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Data Engineering Training Courses

Data Engineers collect, store, process, and analyze large data sets to make better decisions. They work with databases, data warehouses, and data lakes.

Our Data Engineering training courses teach attendees how to use Python, PySpark, Azure, Google Cloud (GCP), Apache Airflow, and more to manage data pipelines and extract insights. Accelebrate offers customized Data Engineer training solutions tailored to your specific needs. If you have a particular project in mind, we can work with you to develop a learning plan that will help you achieve your goals. All courses can be delivered live, online or in person at your site for your team of 3 or more attendees.

Introduction to Data Engineering with Python
Intermediate Data Engineering with Python
Data Engineering with Python and PySpark
Data Engineering Boot Camp
Data Science and Data Engineering for Architects
Data Engineering, ETL, and DataOps
Data Engineering on Microsoft Azure (DP-203)
Introduction to Apache Airflow
Advanced Apache Airflow
Comprehensive Apache Airflow
Apache Airflow for Machine Learning Operations
Workflow Management with Apache Airflow
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Read our article on Python and PySpark for Data Engineering to find out how these two technologies work together to help data engineers glean insights from massive data sets.

What are the skills required for a Data Engineer?

  • Programming languages: Data Engineers typically need to be proficient in Python, Java, or Scala.
  • Data storage and processing: Data Engineers must be familiar with data storage and processing technologies, such as Hadoop, Hive, and Spark.
  • Data analysis: Data Engineers need to be able to analyze data using tools such as SQL, R, and Python.
  • Cloud computing: Data Engineers must be familiar with AWS, Azure, or GCP.

What are the challenges of Data Engineering?

  • Data volume: The amount of data that businesses collect and store is growing exponentially. This can make it difficult to store, process, and analyze data.
  • Data quality: Data quality is a significant challenge for Data Engineers. Data can be corrupted, incomplete, or inaccurate, making it challenging to get accurate results from data analysis.
  • Data security: Data Engineers must ensure that data is secure and protected from unauthorized access.

Learn More About Data Engineering

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