SQL, ETL and BI Fundamentals

Overview

Syllabus

Courses

• 0 reviews

  1 week, 3-4 hours a week, 3-4 hours a week

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  This mini-course provides a practical introduction to commonly used Linux / UNIX shell commands and teaches you basics of Bash shell scripting to automate a variety of tasks. 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 perform the labs.

  In this course you will work with general purpose commands, like id, date, uname, ps, top, echo, man; directory manageent 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 create simple to intermediate 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.

  This course provides essential hands-on skills 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.

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  5 weeks, 2-4 hours a week, 2-4 hours a week

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  Well-designed and automated data pipelines and ETL processes are the foundation of a successful Business Intelligence platform. Defining your data workflows, pipelines and processes early in the platform design ensures the right raw data is collected, transformed and loaded into desired storage layers and available for processing and analysis as and when required.

  This course is designed to provide you the critical knowledge and skills needed by Data Engineers and Data Warehousing specialists to create and manage ETL, ELT, and data pipeline processes.

  Upon completing this course you’ll gain a solid understanding of Extract, Transform, Load (ETL), and Extract, Load, and Transform (ELT) processes; practice extracting data, transforming data, and loading transformed data into a staging area; create an ETL data pipeline using Bash shell-scripting, build a batch ETL workflow using Apache Airflow and build a streaming data pipeline using Apache Kafka.

  You’ll gain hands-on experience with practice labs throughout the course and work on a real-world inspired project to build data pipelines using several technologies that can be added to your portfolio and demonstrate your ability to perform as a Data Engineer.

  This course pre-requisites that you have prior skills to work with datasets, SQL, relational databases, and Bash shell scripts.

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  6 weeks, 2-3 hours a week, 2-3 hours a week

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  Today’s businesses are investing significantly in capabilities to harness the massive amounts of data that fuel Business Intelligence (BI). Working knowledge of Data Warehouses and BI Analytics tools are a crucial skill for Data Engineers, Data Warehousing Specialists and BI Analysts, making who are amongst, the most valued resources for organizations.

  This course prepares you with the skills and hands-on experience to design, implement and maintain enterprise data warehouse systems and business intelligence tools. You’ll gain extensive knowledge on various data repositories including data marts, data lakes and data reservoirs, explore data warehousing system architectures, deepen on data cubes and data organization using related tables. And analyze data using business intelligence like Cognos Analytics, including its reporting and dashboard features, and interactive visualization capabilities.

  This course provides hands-on experience with practice labs and a real-world inspired project that can be added to your portfolio and will demonstrate your proficiency in working with data warehouses. Skills you will gain include building data warehouses, Star/Snowflake schemas, CUBEs, ROLLUPs, Materialized Views/MQTs, reports and dashboards.

  This course assumes prior SQL and relational database experience.

• 0 reviews

  5 weeks, 2-4 hours a week, 2-4 hours a week

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  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 necessary for anyone who wants to start a career in Data Engineering, Data Analytics or Data Science. The purpose of this course is to introduce relational database (RDBMS) concepts and help you learn and apply foundational and intermediate knowledge of the SQL language.

  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 also work with functions, perform sub-selects and nested queries, as well as access multiple tables in the database.

  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.

Taught by

Ramesh Sannareddy, Rav Ahuja, Jeff Grossman and Yan Luo

Tags

• united states