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What are the best practices for data analysis in a hybrid data modeling-engineering role?

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Data modeling and data engineering are two essential skills for data analysis, but they are not the same. Data modeling is the process of designing and defining the structure, relationships, and constraints of data in a system, while data engineering is the process of building and maintaining the pipelines, platforms and tools that enable data analysis. In this article, we will explore the differences and similarities between data modeling and data engineering and how to apply best practices for data analysis in a hybrid data modeling-engineering role.

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1 Data modeling basics

Data modeling is the art and science of creating a representation of data that suits a specific purpose, such as a database, a report or a dashboard. Data modeling involves identifying the entities, attributes and relationships that make up the data and applying rules and constraints to ensure data quality and integrity. Data modeling can be done at different levels of abstraction, such as conceptual, logical and physical, depending on the level of detail and complexity required.

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2 Data engineering basics

Data engineering is the discipline of building and managing the infrastructure and processes that enable data analysis. Data engineering involves designing, developing, testing and deploying data pipelines, platforms and tools that collect, store, transform and deliver data to analysts, scientists and other stakeholders. Data engineering also involves ensuring data security, reliability scalability and performance, as well as integrating data from various sources and formats.

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3 Data modeling vs data engineering

Data modeling and data engineering are both crucial for data analysis, but they have different goals and responsibilities. Data modeling focuses on the what and why of data, while data engineering focuses on the how and where of data. Data modeling defines the data requirements and specifications, while data engineering implements the data solutions and systems. Data modeling is more concerned with the business logic and semantics of data, while data engineering is more concerned with the technical aspects and challenges of data.

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Kate Strachnyi

Data & AI Content Creator at DATAcated? | O'Reilly Author (ColorWise)
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Data modeling and data engineering are crucial for data analysis, with different goals and responsibilities. Data modeling translates business data requirements into a logical data model, ensuring data accuracy and accessibility. Data engineering designs and implements technical solutions for storing, processing, and analyzing large amounts of data efficiently. It involves working with tools like databases, data pipelines, and cloud computing. Both roles complement each other in building effective data pipelines for better-informed decisions.

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Aysha Abdul Azeez

Masters in Applied Data Science | Technical Documentation
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In a hybrid data modelling-engineering role, alignment with business objectives is prioritized through close collaboration with stakeholders. Data quality is upheld using effective modelling techniques, and robust documentation and version control are implemented for efficient code management. Embracing automation tools, workflows are streamlined, and a commitment to staying updated on evolving technologies and industry trends is maintained.

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4 Data modeling-engineering hybrid role

In some cases, data analysts may need to perform both data modeling and data engineering tasks, depending on the project and the organization. This hybrid role requires a combination of skills and knowledge from both domains, such as data architecture, data warehousing, data integration, data quality, data governance, data visualization and data analysis. A data modeling-engineering hybrid role can be challenging, but also rewarding, as it allows data analysts to have more control and influence over the entire data lifecycle.

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Sunny Omotoye

Cybersecurity Engineer|Analyst
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Hybrid data modeling and data engineering role requires strong technical skills to succeed. No doubt, strong foundational knowledge in both data modeling and data engineering will be need. Among the technical skills that are essential are knowledge of SQL, data warehousing techniques, data modeling concepts, ETL (extract, transform, load), R or Python programming language, and visualization tools. Aside from the aforementioned, ability to collaborate with team members and stakeholders is important, attention to detail, and continuous learning. Since the field of data analytic is constantly evolving, there will be need to be up to date with latest concepts and techniques in this field.

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5 Best practices for data modeling-engineering hybrid role

To succeed in a data modeling-engineering hybrid role, data analysts need to understand the business problem and the data needs of the stakeholders and communicate clearly and frequently with them. They should also choose the appropriate data modeling approach and level of abstraction and document the data model and its assumptions and limitations. Additionally, data engineers should use the right tools and frameworks and follow the principles of data engineering, such as modularity, reusability, testability and maintainability. They should also validate and monitor the data quality and integrity and handle data errors and anomalies gracefully. Moreover, they should optimize the data performance and efficiency and leverage the cloud and other technologies to scale and automate the data processes. Finally, they should visualize and present the data insights and recommendations and solicit feedback and improvement suggestions.

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Ismael Chang Ghalimi

CEO @ STOIC
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Whether you have a modeling-engineering hybrid role or are part of a team made of data modelers and data engineers, using a single tool covering the end-to-end data lifecycle (modeling, engineering, analysis, and beyond) will streamline the process, facilitate incremental and iterative developments, reduce cycle times, and lower error rates Most importantly, it will improve the overall user experience.

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6 Resources for data modeling-engineering hybrid role

If you're looking to learn more about data modeling and data engineering and how to use them in data analysis, there are a few resources you can explore. Steve Hoberman's book, "Data Modeling Made Simple: A Practical Guide for Business and IT Professionals," is a great place to start. Additionally, Paul Crickard's "Data Engineering with Python: Work with massive datasets to design data models and automate data pipelines using Python" is a great resource. DataCamp's "Data Engineering for Everyone" course is also a great option. For more in-depth learning, Coursera's Data Engineering, Big Data and Machine Learning Specialization and Udemy's Data Modeling and Relational Database Design are both great options.

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What are the best practices for data analysis in a hybrid data modeling-engineering role?

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6

1 Data modeling basics

2 Data engineering basics

3 Data modeling vs data engineering

4 Data modeling-engineering hybrid role

5 Best practices for data modeling-engineering hybrid role

6 Resources for data modeling-engineering hybrid role

Data Analysis

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What are the best practices for data analysis in a hybrid data modeling-engineering role?

1

2

3

4

5

6

1 Data modeling basics

2 Data engineering basics

3 Data modeling vs data engineering

4 Data modeling-engineering hybrid role

5 Best practices for data modeling-engineering hybrid role

6 Resources for data modeling-engineering hybrid role

Data Analysis

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