登录查看更多内容

You’re looking to develop your career in Data Warehousing. What skills do you need to focus on?

由人工智能和领英社区提供技术支持

Data warehousing is a process of collecting, integrating, and analyzing data from various sources to support business intelligence, analytics, and decision making. It is a vital skill for any organization that wants to leverage data to gain insights, improve performance, and create value. If you’re looking to develop your career in data warehousing, you need to focus on some key skills that will help you design, build, and maintain effective data warehouses. Here are some of the most important ones.

此文章中的业界达人

由社区从 10 条内容中精选。了解更多

Shreyas Shukla

AI-fluencer & Trainer || IIT Kharagpur Alum || Microsoft Azure Certified Data Engineer Associate || 3 × Azure || Python…
Aman Gambhir

Microsoft Certified Data Engineer | Top Icons Of India 2024 | LinkedIn Top Voice | Interviewer and Tech Recruiter |…
AAMIR P

Senior Software Engineer at Tiger Analytics | Padma Shri Award nominee for the year 2023 | Author of 25+ books |…

1 Database design

A data warehouse is essentially a database that stores data from different sources in a consistent and organized way. To create a data warehouse, you need to understand the principles of database design, such as data modeling, normalization, denormalization, schemas, and indexes. You also need to be familiar with the different types of data warehouse architectures, such as star, snowflake, and galaxy, and how they affect the performance, scalability, and complexity of the data warehouse.

添加您的观点

Shreyas Shukla

AI-fluencer & Trainer || IIT Kharagpur Alum || Microsoft Azure Certified Data Engineer Associate || 3 × Azure || Python || SQL || 5?SQL HackerRank
举报内容
Database includes concepts like modeling, schemas, indexes, normalization , etc. Besides, you need to be aware of architectures of data warehouses like star, snowflake etc. and how they are different from each other.

已翻译

赞
Aman Gambhir

Microsoft Certified Data Engineer | Top Icons Of India 2024 | LinkedIn Top Voice | Interviewer and Tech Recruiter | 15K+ Brains | 50M+ Views | Helping Job Seekers | Humor In The Hustle | All the opinions are personal
举报内容
Understand database concepts and be proficient in designing efficient and scalable database structures. This includes knowledge of normalization, indexing, and schema design.

已翻译

赞
AAMIR P

Senior Software Engineer at Tiger Analytics | Padma Shri Award nominee for the year 2023 | Author of 25+ books | Badminton Player | Udemy Instructor | Public Speaker | Podcaster | Chess Player | Coder | Yoga Volunteer |
举报内容
Inquire about the candidate's proficiency in data modeling techniques, including conceptual, logical, and physical data modeling. Assess their ability to translate business requirements into a conceptual data model, refine it into a logical data model, and implement it as a physical data model tailored to the data warehouse platform.

已翻译

赞

2 Data integration

Data integration is the process of extracting, transforming, and loading (ETL) data from various sources into the data warehouse. It involves cleaning, validating, and transforming data to ensure its quality, accuracy, and compatibility. You need to master the tools and techniques for data integration, such as SQL, scripting languages, ETL tools, data pipelines, and workflows. You also need to be able to handle data integration challenges, such as data inconsistency, duplication, and latency.

添加您的观点

Aman Gambhir

Microsoft Certified Data Engineer | Top Icons Of India 2024 | LinkedIn Top Voice | Interviewer and Tech Recruiter | 15K+ Brains | 50M+ Views | Helping Job Seekers | Humor In The Hustle | All the opinions are personal
举报内容
Learn techniques for integrating data from multiple sources into a data warehouse. This includes ETL (Extract, Transform, Load) processes, data migration, and data synchronization.

已翻译

赞
AAMIR P

Senior Software Engineer at Tiger Analytics | Padma Shri Award nominee for the year 2023 | Author of 25+ books | Badminton Player | Udemy Instructor | Public Speaker | Podcaster | Chess Player | Coder | Yoga Volunteer |
举报内容
Discuss the candidate's familiarity with scripting languages commonly used in data integration tasks, such as Python, Perl, or PowerShell. Evaluate their ability to write scripts to automate data extraction, transformation, and loading tasks, as well as to perform data validation and cleansing operations.

已翻译

赞

3 Data analysis

Data analysis is the process of querying, exploring, and interpreting data from the data warehouse to generate insights, reports, and dashboards. It requires a strong knowledge of SQL, the most common language for querying data warehouses, as well as other analytical tools and frameworks, such as Excel, Power BI, Tableau, Python, and R. You also need to have a good understanding of business logic, metrics, and KPIs, as well as data visualization and storytelling skills.

添加您的观点

Aman Gambhir

Microsoft Certified Data Engineer | Top Icons Of India 2024 | LinkedIn Top Voice | Interviewer and Tech Recruiter | 15K+ Brains | 50M+ Views | Helping Job Seekers | Humor In The Hustle | All the opinions are personal
举报内容
Develop skills in analyzing data stored in a data warehouse to derive insights and make informed decisions. This includes knowledge of SQL and data visualization tools.

已翻译

赞
AAMIR P

Senior Software Engineer at Tiger Analytics | Padma Shri Award nominee for the year 2023 | Author of 25+ books | Badminton Player | Udemy Instructor | Public Speaker | Podcaster | Chess Player | Coder | Yoga Volunteer |
举报内容
Evaluate the candidate's ability to explore and discover patterns, trends, and relationships within the data warehouse using exploratory data analysis (EDA) techniques. Inquire about their experience with data profiling, data visualization, and data mining techniques to uncover hidden insights and identify actionable opportunities.

已翻译

赞

4 Data governance

Data governance is the practice of managing and controlling the data in the data warehouse, ensuring its security, quality, compliance, and availability. It involves defining and enforcing policies, standards, and roles for data access, usage, and maintenance. You need to be aware of the best practices and regulations for data governance, such as data privacy, data ethics, and data audit. You also need to be able to monitor and troubleshoot data issues, such as data breaches, errors, and anomalies.

添加您的观点

Aman Gambhir

Microsoft Certified Data Engineer | Top Icons Of India 2024 | LinkedIn Top Voice | Interviewer and Tech Recruiter | 15K+ Brains | 50M+ Views | Helping Job Seekers | Humor In The Hustle | All the opinions are personal
举报内容
Understand the principles of data governance and how to implement them in a data warehousing environment. This includes ensuring data quality, security, and compliance with regulations.

已翻译

赞

5 Data engineering

Data engineering is the discipline of designing, developing, and optimizing the data infrastructure and systems that support the data warehouse. It covers aspects such as data storage, data processing, data distribution, and data orchestration. You need to have a solid background in computer science, software engineering, and cloud computing, as well as proficiency in programming languages, frameworks, and platforms, such as Java, Python, Spark, Hadoop, AWS, and Azure.

添加您的观点

AAMIR P

Senior Software Engineer at Tiger Analytics | Padma Shri Award nominee for the year 2023 | Author of 25+ books | Badminton Player | Udemy Instructor | Public Speaker | Podcaster | Chess Player | Coder | Yoga Volunteer |
举报内容
Discuss the candidate's familiarity with data processing frameworks and technologies for transforming and analyzing data at scale, such as Apache Spark, Apache Flink, or Apache Beam. Evaluate their ability to design and implement data processing pipelines that ingest, transform, and load data into the data warehouse efficiently. Assess the candidate's knowledge of data pipeline monitoring and management practices, including logging, error handling, and performance optimization. Inquire about their experience in implementing monitoring and alerting mechanisms to detect and respond to data pipeline failures and performance bottlenecks.

已翻译

赞

6 Data literacy

Data literacy is the ability to understand, communicate, and use data effectively and responsibly. It is a crucial skill for any data warehousing professional, as it enables you to collaborate with stakeholders, customers, and users across the organization and beyond. You need to develop your data literacy skills, such as critical thinking, problem-solving, data ethics, and data culture, as well as your soft skills, such as communication, presentation, and teamwork.

添加您的观点

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

添加您的观点

Fawad A. Qureshi

Field CTO @ Snowflake | LinkedIn Learning Instructor | Sustainability ??, Data Strategy, Business Transformation
举报内容
I would suggest the following meta concepts before diving deep into tactical topics: Data Modeling and Design Scalable processing (thinking parallel) Story telling Systems thinking

已翻译

赞

Data Warehousing

+ 关注

给文章评分

我们借助人工智能创建了此文章。您认为这篇文章怎么样？

很棒不太好

举报此文章

查看全部

You’re looking to develop your career in Data Warehousing. What skills do you need to focus on?

1

2

3

4

5

6

7

1 Database design

2 Data integration

3 Data analysis

4 Data governance

5 Data engineering

6 Data literacy

7 Here’s what else to consider

Data Warehousing

给文章评分

感谢您的反馈

更多Data Warehousing相关文章

更多相关阅读内容

You’re looking to develop your career in Data Warehousing. What skills do you need to focus on?

1

2

3

4

5

6

7

1 Database design

2 Data integration

3 Data analysis

4 Data governance

5 Data engineering

6 Data literacy

7 Here’s what else to consider

Data Warehousing

给文章评分

感谢您的反馈

查看其他技能