The mode of instruction for all presentations and laboratory sessions is conducted exclusively in the English language.
Having strong analytical skills means being able to approach problems logically and systematically, breaking them down into smaller parts to understand them better.
People who possess these skills are highly valued by employers, as they are better able to work in teams, build relationships, and contribute to a positive and productive work environment.
A prerequisite for attending our course is the completion of a high school diploma or equivalent.
The course schedule comprises of a total of 20 hours per week, with classes running for 4 hours each day from Friday, Saturday and Sunday 11:00 AM - 3:00 PM ET.
Introduction To Data Engineering
Data Engineering is the field of computer science that focuses on designing, developing, and maintaining systems for managing large and complex datasets. It involves building systems for data collection, storage, processing, and analysis, as well as developing algorithms and tools for data integration, transformation, and cleaning.
Data Engineering in Cloud
Cloud-based data engineering enables organizations to leverage the power of big data technologies, such as Apache Hadoop, Apache Spark, and Apache Kafka, without the need for expensive hardware and infrastructure.
Data Storage and Retrieval
Data Storage and Retrieval involves designing and implementing effective data retrieval systems, such as search engines, data warehouses, and data lakes. These systems enable organizations to retrieve and analyze data quickly and efficiently, providing valuable insights and driving informed decision-making.
Data Ingestion involves extracting data from various sources, such as databases, applications, APIs, and data streams. The data is then transformed into a format that can be easily processed and analyzed, using tools such as data pipelines, data integration platforms, and ETL tools.
Batch data processing is commonly used for large-scale data processing, such as data analysis, machine learning, and data warehousing. It involves processing data in parallel, using distributed computing systems, such as Hadoop, Spark, or MapReduce.
Data Processing - Streaming
Streaming data processing is commonly used for applications that require real-time data analysis, such as fraud detection, stock market analysis, and IoT (Internet of Things) applications. It involves processing data in parallel, using distributed computing systems, such as Apache Kafka, Apache Flink, or Apache Storm.
Data Quality and Governance
Data Quality and Governance is a critical component of data management, particularly in industries such as finance, healthcare, e-commerce, and technology. It involves a wide range of technologies and tools, including data quality management software, data profiling tools, and data governance frameworks.
Data modeling refers to the process of creating a conceptual representation of data, which enables the organization to understand and manage its data more effectively. It involves designing and building a structure for data that reflects the relationships between different data entities, and provides a framework for organizing and managing data.
Data Analysis and Visualization
Data analysis and visualization are critical components of data management and data-driven decision-making. They enable organizations to identify trends, patterns, and correlations in their data, and to make informed decisions based on these insights.
Data governance is the process of managing the availability, usability, integrity, and security of data used in an organization. It involves establishing policies and procedures for data management, including data acquisition, storage, processing, and analysis, as well as defining roles and responsibilities for data management.
Welcome to the exciting world of data engineering! My name is Haseeb, and I am thrilled to be your instructor for this course. With over 15 years of industry experience, I have specialized in data engineering technologies, including Apache Flink, which I contributed myself. As a data engineer, my passion is to design, build, and maintain robust data pipelines and systems that deliver insights and value to organizations. In this course, we will explore the key technologies, tools, and best practices used in data engineering, including Spark, Flink, Data modeling, Datawarehouse among others. Get ready to embark on a fascinating journey that will equip you with the skills and knowledge you need to succeed in this dynamic field!
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