We have updated this course on October 7, 2022 to correct a problem that was impacting course completion. If you are enrolled in the course and have not completed it, your progress may be impacted. We apologize for any inconvenience this may cause
Languages Available: Deutsch | Español (Latinoamérica) | Español (España) | Français | Bahasa Indonesia | ไทย | Italiano | 日本語 | 한국어 | Português (Brasil) | 中文（简体) | 中文（繁體) | Tiếng ViệtIn this self-paced course, you learn about the process for planning data analysis solutions and the various data analytic processes that are involved. This course takes you through five key factors that indicate the need for specific AWS services in collecting, processing, analyzing, and presenting your data. This includes learning basic architectures, value propositions, and potential use cases. The course introduces you to the AWS services and solutions to help you build and enhance data analysis solutions.Intended AudienceThis course is intended for:Data architectsData scientistsData analystsCourse ObjectivesIn this course, you will learn how to:Identify the characteristics of data analysis solutions and the characteristics that indicate such a solution may be requiredDefine types of data including structured, semistructured, and unstructured dataDefine data storage types such as data lakes, AWS Lake Formation, data warehouses, and the Amazon Simple Storage Service (Amazon S3)Analyze the characteristics of and differences in batch and stream processingDefine how Amazon Kinesis is used to process streaming dataAnalyze the characteristics of different storage systems for source dataAnalyze the characteristics of online transaction processing (OLTP) and online analytical processing (OLAP) systems and their impact on the organization of data within these systemsAnalyze the differences of row-based and columnar data storage methodsDefine how Amazon EMR, AWS Glue, and Amazon Redshift each work to process, cleanse, and transform data within a data analysis solutionAnalyze the concept of atomicity, consistency, isolation, and durability (ACID) compliance as well as basic availability, soft state, eventual consistency (BASE) compliance and how an extract, transform, load (ETL) process can help to ensure complianceExplore the concept of data schemas and understand how they define data and how this information is stored in metastoresAnalyze the concept of data versus informationRecognize the ways to analyze data to produce information for reports using tools such as Amazon QuickSight and Amazon AthenaDefine how AWS services work together to visualize dataPrerequisitesWe recommend that attendees of this course have the following prerequisites:Working knowledge of database conceptsBasic understanding of data storage, processing, and analyticsExperience with enterprise IT systemsDelivery MethodThis course is delivered through a mix of:Digital trainingDuration3 Hours 30 MinutesCourse OutlineThis course covers the following concepts:• Lesson 1: Introduction to data analysis solutions - Data analytics and data analysis concepts - Introduction to the challenges of data analytics• Lesson 2: Volume – data storage - Introduction to Amazon S3 - Introduction to data lakes - Introduction to data storage methods• Lesson 3: Velocity – data processing - Introduction to data processing methods - Introduction to batch data processing - Introduction to stream data processing• Lesson 4: Variety – data structure and types - Introduction to source data storage - Introduction to structured data stores - Introduction to semistructured and unstructured data stores• Lesson 5: Veracity – cleansing and transformation - Understanding data integrity - Understanding database consistency - Introduction to the ETL process• Lesson 6: Value – reporting and business intelligence - Introduction to analyzing data - Introduction to visualizing data• Lesson 7: Key Takeaways - Putting the pieces together - What’s next
Industry Partners
