Description
At Sixe Engineering we have been providing official IBM training around the world for over 12 years. Get the best training from our specialists in Europe. We have important discounts and offers for two or more students.
Course details
| IBM course code: DW606G | Category: IBM Open Platform / IBM Open Platform |
| Delivery: Online & on-site** | Course length in days: 2 |
Target audience
This intermediate training course is for those who want a foundation of IBM BigInsights. This includes: Big data engineers, data scientist, developers or programmers, administrators who are interested in learning about IBM's Open Platform with Apache Hadoop.
Desired Prerequisites:
None, however, knowledge of Linux would be beneficial.
Instructors
The great majority of the IBM courses we offer are taught directly by our engineers. This is the only way we can guarantee the highest quality. We complement all the training with our own materials and laboratories, based on our experience during the deployments, migrations and courses that we have carried out during all these years.
Added value
Our courses are deeply role oriented. To give an example, the needs for technology mastery are different for developer teams and for the people in charge of deploying and managing the underlying infrastructure. The level of previous experience is also important and we take it very seriously. That is why beyond (boring) commands and tasks, we focus on solving the problems that arise in the day to day of each team. Providing them with the knowledge, competencies and skills required for each project. In addition, our documentation is based on the latest version of each product.
Agenda and course syllabus
Unit 1: IBM Open Platform with Apache Hadoop
- Exercise 1: Exploring the HDFS
Unit 2: Apache Ambari
- Exercise 2: Managing Hadoop clusters with Apache Ambari
Unit 3: Hadoop Distributed File System
- Exercise 3: File access and basic commands with HDFS
Unit 4: MapReduce and Yarn
- Topic 1: Introduction to MapReduce based on MR1
- Topic 2: Limitations of MR1
- Topic 3: YARN and MR2
- Exercise 4: Creating and coding a simple MapReduce job
- Possibly a more complex second Exercise
Unit 5: Apache Spark
- Exercise 5: Working with Spark's RDD to a Spark job
Unit 6: Coordination, management, and governance
- Exercise 6: Apache ZooKeeper, Apache Slider, Apache Knox
Unit 7: Data Movement
- Exercise 7: Moving data into Hadoop with Flume and Sqoop
Unit 8: Storing and Accessing Data
- Topic 1: Representing Data: CSV, XML, JSON, and YAML
- Topic 2: Open Source Programming Languages: Pig, Hive, and Other [R, Python, etc]
- Topic 3: NoSQL Concepts
- Topic 4: Accessing Hadoop data using Hive
- Exercise 8: Performing CRUD operations using the HBase shell
- Topic 5: Querying Hadoop data using Hive
- Exercise 9: Using Hive to Access Hadoop / HBase Data
Unit 9: Advanced Topics
- Topic 1: Controlling job workflows with Oozie
- Topic 2: Search using Apache Solr
- No lab exercises
Do you need to adapt this syllabus to your needs? Are you interested in other courses? Ask us without obligation.
Locations for on-site delivery
- Austria: Vienna
- Belgium: Brussels, Ghent
- Denmark: Cophenhagen
- Estonia: Tallinn
- Finland: Helsinki
- France: Paris, Marseille, Lyon
- Germany: Berlin, Munich, Cologne, Hamburg
- Greece: Athens, Thessaloniki
- Italy: Rome
- Louxemburg: Louxembourg (city)
- Netherlands: Amsterdam
- Norway: Oslo
- Portugal: Lisbon, Braga, Porto, Coimbra
- Slovakia: Bratislava
- Slovenia: Bratislava
- Spain: Madrid, Sevilla, Valencia, Barcelona, Bilbao, Málaga
- Sweden: Stockholm
- Turkey: Ankara
- United Kingdom: London
