Organizations have more data at their disposal today than ever before. The vast amount of data that organizations are capturing, along with their desire to extract meaningful insights is driving an urgent demand for Data Engineers.
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Organizations have more data at their disposal today than ever before. The vast amount of data that organizations are capturing, along with their desire to extract meaningful insights is driving an urgent demand for Data Engineers.
Organizations have more data at their disposal today than ever before. The vast amount of data that organizations are capturing, along with their desire to extract meaningful insights is driving an urgent demand for Data Engineers.
Data Engineers play a fundamental role in harnessing data that enable organizations to apply business intelligence for making informed decisions. Today’s Data Engineers require a broad set of skills to develop and optimize data systems and make data available to the organization for analysis.
This Professional Certificate provides you the job-ready skills you will need to launch your career as an entry level data engineer.
Upon completing this Professional Certificate, you will have extensive knowledge and practical experience with cloud-based relational databases (RDBMS) and NoSQL data repositories, working with Python, Bash and SQL, processing big data with Apache Hadoop and Apache Spark, using ETL (extract, transform and load) tools, creating data pipelines, using Apache Kafka and Airflow, designing, populating, and querying data warehouses and utilizing business intelligence tools.
Within each course, you’ll gain practical experience with hands-on labs and projects for building your portfolio. In the final Capstone project, you’ll apply your knowledge and skills attained throughout this program and demonstrate your ability to perform as a Data Engineer.
This program does not require any prior data engineering or programming experience.
What you'll learn
- Describe the core concepts, processes, tools and technologies in the field of data engineering.
- Demonstrate your aptitude with RDBMS fundamentals including design & creation of databases, schemas, tables; DB administration, security & working with MySQL, PostgreSQL & IBM Db2.
- Demonstrate your proficiency with SQL query language, SELECT, INSERT, UPDATE, DELETE statements, database functions, stored procs, working with multiple tables, JOINs, & transactions.
- Explain NoSQL and big data concepts including practice with MongoDB, Cassandra, IBM Cloudant, Apache Hadoop, Apache Spark, SparkSQL, SparkML, Spark Streaming.
- Describe ETL tools, data pipelines using Python, shell scripts with Linux, Apache Airflow and Apache Kafka.
- Describe Data Lakes, Data Marts and Enterprise Data Warehouses (EDW) and design them using Star and Snowflake schemas.
- Design and populate Data Warehouses and analyze their data with Business Intelligence (BI) tools like Cognos Analytics.
What's inside
14 courses
Python Basics for Data Science
(21 hours)
Python Basics for Data Science
(21 hours)
SQL for Data Science
(12 hours)
SQL for Data Science
(12 hours)
SQL Concepts for Data Engineers
(4 hours)
SQL Concepts for Data Engineers
(4 hours)
Relational Database Basics
(10 hours)
Relational Database Basics
(10 hours)
Data Engineering Basics for Everyone
(38 hours)
Data Engineering Basics for Everyone
(38 hours)
Python for Data Engineering Project
(4 hours)
Python for Data Engineering Project
(4 hours)
NoSQL Database Basics
(12 hours)
NoSQL Database Basics
(12 hours)
Big Data, Hadoop, and Spark Basics
(15 hours)
Big Data, Hadoop, and Spark Basics
(15 hours)
Apache Spark for Data Engineering and Machine Learning
(7 hours)
Apache Spark for Data Engineering and Machine Learning
(7 hours)
Linux Commands & Shell Scripting
(3 hours)
Linux Commands & Shell Scripting
(3 hours)
Building ETL and Data Pipelines with Bash, Airflow and Kafka
(15 hours)
Building ETL and Data Pipelines with Bash, Airflow and Kafka
(15 hours)
Relational Database Administration (DBA)
(20 hours)
Relational Database Administration (DBA)
(20 hours)
Data Warehousing and BI Analytics
(15 hours)
Data Warehousing and BI Analytics
(15 hours)
Data Engineering Capstone Project
(15 hours)
Data Engineering Capstone Project
(15 hours)Save this collection
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