What Is Data Engineering | Data Engineering Explained | How To Become A Data Engineer | Intellipaat

Summary

This video delves into the evolving landscape of data engineering, emphasizing its critical role in helping organizations leverage data for effective decision-making. Despite the potential of data, a high failure rate of data projects has been observed, often due to inadequate infrastructure. Data engineers are essential for creating and managing data pipelines, ensuring data is structured and accessible for data scientists. The video highlights the key skills required for data engineers, including proficiency in programming languages, ETL systems, and cloud technologies, distinguishing their role from that of data scientists.

Takeaways

• 😀 Data engineering is essential for transforming raw data into usable formats for decision-making.
• 📊 Organizations are increasingly recognizing the value of data in driving business decisions.
• ❗ Despite the potential, 85% of big data projects failed due to unreliable data infrastructure.
• 🚫 In 2019, 87% of data science projects did not reach production, highlighting the challenges in the field.
• 📈 The world is generating approximately 435 exabytes of data per day, emphasizing the need for effective data management.
• 🔧 Data engineers create data pipelines and build warehouses to streamline data accessibility across organizations.
• 📚 Data engineering is distinguished from data science by its focus on software engineering related to data.
• 🔍 Key skills for data engineers include expertise in SQL, Python, ETL systems, and cloud technologies like AWS and Azure.
• 🏆 Data engineering roles are highly sought after, ranked alongside data scientists and machine learning engineers in job demand.
• 💡 Understanding the difference between data engineers and data scientists is crucial for leveraging data effectively.

Q & A

What is data engineering?

Data engineering is the process of collecting, managing, and converting raw data into usable information for analysis, focusing on the creation and maintenance of data pipelines.

Why has the importance of data engineering increased?

The growing realization among organizations that data is a valuable asset for making key business decisions has led to an increased demand for data engineers to ensure that data is structured and accessible.

What was stated in Gartner's 2017 report about big data projects?

Gartner's 2017 report indicated that 85% of big data projects fail, primarily due to unreliable data infrastructure and misalignment of roles.

What are some challenges that led to the failure of data projects?

Challenges include insufficient data infrastructure, misalignment between data scientists and their tasks, and issues with data modeling that prevent optimal data usage.

How do data engineers differ from data scientists?

Data engineers focus on building data pipelines and managing data infrastructure, while data scientists specialize in analyzing data using statistical methods and machine learning techniques.

What skills are essential for a data engineer?

Essential skills for a data engineer include proficiency in programming languages like SQL and Python, expertise in database management, knowledge of ETL systems, familiarity with data warehousing tools, and understanding cloud computing platforms.

What programming languages should a data engineer know?

A data engineer should be proficient in programming languages such as SQL, Python, and R.

What role do data pipelines play in data engineering?

Data pipelines are crucial for collecting, transforming, and transporting data from various sources into a centralized system, ensuring it is ready for analysis.

Which tools are important for data warehousing?

Important tools for data warehousing include Apache Kafka, Spark, and Hadoop, which help in managing and processing large datasets.

What trends were noted regarding the demand for data engineering roles?

Reports have ranked data engineering roles alongside data scientists and machine learning engineers as among the most in-demand jobs in the tech industry.