We close Chapter 29 with a crucial aspect when a company accumulates a lot of data: data governance. Having a data lake (subchapter 29.1) full of valuable information is great, but it raises serious questions: who can see which data? How do you protect sensitive information? How do you control access centrally when you have data from the entire company? To answer this, AWS offers Lake Formation: a service to build, secure, and govern your data lake centrally.
The problem: a data lake without control is a risk
A data lake gathers a lot of data from the entire company in one place (S3). That’s powerful, but also dangerous if you don’t control who accesses what:
In the data lake there is all kinds of data: - Public data (product catalog) - Internal data (sales) - SENSITIVE data (customer personal data, finances...) → NOT everyone should be able to see EVERYTHING
Without good access control:
- Anyone with access to the lake could see sensitive data they shouldn’t (a serious risk, remember privacy and compliance from Chapter 23).
- Managing permissions “by hand” over millions of files in S3 would be unfeasible and error-prone.
- It would be difficult to prove (to auditors, for regulations) that the data is well protected.
You need a centralized and granular way to govern who accesses which data. That’s Lake Formation.
What is Lake Formation
AWS Lake Formation is a service that makes it easier to build, secure, and govern a data lake centrally. Its most prominent feature is granular and centralized access control to data: defining, from a single place, who can access which data (down to specific tables and columns), in a simple way.
Lake Formation (centralized data lake governance): ├── build the data lake more easily ├── control access in a GRANULAR and centralized way │ "this team sees the sales table, but NOT the personal data column" └── audit who accesses what
Analogy: Lake Formation is like the access control and security system of a large library or national archive. It’s not enough to have all the documents stored (that’s the data lake); you need to control who can enter which section: the general public accesses the common room, accredited researchers access special archives, and only authorized personnel access confidential documents. Lake Formation is that system that, from a central point, decides and monitors who accesses each part of your data.
What Lake Formation brings you
- Build the data lake more easily
It helps set up the data lake more easily: it makes it easier to bring in data, organize it, and catalog it (works together with Glue, subchapter 29.1). It simplifies the steps to create the lake.
- Granular and centralized access control
This is the star feature. From a single place, you define who can access which data, in great detail:
Examples of granular permissions with Lake Formation:
- "The marketing team can see the customers table,
but NOT the email and phone columns" (column level)
- "The finance team sees the complete sales data"
- "Analysts only see aggregated data, not individual data"Instead of managing permissions file by file in S3 (chaos), you define clear rules at the data level (databases, tables, columns), centrally. This connects with the least privilege we saw in IAM (subchapter 7.2): everyone accesses only the data they need.
- Protect sensitive data
Thanks to that granular control, you can protect sensitive information (personal, financial data) ensuring that only those who should see it can, while others access the rest. It’s key for privacy compliance.
- Auditing and compliance
It allows you to log and demonstrate who accesses which data, which is essential for audits and to comply with regulations (links to compliance in Chapter 23). You have a central view of your data security.
Why it matters: from “data chaos” to “governed data lake”
The great value of Lake Formation is turning a potentially chaotic and insecure data lake into a governed one: where you know exactly who accesses what, you protect sensitive data, and you can prove it. Without governance, a data lake full of valuable data is also a ticking time bomb for security and compliance. With Lake Formation, it’s a secure and well-controlled asset.
Without governance: data lake = lots of data + uncontrolled access = RISK With Lake Formation: data lake = lots of data + controlled access = SECURE ASSET
Real-world example: a healthcare company has a data lake with patient data (very sensitive), operational data, and public data. They use Lake Formation to govern it. They define, centrally: researchers access anonymized and aggregated data (without seeing identities), authorized medical staff access the full data of their patients, and the marketing team only accesses public data. Columns with identifiable personal data are protected and only visible to those with explicit authorization. When a data protection audit arrives, the company easily demonstrates who accesses what. What would be a huge legal risk without governance, with Lake Formation is a controlled, secure, and compliant system.
How Chapter 29 closes
Lake Formation completes the data platform we have built in this chapter:
S3 + Glue + Athena (29.1) → store and query the data lake Kinesis (29.2) → ingest real-time data Redshift (29.3) → fast, large-scale analytics (data warehouse) Lake Formation (this) → GOVERN and SECURE everything (who accesses what)
The first pieces build and exploit the data; Lake Formation ensures that all of it is secure, controlled, and compliant. A complete data platform needs both: capability and governance.
What you should remember
- A data lake gathers a lot of data (including sensitive data) from the entire company; without access control, it’s a serious security and compliance risk, and managing permissions “by hand” over millions of files is unfeasible.
- AWS Lake Formation makes it easier to build, secure, and govern a data lake centrally. Like the access control system of a large archive.
- Its star feature is granular and centralized access control: you define from a single place who accesses which data, down to the level of tables and columns (in line with IAM’s least privilege), instead of managing loose files in S3.
- It brings: easier lake setup, protection of sensitive data (key for privacy), and auditing/compliance (demonstrating who accesses what).
- It turns a chaotic and insecure data lake into a governed and secure one: the difference between a risk and an asset. Capability (29.1-29.3) plus governance (Lake Formation) = complete data platform.
You’ve completed Chapter 29 and mastered data platforms in AWS: data lakes, streaming, data warehouse, and data governance! In Chapter 30 we’ll return to the realm of large-scale organization: how to structure multiple accounts and landing zones for large enterprises.
Cloud, AWS & Terraform — From Zero to Expert
Chapter 1 · What is cloud computing
- 1.1 The traditional client-server model
- 1.2 Problems the cloud came to solve
- 1.3 On-premise vs cloud vs hybrid
- 1.4 The three service models: IaaS, PaaS, SaaS
- 1.5 The five pillars of cloud (according to NIST)
- 1.6 Real advantages: elasticity, pay-as-you-go, global availability
Chapter 2 · The cloud market and major providers
- 2.1 AWS, Azure and GCP: differences and market share
- 2.2 Why learn AWS first
- 2.3 Concepts that are universal among providers
Chapter 3 · Regions, availability zones and edge
- 3.1 What is an AWS region and how to choose it
- 3.2 Availability Zones: high availability by design
- 3.3 Edge locations and CloudFront
- 3.4 Latency, resilience and data sovereignty
Chapter 4 · Compute: EC2
- 4.1 Instances: types, families and when to choose each
- 4.2 AMIs, key pairs and Security Groups
- 4.3 Instance lifecycle
- 4.4 Elastic IPs and Placement Groups
- 4.5 Savings Plans vs Reserved vs On-Demand vs Spot
Chapter 5 · Storage: S3
- 5.1 Buckets, objects and keys
- 5.2 Storage classes (Standard, IA, Glacier…)
- 5.3 Versioning and object lifecycle
- 5.4 Bucket policies and ACLs
- 5.5 Static website hosting
Chapter 6 · Networking: VPC
- 6.1 What is a VPC and why you need it
- 6.2 Public and private subnets
- 6.3 Internet Gateway and NAT Gateway
- 6.4 Route Tables and Network ACLs
- 6.5 VPC Peering and endpoints
Chapter 7 · Identity and access: IAM
- 7.1 Users, groups, roles and policies
- 7.2 The principle of least privilege
- 7.3 Identity-based vs resource-based policies
- 7.4 MFA and temporary credentials (STS)
- 7.5 IAM security best practices
Chapter 8 · Managed databases
- 8.1 RDS: engines, Multi-AZ and read replicas
- 8.2 Aurora and its advantages over vanilla RDS
- 8.3 DynamoDB: key-value / document model
- 8.4 ElastiCache for in-memory cache
- 8.5 When to use each type of database
Chapter 9 · Why Infrastructure as Code
- 9.1 Problems with manual provisioning
- 9.2 Declarative vs imperative IaC
- 9.3 Terraform vs CloudFormation vs Pulumi vs CDK
- 9.4 The plan → apply → destroy cycle
Chapter 10 · HCL: the Terraform language
- 10.1 Resource, variable, output, locals blocks
- 10.2 Data types: string, number, bool, list, map, object
- 10.3 Expressions, references and built-in functions
- 10.4 Conditionals and loops (count, for_each, for)
Chapter 11 · Providers and state
- 11.1 How the AWS provider works
- 11.2 The terraform.tfstate file and its importance
- 11.3 Local state vs remote state (S3 + DynamoDB)
- 11.4 Essential commands: init, plan, apply, destroy, fmt, validate
Chapter 12 · Your first real infrastructure in Terraform
- 12.1 Create a VPC with subnets from scratch
- 12.2 Launch a public EC2 instance
- 12.3 Associate a Security Group and an Elastic IP
- 12.4 Outputs and references between resources
- 12.5 Team workflow: PR review of plans
Chapter 13 · Load balancing and auto scaling
- 13.1 Application Load Balancer vs Network Load Balancer
- 13.2 Target Groups, listeners and rules
- 13.3 Auto Scaling Groups: policies and metrics
- 13.4 Warm pools and lifecycle hooks
Chapter 14 · Serverless with Lambda
- 14.1 The Lambda execution model
- 14.2 Triggers: API Gateway, S3, DynamoDB Streams, SQS
- 14.3 Dependency management and layers
- 14.4 Cold starts and strategies to reduce them
- 14.5 Limits and anti-patterns
Chapter 15 · Messaging and events
- 15.1 SQS: standard vs FIFO queues, DLQ
- 15.2 SNS: topics, subscriptions, fan-out
- 15.3 EventBridge: event buses and rules
- 15.4 Patterns: pub/sub, decoupling, saga
Chapter 16 · Content delivery and DNS
- 16.1 Route 53: record types and routing policies
- 16.2 CloudFront: distributions, caches and origins
- 16.3 ACM: free SSL/TLS certificates
- 16.4 WAF integrated with CloudFront
Chapter 17 · Containers on AWS
- 17.1 Docker: quick review of key concepts
- 17.2 ECR: private image registry
- 17.3 ECS: task definitions, services, Fargate vs EC2
- 17.4 EKS: when Kubernetes and when not
Chapter 18 · Modules: reuse and composition
- 18.1 Anatomy of a Terraform module
- 18.2 Input variables, outputs and dependencies
- 18.3 Local modules vs Terraform Registry modules
- 18.4 Module versioning with Git tags
- 18.5 Design of generic vs domain-specific modules
Chapter 19 · Workspaces and environment management
- 19.1 Terraform workspaces: use cases and limitations
- 19.2 Directory strategy per environment (dev/stg/prod)
- 19.3 Terragrunt: DRY for environment configurations
- 19.4 Environment variables and .tfvars files
Chapter 20 · Remote backends and locking
- 20.1 Configure S3 + DynamoDB as backend
- 20.2 State locking: avoiding team corruption
- 20.3 State migration between backends
- 20.4 terraform import: bring existing resources into state
Chapter 21 · Infrastructure testing
- 21.1 Terraform validate and fmt in CI
- 21.2 Checkov and tfsec: static security analysis
- 21.3 Terratest: integration tests in Go
- 21.4 Contract testing between modules
Chapter 22 · Terraform in CI/CD
- 22.1 Basic pipeline: lint → plan → apply in GitHub Actions
- 22.2 Atlantis: GitOps for Terraform
- 22.3 Terraform Cloud / HCP Terraform
- 22.4 Drift detection and automatic reconciliation
Chapter 23 · Defense in depth
- 23.1 AWS Organizations and Service Control Policies
- 23.2 AWS Config: continuous compliance
- 23.3 GuardDuty: threat detection
- 23.4 Security Hub: centralized view
- 23.5 KMS: key management and rotation
- 23.6 Secrets Manager vs Parameter Store
Chapter 24 · Observability: logs, metrics and traces
- 24.1 CloudWatch Logs, metrics and alarms
- 24.2 CloudWatch Dashboards and Contributor Insights
- 24.3 X-Ray: distributed tracing
- 24.4 OpenTelemetry on AWS
- 24.5 Managed Grafana and Managed Prometheus
Chapter 25 · Cost optimization
- 25.1 AWS Cost Explorer and budgets with alerts
- 25.2 Trusted Advisor and Compute Optimizer
- 25.3 Rightsizing: how to detect overprovisioning
- 25.4 Savings Plans vs Reserved Instances: strategic decision
- 25.5 FinOps: culture and processes to control spending
Chapter 26 · High availability and disaster recovery
- 26.1 RTO and RPO: defining objectives
- 26.2 Strategies: backup/restore, pilot light, warm standby, multi-site
- 26.3 Route 53 health checks and automatic failover
- 26.4 AWS Backup: centralized backup policy
Chapter 27 · AWS Well-Architected Framework
- 27.1 The six pillars: operational excellence, security, reliability, performance efficiency, cost optimization, sustainability
- 27.2 Well-Architected Tool: formal reviews
- 27.3 How to apply the framework in design decisions
Chapter 28 · Serverless architectures at scale
- 28.1 Event-driven architecture with Lambda + EventBridge
- 28.2 Saga pattern for distributed transactions
- 28.3 Step Functions: orchestration of complex workflows
- 28.4 Lambda@Edge and CloudFront Functions
Chapter 29 · Data platforms on AWS
- 29.1 Data Lake with S3, Glue and Athena
- 29.2 Kinesis Data Streams and Firehose for streaming
- 29.3 Redshift: data warehousing at scale
- 29.4 Lake Formation: data governance
Chapter 30 · Multi-account and landing zones
- 30.1 Why separate workloads into different accounts
- 30.2 AWS Control Tower and Account Factory
- 30.3 Centralized log and security management
- 30.4 Terraform at multi-account scale with shared modules
Chapter 31 · Platform Engineering and Internal Developer Platform
- 31.1 Golden paths and abstractions over Terraform
- 31.2 AWS Service Catalog
- 31.3 Backstage as a developer portal
- 31.4 Terraform modules as internal product
Chapter 32 · Relevant AWS certifications
- 32.1 Cloud Practitioner: is it worth it?
- 32.2 Solutions Architect Associate → Professional
- 32.3 DevOps Engineer Professional
- 32.4 Specialty: Security, Database, Networking
- 32.5 HashiCorp Terraform Associate
Chapter 33 · Projects to consolidate what you've learned
- 33.1 Project 1: serverless blog (S3 + CloudFront + Lambda + DynamoDB)
- 33.2 Project 2: REST API with ECS Fargate + RDS + ALB
- 33.3 Project 3: data platform with Glue + Athena + Redshift
- 33.4 Project 4: multi-account landing zone with Terraform and Control Tower
