop: Hadoop is a collection of tools, namely, HDFS (Hadoop Distributed File System), MapReduce, etc. It acts as a foundation framework for storing and analyzing information.
5. Julia: Julia is yet another general-purpose programming language that is easy to learn. It has the capability to be used solely in data projects for prototyping and production.

How a data Engineer can bring value to your business

• Designing architectures: developing, testing and maintaining large-scale architectures, i.e. databases. Ensuring architecture will support the business requirements.
• Discover opportunities for data acquisition. Explore and examine data to find hidden patterns.
• Developing data set processes for data modeling, production and mining.
• Recommend ways to improve data reliability, efficiency and quality.

Wrapping up

Data engineers work closely with data scientists and data analysts and reform data before they can process data and design models to work on. Analytics are built upon layers, and foundational work such as building data warehousing is an essential prerequisite for scaling a growing organization.