30 days of Data Engineering: Day 1
Greetings, folks! The primary objective of the 30-day Data Engineering program, with many interesting projects, is to comprehend Data Engineering from a practical viewpoint and get practical hands-on experience by implementing projects (while avoiding excessive theoretical discussions).
Day 1 — What is Data Engineering; Why Data Engineering; Data Engineers vs. ML Engineers & Data Scientists; Purpose, Scope, and Responsibilities
What is Data Engineering
Why Data Engineering
Data Engineers — ML Engineers — Data Scientists
Purpose and Scope
What is Data Engineering
Data engineering involves the series of activities that prepare data for various applications, including analysis and machine learning. This includes tasks such as data ingestion, cleaning, and transformation, in addition to building and maintaining the essential infrastructure and systems that store, process, and permit access to the data.
Data Engineering aims to ensure that data can be readily utilized and comprehended by other members of the data team, such as machine learning engineers and data scientists.
To put it simply, Data Engineering is the fundamental process of design and implementation for collecting, storing, processing, and analyzing a vast amount of data on a large scale.
To put it straight, In data engineering, The primary responsibility is to develop and manage large-scale data processing systems that prepare both structured and unstructured data. Our goal is to utilize this data to perform analytical modeling and make data-driven decisions.
Data engineering’s objective is to ensure the accessibility of high-quality data for analysis and to facilitate effective decision-making based on data.
The Data Engineering ecosystem comprises four essential components, namely —
Data — This refers to various types, formats, and sources of data.
Data stores and repositories — These are databases, data warehouses, data marts, data lakes, and big data stores that store and process data, both relational and non-relational.
Data Pipelines — The process of collecting, gathering, cleaning, processing, and transforming data from multiple sources to make it suitable for analysis.
Analytics and Data-driven Decision-Making — This component involves making the processed data available for business analytics, visualization, and data-driven decision-making.
Why Data Engineering?
The Data Engineering lifecycle involves building/architecting data platforms, designing and implementing data stores and repositories, and processing and analyzing data. This encompasses activities such as data gathering, importing, cleaning, pre-processing, querying, analyzing data, performance monitoring, evaluation, optimization, and fine-tuning of systems and processes.
Engaging in Data Engineering offers several benefits, including—
1. The ability to handle and process heterogeneous data formats, resulting in high-quality data that can be used for production.
2. The capability to work with large volumes of data and extract optimal value from it.
3. Automation of data pipelines and streams.
4. Efficient use of metadata.
5. The potential to extract valuable insights from real-time refined data.
How Data Engineers are different from ML Engineers and Data Scientists?
To put it simply, a Data Engineer is accountable for ensuring high-quality data is available from diverse sources, maintaining databases, constructing data pipelines, performing data queries, preprocessing data, conducting feature engineering, working with Apache Hadoop and Spark, and developing data workflows utilizing Airflow.
In contrast, Data Scientists and ML Engineers are responsible for developing ML algorithms, building data and ML models, deploying them, possessing statistical and mathematical knowledge, and evaluating, enhancing, and optimizing outcomes.
Even though there is some overlap between the two, data engineers and machine learning engineers perform separate duties. Data engineers concentrate on designing and constructing the infrastructure and systems that store and process data, while machine learning engineers focus on constructing and deploying machine learning models. Meanwhile, data scientists analyze and interpret data to gain insights and make decisions.
Purpose, Scope, and Responsibilities
The scope of data engineering encompasses a broad array of responsibilities, including designing data pipelines, managing data warehousing, and utilizing advanced big data technologies such as Hadoop and Spark. Collaboration with data scientists and machine learning engineers is also essential to ensure that data is accessible and comprehensible to other members of the data team.
The primary duty of data engineers is to construct a highly effective data infrastructure capable of processing vast quantities of data from diverse sources.
To summarize, this series aims to provide hands-on experience while simultaneously touching on essential theoretical concepts.
Concluding for Day 1!
Please visit my next blog for Day 2 and more.
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Keep learning and coding
