Data Lake Mastery: The Key to Big Data & Data Engineering

Design a scalable data lake architecture with storage, processing, governance, metadata management, and IAM-based access control and orchestration to enable secure data flow across diverse sources.

Trace the data flow in a data lake from ingestion to insights, highlighting schema on read, metadata governance, and consumption via BI tools.

Explore a practical, multi-layer data lake architecture with landing, raw, curated, and consumption zones, plus an optional exploration area, to enable governance, security, schema on read, and scalable analytics.

Discover how to implement data lake zones in AWS S3 with Glue for cataloging and transformations, convert to parquet, ingest via Lambda and Kinesis, and query with Athena for insights.

Explore common data lake formats—json, csv, parquet, avro, and an orc—and learn why row and columnar formats matter for ingestion, analytics, and storage across landing, curated, and exploratory zones.

Explore data ingestion methods for a data lake, including batch and streaming ingestion, real-time analytics, and the ETL versus ELT paradigms, with hybrid approaches for curated and raw data zones.

Explore batch ingestion as the default method for loading data into a data lake. Ingest data in large batches at set intervals, ideal for historical data and non real-time needs.

Discover how metadata management and a data catalog organize ingestion and enable discovery in a data lake, with automated Glue crawlers and profiling for quality.

Design a simplified data lake for a retail company, set up the data ingestion pipeline, and perform basic processing, quality checks, and metadata management for batch and event-driven ingestion.

Set up an S3 source and raw zone, then use a glue crawler to build the data catalog and auto-detect schemas for subsequent ingestion.

Create an etl job in AWS Glue to ingest data from S3 into the raw data zone of the data lake. Use csv or inferred schema and update data catalog.

Explore ingestion patterns for data lakes, focusing on change data capture, log ingestion, and event-driven ingestion for near real-time data and efficient data synchronization.

Set up event-driven ingestion with AWS S3 and Lambda by triggering on new source-bucket files to move them into the target data lake, automating data ingestion.

Enhance an AWS Lambda data ingestion function with data profiling by creating a log folder in the target bucket to track rows, columns, and missing data.

Master in-place querying with AWS Athena to bridge data ingestion and analysis in a data lake, enabling ad hoc, cost-efficient insights directly from S3 using standard SQL.

Query and analyze data in a data lake with AWS Athena, the serverless analytics service, using the Glue data catalog to run ad hoc SQL queries and compute revenue.

Explore data streaming as a real-time alternative to batch processing, detailing producers, stream ingestion, and processing in a data lake using AWS with Kafka or Kinesis.

Set up AWS Kinesis data stream and deploy Kinesis Data Generator to simulate data. Create a CloudFormation IAM role with administrator access, monitor the stream, and delete stream and role.

Monitor and troubleshoot data ingestion pipelines in a data lake by tracking kpis, configuring alerts, collecting logs, and using dashboards to ensure proactive reliability.

Explore practical monitoring and troubleshooting for AWS Glue ETL jobs by viewing logs and metrics, and setting alerts. Build dashboards to monitor health and performance using CloudWatch observability metrics.

Master data storage management using s3 as the data lake backbone, covering buckets, objects, lifecycle management, archival, redundancy, replication, cataloging, ingestion, and querying.

Plan a multi-zone data lake with raw, transformed, curated, and exploratory zones, plus an optional landing zone. Separate production and development with AWS accounts and use buckets, folders, and metadata.

Partition data with folder and bucket structure plus metadata to simplify data management, enable archival, and boost query performance by scanning only relevant subsets via glue catalog and Athena.

Organize a data lake with a bucket level that defines raw, transformed, and curated zones, and partition data by date and use case to optimize queries.

Set up an s3 bucket with London and New York folders, run a Glue crawler to auto-create partitions in the data catalog, and query with Athena to see partitioned results.

Ensure schema consistency across csv and parquet data to avoid issues in queries; learn how column order, missing columns, and renamed fields affect Athena results and data retrieval.

Learn how data lifecycle management in data lakes balances cost and accessibility by moving data across S3 storage classes—from standard to glacier—while automating lifecycle rules for archiving and deletion.

Explore storage classes, upload a file, set and automate storage class transitions via lifecycle rules, compare costs across standard, infrequent access, Glacier, and intelligent-tiering.

Create and manage lifecycle rules to move data between storage classes, including intelligent-tiering and glacier instant retrieval after 180 days, and expire objects, with filters by prefix, tags, or size.

Intelligent-tiering automatically moves data between frequent, infrequent, and archive tiers based on access patterns. Configure lifecycle rules to optimize cost and control archive options.

Master versioning in data lakes by creating immutable file versions and preserving change history. This enables revert to previous states for disaster recovery, data integrity, regulatory compliance, and cost management.

Enable bucket versioning in S3 and observe how updates create versions and delete markers. Learn how lifecycle rules treat current versus non current versions and when deletion becomes permanent.

Explore cross-region replication to copy and synchronize data between source and destination buckets, boosting disaster recovery, lower latency, and higher availability while weighing costs and compliance.

Create a versioned AWS source bucket and a destination bucket in another region, then configure a replication rule to enable cross-region replication.

Tie backups and recovery to your data protection strategy, using versioning and replication for granular recovery and redundancy. Identify data, set backup frequency with RTO/RPO targets, and test recovery regularly.

Configure an on-demand backup for an S3 source bucket with AWS Backup, set the retention period, and restore the recovery point while addressing ACL and bucket ownership issues.

Learn to create a backup plan in aws backup service, specifying daily or hourly schedules, retention windows, continuous backups for point-in-time recovery, and storage options including vaults and resource scope.

Transform raw data in data lakes using schema on read and elt for flexible analysis. Explore the multi-zone design—from raw to curated—and tools like AWS, Hadoop, Spark, Athena, and Redshift.

Explore how Hadoop provides distributed storage with the Hadoop distributed file system and MapReduce processing, delivering fault tolerance and scalability, contrasting with cloud data lake options like S3.

Spark delivers fast in-memory processing, outpacing Hadoop MapReduce, with scalable clustering and RDDs; it supports Scala, Python, and Java for real-time streaming and machine learning.

Learn how AWS Glue provides a serverless, fully managed data integration ecosystem for data lakes. Use the AWS Glue data catalog, crawlers, and etl jobs with Spark and streaming Spark.

Learn to build ETL pipelines with visual ETL, notebooks, and manual scripting, perform light transformations, deduplicate data, and store results in parquet in an s3-backed data lake.

Enable glue bookmarks to remember loaded data and update state information, allowing incremental loads where subsequent runs process only new records from the source bucket into parquet targets.

Process data streams end-to-end by connecting a Kinesis stream to Glue, applying Spark Streaming ETL transformations, and loading results into an S3 data lake.

Optimize data lake costs and performance by using parquet or ORC formats, partitioning data, applying pushdown predicates, and automating incremental processing with data lifecycle management.

Understand how to orchestrate data-lake workflows using step functions, lambda, glue, and eventbridge to automate ingestion, validation, and transformation from raw to cleansed zones.

Automate the end-to-end data ingestion, validation, and ETL for daily retail sales data using AWS Step Functions state machine, EventBridge triggers, and Lambda validation to derive insights.

Create and connect AWS resources: buckets, lambda, ETL, and a Step Functions state machine, triggered by EventBridge to validate, transform to parquet, and load into the sales data processed bucket.

Set up an AWS Step Functions state machine that invokes a Lambda function, uses a choice on status, and starts a Glue ETL job aggregate data triggered by file upload.

Configure EventBridge to trigger a Step Functions state machine and a lambda when a new S3 object arrives, starting the etl job and moving data to the process bucket.

Explore conditional logic in the state machine that prevents the ETL from triggering when a file’s schema or format differs, ensuring data validation fails and the ETL does not run.

Discover how in a data lake, schema on read enables flexible analysis, decentralized analytics, and use cases from data exploration to machine learning and BI, with cataloging and ETL workflows.

Explore a data lake with schema on read, ELT, and ad hoc queries in Athena, loading raw sales data, creating tables, joining sales and products, and deriving insights.

Connect your data lake to Power BI by installing the Athena ODBC driver, creating an IAM user with access keys, and loading the data catalog tables for visualization.

Explore connecting your data lake to AWS QuickSight for business analytics, configuring data sources like S3 and Athena, and visualizing data with SPICE in-memory analytics.

Connect a sageMaker notebook to your data lake for exploratory machine learning in a fully managed environment, configuring roles and access to S3, Athena, and the Glue data catalog.

Explore data in a data lake by using notebooks to connect to Athena, load data into a data frame, run queries, install this py Athena package, and visualize sales insights.

Explore why monitoring a data lake is essential for data quality, performance, security, and cost management, and learn how to address these challenges with effective monitoring options.

Explore a toolset for monitoring a data lake, using metrics, alarms, dashboards, and logs in AWS CloudWatch and AWS CloudTrail to maintain performance, reliability, and security proactively.

Configure data lake monitoring by enabling Athena query metrics and publishing them to CloudWatch, then build dashboards and alarms for proactive performance insights.

Set up dashboards in CloudWatch to monitor query performance with line charts and data tables. Tune refresh intervals and metrics like total execution time; add alarms for Athena and Glue.

Set up alarms in CloudWatch by selecting metrics, defining thresholds, and configuring anomaly detection and SNS email alerts to detect failing queries quickly.

Analyze logs to trace event history and troubleshoot ETL jobs in AWS Glue, using resource logs, CloudWatch log groups and streams, and EventBridge integration for failures.

Master access control in data lakes by applying authentication and authorization, enforcing least privilege with IAM roles and policies in AWS.

apply the principle of least privilege to grant only the minimum access needed, reducing attack surface, preventing accidents, and protecting sensitive data through role-based access control.

Learn to implement role-based access control in AWS by mapping users to groups and policies, defining roles, and reviewing permissions for analysts and engineers.

Implement access control in AWS by creating IAM users and a data analysts group, assigning policies (S3 read-only or full access), and enabling console login to validate RBAC.

Test RBAC by logging in as a distinct IAM user in the AWS console, validating access to S3 buckets and Glue ETL jobs, and refining permissions with a custom policy.

Create a custom policy for the data analyst group, granting specific S3 permissions with wildcard resources, using visual and json approaches, and test access.

Adopt defense-in-depth for data lakes with a multi-layer security strategy: perimeter firewalls and ACLs, VPC segmentation, IAM least-privilege, monitoring with CloudWatch and GuardDuty, and encryption at rest and in transit.

Configure AWS CloudTrail to track user activity and API usage by creating a trail, storing logs in an S3 bucket, sending to CloudWatch, and enabling data events and insights.

Encrypt data at rest and in transit to protect sensitive information and meet GDPR. Enable S3 default encryption using AWS managed keys, with optional KMS and dual-layer encryption.

see encryption in action for data in rest in AWS S3 with default S3 managed key and automatic decryption on read, and encryption in transit via https for data transfer.

Set up and apply tags as key-value pairs to resources like Glue jobs and S3 objects. Use tags for cost allocation and lifecycle management.

Explore how tags drive lifecycle rules and cost allocation in a data lake, enabling tag-based archiving actions and cost reporting by department.