AWS Data Engineer Associate (DEA-C01) Exam Prep Course

Explore what AWS is and how its certification prep frames your data engineering path, with an overview of services, exam structure, and key cloud benefits.

Outline learning paths for AWS certification levels—foundational, associate, professional—and compare video and document-based study methods. Break down AWS services by category with practice questions and hands-on exercises.

Explore the AWS certification exam structure across foundational, associate, and professional levels, highlighting the 2024 associate lineup (SysOps, Developer, Solutions Architect, Data Engineer, ML Engineer) and the exam format.

Create a new AWS account and configure an IAM user with administrator access to manage services via the console, CLI, Cloud Shell, and SDK using access keys.

Introduce key AWS services for the data engineer exam and explain evolving data analysis trends, including generative AI, batch and stream data, and unstructured data.

Learn how a data pipeline collects data from company databases and unstructured datasets, and ingests the load data through streaming, batching, manual uploads, or automated API integration.

Explore data lakes, data warehouses, and data marts; learn that data lakes store native-format data, data warehouses centralize data for reporting and analysis, and data marts serve focused business needs.

Explore ETL processing: extract from databases, APIs, flat files, transform and clean data, load into targets. Compare ETL and ELT approaches, including cloud warehouses such as Amazon Redshift for analysis.

Learn analytics and visualization with AWS managed services across the data pipeline, featuring Kinesis, Data Migration, S3, Lake Formation, Redshift, Athena, QuickSight, OpenSearch, SageMaker, and security.

Explore data types and formats—structured, unstructured, and semi-structured—alongside common formats like CSV, JSON, Avro, and Parquet, with snappy compression, and AWS data services including migration, Kinesis, SQS, and Kafka.

Explore Amazon Kinesis as a data analytics suite for collecting, processing, and analyzing streaming and batch in real time, with Kinesis Data Streams, Data Firehose, Flink, and Kinesis Video Streams.

Explore Amazon Kinesis Data Streams (KDS) for real-time ingestion and processing of logs, IoT data, and other streams with automatic scaling and multi-availability-zone durability.

Explore consuming data from Kinesis Data Streams with the KCL and KQL, enabling multi-threaded reads across shards, shard sharing for load balancing, and DynamoDB-backed lease and checkpoint management.

Explore Kinesis Data Streams features, including provisioned and on-demand modes, shard management, enhanced fan-out with 40 MB/s total throughput, and low-latency real-time processing.

Learn to diagnose and mitigate common Kinesis data streams issues, such as hot shards, data duplication, and cross-shard ordering, using partition key strategies, shard scaling, and sequence-aware processing.

Demonstrate creating a Kinesis data stream, selecting on-demand or provisioned capacity, and putting and getting records. Explain shard configuration, the 24-hour time-to-live, and basic monitoring and data viewing options.

Group streaming data in near real time with Data Firehose, buffering by fixed size or time interval, transform via Lambda, and deliver to redshift, S3, OpenSearch, or Splunk.

Submit records from a Kinesis data stream to a Kinesis data Firehose, transform data with a Lambda function, and store results in an S3 bucket; validate with demo data.

Harness the free managed service for Apache Flink to process real-time data from Kinesis Data Streams with millisecond latency, using SQL for analytics and no cluster management.

Explore the Amazon managed service for Apache Flink through studio notebook blueprint, analyzing real-time stock data from MSK, using Apache Zeppelin and UDF, with no local setup.

Explore Amazon SQS, a free managed message queue service for microservices. Compare standard and FIFO queues, visibility timeouts, delay queues, and dead-letter queues.

Explore Amazon MQ, a message queuing service based on Apache MQ and Rabbit MQ that supports JMS and MQTT protocols, and compare it with SQS for AWS migrations.

Learn how MSK enables real time data ingestion, storage, and processing with a managed Kafka cluster, Apache Flink support, and security via VPC, IAM, and encryption.

Explore data migration with the snow family, physical devices that transfer terabytes from on-prem to AWS, offering secure, encrypted, durable transfers when networks are restricted; network transfer suits small data.

Explore the AWS transfer family for migrating data to S3 and EFS using SFTP, FTPS, and FTP; configure IAM roles and directory service or API gateway authentication; compare DataSync.

Explore AWS data exchange, a marketplace service to subscribe to data from providers stored in S3, connect via APIs, and import or export data to your data pipeline with pricing.

Leverage Amazon AppFlow to securely transfer data between SaaS apps such as Salesforce, ServiceNow, Slack, and AWS, supporting event-driven and scheduled transfers with scalable flows and encryption.

Explore storage and data management with AWS S3, Lake Formation, EBS, and EFS; compare object, block, and file storage, and review use cases like archiving and backing up.

Explore Amazon S3, the available and durable object storage with unlimited capacity, charged based on storage you use, offering data protection with MFA authentication and encryption, buckets, and lifecycle management.

Explore Amazon S3 access control and security features, including IAM and bucket policies, ACLs, encryption at rest (server-side and client-side), signed URLs, CORS, and object locking with versioning.

Master S3 operations like getobject and list bucket with filters, batch operations, and multi-part upload. Apply transfer acceleration and S3 select to extract data subsets.

Explore AWS lake formation, connecting S3 and relational data with Glue crawlers and data catalog to deduplicate, transform, and enforce fine-grained access via IAM at bucket, table, and column levels.

Explore Amazon EBS, EFS, and EC2 instance store, comparing block, file, and local storage, including persistence, redundancy across availability zones, and attach/detach workflows, with encryption steps.

Explore AWS database services, including relational databases like Aurora and Redshift, and NoSQL options such as DynamoDB, with managed patches, backups, high availability, and pay-as-you-go pricing.

Explore Amazon RDS by examining instance types and families, including general purpose and memory optimized options, with storage: general purpose ssd, provisioned iops, magnetic hdd, and scaling below 10% space.

Discover how the Amazon RDS proxy pools and shares connections to boost scalability, improve read performance by casting results, and support IAM and VPC security with configurable connections and timeouts.

Amazon RDS read replicas are asynchronously replicated up to five instances to offload read processing and enable disaster recovery, migration, and cross-region use, with engine-specific constraints.

Learn how LDS backups work with automatic backups, manual snapshots, and point-in-time recovery that create a new DV instance from transaction logs stored in S3.

Explore RDS enhanced monitoring with OS level metrics like CPU, memory, and disk IO, plus Performance Insights for SQL level visibility of rate events and database load.

Learn to create and configure an Amazon RDS MySQL database on the free tier, choosing standard vs easy creation, setting up VPC, security groups, authentication, storage, backups, and monitoring.

Create an RDS read replica from the primary, possibly in a different region, and align encryption settings. Then take a snapshot, copy to encrypt, and restore, noting the separate endpoints.

Explore Amazon Aurora, a MySQL and PostgreSQL compatible, cloud-optimized managed relational database with high durability, cross-zone replication across multiple availability zones, and continuous backups to Amazon S3.

Explore Aurora global database that replicates across a primary region and up to five secondaries, enabling sub-60-second failover and latency-based routing to the nearest region.

Explore Amazon Aurora backups, including continuous automatic backups, point-in-time recovery within five minutes, and backtrack up to 72 hours to quickly rewind or clone databases.

Create an Amazon Aurora database from the RDS console, select MySQL compatibility, configure production settings, master username/password or secret manager, storage, replicas, and endpoints, then enable monitoring and performance insights.

Explore restoring an Amazon Aurora database via automated backups and point-in-time recovery, creating snapshots, and performing backtrack and failover to ensure rapid high availability.

Explore Amazon Redshift architecture, with leader and compute nodes, where the leader coordinates queries and compute nodes store data, enabling MPP and distribution styles such as key, even, and all.

Learn how Amazon Redshift distribution styles—key distribution, all distribution, and even distribution—optimize joins and parallel processing, reducing data movement and boosting query performance.

Explore Amazon Redshift node types, including dense compute with SSDs for small, compute-heavy workloads and dense storage with HDDs for large datasets, plus R3 nodes with S3 storage.

Optimize Amazon Redshift performance by reducing disk IO through columnar storage, data compression, and 13 encoding types; apply zoning and workload management to manage queues, memory, and query slots.

Master elastic resize for quick scaling of Redshift clusters, contrast it with classic resize for node type changes, and leverage concurrency scaling and S3 caching for scalable data consistency performance.

Master redshift ddl concepts, including create table, alter table, and create external table for redshift spectrum to query s3 data; note deletes and updates are logical deletions.

Master the data manipulation language (DML) in Amazon Redshift. Use copy for high-parallelism bulk loads from S3 and DynamoDB, and insert for data additions while preparing, executing, and explaining queries.

Explore data control language (DCL) in Amazon Redshift by setting permissions for specific users or roles with IAM and Lake Formation, controlling access to tables and clusters.

Explore the security features of Amazon Redshift, including data encryption, network security, logging, and auditing, and review access control at the table, schema, function, and column levels.

Redshift Spectrum lets you query data stored in S3 directly, scales to exabytes, and charges only for data, with no upfront costs, while AWS guru enables schema discovery and cataloging.

Discover how redshift handles real-time streaming data with a managed apache flink service and late shift for batch analysis, and query data on S3 with redshift spectrum and athena.

Perform a hands-on Redshift session querying CSV data in S3 with a new editor that auto-fetches external table schemas. Create cluster and run queries against S3 data without loading it.

Explore DynamoDB, a serverless NoSQL key-value database with partition and sort keys, offering scalable throughput, unlimited storage, particle queries, and automatic replication across three availability zones for high availability.

Explore DynamoDB capacity mode by comparing on-demand and provisioned options, how auto scaling adjusts read and write capacity units (RCU and WCU), and the pricing and cost structure.

Explore DynamoDB keys and indexes, including partition keys, sort keys, local and global secondary indexes, and how to balance performance and cost through careful index design.

Explore DynamoDB streams to capture item-level changes (insert, update, delete) stored 24 hours, with record and shard id enabling global tables and feeding into Amazon Redshift and AWS Lambda.

Dive into DynamoDB accelerator (DAX), an in-memory cache that speeds read-heavy workloads to microsecond responses through a write-through strategy that writes to both cache and DynamoDB, with a five-minute TTL.

Enable DynamoDB global tables across regions to enable multi-master reads and writes, and ensure streams are enabled with consistent name, write capacity, encryption, ttl, and global secondary index.

Learn how DynamoDB query and scan operate, when to use each for efficient data retrieval with partition key, and the role of batch vs transactions in processing large data sets.

Explore DynamoDB backups, including on-demand full backups and point-in-time recovery, enabling restore for up to 35 days. Restores create tables and require reconfiguring indexes, capacity, encryption, and settings after restoration.

Discover DynamoDB security: IAM access control for tables, indexes, and streams, plus encryption and network isolation; compare interface and gateway VPC endpoints to simplify management with a routing table entry.

Explore how to create an Amazon DocumentDB cluster, choosing between instance-based and elastic clusters. Set shard count or instances, VPC, subnet, security group, and authentication, then monitor with CloudWatch.

Learn how to configure a DynamoDB table, including setting the partition key and optional sort key. Explore on-demand or provisioned capacity, composite keys, adding items, and encryption at rest.

Learn to use DynamoDB scan and query through a hands-on session that builds a test product table and demonstrates filtering, projection, and batch write via command line interface and console.

Perform a DynamoDB demo of GSI and LSI, including creating an LSI at table setup and a GSI with SKU as partition key and price as sort key for queries.

Explore Amazon MemoryDB for Redis, a fully managed in-memory database offering high performance, durability with multi-availability zones, strong primary and eventual replica consistency, and scalable clusters, shards, and replicas.

Explore Amazon DocumentDB, a MongoDB-compatible, document-based, schemaless database with a flexible hierarchical structure, scalable architecture, elastic clusters, and high availability.

Learn to create and configure an Amazon DocumentDB cluster, choosing between instance-based and elastic clusters, setting shards, security, and MongoDB API connectivity.

Explore Amazon Neptune's graph database with Gremlin and Sparql queries in a VPC-deployed cluster. Learn primary and read replicas, failover priorities, six data copies, and auto-scaling storage to 64 TB.

Launch an amazon neptune database in the console, choose provisioned or serverless, set capacity units and network security, then connect via jupyter or sagemaker to run samples.

Explore ETL processing concepts and AWS tools for batch and streaming ETL, including extract-transform-load, ELT adoption, and services like AWS Glue, EMR, Lambda, and Apache Flink.

Explore AWS Glue, a comprehensive etl tool with a crawler that gathers schema from relational and semi-structured data, stores it in the data catalog, and enables Glue Studio workflows.

Discover how the Glue crawler extracts data, infers schemas, and populates the Glue Data Catalog across databases, streaming data, and S3, with registry support for streaming.

Learn how AWS Glue ETL jobs perform data transformations with PySpark, dynamic frames, and data frames, using built-in functions like drop, split, filter, and join; explore DataBrew and Ray integration.

Master aws glue workflow concepts, including dpu, bookmarks, and event triggers. Use glue studio with dag-based etl tasks for scheduling and streaming workloads.

Perform hands-on with Glue crawler and Athena to extract csv schema from S3, create Glue database and table, and run Athena queries to access the data.

Explore AWS Lambda, an event-driven serverless service that runs code in response to events, with pay-per-use execution, API Gateway and DynamoDB integration, layers, concurrency controls, and quotas.

Explore Amazon EMR and the Hadoop ecosystem, leveraging Apache Spark, Hive, and Presto to process petabytes of data with elastic MapReduce, scalable compute and storage, and S3 persistence.

Explore the Hadoop ecosystem on emr, including MapReduce, Spark, Hive, HBase, Presto, with yarn as the resource manager and hdfs as the storage backbone.

Explore the EMR architecture with master, core, and task nodes, including HDFS storage, MapReduce tasks, and stateless task execution, with scalable instance groups and fleets for on-demand workloads.

Explore two EMR storage options: local HDFS on cluster disks for fast, temporary data, and EMR HDFS on S3 for scalable, shared data storage, with EBS as an optional enhancement.

Demonstrate launching an EMR on EC2, running a PySpark job on King County health inspection data, and managing steps to terminate the cluster to control costs.

Understand how AWS Step Functions uses state machines to coordinate distributed applications and microservices. Explore processing, conditional branching, map and wait states, ASL, standard vs express workflows, and SQS comparisons.

Explore how Amazon API gateway provides a secure interface for web apps, supporting HTTP, REST, and WebSocket APIs, integrating with Lambda and managing endpoints, stages, throttling, quotas, and mapping templates.

Configure usage plans to set API key access and quotas. Improve performance with caching and gzip encoding, and secure access with client certificates and Cognito or Lambda authorizer.

Explore how api gateway, lambda, and dynamodb enable a serverless book sharing app that uses post and get requests with json data, including security tokens, throttling, and exponential backoff.

Learn how Amazon MWAA provides a managed Airflow environment on AWS, with DAG storage in S3, execution on EC2, and monitoring via CloudWatch, plus core DAG concepts and use cases.

Explore AWS analytics and visualization tools for interactive analytics, including Athena, OpenSearch, Redshift, EMR, Kinesis, QuickSight, and SageMaker, with real-time processing via Apache Flink and Spark Streaming.

Learn how Amazon Athena enables serverless pay-per-query analysis on data stored in S3 using standard SQL, supporting Parquet, CSV, JSON, and more, with native Glue integration and a schema-on-read model.

Learn Amazon Athena features, including table creation by DDL or Glue data catalog, work groups for access control, and federated queries with user defined functions across multiple data sources.

Improve Amazon Athena performance and cost efficiency in the AWS data engineer training by compressing data, using columnar formats, and optimizing file sizes to 128–512 MB with partitioning and bucketing for selective queries.

OpenSearch is a distributed search and analytics service for logs, using documents, indices, and shards. Learn master nodes, primary and replica shards, and ultrawarm storage with s3 deletion by index.

Explore OpenSearch performance tuning through shard sizing and count, bulk indexing, deep pagination with search after, and advanced full-text search features like phrase, proximity, and fuzzy search.

Explore how OpenSearch uses a cluster matrix to monitor health and status, and analyze node and shard metrics. Examine index and raw logs and thresholds for long-running document operations.

Explore OpenSearch capabilities for API and browser based analysis, using dashboards to search, visualize, and report on data, and manage backups with automatic hourly snapshots and manual S3 snapshots.

Explore Amazon SageMaker, an integrated AI and machine learning environment with a Jupyter Notebook, support for TensorFlow, PyTorch, and MXNet, and scalable training.

Explore Amazon QuickSight as a free managed BI tool that scales to many users without infrastructure management, offering rich charts and spice data support, with standard and enterprise plans.

Learn how calculated fields and AWS Glue preprocessing improve data loading into QuickSight, while ML insights, QuickSight Q, and Spice enable fast analytics, anomaly detection, forecasting, and natural language queries.

Explore Amazon QuickSight security roles, including admin, author, and leader, plus row-level and column-level security, rule-based access, and the need for IAM policies to restrict data sources.

Explore amazon quicksight's line, bar, combo, waterfall, heatmap, treemap, scatter, bubble charts, maps, histograms, and box-and-whisker diagrams for time-series, correlation, and distribution visualization.

Explore security domain concepts like IAM, data encryption, data governance, monitoring and logging, networking. Learn how VPC isolation, firewall rules, and Amazon Macie and Amazon Detective protect data at rest.

Learn how AWS IAM manages access with policies, users, groups, and roles; enforce least privilege, MFA, and secure handling of passwords and access keys with environment variables and key rotation.

Define how IAM policies set permitted actions on resources and attach to identities or resources. Compare inline versus managed policies, identity-based versus resource-based, JSON structure with version 2012-10-17 and conditions.

Explore cross-account IAM settings, including how an EC2 to S3 action is constrained by identity and resource policies. Identify how permission boundaries, SCPs, and role switching govern access across accounts.

AWS STS provides temporary credentials via assume role, enabling cross-account access with short-lived access keys, secret access keys, and session tokens governed by trust policies.

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

Learn how AWS Secrets Manager centrally manages secret lifecycles, enables automatic rotation with Lambda, and differs from Parameter Store for securely managing credentials and use cases and exam focus.

Amazon Macie detects PII and PHI in S3 with machine learning, enabling monitoring, KMS encryption, and PCI/DSS/GDPR/HIPAA compliance; it cannot encrypt or mask data by itself.

Explore how Amazon GuardDuty analyzes CloudTrail S3, VPC flow, and DNS logs to detect threats such as compromised credentials and data access anomalies, triggering actions via CloudWatch events and EventBridge.

Explore Amazon Detective, a service that collects logs from AWS services and uses machine learning to investigate security issues, integrating with GuardDuty, Security Hub, CloudTrail, and VPC flow logs.

Amazon CloudWatch provides integrated monitoring of logs and metrics, triggers alarms via SNS, and supports CloudWatch logs, VPC flow logs, and Lambda integration.

Explore AWS Config, a service that logs resource changes, evaluates compliance with rules via AWS managed and customer managed rules, supports conformance packs, and triggers fixes via System Manager automation.

Learn AWS network security fundamentals, including VPCs with private and public subnets, internet gateway and Nat gateway, a virtual gateway for on-premise connections, and security groups to control access.

Discover how AWS firewall services protect resources across application and network layers, including AWS WAF, Route 53 DNS firewall, AWS Shield, and Firewall Manager, and distinguish stateful from stateless rules.

Capture IP traffic metadata with VPC flow logs. Record source and destination IP, port, protocol, packet and byte counts, rejects, and store data in CloudWatch, S3, or Kinesis Data Firehose.

Discover how VPC peering uses a private network and private IPs to securely connect VPCs across accounts and regions, avoiding public IPs, CIDR overlap issues, and non-transitive limits.

Understand VPC endpoints to access resources privately without exposing your IP, using interface endpoints with PrivateLink and gateway endpoints for S3 and DynamoDB.

Explore how vpn creates a secure tunnel over the internet with encapsulation. Review AWS client vpn, site to site vpn, ipsec vs OpenVPN, and AWS global accelerator.

Explore how a content delivery network caches content between clients and origin servers to reduce origin load and improve response times, with Amazon CloudFront as the global edge CDN.

Explores CloudFront security features, including ssl encryption by default, shield protection, geo and ip-based access, OAI-protected S3 origins, and signed URLs and cookies for controlled access.

Explore edge functions at CloudFront edge locations to customize requests and responses, enable basic authentication, and reduce origin load using CloudFront functions and lambda at edge.

Explore how Amazon Route 53 resolves DNS queries from root servers, uses alias records for AWS resources, provides edge location service, health checks, and policy based routing for high availability.

Automate continuous integration, testing, and deployment using AWS CodeBuild, CodeDeploy, and CodePipeline, with Git repositories, buildspec and appspec configurations, and canary or linear deployment strategies.

Learn to manage costs with AWS budgets, alerts, dashboards, Cost Explorer, and Cost and Usage Reports; compare saving plans and reserved instances; tag resources to organize reports.

Learn to navigate practice questions and the Udemy practice test, selecting the best solutions that meet multiple requirements and reviewing explanations to prepare for the AWS data engineer exam.