Prepare for a career in the field of data warehousing. In this program, you’ll learn in-demand skills like SQL, Linux, and database architecture to get job-ready in less than 3 months.

Data warehouse engineers design and build large databases called data warehouses, used for data and business analytics. They work closely with data analysts, data scientists, and project management to power analysis that enable insights and inform decision-making.

This program will teach you the foundational data warehousing skills employers are seeking for entry level data warehouse roles. This program will not only help you start your career in data warehousing, but also provides a strong foundation for future career development in other paths such as Business Intelligence (BI) roles.

You’ll learn the latest tools used by professional data warehouse engineers including Relational Database Management Systems (RDBMS), PostgreSql, and MySQL. Alongside these tools, learn how to use Linux/UNIX shell scripts to automate repetitive tasks and build data pipelines and Extract, Transform and Load (ETL) data. You’ll also work with data warehouses and query them using SQL and BI tools.

When you complete the full program, you’ll have a portfolio of projects and a Professional Certificate from IBM to showcase your expertise. You’ll also earn an IBM Digital badge and will gain access to career resources to help you in your job search, including mock interviews and resume support.

This course provides a practical understanding of common Linux / UNIX shell commands. In this beginner friendly course, you will learn about the Linux basics, Shell commands, and Bash shell scripting.   

You will begin this course with an introduction to Linux and explore the Linux architecture. You will interact with the Linux Terminal, execute commands, navigate directories, edit files, as well as install and update software. 

Next, you’ll become familiar with commonly used Linux commands. You will work with general purpose commands like id, date, uname, ps, top, echo, man; directory management commands such as pwd, cd, mkdir, rmdir, find, df; file management commands like cat, wget, more, head, tail, cp, mv, touch, tar, zip, unzip; access control command chmod; text processing commands - wc, grep, tr; as well as networking commands - hostname, ping, ifconfig and curl.  

You will then move on to learning the basics of shell scripting to automate a variety of tasks. You’ll create simple to more advanced shell scripts that involve Metacharacters, Quoting, Variables, Command substitution, I/O Redirection, Pipes & Filters, and Command line arguments. You will also schedule cron jobs using crontab. 

The course includes both video-based lectures as well as hands-on labs to practice and apply what you learn. You will have no-charge access to a virtual Linux server that you can access through your web browser, so you don't need to download and install anything to complete the labs. 

You’ll end this course with a final project as well as a final exam. In the final project you will demonstrate your knowledge of course concepts by performing your own Extract, Transform, and Load (ETL) process and create a scheduled backup script. 

This course is ideal for data engineers, data scientists, software developers, and cloud practitioners who want to get familiar with frequently used commands on Linux, MacOS and other Unix-like operating systems as well as get started with creating shell scripts.

Hands-on Introduction to Linux Commands and Shell Scripting

In this course you will apply a variety of data warehouse engineering skills and techniques you have learned as part of the previous courses in the IBM Data Warehouse Engineer Professional Certificate. You will assume the role of a Junior Data Engineer who has recently joined the organization and be presented with a real-world use case that requires a data warehouse engineering solution.

Data Warehousing Capstone Project

Get started with Relational Database Administration and Database Management in this self-paced course!  

This course begins with an introduction to database management; you will learn about things like the Database Management Lifecycle, the roles of a Database Administrator (DBA) as well as database storage. You will then discover some of the activities, techniques, and best practices for managing a database.  

You will also learn about database optimization, including updating statistics, slow queries, types of indexes, and index creation and usage. You will learn about configuring and upgrading database server software and related products. You’ll also learn about database security; how to implement user authentication, assign roles, and assign object-level permissions. And gain an understanding of how to perform backup and restore procedures in case of system failures.  

You will learn how to optimize databases for performance, monitor databases, collect diagnostic data, and access error information to help you resolve issues that may occur. Many of these tasks are repetitive, so you will learn how to schedule maintenance activities and regular diagnostic tests and send automated messages of the success or failure of a task. The course includes both video-based lectures as well as hands-on labs to practice and apply what you learn. 

This course ends with a final project where you will assume the role of a database administrator and complete a number of database administration tasks across many different databases.

Relational Database Administration (DBA)

Delve into the two different approaches to converting raw data into analytics-ready data. One approach is the Extract, Transform, Load (ETL) process. The other contrasting approach is the Extract, Load, and Transform (ELT) process. ETL processes apply to data warehouses and data marts. ELT processes apply to data lakes, where the data is transformed on demand by the requesting/calling application.  

In this course, you will learn about the different tools and techniques that are used with ETL and Data pipelines. Both ETL and ELT extract data from source systems, move the data through the data pipeline, and store the data in destination systems. During this course, you will experience how ELT and ETL processing differ and identify use cases for both. You will identify methods and tools used for extracting the data, merging extracted data either logically or physically, and for loading data into data repositories.  

You will also define transformations to apply to source data to make the data credible, contextual, and accessible to data users. You will be able to outline some of the multiple methods for loading data into the destination system, verifying data quality, monitoring load failures, and the use of recovery mechanisms in case of failure.  

By the end of this course, you will also know how to use Apache Airflow to build data pipelines as well be knowledgeable about the advantages of using this approach. You will also learn how to use Apache Kafka to build streaming pipelines as well as the core components of Kafka which include: brokers, topics, partitions, replications, producers, and consumers. 

Finally, you will complete a shareable final project that enables you to demonstrate the skills you acquired in each module.

ETL and Data Pipelines with Shell, Airflow and Kafka

Are you ready to dive into the world of data engineering? In this beginner level course, you will gain a solid understanding of how data is stored, processed, and accessed in relational databases (RDBMSes). You will work with different types of databases that are appropriate for various data processing requirements. 

You will begin this course by being introduced to relational database concepts, as well as several industry standard relational databases, including IBM DB2, MySQL, and PostgreSQL. Next, you’ll utilize RDBMS tools used by professionals such as phpMyAdmin and pgAdmin for creating and maintaining relational databases. You will also use the command line and SQL statements to create and manage tables. 

This course incorporates hands-on, practical exercises to help you demonstrate your learning. You will work with real databases and explore real-world datasets. You will create database instances and populate them with tables and data. 

At the end of this course, you will complete a final assignment where you will apply your accumulated knowledge from this course and demonstrate that you have the skills to: design a database for a specific analytics requirement, normalize tables, create tables and views in the database, load and access data. 

No prior knowledge of databases or programming is required. 

Anyone can audit this course at no-charge. If you choose to take this course and earn the Coursera course certificate, you can also earn an IBM digital badge upon successful completion of the course.

Introduction to Relational Databases (RDBMS)

Much of the world's data lives in databases. SQL (or Structured Query Language) is a powerful programming language that is used for communicating with and manipulating data in databases. A working knowledge of databases and SQL is a must for anyone who wants to start a career in Data Engineering, Data Warehousing, Data Analytics, Data Science or Business Intelligence. The purpose of this course is to help you learn and apply foundational and intermediate knowledge of the SQL language, and become familiar with many relational database (RDBMS) concepts along the way.

You will start with performing basic Create, Read, Update and Delete (CRUD) operations using CREATE, SELECT, INSERT, UPDATE and DELETE statements. You will then learn to filter, order, sort, and aggregate data. You will work with functions, perform sub-selects and nested queries, as well as JOIN data in multiple tables. You will also work with VIEWS, transactions and create stored procedures.

The emphasis in this course is on hands-on, practical learning. As such, you will work with real database systems, use real tools, and real-world datasets. You will create a database instance in the cloud. Through a series of hands-on labs, you will practice building and running SQL queries. At the end of the course you will apply and demonstrate your skills with a final project.

The SQL skills you learn in this course will be applicable to a variety of RDBMSes such as MySQL, PostgreSQL, IBM Db2, Oracle, SQL Server and others.

No prior knowledge of databases, SQL or programming is required, however some basic data literacy is beneficial.

SQL: A Practical Introduction for Querying Databases

Kickstart your Data Warehousing and Business Intelligence (BI) Analytics journey with this self-paced course. You will learn how to design, deploy, load, manage, and query data warehouses and data marts. You will also work with BI tools to analyze data in these repositories. 

You will begin this course by understanding different kinds of analytics repositories including data marts, data warehouses, data lakes, data lakehouses, and data reservoirs, and their functions and uses. They are designed to enable rapid business decision making through accurate and flexible reporting and data analysis. 

A data warehouse is one of the most fundamental business intelligence tools in use today, and one that successful Data Engineers must understand. In this course, you will learn to design, model and implement data warehouses and explore data-warehousing architectures such as Star and Snowflake schemas. You will also learn how to populate data warehouses using ETL and ELT processes, verify data, query data and how to use Cubes, Rollups, and materialized views/tables. 

You will become familiar with different BI tools used by experts in the industry such as IBM Cognos Analytics, Tableau, and Microsoft PowerBI. You will also use a BI tool to create data visualizations and build interactive dashboards to gain insights from data. 

The hands-on labs in this course will enable you to apply what you learn and gain a practical knowledge of Data Warehousing and BI Analytics. You will work with repositories like MySQL, PostgreSQL, and IBM Db2. You will also use BI tools like Cognos Analytics. At the end of this course, you will complete a project to demonstrate the skills you acquired in each module.

Getting Started with Data Warehousing and BI Analytics

Start your journey in one of the fastest growing professions today with this beginner-friendly Data Engineering course! You will be introduced to the core concepts, processes, and tools you need to know in order to get a foundational knowledge of data engineering. as well as the roles that Data Engineers, Data Scientists, and Data Analysts play in the ecosystem. 

You will begin this course by understanding what is data engineering as well as the roles that Data Engineers, Data Scientists, and Data Analysts play in this exciting field. Next you will learn about the data engineering ecosystem, the different types of data structures, file formats, sources of data, and the languages data professionals use in their day-to-day tasks. 

You will become familiar with the components of a data platform and gain an understanding of several different types of data repositories such as Relational (RDBMS) and NoSQL databases, Data Warehouses, Data Marts, Data Lakes and Data Lakehouses. You’ll then learn about Big Data processing tools like Apache Hadoop and Spark. You will also become familiar with ETL, ELT, Data Pipelines and Data Integration. 

This course provides you with an understanding of a typical Data Engineering lifecycle which includes architecting data platforms, designing data stores, and gathering, importing, wrangling, querying, and analyzing data. You will also learn about security, governance, and compliance.  

You will learn about career opportunities in the field of Data Engineering and the different paths that you can take for getting skilled as a Data Engineer. You will hear from several experienced Data Engineers, sharing their insights and advice. 

By the end of this course, you will also have completed several hands-on labs and worked with a relational database, loaded data into the database, and performed some basic querying operations.

IBM Data Warehouse Engineer

About the Program

Description

Deliverables

0 Credits

Academic Excellence

Akamai Network Engineering$375

Google IT Automation with Python$375

Google IT Support$375

IBM Data Engineering$375

IBM Full Stack Software Developer$375

Meta Database Engineer$375

IBM Cybersecurity Analyst$375

Akamai Consulting and Support$375

IBM IT Support$375

Information Technology Specialist: Networking$236.25