This course is now fully-booked! If you would like to attend the next “Introduction to Hadoop” course in November please click HERE.
This two-day training course explores major characteristics and functionalities of Apache Hadoop platform and its ecosystem of tools for Big Data processing and analysis. The course provides a first-hand practical experience in Hadoop Distributed File System (HDFS) and MapReduce frameworks by the means of a number of presentations and tutorials during which the attendees will learn how to design and perform simple MapReduce programs to process the data and calculate a set of statistics. The course can also serve as a gentle introduction to the basics of Java programming language and essential Hadoop File System shell commands. During the course you will learn to:
understand the features, major characteristics, architecture and operations of Hadoop and its ecosystem including, Yet Another Resource Negotiator (YARN), Hadoop Distributed File System, MapReduce programming framework and other Hadoop-related tools e.g. HBase, Hive, Cassandra, Mahout and Pig,
monitor and diagnose the performance of Apache Hadoop clusters and their resources using Apache Ambari and control the deployed services through Apache ZooKeeper,
manage large datasets in Hadoop Distributed File System (HDFS) using Hadoop File System shell commands,
design and execute simple MapReduce parallel programs (written in Java) to calculate various statistics and control their performance in real-time,
implement learnt skills to build and provision Hadoop-based Big Data applications.
During the course the attendees will perform several simple MapReduce jobs on a Linux-based Mind Project Hadoop cluster.
The course will run for 2 days from 9:30am until ~5:00pm on each day and will consist of alternating lecture-style presentations and practical tutorials. The example datasets used during tutorial sessions will come from social sciences, economics and business fields, however the contents may vary depending on specific interests of participants (based on the Participant’s Skills Inventory). There will be two 15-minute coffee/tea breaks and one 1-hour lunch break on each day.
What is included?
Apart from the contents of the course, Mind Project will provide the participants with the following:
- a digital (USB memory stick) Course Manual including all presentation slides, Hadoop course script files, datasets and a list of reference books and online resources,
- additional home exercises and all data sets available to download,
- Wi-Fi access,
- Central London location – a 1-min walk from the Barbican station, 5 minutes away from Farringdon and St. Paul’s stations, 15 minutes from the Liverpool Street Station,
- networking opportunity,
- Mind Project course attendance certificate.
In order to fully benefit from the training course, we recommend that attendees bring their personal WiFi-enabled laptops to the session with at least one of the following web browsers installed: Chrome, Safari, Mozilla Firefox and/or Internet Explorer. Also, the laptops should be equipped with a simple text editor suitable for code/script typing e.g. Notepad++ (for Windows users) or TextWrangler (for Mac users). Please be advised that we do not recommend the following applications: WordPad, Gedit or TextEdit.
This course is targeted at IT literate users with interest in Big Data processing and architecture. No prior exposure to the Hadoop ecosystem and its tools is required.
Participants are encouraged to complete the online Participant’s Skills Inventory to allow Mind Project and our course tutors to customise the contents of the course depending on the level of participants’ knowledge and their areas of interest. The data obtained through the Participant’s Skills Inventory will be held fully-confidential and will only be used to provide a quality data analysis training.
Deadline for registrations
The deadline for registrations on this training course is Friday, 13th of October 2017 at 16:00 London (UK) time. However, Mind Project reserves the right to end the registration process earlier if all places are booked before the deadline.
Prices and discounts
- £375 + VAT (£450) per person for the whole course (regular fee).
- £250 + VAT (£300) per person for the whole course for UK registered undergraduate and postgraduate students, and representatives of registered charitable organisations (discounted fee).
- For group bookings of 4 and more participants, please contact us directly.
Please mind that the course fee DOES NOT include the following:
- transport to and from the venue,
- accommodation and lunch.
Please contact us should you have any questions about this course. You may also want to visit the Training Courses – Frequently Asked Questions website, which gives further practical details about Mind Project training courses. You can book your place on the course by clicking Book ticket button in the top section of the course page. Please note that we accept all major credit/debit cards (through the PayPal and Stripe systems) and BACS payments. We can only confirm fully-paid bookings. Please contact us for other payment options e.g. if a Purchase Order is required. Please read Training & Events Terms & Conditions before your purchase.
Course feedback and testimonials
We have received the following testimonials for the previous editions of this course:
- “Great introduction to Hadoop from a clearly knowledgable demonstrator.” – Feargal Egan, GResearch
- “Really great course, liked the practical example.”
- “Great course, focuses on key areas of Hadoop.”
- “Good course for those who who want to learn Hadoop from the ground up.”
Based on the anonymous feedback forms we have also received the following comments from our attendees:
- “Good pace, great practical.”
- “Good balance between theory and practice.”
- “I was surprised how much we got through. It was very well pitched and paced. I can now go and use the software.”
- “Good coverage of the essential basics, HDFS, MapReduce etc.”
The course will be held at CAP House, 1st Floor, 9-12 Long Lane, London, EC1A 9HA. Please see the map below.