AWS Cloud Practitioner Slides PDF

# AWS Certified Cloud Practitioner Slides v2.0 ## RDS Solution Architecture * Elatic Load Balancer * EC2 Instances * Possibly in an ASG * Read/write * Amazon RDS * SQL (relational) Database ## Amazon Aurora * Aurora is a proprietary technology from AWS (not open sourced) *...

# AWS Certified Cloud Practitioner Slides v2.0 ## RDS Solution Architecture * Elatic Load Balancer * EC2 Instances * Possibly in an ASG * Read/write * Amazon RDS * SQL (relational) Database ## Amazon Aurora * Aurora is a proprietary technology from AWS (not open sourced) * PostgreSQL and MySQL are both supported as Aurora DB * Aurora is "AWS cloud optimized" and claims 5x performance improvement over MySQL on RDS, over 3x the performance of Postgres on RDS * Aurora storage automatically grows in increments of 10GB, up to 64 TB. * Aurora costs more than RDS (20% more) - but is more efficient * Not in the free tier ## RDS Deployments: Read Replicas, Multi-AZ ### Read Replicas: * Scale the read workload of your DB * Can create up to 15 Read Replicas * Data is only written to the main DB * Amazon * RDS replication * RDS replication * RDS ### Multi-AZ: * Failover in case of AZ outage (high availability) * Data is only read/written to the main database * Can only have 1 other AZ as failover * Amazon * RDS Replication cross AZ * RDS * Read Replica * read * writes * Main * read * read * Read Replica * read * Main * writes * read * Failover DB * Failover in case of issues with Main DB ## RDS Deployments: Multi-Region * Multi-Region (Read Replicas) * Disaster recovery in case of region issue * Local performance for global reads * Replication cost * Amazon * RDS * Amazon * RDS * Amazon * RDS * Read Replica * read * Main * writes * Main * writes * Read Replica * read * read * Application(s) * Application(s) * Application(s) ## Amazon ElastiCache Overview * The same way RDS is to get managed Relational Databases... * ElastiCache is to get managed Redis or Memcached. * Caches are in-memory databases with high performance, low latency * Helps reduce load off databases for read intensive workloads * AWS takes care of OS maintenance / patching, optimizations, setup, configuration, monitoring, failure recovery, and backups ## ElastiCache Solution Architecture - Cache * Elastic Load Balancer * Read/write from cache * Fast * ElastiCache * In-memory database * EC2 Instances * Possibly in an ASG * Read/write From DB * Slower * Amazon RDS * SQL (relational) Database ## DynamoDB * Fully Managed * Highly available with replication across 3 AZ * NoSQL database - not a relational database * Scales to massive workloads, distributed "serverless" database * Millions of requests per second, trillions of row, 100s of TB of storage * Fast and consistent in performance * Single-digit millisecond latency - low latency retrieval. * Integrated with IAM for security, authorization, and administration. * Low cost and auto scaling capabilities ## DynamoDB - Type of Data * DynamoDB is a key/value database ### Products | Partition Key | Sort Key | Type | Attributes | |---|---|---|---| | Product ID | | Book ID | Odyssey, Homer, 1871 | | | | Album ID | 6 Partitas, Bach | | | Album ID: Track ID | Partita No. 1 | | | | Movie ID | The Kid | Drama, Comedy, Chaplin | ## DynamoDB Accelerator - DAX * Fully Manged in-memory cache for DynamoDB * 10x performance improvement - single-digit millisecond latency to microseconds latency - when accessing your DynamoDB tables * Secure, highly scalable & highly available * *Difference with ElastiCache at the CCP level* : DAX is only used for and is integrated with DynamoDB, while ElastiCache can be used for other databases. ## Redshift Overview * Redshift is based on PostgreSQL, but it's not used for OLTP. * It's OLAP - online analytical processing (analytics and data warehousing) * Load data once every hour, not every second. * 10x better performance than other data warehouses, scale to PBs of data. * Columnar storage of data (instead of row based). * Massively Parallel Query Execution (MPP), highly available. * Pay as you go based on the instances provisioned. * Has a SQL interface for performing the queries. * Bl tools such as AWS Quicksight or Tableau Integrate with it. ## Amazon EMR * EMR stands for "Elastic Mapreduce" * EMR helps creating Hadoop clusters (Big Data) to analyze and process a vast amount of data. * The clusters can be made of hundreds of EC2 instances. * Also supports Apache Spark, HBase, Presto, Flink ... * EMR takes care of all the provisioning and configuration. * Auto-scaling and integrated with Spot instances. * Use cases: data processing, machine learning, web indexing, big data... ## Athena Overview * Fully Serverless database with SQL capabilities. * Used to query data in S3. * Pay per query. * Output results back to S3. * Secured through IAM. * Use Case: one-time SQL queries, serverless queries on S3, log analytics. ## Amazon QuickSight * Serverless machine learning-powered business intelligence service to create interactive dashboards. * Fast, automatically scalable, embeddable, with per-session pricing. * Use cases: * Business analytics * Building visualizations * Perform ad-hoc analysis * Get business insights using data * Integrated with RDS, Aurora, Athena, Redshift, S3... ## DocumentDB * Aurora is an "AWS-implementation" of PostgreSQL / MySQL ... * DocumentDB is the same for MongoDB (which is a NoSQL database) * MongoDB is used to store, query and index JSON data. * Similar "deployment concepts" as Aurora. * Fully Managed, highly available with replication across 3 AZ. * Aurora storage automatically grows in increments of 10GB, up to 64 TB. * Automatically scales to workloads with millions of requests per seconds ## Amazon Neptune * Fully managed graph database. * A popular *graph dataset* would be a social network: * Users have friends * Posts have comments * Comments have likes from users * Users share and like posts... * Highly available across 3 AZ, with up to 15 read replicas. * Build and run applications working with highly connected datasets - optimized for these complex and hard queries. * Can store up to billions of relations and query the graph with milliseconds latency. * Highly available with replications across multiple AZs * Great for knowledge graphs (Wikipedia), fraud detection, recommendation engines, social networking. ## Amazon QLDB * QLDB stands for "Quantum Ledger Database". * A ledger is a book recording financial transactions. * Fully Managed, Serverless, High available, Replication across 3 AZ * Used to review history of all the changes made to your application data over time. * Immutable system: no entry can be removed or modified, cryptographically verifiable. * 2-3x better performance than common ledger blockchain frameworks, manipulate data using SQL. * *Difference with Amazon Managed Blockchain:* no decentralization component, in accordance with financial regulation rules. ## Amazon Managed Blockchain * Blockchain makes it possible to build applications where multiple parties can execute transactions without the need for a trusted, central authority. * Amazon Managed Blockchain is a managed service to: * Join public blockchain networks * Or create your own scalable private network. * Compatible with the frameworks Hyperledger Fabric & Ethereum. ## AWS Glue * Managed extract, transform, and load (ETL) service. * Useful to prepare and transform data for analytics. * Fully serverless service. * Glue Data Catalog: catalog of datasets, can be used by Athena, Redshift, EMR. ## DMS Database Migration Service * Quickly and securely migrate databases to AWS, resilient, self-healing. * The source database remains available during the migration. * Supports: * Homogeneous migrations: ex. Oracle to Oracle * Heterogeneous migrations: ex Microsoft SQL Server to Aurora ## Databases & Analytics Summary in AWS * Relational Databases - OLTP: RDS & Aurora (SQL) * Differences between Multi-AZ, Read Replicas, Multi-Region * In-memory Database: ElastiCache * Key/Value Database: DynamoDB (serverless) & DAX (cache for DynamoDB) * Warehouse - OLAP: Redshift (SQL) * Hadoop Cluster: EMR * Athena: query data on Amazon S3 (serverless & SQL) * QuickSight: dashboards on your data (serverless) * DocumentDB: "Aurora for MongoDB" (JSON - NoSQL database) * Amazon QLDB: Financial Transactions Ledger (immutable journal, cryptographically verifiable) * Amazon Managed Blockchain: managed Hyperledger Fabric & Ethereum blockchains * Glue: Managed ETL (Extract Transform Load) and Data Catalog service * Database Migration: DMS * Neptune: graph database ## Other Compute Section ## What is Docker? * Docker is a software development platform to deploy apps. * Apps are packaged in containers that can be run on any OS. * Apps run the same, regardless of where they're run: * Any machine * No compatibility issues * Predictable behavior * Less work * Easier to maintain and deploy. * Works with any language, any OS, any technology * Scale containers up and down very quickly (seconds) ## Docker on an OS ## Where Docker Images are Stored? * Docker images are stored in Docker Repositories. * Public: Docker Hub: [https://hub.docker.com/](https://hub.docker.com/) * Find base images for many technologies or OS: * Ubuntu * MySQL * NodeJS, Java... * Private: Amazon ECR (Elastic Container Registry) ## Docker versus Virtual Machines * Docker is "sort of" a virtualization technology, but not exactly. * Resources are shared with the host => many containers on one server. ## ECS * ECS = Elastic Container Service * Launch Docker containers on AWS * You must provision & maintain the infrastructure (the EC2 instances) * AWS takes care of starting/stopping containers. * Has integrations with the Application Load Balancer. ## Fargate * Launch Docker containers on AWS. * You do not provision the infrastructure (no EC2 instances to manage) - simpler! * Serverless offering. * AWS just runs containers for you based on the CPU/RAM you need. ## ECR * Elastic Container Registry. * Private Docker Registry on AWS. * This is where you store your Docker images so they can be run by ECS or Fargate.