Welcome to analyzing big data with SQL, I'm Ian Cook. This course teaches the fundamentals of the SQL SELECT statement, which is the most important part of the SQL language, and the one part you definitely need to know if you're doing data analysis. SQL is an essential skill for data analysts, and data scientists. It's the standard language for accessing tabular data, and it's ubiquitous. Whether you're working with data in a relational database, in a data warehouse, or in a set of files on a cluster or in Cloud storage, in all these cases you can use SQL to retrieve and analyze this data. Popular applications for analytics, and business intelligence virtually all use SQL to retrieve or analyze data. Also data scientists, data engineers, and Machine Learning developers who use other languages like Python and R, often also use SQL to connect their code to a wide variety of different data sources. So you can use SQL to create data of all sizes across numerous different systems, and in this course you'll gain SQL skills that apply to all of these systems. But there's a particular emphasis in this course on a newer breed of SQL engines. Open source distributed SQL engines that can query extremely large datasets, engines like Hive, Impala, Presto, and Drill. These engines are a cornerstone of modern analytic database systems. As data has grown bigger and bigger at an ever accelerating rate, it's become impractical to store it in relational databases, and too expensive to store it in traditional data warehouses. So systems built with these newer distributed SQL engines have increasingly replaced those older systems. In this course, you'll have an opportunity to get hands-on experience using two of these engines, Hive and Impala. These are the most widely deployed of the open source SQL engines, and they are the standard in numerous companies and organizations. If you're new to SQL, this course is a great place to learn it. It teaches the basics of the SQL SELECT statement one step at a time, and gives you opportunities to practice writing and running SQL SELECT statements to answer real-world types of questions. If you're already familiar with SQL, maybe you've used it to query relational databases, then this course will help you cement your knowledge and update your skills, so you can be proficient with modern distributed SQL engines. This course is the second in the specialization modern big data analysis with SQL from Cloudera. The sequence of courses in this specialization is designed to provide excellent preparation for the Cloudera Certified Associate Data Analyst certification exam. This certification was created to identify qualified data analysts, with a talent for using SQL to analyze big data. It's a great way to stand out and be recognized by potential employers. You can earn this certification by taking a hands-on practical exam using the same SQL engines that this specialization teaches, Hive and Impala. If you're interested in this certification, you should take the Honors track of this course. In the Honors lessons you'll have the opportunity to gain some additional skills, learn a few more techniques, and become more resourceful as a data analyst using SQL with these big data engines. If you have not already taken the first course in this specialization, foundations for SQL on big data, then I would definitely encourage you to take that course before continuing with this second course. That first course teaches the key concepts behind relational databases, SQL, and big data. The final part of that first course shows how to set up the virtual machine that you'll use for hands-on exercises, and practice in this course. Please go ahead now and watch the next video which gives a whirlwind review of that first course, that should help you decide whether to take it if you haven't already. Whether you are looking for a basic intuitive introduction to data analysis with SQL or you're eager to establish your credentials as a big data analytics professional, this course and the specialization it's a part of, will help you to get there.