登录查看更多内容

What are the most important data engineering skills to master?

由人工智能和领英社区提供技术支持

Data engineering is the process of designing, building, and maintaining data pipelines that transform raw data into valuable insights for analysis and decision making. Data engineers work with various tools and technologies to handle large volumes of structured and unstructured data, and ensure its quality, security, and availability. Data engineering is a crucial skill for anyone who wants to work with data, especially in the fields of statistics, machine learning, and data science. But what are the most important data engineering skills to master? Here are six key areas that every data engineer should know and practice.

此文章中的业界达人

由社区从 2 条内容中精选。了解更多

Temitope Oba

Data Analyst|Business Intelligence Analyst|Visualization Specialist|Data Scientist|Machine Learning Enthusiast
Garima Bajpai

data science novice

1 Data Modeling

Data modeling is the skill of creating logical and physical representations of data, such as schemas, tables, views, and indexes. Data modeling helps to organize data in a way that makes sense for the business needs and the data analysis goals. Data modeling also helps to optimize the performance, scalability, and reliability of data pipelines. Data engineers should be familiar with different data modeling techniques, such as relational, dimensional, document, graph, and key-value, and how to choose the best one for each use case.

添加您的观点

Garima Bajpai

data science novice
举报内容
Mastering key data engineering skills is crucial for success. Prioritize data modeling, understanding how to structure and organize data efficiently. Proficiency in ETL (Extract, Transform, Load) processes is vital for seamless data flow. Acquire expertise in database technologies like SQL and NoSQL for effective data manipulation. Familiarize yourself with data warehousing concepts and cloud platforms. Lastly, grasp data quality and governance principles to ensure reliable and accurate datasets. These skills collectively form a robust foundation, empowering data engineers to navigate complex challenges in designing and maintaining efficient data pipelines.

已翻译

赞

2 SQL and NoSQL

SQL and NoSQL are two types of languages that data engineers use to interact with data. SQL stands for Structured Query Language, and it is used to query, manipulate, and analyze data stored in relational databases. NoSQL stands for Not Only SQL, and it is used to work with data stored in non-relational databases, such as MongoDB, Cassandra, and Redis. Data engineers should be proficient in both SQL and NoSQL, and know how to write efficient and complex queries, join data from multiple sources, apply functions and aggregations, and troubleshoot errors.

添加您的观点

3 Programming and Scripting

Programming and scripting are skills that data engineers use to automate tasks, create data pipelines, and perform data analysis. Programming languages, such as Python, R, Java, and Scala, are used to write code that can process, transform, and manipulate data, as well as implement algorithms and models. Scripting languages, such as Bash, PowerShell, and Perl, are used to write scripts that can execute commands, control workflows, and integrate tools and systems. Data engineers should be comfortable with at least one programming language and one scripting language, and know how to use libraries, frameworks, and APIs that support data engineering.

添加您的观点

4 Data Warehousing and Data Lake

Data warehousing and data lake are two types of architectures that data engineers use to store and manage data. A data warehouse is a centralized repository of structured and processed data that is optimized for analytical queries and reporting. A data lake is a distributed repository of raw and unprocessed data that is optimized for storage and scalability. Data engineers should know how to design, build, and maintain data warehouses and data lakes, and how to use tools and platforms that enable data ingestion, extraction, loading, and transformation.

添加您的观点

5 Cloud Computing and Big Data

Cloud computing and big data are two domains that data engineers need to master to handle the increasing volume, variety, and velocity of data. Cloud computing is the delivery of computing services over the internet, such as servers, storage, databases, networks, and software. Big data is the term used to describe data sets that are too large or complex to be processed by traditional methods. Data engineers should know how to use cloud computing and big data technologies, such as AWS, Azure, GCP, Hadoop, Spark, Kafka, and Hive, to create scalable, reliable, and cost-effective data solutions.

添加您的观点

6 Data Quality and Data Governance

Data quality and data governance are two aspects of data engineering that ensure the accuracy, consistency, and security of data. Data quality is the measure of how well data meets the expectations and requirements of the data users and consumers. Data governance is the set of policies, standards, and processes that define how data is collected, stored, accessed, and used. Data engineers should know how to implement data quality and data governance best practices, such as data validation, data cleaning, data documentation, data lineage, data catalog, and data access control.

添加您的观点

Temitope Oba

Data Analyst|Business Intelligence Analyst|Visualization Specialist|Data Scientist|Machine Learning Enthusiast
举报内容
This is definitely one of the top 3 skills a Data engineer should have. Beyond the tools and ability to use them, the skills need will be Data migration and modelling, data warehousing and provisioning and data quality and governance implementation. These 3 are important skills regardless of the tools, type of data being worked on(whether it be structured, semi-structured or unstructured), or the kind of repository employed(datalake or data warehouse or lakehouse).

已翻译

赞

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

Statistics

+ 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

What are the most important data engineering skills to master?

1

2

3

4

5

6

7

1 Data Modeling

2 SQL and NoSQL

3 Programming and Scripting

4 Data Warehousing and Data Lake

5 Cloud Computing and Big Data

6 Data Quality and Data Governance

7 Here’s what else to consider

Statistics

给文章评分

感谢您的反馈

更多Statistics相关文章

更多相关阅读内容

What are the most important data engineering skills to master?

1

2

3

4

5

6

7

1 Data Modeling

2 SQL and NoSQL

3 Programming and Scripting

4 Data Warehousing and Data Lake

5 Cloud Computing and Big Data

6 Data Quality and Data Governance

7 Here’s what else to consider

Statistics

给文章评分

感谢您的反馈

查看其他技能