Big Data and the Data Lake from the Analyst Perspective (2-Hour Lab) - Scott Shaw
The concept of a data lake is emerging as a popular way to organize and build the next generation of data systems to master new big data challenges. Organizations are seeking to create data lakes because they manage and use data with increased volume, variety, and unprecedented velocity. However, how can data professionals and analysts access the data lake for the expected insights at the velocity required by the business?
In this two-hour hands-on lab, we will take every day relevant business scenarios to learn how to spring business value from data held in the data lake, in order to answer key questions:
- How do we import structured, semi-structured and unstructured data into our data lake?
- How can we access unstructured and semi-structured data to derive value for the enterprise?
- How do we do analytics and reporting over the data lake in Hadoop?
- What are the similarities and differences between accessing data held in a data lake in terms of architecture and syntax?
We will springboard from SQL to learn how to do familiar activities such as import, access and visualize data in our data lake. To do this, we will use technology such as Hive, Hue, and the Hadoop Distributed Files System (HDFS) as a data source.
Come and join Scott for this practical two-hour lab which will build on your existing SQL skills to demystify Big Data and the Data Lake for analysts.
This session is aimed at people who want to keep pace with the latest Big Data technology by adopting the latest techniques to explore the data lake, building on their existing analytical knowledge.
Register for "Big Data and the Data Lake from the Analyst Perspective"