Kx Introductory Workshop, Americas
6 Aug 2020, 9:30 am
Time Zone: ET
Kx Workshops are aimed at both a technical and business audience. Developers new to the technology will find the session very useful in gaining a quick guide to many features of the q language. Business managers will gain an understanding of what makes kdb+ different, and will leave with a thorough overview of the power of kdb+ and the range of uses to which it can be put. The workshops are a great way to learn about the technology in a hands-on environment where you will solve real business problems.
This one-day hands-on workshop explains how kdb+ and q language can address today’s data challenges. The program highlights the advantages of using q and its API’s to analyze massive data sets and extract information to drive business decisions.
The Workshop will start with an Introduction to Kx, kdb+, q and the challenges of working with big data. We will then connect to our remote servers and dive straight into working with kdb+ using JupyterQ notebooks.
Please note we require all attendees to enable their cameras during the workshop to aid our trainers with visual cues on how you are progressing with the material and increase interactivity.
The Kx Introductory Workshops are free of charge.
The workshop will be carried out in 3 sections:
1. Data Exploration
This is the longest session and dives straight into working with the NYC Taxi trip database.
This session covers examples that show how Kx can be used to analyze billions of records, including:
• Grouping, aggregations and time-based joins
• Joining data with other data sets
• Statistical analysis
• Saving Data to Disk
2. q Language
In this session, we take a detailed look at the q language and what features make it an excellent choice for analyzing and transforming your data.
• Atom Operations
• Vector Operations
• Temporal Arithmetic
3. Loading Data & IPC
This session explains the management of real-time data feeds, including:
• Importing data from CSV and JSON
• Publishing, analyzing and storing high volume data
• Performance characteristics of data streams
• Defining event handlers and analytics in real-time
• Setting up a feed subscriber to maintain and display real-time metrics
• Connecting to kdb+ via HTTP and Websocket