In today’s era where data holds value, the role of data engineering has become increasingly crucial. In this blog post, we'll dive deeper into the world of data engineering, exploring its importance, key concepts, and the tools and technologies that drive this field forward.
I. Introduction to Data Engineering
A. What is Data Engineering?
Data engineering can be described as a kind of software engineering but they only focus deeply on data like:- data infrastructure, data mining, data warehousing, data crunching, etc. Data engineering involves collecting, storing, and analyzing data in a data warehouse before serving it to various stakeholders. Data engineers work in a variety of settings to build system that collect, manage and convert raw data into usable information for data scientist and business analyst to interpret.
B. Importance of Data Engineering
In this era driven by data, the practice of data engineering is essential as it enables companies to make decisions based on insights derived from data streamlines operations and upholds standards for data security and compliance. Different organizations that prioritize investments in data engineering capabilities are better positioned to thrive in today’s landscape and capitalize on opportunities emerging from the transformation.
C. Role of Data Engineers in the Data Ecosystem
Data engineers serve as connectors between sources of information and end users seeking insights. Collaborating closely with professionals like data scientists, analysts and stakeholders they ensure that information is accessible, trustworthy and usable. Furthermore their responsibilities encompass designing and implementing pipelines for managing data flow, alongside developing storage solutions to cater to the organizations evolving requirements.
Data engineers are responsible, for extracting data from sources refining or organizing it. Then loading it into a database for analytical purposes. Their critical role includes ensuring the accuracy and optimization of data usage by data scientists.
D. Workflow of Data Engineers
Data will be generated in a corporation by several teams using a range of systems such as databases, APIs, streaming events, and file servers. This is the data needed by multiple teams to do various analyses.
Generally, the incoming data is in different formats and sizes from different sources and that data is stored into an archival/analytics system like a data warehouse or a data lake. When the data is in data warehouse, it will be cleaned, transformed into a mutually agreed format between the stakeholders.
The data engineering team will create and maintain pipelines and procedures, such as ETL/ELT, for data ingestion and transformation across all data received in the data warehouse.
II. Key Concepts in Data Engineering
A. Data Storage and Management
Data engineers are adept at utilizing database systems, which are computerized repositories for storing vast amounts of data. These databases can be categorized into SQL or NoSQL variants. Applications often rely on databases to deliver specific functionalities; for instance, an online store utilizes a database to manage product data such as prices and inventory levels. Conversely, databases designated for analysis purposes hold data tailored for analytical tasks. While further nuances will be explored in subsequent chapters, it's crucial to recognize that the data engineer's realm predominantly revolves around databases
B. Data Processing and Transformation
Data engineers rely on specialized tools to efficiently process data, which may involve tasks such as cleansing, aggregating, or integrating data from various sources. Given the often massive volumes of data involved, parallel processing becomes indispensable. This approach distributes the workload across clusters of machines, enabling data engineers to tackle processing tasks effectively. These tools often offer a simplified interface, abstracting the complexities of underlying architectures, and providing a user-friendly API for seamless integration into data engineering workflows.
III. Tools and Technologies in Data Engineering
A. Database Management Systems
Database management systems (DBMS) are software tools that enable data storage, retrieval, and manipulation. Popular DBMS include MySQL, PostgreSQL, Oracle, and SQL Server. Data engineers use DBMS to store and manage structured data efficiently.
B. Extract, Transform and Load
ETL is a data progress where in we take data from one or multiple source, apply some changes to it and then load it in a new data warehouse or other unified data repository. ETL serves as a foundation for the works teams such as data analytics and machine learning. ETL cleanses and organizes data using set of business rules to fulfill a specific business needs such as monthly reporting but it may also handle more advanced analytics.
C. Big Data Technologies
In the realm of data engineering, the abundance of commonly used tools offers a wealth of options for professionals to select from. For databases, MySQL and PostgreSQL stand out as popular choices. When it comes to processing data, Apache Spark and Apache Hive are widely embraced. As for scheduling tasks, tools such as Apache Airflow, Apache Oozie, and the straightforward bash tool, Cron, provide effective solutions. It's worth noting that while these examples are prevalent, organizations may also opt to develop proprietary tools internally to meet their specific needs.