AWS Certified Data Analytics Specialty (DAS-C01) Training

Explore AWS fundamentals, certification prep, and service introductions, including how to set up an AWS account, key benefits of cloud deployment, and core services like S3 and EC2.

Explore learning paths for AWS certification across foundational, associate, and professional levels, with guidance on video versus document study, adaptive methods, and practice questions with hands-on console exercises.

Get an overview of the AWS certification exam, including three levels, no prerequisites, 2024 changes (data engineer associate; AI practitioner and ML engineer), and exam format for preparation.

Create a new AWS account and configure an IAM user with MFA for secure access. Learn to use the Management Console, CLI, Cloud Shell, and SDK in a chosen region.

Introduce AWS data analytics services for the DAS-C01 exam and the rationale behind domain keys, then examine trends in generative AI, batch and streaming data, and diverse analytics methods.

Explore how a data pipeline collects data from diverse sources, ingests it via streaming, batching, or APIs, and handles structured, unstructured, and semi-structured data.

Explore data lakes, data warehouses, and data marts: lakes store data in native format with structured, semi-structured, and unstructured data; warehouses enable reporting and analysis, and marts target business areas.

Learn etl processing—extract from databases, APIs, and flat files; transform and clean data, addressing duplicates, missing values, and invalid numbers, with elt in Amazon Redshift.

Explore AWS data analytics workflows from data collection to visualization using Kinesis, S3 data lakes, Redshift, and QuickSight, with OpenSearch, Athena, and SageMaker supporting analytics and security across pipelines.

Explore data types and formats—structured, unstructured, and semi-structured—along with csv, json, avro, and orc, and learn how compression and columnar storage boost analytics.

Explore Amazon Kinesis as a core data analytics service, covering data collection streaming and batch, real-time processing with Kinesis Data Streams, Firehose, and Apache Flink for streaming analytics.

Explore Kinesis data streams for real-time ingestion by producers, with shards and partitions, scalable throughput, and integration with EMR, Lambda, Redshift, and S3.

Introduce the consumer side of Kinesis data streams, building multi-threaded applications that read data via the KCL or Kql, while DynamoDB manages leases and checkpoints for shard coordination.

Explore provisioned and on-demand Kinesis data streams, enhanced fan-out for 20 consumers at 40 MB/s, and low latency via http/2, with 24-hour retention up to 365 days.

Explore common Kinesis data streams issues, including hot shards, throughput limits, data duplication, and ordering across shards; learn remedies with random partition prefixes, unique producer IDs, and shard scaling.

Perform a hands-on with Kinesis Data Streams, creating a stream, selecting capacity mode, and sending and consuming records using the SDK and KPL, with monitoring via CloudWatch.

Explore how Data Firehose groups streaming data by size or time for near real time processing, uses lambda for transformations, and delivers to S3, Redshift, OpenSearch, or Splunk.

Submit records from a Kinesis data stream through Firehose, transform them with a Lambda function, and store the converted data in an S3 bucket for verification.

Explore the free managed service for Apache Flink that processes real-time streaming data at scale in AWS with millisecond latency, enabling SQL analytics, Studio Notebook, and Apache Zeppelin integration.

Analyze real-time stock data from MSK with the managed service for Apache Flink using studio notebooks and Zeppelin. Deploy via blueprint to avoid local setup and study streaming analytics.

Explore Amazon SQS, a fully managed message queue enabling loosely coupled, asynchronous microservices; compare standard and FIFO queues, polling, visibility timeout, delay queues, and dead-letter queues for reliable message processing.

Create and test standard and FIFO queues in AWS SQS, configure visibility timeout, delivery delay, and deduplication, and verify with messages and a dead-letter queue.

Explore Amazon MQ, a message queuing service based on Apache MQ and RabbitMQ, supporting JMS and MQTT; compare it with SQS for on AWS use and migration compatibility.

Explore Amazon managed streaming for Apache Kafka, a fully managed Apache Kafka service for real-time data ingestion, storage, and processing, with security via VPC, IAM, and encryption.

The snow family uses physical storage devices to transfer terabytes from on premises to AWS and export data from S3, ideal for network-restricted environments.

Learn how AWS Transfer Family enables data transfer to S3 and EFS via SFTP, FTPS, and FTP, with VPC endpoints and IAM role authentication, plus DataSync for migrations and backups.

Migrate databases with AWS Database Migration Service, using full, incremental, or differential migrations and continuous replication; configure replication instances and endpoints, monitor progress, and apply schema conversion with SCT.

Explore AWS data exchange, a marketplace service, to subscribe to provider data, store in S3, connect via APIs, and manage import/export for your data pipeline, with variable pricing.

Amazon AppFlow is a free managed integration service that securely transfers data between SaaS apps like Salesforce and ServiceNow and AWS, enabling event-driven flows with filtering and encryption.

Explore data types and storage services, including Amazon S3, Lake Formation, EBS, and EFS. Understand object, block, and file storage and how they map to relational, NoSQL, in-memory, and Redshift.

Learn how Amazon S3 provides highly durable, unlimited capacity object storage billed by usage, with redundancy across multiple availability zones, diverse storage classes, lifecycle management, and Glacier archiving.

Control access in S3 with IAM policies, bucket policies, and ACLs, use signed URLs, encryption, and enable versioning and object locking for data protection.

Explore Amazon S3 features, including batch operations, multipart uploads, transfer acceleration, and S3 select, using GetObject and list bucket operations with prefix or size filtering.

Explore how to mask CSV data using S3 Object Lambda access point and a Lambda function, configure permissions, and test with a sample dataset in AWS data analytics training.

Configure cross-origin resource sharing for Amazon S3 by creating two buckets, enabling static website hosting, and setting policies to allow public get object requests between the buckets.

Set up Amazon S3 event notifications to trigger a Lambda function. Upload to the input folder; save uppercase output to the output folder to avoid infinite loops.

Explore Amazon EBS, EFS, and EC2 instance store, their differences and use cases for persistent storage; learn when to attach, scale, and encrypt across AZs and single assets.

Explore AWS database services, compare relational RDBs like Oracle and MySQL with NoSQL options such as DynamoDB and DocumentDB, and learn managed patching, automatic failover, read replicas, and security features.

Explore Amazon RDS by examining RDS elements, instance types and families, storage options including general purpose SSD, provisioned IOPs, magnetic for legacy compatibility, and auto-scaling behavior.

Scale applications with Amazon RDS proxy, a managed, highly available database proxy that pools connections. Secure IAM and VPC security groups, and improve read performance via caching.

Learn how lead replicas asynchronously mirror the primary, with up to five read replicas to offload reads, promote to primary, and support disaster recovery, migration, and cross-region uses.

Understand Amazon RDS backups, including automatic backups, manual snapshots, and point-in-time recovery that create a new DB instance and cannot restore to an existing one from S3 transaction logs.

Enable enhanced monitoring for detailed OS level metrics such as CPU usage, memory usage, and disk IO, and enable performance insights to view the impact on database load.

Perform a hands on creation and configuration of an Amazon RDS MySQL database in the console, using the free tier, and configure credentials, VPC, security groups, backups, and monitoring.

Configure and create a read replica from the primary instance across regions, review the separate endpoint, and learn encryption rules that cannot be changed after snapshot restoration.

Discover Amazon Aurora, a fully managed MySQL/PostgreSQL compatible relational database that scales compute and storage independently, with multi-AZ replication, S3 backups, and fast failover for high availability and low cost.

Discover Aurora global database across regions with one primary, up to five secondaries, under-60s replication, nearest-region latency, cross-region replication, and up to two writable masters in MySQL multi-master.

Explore Aurora backups, offering more extensive backup capabilities than RDS, including continuous automatic backups, point-in-time recovery, cloning, and backtrack up to 72 hours.

Create and monitor an Aurora database from the RDS console, selecting MySQL compatibility, configuring cluster settings, backups, multi-availability zones, endpoints, monitoring, and performance insights for optimization.

Learn how Aurora promotes the largest read replica to writer during failover. Failover priorities guide promotion, and AWS recommends keeping read replicas the same size for stability.

Introduce Amazon Redshift, a free managed data warehouse service to store and analyze petabytes of data. Benefit from scalable, cost effective, high performance with columnar storage, compression, and automated backups.

Explore the redshift architecture, including the cluster, leader node, and compute nodes, and how they coordinate queries using MPP and distribution styles such as key, even, and all.

Discover how Amazon Redshift distributes data across nodes using key, even, and old style distribution to optimize joins, local processing, and replication for small lookup tables.

Explore redshift node types: dense compute for under 2 TB with SSD, dense storage for massive data on HDD, and RA3 with independent compute and storage via SSD and S3.

Explore how Amazon Redshift boosts performance by reducing disk i/o through columnar storage and data compression. Learn how zoning and workload management optimize queries, memory, and slots.

Scale Amazon Redshift by resizing compute capacity with elastic resize or classic resize, where elastic resize adds or removes nodes quickly (without changing node type) and may require vacuuming to reclaim disk space, while classic resize changes node types for scaling up. Enable concurrent scaling to improve performance on clusters while keeping the endpoint unchanged, with a leader node coordinating queries and a caching layer for S3 storage that preserves data consistency and yields linear performance gains.

Learn Redshift data definition language (DDL) commands, including create and alter table, external tables with Redshift Spectrum on S3, and vacuum and analyze for query plan optimization.

Master Amazon Redshift dml by using copy for fast parallel loads from S3 and DynamoDB, and insert for small batches. Use prepare and explain to optimize query performance.

Explore data control language (DCL) in Redshift, managing permissions via IAM and lake formation for specific users, roles, or tables, plus commands for cluster management and system information.

Explore redshift security features, including data encryption, network security, logging, and auditing, with table, database, schema, function, and column-level access controls, plus cluster encryption only at creation.

Explore Amazon Redshift Spectrum to query data directly on S3 without loading, enabling exabyte-scale analytics. Enjoy cost-efficient, pay-per-query pricing with AWS Glue-integrated schema discovery and cataloging.

Learn how Amazon Redshift supports batch data analysis and streaming ingestion, use the managed Apache Flink service for real-time analytics, and query data on S3 with Redshift Spectrum and Athena.

Perform a hands-on redshift query on csv data stored in s3, using the editor to fetch external table schemas and run queries against a created cluster.

Explore Amazon DynamoDB, a fully managed key value database with partition and sort keys, offering serverless scalability, unlimited storage, and NoSQL queries for content, catalogs, and user profiles.

Explore DynamoDB capacity mode, including on-demand and provisioned options, pricing, auto scaling, and capacity units (RCU and WCU), plus backups, point-in-time recovery, and export to S3.

Explore how DynamoDB partitions data with a partition key and sort key, and how local and global secondary indexes improve queries while balancing performance and cost.

DynamoDB streams capture a sequence of item-level changes stored for 24 hours, enabling cross-region replication, global tables, change analysis, and integration with Amazon Redshift or AWS Lambda to trigger actions.

Explore DAX, a fully managed in-memory cache for DynamoDB that speeds reads to microseconds via write-through caching to cache and DynamoDB, with a default time-to-live of five minutes.

Learn how DynamoDB global tables enable multi-master reads and writes across regions with streams and consistent updates. Ensure name, write capacity, encryption, time to live, and global secondary index settings.

Compare DynamoDB query and scan operations, understand performance, one megabyte limits and pagination, and distinguish batch versus transactional processing with all-or-nothing guarantees, illustrated by a hotel reservation example.

Learn how DynamoDB backups work with on-demand backups and point-in-time recovery, including restoration, retention up to 35 days, and reconfiguring settings after restore.

Explore the security features of DynamoDB, including IAM access control for tables, indexes, and streams, encryption, and network isolation—gateway VPC endpoints that simplify access via routing tables.

Create and configure a DynamoDB table named test product with a partition key product ID and optional sort key, then compare on-demand and provisioned capacity and explore composite keys.

Perform a hands-on DynamoDB session focusing on scanning and querying a test product three table, including batch write, projection, and key condition examples.

Demonstrate creating DynamoDB tables with local secondary indexes and global secondary indexes, setting the LSI price as sort key and GSI sku and price, then query via the GSI.

Explore Memorydb for Amazon Redis, a fully managed in-memory database delivering high performance, multi-availability zone durability, primary and replica consistency, backups up to 35 days, and up to 500 nodes.

Explore Amazon DocumentDB, a MongoDB-compatible, schemaless document database with a JSON-like structure, scalable multi-AZ deployment, and MongoDB API migration within a VPC.

Learn to create an Amazon DocumentDB cluster, compare instance-based and elastic clusters, configure shard count and capacity, VPC, subnets, authentication, and monitor with CloudWatch metrics.

Amazon Neptune demonstrates a sample graph database with nodes Alice, Bob, and Charlie connected by edges, using Gremlin and Sparql, deployed in a VPC with a primary and read-only replicas.

Launch and configure an Amazon Neptune database, choosing provisioned or serverless capacity, set engine and cluster options, enable Jupyter notebook via SageMaker, and deploy with CloudFormation for quick setup.

Explore ETL processing concepts, comparing batch ETL with ELT in cloud data warehouses, and review AWS services for batch and streaming ETL, including AWS Glue, EMR, Lambda, and Flink.

Learn how AWS Glue provides an etl tool with a crawler, data catalog, and schema registry to run etl jobs, apply data quality rules, and automate workflows in Glue Studio.

Explore AWS Glue crawler capabilities, populating the AWS Glue Data Catalog with schemas and storage details from databases, streaming data, and S3, and leverage the schema registry for real-time streaming.

Master aws glue etl, built on apache spark and python, with pyspark transforms like drop, split, filter, and join, plus databrew data cleaning and ray for parallel processing.

Explore AWS Glue workflows that orchestrate ETL tasks with worker types and DPUs, enable bookmarks for incremental processing, and trigger jobs by cron or events in Glue Studio using DAGs.

Learn to use AWS Glue crawler to extract schema from CSV data in S3, create a data catalog database, and run Athena queries to analyze the dataset end-to-end.

Learn how AWS Lambda enables event driven, serverless computing, integrates with API Gateway and DynamoDB, manages IAM permissions, layers, and versions, and monitors concurrency and execution cost.

Configure asynchronous invocation for an AWS Lambda function, deliberately trigger a failure, and route failed events to an SQS dead letter queue while validating retries and monitoring.

Configure AWS Lambda event source filtering with SQS, set up the queue and Python function, attach IAM roles, and validate filtered messages using CloudWatch logs.

Explore Amazon EMR, a large-scale distributed platform using Spark, Hive, and Presto to handle petabytes of data, with Elastic MapReduce and S3-based data persistence.

Explore the Hadoop ecosystem and Amazon EMR features, including MapReduce, HDFS, Spark, Hive, HBase, and Presto, plus cluster configuration and a word-count example.

Understand the EMR architecture with master, core, and task nodes; master manages cluster, core stores HDFS and runs MapReduce, tasks are stateless, and auto or managed scaling adjusts instance groups.

Explore two storage options for Amazon EMR—HDFS on local cluster disks and the EMR file system backed by S3—highlighting node life cycle effects and scalability.

Launch an EMR cluster on EC2 to run a PySpark analysis of a health inspection dataset in S3, using EMR steps to manage the workflow.

Explore how AWS step functions coordinates multiple services into scalable workflows using state machines, parallel processing, and conditional branches, contrasting with SQS for decoupled, linear tasks.

Engage in a hands-on AWS Step Functions session to build and run a state machine using JSON and the graphical interface, including wait, parallel, and yes/no branching.

Discover how Amazon API Gateway provides an interface for web apps to create, secure, publish, and monitor HTTP, REST, and WebSocket APIs with endpoints, stages, and DynamoDB-backed Lambda integration.

Explore AWS API Gateway features, including usage plans with key request limits, caching, encoding, client certificates, and IAM, Cognito, or Lambda authenticators; pricing depends on API calls and data transfer.

Understand how a serverless book selling app uses API gateway, Lambda, and DynamoDB to post and fetch books, with security tokens, throttling, and exponential backoff.

Perform a hands-on setup of api gateway with lambda aliases to separate development and production. Implement canary routing and weighted traffic for a new feature.

Explore Amazon MWAA, the managed airflow service, to orchestrate pipelines with dags, s3 docs, ec2 execution, and cloudwatch monitoring, and learn dag vs undirected graphs for workflow dependencies.

Explore analysis, visualizations, and interactive analytics with AWS tools like Athena, OpenSearch, Redshift, QuickSight, plus EMR and SageMaker for real-time processing and machine learning.

Discover Amazon Athena, a serverless tool for querying S3 data with Presto distributed SQL, pay-per-query, supporting packet, CSV, JSON, ORC, and Avro formats.

Query data with Amazon Athena by defining DDL manually or via the Glue data catalog, and use work groups and UDFs with the federation SDK for federated queries across sources.

Learn how to optimize Amazon Athena performance by compressing data, using columnar formats, and applying partitioning and bucketing to reduce I/O and ensure file sizes between 128 and 512 MB.

Learn the Amazon OpenSearch architecture as a real-time distributed search and analytics service, covering documents, indices, shards, data and master nodes, and ultrawarm storage with EBS or S3.

Improve OpenSearch performance by tuning shard size ten to fifty gigabytes and shard count twenty-to-fifty, use bulk indexing, and apply search after with phrase search, proximity, fuzzy, and language-based search.

Explore OpenSearch metrics for nodes and shards to monitor health and load balancing; include index, throw logs, and search throw logs for document additions, deletions, and updates with time threshold.

Explore OpenSearch dashboards for API and browser based analysis to search, visualize, and report on data, with log analysis and system monitoring, plus hourly automatic and manual backups to S3.

Provision a minimal OpenSearch domain, ingest sample web log data, and build a browser-share dashboard using an index, Discover, and a pie chart visualization.

Explore Amazon SageMaker, a fully managed AI and machine learning environment that builds, trains, and deploys models with a Jupyter Notebook and optimized algorithms, supporting TensorFlow, PyTorch, and MXNet.

Explore Amazon QuickSight, a managed BI tool that scales to hundreds of thousands of users and uses an in-memory engine for high performance without loading data; compare standard and enterprise.

Explore how QuickSight uses calculated fields, AWS Glue preprocessing, and Spice for analytics on large data sets, with ML insights including anomaly detection, forecasting, and QuickSight Q natural language queries.

Explore QuickSight roles—admin, author, and leader—and how they govern permissions, dashboards, and data analysis, plus row-level and column-level security and data access rules.

Explore QuickSight graph types for data visualization, including line charts, bar charts, combo and waterfall charts, pie charts, heatmaps, scatter maps, histograms, and box and whisker diagrams.

Sign up for Amazon QuickSight, upload CSV data, create datasets, build bar and donut charts, add a profit field, publish the dashboard, and apply column-level security.

Explore AWS security, covering IAM, data encryption, governance, monitoring, logging, and networking; learn VPC isolation, firewall rules by IP and location, and data-at-rest protection with Amazon Macie and Amazon Detective.

Learn how identity and access management controls resource permissions in AWS IAM, using users, groups, roles, and policies with authentication options, MFA, least privilege, and security best practices.

Explore AWS IAM policies in JSON, covering identity-based, resource-based, inline, and managed options. Learn how statements, actions, effects, ARNs, and conditions govern access for services like S3, EC2, and Lambda.

Explore cross-account IAM in AWS, detailing how identity and resource policies govern actions across accounts, use of permission boundaries and SCPs, and switching roles for secure access.

Set up a dummy and main administrator account, then create an IAM role with an external id to prevent the confused deputy problem and verify behavior with STS role commands.

Demonstrate a hands-on permission boundary in AWS IAM, showing how a two-part boundary allows all IAM actions yet denies attaching admin access to other users.

Learn how AWS STS issues temporary credentials via assume role for cross-account access, with expiration and dynamic generation, enabling secure CLI and environment variables usage.

Explore Apache Ranger, an open source framework for fine-grained, row and column level access, data filtering, masking, and centralized auditing via CloudWatch across the Hadoop ecosystem, contrasting with IAM.

Explore how AWS KMS enables creation and management of cryptographic keys for encryption of data at rest and in transit using symmetric, asymmetric, and hybrid methods.

Explore encryption with AWS KMS data keys: create a key, generate a data key, encrypt and decrypt files, and delete the key after a seven-day wait.

Learn how AWS Secrets Manager centralizes secret lifecycles and enables automatic rotation with Lambda to securely store credentials. Compare Secrets Manager with Parameter Store for use cases and exam relevance.

Learn to create and configure secrets in aws secrets manager, select secret types, set key-value pairs, and manage rotation options with cloud shell and cli, including retrieve, update, and delete.

The lecture introduces Amazon Macie for S3 governance, detecting sensitive information with ML, enabling monitoring, KMS encryption, and CloudWatch alerts, and identifying PII and PHI to support GDPR and PCI.

Enable Amazon Macie to detect PII data in S3 buckets and run a one-time discovery job on a sample sensitive data CSV to reveal findings.

GuardDuty analyzes storage and logging services, including CloudTrail S3, VPC flow logs, and DNS logs, to detect threats such as compromised credentials and unusual access patterns. Enable from the console.

Enable Amazon GuardDuty, explore the findings dashboard, and simulate threats using sample findings to learn how GuardDuty detects anomalies across EC2, S3, and other services.

Discover Amazon Detective, a service that automatically collects logs from CloudTrail, VPC flow logs, and GuardDuty, and uses machine learning to investigate security issues. Integrates with Security Hub and GuardDuty.

Learn Amazon CloudWatch, the integrated operational monitoring service for AWS resources, collecting logs and metrics, triggering alerts via SNS and Lambda, and routing events with EventBridge.

Create an IAM role for Lambda, build a Python 3.11 function that emits CloudWatch EMF logs as JSON, deploy, test, and view custom metrics in CloudWatch under the specified namespace.

Explore AWS CloudTrail, which records user activity and API calls as management and data events, logs timestamps and resources, and enables insights for anomaly detection; data events require explicit enablement.

Explore how aws config logs resource changes, enforces ssh configurations for ec2, evaluates compliance with config rules, and automates fixes via system manager across accounts and regions.

learn to configure an AWS config rule to stop non-compliant EC2 instances by enforcing a specific instance type and enabling remediation actions.

Design an aws vpc with private and public subnets, internet and nat gateways, and vpn connections to on-premises, where security groups act as firewall rules across availability zones.

VPC flow logs capture IP traffic from network interfaces and can be stored in CloudWatch logs, S3, or Kinesis Data Firehose, revealing source and destination IPs, ports, protocol, and timestamps.

Set up a VPC flow log workflow by creating an S3 bucket and EC2 instance, configuring IAM roles, saving logs to S3, and querying them with Amazon Athena.

Connect VPCs via private IPs with VPC peering for secure, hardware-free communication. Prevent CIDR overlaps and avoid transitive peering; VPN, Direct Connect, Internet Gateway, and VPC endpoints are not supported.

Explore VPC endpoints to access AWS services privately from your VPC using interface and gateway endpoints, via PrivateLink, with S3 or DynamoDB, and DNS or ENI considerations.

Set up and test a vpc endpoint for s3 by creating gateway and interface endpoints, attaching an iam role with s3 read access, and validating access from an ec2 instance.

Learn how VPN and Direct Connect enable secure, private communication over the internet through virtual tunnels, SSL/IPsec encryption, and authentication, including AWS Client VPN and site-to-site VPN options.

Explore AWS Direct Connect basics, including dedicated and hosted connections, private/public/transit virtual interfaces, and using Direct Connect Gateway and Transit Gateway for multi-region connectivity, with VPN backup and resilience options.

Leverage Amazon CloudFront, a global content delivery network, to reduce origin load and latency via edge locations and caching, with DDoS protection, reporting, and pay-as-you-go pricing.

Detail CloudFront security features, including SSL encryption, WAF and Shield protection, origin access identity for S3, HTTP header checks for ALBs, and signed URLs and cookies for private content.

Explore CloudFront edge functions, compare CloudFront functions and Lambda@Edge, and learn to implement authentication, authorization, and response handling at the edge to reduce origin load.

Configure CloudFront origin access control (OAC) to block direct access to an S3 bucket and deliver content only via CloudFront, by creating a private bucket, a distribution, and bucket policy.

Set up a CloudFront distribution with an S3 origin and insert a custom header using a CloudFront function. Then rewrite a page URL with Lambda@Edge to serve S3 origin content.

Amazon Route 53 provides domain resolution, DNS routing, and health checks, with an edge location service and routing policies such as simple, latency, geolocation, geo proximity, multivalue, and weighted.

Configure two S3 static websites and implement Route 53 failover with CNAME records and health checks to switch from a primary to a secondary site.

Learn how continuous integration, delivery, and deployment automate building, testing, and releasing code with AWS CodeBuild, CodeDeploy, and CodePipeline, and how Git workflows and deployment targets fit the workflow.

Connect AWS to an external GitHub repository via the GitHub integration in the AWS console, using collections (formerly Codestar) to authorize and trigger builds with CodeBuild or CodePipeline.

Run a hands-on workflow to build a containerized app with CodeBuild using a Dockerfile. Integrate GitHub as source, build with build spec yaml, push to ECR, and deploy to ECS.

Demonstrates in-place deployment with AWS CodeDeploy to an EC2 instance using S3 as the source and revision location, with versioned zip apps.

Perform a hands-on walk-through of deploying a static website with AWS CodePipeline and S3. Learn to integrate GitHub, a build stage, and automatic updates via index.html changes.

Explore AWS cost management tools, including budgets, cost explorer, and cost and usage reports, to monitor and forecast spending and optimize with saving plans, reserved instances, and tags.

Learn how to tackle AWS data analytics specialty exam questions using a sample scenario with Amazon Kinesis Data Firehose, Amazon Redshift, and Amazon QuickSight, plus exam strategy and practice tests.