Official Video/Training Resources

Exam Readiness: AWS Certified Big Data - Speciality (Digital)

Preparation Steps

  1. Review Exam Guide and Sample Questions
  2. Practice with Qwiklabs and Exam Prep Quests
  3. Review AWS FAQ
  4. Study AWS Whitepapers
  5. Practice Exam
  6. Exam

Big Data is Breaking Traditional IT Infrastructure

  • Variety
  • Velocity
  • Volume

Evolution of Big Data Processing

  • Descriptive (Historical)
    • Dashboards, traditional query, and reporting
  • Real-time
    • Click-stream analysis, ad bidding, streaming data
  • Predictive

Types of Services


  • Amazon EC2
  • Kafka
  • Cassandra


  • Amazon EMR
  • Amazon RDS
  • Amazon Elasticsearch
  • Amazon ElasticCache
  • Amazon Redshift


  • AWS Lambda
  • Amazon S3
  • Amazon API Gateway
  • Amazon Cognito
  • Amazon DynamoDB
  • Amazon CloudWatch
  • Amazon Kinesis
  • Amazon SQS
  • AWS IoT

AWS Big Data Services


  • ISV connectors
  • AWS Direct Connect
  • AWS Snowball
  • Amazon S3 Transfer Acceleration
  • Kinesis Data Firehose
  • AWS IoT
  • AWS Glue
  • AWS Step Functions


  • Aurora
  • Amazon Elasticsearch
  • Amazon Kinesis
  • Amazon DynamoDB
  • Amazon S3


  • Amazon EMR
  • EC2
  • AWS Lambda
  • Amazon Redshift
  • Amazon Athena
  • Amazon AI
  • Kinesis Data Analytics


  • Kibana
  • AWS Marketplace
  • IPython/Jupyter
  • Zeppelin
  • Amazon QuickSight

Amazon EMR and Kerberos

  • Amazon EMR 5.10.0 and later supports Kerberos
  • Services and users that authenticate are called principals
  • Principles exist in a Kerberos realm
  • The Key Distribution Center (KDC) manages authentication
  • The KDC issues tickets
  • Principles can be from other Realms. This requires Cross-Realm Trust

Amazon EMR Storage Permissions

  • EMR File System (EMRFS) storage-based permissions
  • Consistent View
  • Data Encryption

Amazon EMR Data Encryption

  • In-Transit Data Encryption for EMRFS traffic between S3 and Cluster nodes
  • TLS Encryption
  • At-Rest Data Encryption

Amazon EMR and Apache Hive

  • Four features of Hive that are specific to Amazon EMR
    • Load table partitions automatically from Amazon S3
    • Specify an off-instance metadata store
    • Write data directly to Amazon S3
    • Access resources located in Amazon S3

Amazon EMR Log Auditing

  • Services
    • Apache Spark
    • Apache HBase
    • Apache Hadoop
    • Apache Tez
    • Apache Flink
  • AWS CloudTrail
    • Amazon EMR API calls
    • AWS KMS API calls
    • Amazon S3 API calls
  • Amazon EMR
    • /JobFlowId/node
    • /JobFlowId/steps/N
    • /JobFlowId/containers

Amazon Redshift Security

  • IAM roles to access data on Amazon S3
  • IAM SSO authentication
  • SSL to secure data in transit
  • Encryption to secure data at rest
  • No direct access to compute nodes
  • Support for Amazon VPC
  • User audit logging and AWS CloudTrail integration
  • SOC 1/2/3, PCI-DSS Level 1, FedRAMP
  • Limit Data Access using Views (know this is possible on the exam)

Amazon Redshift Networking

  • Enhanced VPC routing
    • Query traffic flows only through customer VPC
    • Strict data traffic management
    • Amazon S3 endpoint to access Amazon S3
    • Locked down security groups
    • SSL certificate for each Amazon Redshift cluster

Amazon Redshift IAM Authorization

  • IAM support for data LOAD/UNLOAD
    • IAM roles for LOAD/UNLOAD operations
    • A cluster can have access to specific S3 buckets
    • Simplify credentials management
    • Access to AWS KMS for encryption

Amazon Redshift encryption

  • In Transit
    • Amazon Redshift API calls are made using HTTPS
    • SSL certificate for each Amazon Redshift cluster required
  • At Rest
    • Enable cluster encryption
    • Encrypted via:
      • AWS KMS
      • Hardware security module
    • Supports server-side encryption using SSE-KMS and SSE-S3

Amazon Spark on AWS

  • Spark SQL
    • Can read data from an existing Hive installation
  • Spark Streaming
    • Ingest from kinesis
    • Push to filesystems, and dashboards
  • MLLib (Machine Learning)
    • ML library
  • GraphX
    • Designed to simplify graph analytics tasks

Amazon EMR Sandbox Applications

  • Oozie
    • Workflow scheduler Hadoop Jobs
  • Presto
    • Distributed SQL query engine for GB to PB data
  • Spark
    • Unified analytics engine
  • Sqoop
    • Tool designed for efficiently transferring bulk data between Hadoop and structured data
  • Zeppelin
    • Web-based notebook (similar to Jupyter)
  • Zookeeper
    • Centralized service for maintaining config information

Amazon EMR Spot Instances

  • Master node
  • Core instance fleet
  • Task instance fleet

Amazon Redshift

  • Columns are fast to read
  • Database inserts are expensive and inefficient (update tables)
  • MPP (Massively Parallel Processing cluster).
  • Key Distribution Styles
    • Even: Distribute the rows across the slices in a round-robin fashion (Doesn’t participate in Joins)
    • Key: Distribute according to the values in one column (Columns in Joins are placed together)
    • All: Distribute the entire table to every node (Much longer to load update and insert). Multiplies the data by every node
  • Sort Key:
    • Compound
      • Query predicates use a predicate
    • Interleaved
      • Equal preference to predicates
  • Queues:
    • By default 1 queue with 5 concurrent queries
    • Can have 50 concurrent queries to queue
    • Can specify by each Queue
    • Superuser can always run a job
  • What are the functions of the Amazon Redshift leader node?
    • Acts as a SQL endpoint
    • Stores metadata
    • Generates and coordinates query execution plans
  • What are the AWS services that can be used to load data into Amazon Redshift?
    • DynamoDB
    • EMR
    • DataPipeline
    • Kinesis Data Firehose
    • Amazon S3

Amazon Redshift vs EMR

  • EMR (custom code to analyze large datasets with Spark/Hadoop)
  • Redshift:
    • Complex queries on structured data

Amazon Athena Uses

  • Apache Hive for DDL functionality
    • Complex data types
    • Multitude of formats
    • Supports data partitioning
    • Optimized for query throughput
  • Teradata Presto for SQL queries
    • An in-memory distributed query engine
    • ANSI-SQL compatible with extensions
    • Optimized for latency
  • Best Practices
    • Partitioning
      • Reduce the amount of data scanned
      • Read-only files necessary for queries
    • Compression and file sizes
      • Splittable files allow Athena’s execution engine to split reading of a file by multiple readers
    • Columnar formats for analytics
      • Optimize column-based reads
      • Use Apache Parquet and Apache ORC
  • Asynchronous Interaction Model using Athena API supports:
    • Named queries
    • Column data and metadata
    • Integration with existing data access tools
    • Paginated result sets

Choosing Analytics Services

  • Amazon Redshift
    • Data warehouse for historical analysis and reporting
  • Amazon Redshift Spectrum
    • Extends data warehouse queries to Amazon S3
    • Differentiates performance for complex queries over TBs of data on Amazon S3
    • Improves availability and concurrency on Amazon Redshift
  • Amazon Athena
    • On-demand interactive querying
    • Run simple queries by using standard SQL on Amazon S3