- January 7, 2020 - January 9, 2020
9:30 am - 5:30 pm
- March 17, 2020 - March 19, 2020
9:30 am - 5:30 pm
Big Data on AWS introduces you to cloud-based big data solutions such as Amazon Elastic MapReduce (EMR), Amazon Redshift, Amazon Kinesis and the rest of the AWS big data platform. In this course, we show you how to use Amazon EMR to process data using the broad ecosystem of Hadoop tools like Hive and Hue. We also teach you how to create big data environments, work with Amazon DynamoDB, Amazon Redshift, Amazon QuickSight, Amazon Athena and Amazon Kinesis, and leverage best practices to design big data environments for security and cost-effectiveness.
- Instructor-Led Training (ILT)
- Hands-On Labs
This course allows you to test new skills and apply knowledge to your working environment through a variety of practical exercises.
- Fit AWS solutions inside of a big data ecosystem
- Leverage Apache Hadoop in the context of Amazon EMR
- Identify the components of an Amazon EMR cluster
- Launch and configure an Amazon EMR cluster
- Leverage common programming frameworks available for Amazon EMR including Hive, Pig, and Streaming
- Leverage Hue to improve the ease-of-use of Amazon EMR
- Use in-memory analytics with Spark on Amazon EMR
- Choose appropriate AWS data storage options
- Identify the benefits of using Amazon Kinesis for near real-time big data processing
- Leverage Amazon Redshift to efficiently store and analyze data
- Comprehend and manage costs and security for a big data solution
- Identify options for ingesting, transferring, and compressing data
- Leverage Amazon Athena for ad-hoc query analytics
- Leverage AWS Glue to automate ETL workloads.
- Use visualization software to depict data and queries using Amazon QuickSight
- Orchestrate big data workflows using AWS Data Pipeline
- Individuals responsible for designing and implementing big data solutions, namely Solutions Architects and SysOps Administrators.
- Data Scientists and Data Analysts interested in learning about big data solutions on AWS.
- Basic familiarity with big data technologies, including Apache Hadoop, HDFS, and SQL/NoSQL querying.
- Students should complete the Big Data Technology Fundamentals web-based training or have equivalent experience.
- Working knowledge of core AWS services and public cloud implementation.
- Students should complete the AWS Essentials course or have equivalent experience.
- Basic understanding of data warehousing, relational database systems, and database design.
- Overview of Big Data
- Big Data streaming and Amazon Kinesis
- Using Kinesis to stream and analyze Apache server logs
- Storage Solutions
- Querying Big Data using Amazon Athena
- Using Amazon Athena to analyze log data
- Introduction to Apache Hadoop and Amazon EMR
- Using Amazon Elastic MapReduce
- Storing and Querying Data on DynamoDB
- Hadoop Programming Frameworks
- Processing Server Logs with Hive on Amazon EMR
- Streamlining Your Amazon EMR Experience with Hue
- Running Pig Scripts in Hue on Amazon EMR
- Spark on Amazon EMR
- Processing New York Taxi dataset using Spark on Amazon EMR
- Using AWS Glue to automate ETL workloads
- Amazon Redshift and Big Data
- Visualizing and Orchestrating Big Data
- Managing Amazon EMR Costs
- Securing Big Data solutions
- Big Data Design Patterns
Venue: LIVE Online
Live Online Training
Get the same training you expect in the classroom without leaving your office or home. These are NOT recorded classes. They are LIVE sessions with an expert instructor. We use the latest in video conferencing technologies and audio so you can confidently participate in any class just like being right there in person. We guarantee the effectiveness of our online training delivery approach that we will give you your money back if you are not totally satisfied. Ask us for a demo.
Online class requirements:
- Moderate to fast Internet
- Phone, or Speakers or headset (required in order to hear the instructor/moderator). You can use Voice Over IP (VoIP) or you can dial-in from a regular phone. Whether you decide to use VoIP or a phone, for convenience, we recommend a hands free headset.
- Training software must be installed on your computer (30 trial versions are acceptable)
- RECOMMENDED: Dual Monitors or computers. For optimal online learning experience, we recommend participants have dual monitors or dual computers. Your online classroom credentials allow you to login multiple times from multiple computers. Participants should use one monitor or computer to view instructor/moderator shared screen and another monitor/computer to work in the software.
What happens when you enroll in an online class
When you register for an online class, you will receive a welcome email followed by login access to the Citrix GoToTraining virtual classroom. A workbook (printed copy or eBook) will be sent to you prior to the start of class.
Online Training Advantages
Convenience: You don’t have to travel and can attend from your home, office or anywhere with an internet connection. Our online classes are conducted using GoToTraining, a more robust version of the popular GoToMeeting screen sharing and conferencing platform. To accommodate multiple time zones, courses are typically scheduled from 10am – 5pm Eastern with a one-hour lunch break at 12:30 – 1:30 pm Eastern and a 10-minute break in the morning and afternoon. When conducting custom online course for your group, class times can be modified to accommodate your timezone.
Interactive Learning: Our online training is fully interactive. You can speak and chat with the instructor and classmates at any time. Various interactive techniques are used in every class. Our small class sizes (typically 4 – 8 students), allow our instructors to focus on individual performance and issues and to work closely with you to meet your unique needs. Classes are designed to be a hands-on learning experience, providing opportunities for you to try your new skills while the instructor is available for review, questions, and feedback. You have the option to give the instructor permission to view your computer to provide one-on-one assistance when needed.