- Data Engineering - Home
- Data Engineering - Introduction
- Data Engineering - Data Collection
- Data Engineering - Data Storage
- Data Engineering - Data Processing
- Data Engineering - Data Integration
- Data Engineering - Data Quality & Governance
- Data Engineering - Data Security & Privacy
- Data Engineering - Tools & Technologies
- Data Engineering Useful Resources
- Data Engineering - Useful Resources
- Data Engineering - Discussion
Data Engineering - Introduction
In Data Engineering, data processing and analysis are carried out with the help of high-performance computing. In the context of computing, the data engineering approach adopted is called data flow programming. It represents computations as a directed graph, in which nodes are different operations, and edges represent the flow of data. Implementations like increment computing determines the efficiency of data processing.
Data Engineering involves creating systems and tools to manage the access and flow of the information. Here, the data is easily accessible, well-maintained and ready for analysis. Data engineers build and manage the data infrastructure, making it easier for data analyst and scientists to work with the data.
Data Engineering involves developing, implementing and maintaining systems that transform unique data into high-quality reliable information. This information is used for various purposes, such as machine learning and analysis. Data engineering combines aspects of security, data management, DataOps and data architecture and software engineering. A data engineering is a system that makes the source available for use in analysis or machine learning.
Data Engineering
Data is stored in a wide range of application and one key factor in deciding how to store data is its purpose. Data Engineers optimize storage by compressing, archiving and partitioning data.
Structured data often requires online transaction processing, for which databases are typically used. Relational databases with strong ACID guarantees and SQL queries were once common. NoSQL databases have gained popularity due to their ability to scale horizontally, even though they sacrifice ACID guarantees and reduce the object- relational mismatch.
When structured data needs analytical processing instead of transaction processing, data warehouses are typically used. They support large scale data analysis and manage data flow from databases. Data engineers, business analysts and data scientists access data warehouses using tools like SQL or business intelligence software.
Data Engineering Tools
A data lake is a centralized repository that allows massive amounts of data, whether structured from relational databases or semi-structured and unstructured data or binary data to be stored. This could be done through services available in the public cloud like Microsoft, Amazon or Google..
If the data is less specified, then they are stored as files. There are different options such as:
Object storage manages data using the help of metadata, sometimes assigning an unique key to every file, such as Universally Unique identifier(UUID).
Block storage divides data into equally sized chunks, which often correspond to hard drives or solid-state drives.
File systems organize data hierarchically using nested folders.
Big data is very much popular and captures the interest of many companies. Often, companies use big tools for small data problems, deploying complex systems for minimum data. This trend is driven by various marketing strategies for big data tools.
Data Scientists usually create production data systems, but they often work inefficiently due to limited support and resources from data engineers. Data scientists should focus their time on analytics, machine learning and experimentation. When data engineers handle the foundation tasks, they create a solid base that allows data scientists to excel in their roles.