I wanted to provide you with a comprehensive yet readable guide on modern cloud-based approaches to manage, process, and analyze all the data that keeps pouring into enterprises today. Properly harnessed, data can drive transformative business impact – but only with the right platforms and architecture.
Why Data and Analytics Matters
Before jumping into the technical details around data warehouses and data lakes, it’s important to level set on why data analytics capability is so critical.
Here are some key stats:
- IDC predicts global data volumes touching 175 zettabytes by 2025
- Forbes reports that boosting data-related competencies delivers 12x greater business impact across metrics like profitability, innovation, and competitiveness
- Gartner finds organizations are wasting an average of $12.8 million per year because of poor data quality
So in essence, despite exponential data growth, most firms are unable to harness the information trapped in their siloed on-premises systems. As reporting and analytics remains fragmented across archaic platforms, huge sums are lost across unrealized productivity gains, missed innovations, and flawed strategic decisions.
The firms that will outcompete tomorrow are the ones that can convert raw data into unified, accurate, and accessible insights through techniques like business intelligence, descriptive analytics, predictive modeling, machine learning, and AI.
Overview of Data Warehouses vs Data Lakes
Now that the business context is clear, let’s distinguish two pivotal technology platforms that make realizing analytics success possible:
Data Warehouses…
Data Lakes…
By combining both data lakes (for affordably landing ALL raw information) together with transformed data warehouses (for complex analytics), firms can balance accessibility, performance, and costs.
Evaluating Your Analytics Technology Options with AWS
Given the stakes around advancing data-driven decision making, I want to equip you to evaluate what solutions align to your user profiles, performance requirements and budgets as you consider potential platforms and providers.
Now if your organization is open to cloud, Analytics and big data represent an ideal workload to transition from on-premises appliances given native cloud data bases deliver outsized benefits around scalability and throughput per dollar. Plus, managed offerings reduce your patch and hardware replacement burdens.
Let’s explore top considerations as you assess platforms:
User Personas and Access Requirements
Who will leverage your future state analytics ecosystem? Doing user and access mapping upfront ensures you architect appropriate security, connectivity and querying capabilities.
Common user categories:
- Business analysts producing live or canned reports off structured warehouses
- Data scientists building predictive models leveraging raw lake info
- Developers coding custom transformations and statistics
- External business partners needing selective sourcing
Capture frequency, concurrency allowances, data source needs, and business hours for each group.
Performance and Scalability Requirements
What service levels do your internal teams expect around factors like:
- Query responsiveness during peaks
- Daily/monthly data ingestion volumes
- Frequency of cube refreshes
- Data retention policies
- RTOs and RPOs for failures
Capturing SLAs upfront right-sizes investments and spots gaps.
Budget Tradeoffs
While cost efficiency is tempting, assess budget through total value lens. What’s worth spending more on to accelerate insights and decision making vs areas to conserve?
With the AWS pay-as-you-go approach, outlay adjusts dynamically to your projects. But selectivity remains key to avoid surprise overruns. Core services like S3, Athena and Glacier offer usage-based savings while managed offerings like Redshift require cluster commitments.
Now that you can evaluate vendors and offerings, let’s explore your leading AWS options…
Amazon Redshift
When Best Suited:
- Central repository for clean, processed business data
- Analytics apps needing complex SQL queries
Key Capabilities:
- Columnar storage for high compression
- Massively parallel SQL queries
- Automatic backups for availability
Considerations:
- Not as cost efficient for huge raw or unstructured data
- Can get pricey on large clusters
Amazon Athena
Ideal For:
- Ad hoc analysis over S3 data
- Querying raw file-based data lakes
Key Capabilities:
- Serverless SQL queries on S3
- Supports open data formats like CSV/JSON
- Integrates with AWS Glue catalog
Considerations:
- Query speed less consistent than clusters
- Not for repetitive complex queries
There are a wide range of other purpose-built options like Amazon EMR, Glue and Kinesis that may suit specific analytic workloads or use cases.
Now let‘s shift gears to core architectural considerations as you evaluate building out an AWS analytics modernization roadmap unique to your business…
Key Elements to Factor In Your Blueprint
While tactical choices around data pipelines, security configurations and query optimizations are pivotal, resist the urge to jump straight into tools. Stepping back to architect the complete solution end-to-end equips your teams and platforms to adapt as use cases evolve.
A few areas to map out upfront:
Hybrid Bridging of Cloud and On-Prem Systems
Unless regulations mandate cloud isolation, pragmatic transformation incorporates both zones, playing to the strengths of each. This balances access, data gravity and investment pacing.
Possible integration points:
- VPN secured connectivity between environments
- Network file system mounts to replicate objects
- Hybrid tables to query ground + cloud
- Lambda triggers to initiate workflows
Central Metadata Repository
Catalog files, schemas, metrics, processes and policies in a common catalog to permeate lakes, marts, and warehouses. This accelerates discovery while applying governance. Options like AWS Glue help.
Tagging Standards
Tags on assets like S3 objects, Redshift clusters, or Compute instances allow grouping, filtering and automation. Tags like business unit, data class, env, app can drive resource configs.
Visualization and Business Intelligence Layer
SQL queries satisfy data pros, but casual consumers need intuitive visibility through self-service dashboards, reporting and interactive slice+dice. Blend AWS QuickSight with Tableau or Looker dashboards.
There are so many other pivotal facets like security, data lifecycle management and CI/CD automation that guide analytics success. I‘m happy to explore suitable patterns given where you are starting on the data maturity journey.
Now that we covered architectural considerations, let‘s shift to hands-on implementation…
ETL Pipeline Development and Workflow Orchestration
Populating raw data into affordable storage is the easy part. The complexity lies in keeping disparate sources and targets synchronized through automated pipelines enabling transformation, message handling, recoverability among other facets.
Let‘s walk through key facets of implementing resilient ETL/ELT logic:
1. Pick Your Engine
While coding custom scripts offers ultimate flexibility, start with purpose-built ETL options that handle much of the orchestration complexities like scaling, error handling and operational management natively behind easy to use interfaces.
Managed ETL Services:
- AWS Glue
- Informatica
- Oracle Data Integrator
- Talend
These integrate natively with AWS data platforms. As complexity grows, migrate to battle-tested orchestrators.
2. Map Source to Target Data Flows
Diagram the end to end data pipeline capturing key attributes like:
- Source systems of record
- Landing zones and intermediate staging
- Transformation business logic
- Target database schemas
This logical view eases communication and surfaces gaps upfront.
3. Model Source and Target Schemas
Define the structure expected across source interfaces and the formats needed by target systems. This data modeling ensures compatibility between associated data entities and attributes during mapping.
Options like AWS Glue auto-discover and evolve schemas.
4. Connect Data Sources and Targets
With logical views done, enable physical connectivity by bridging networks and securing credentials across on-prem systems like Oracle and cloud platforms like Amazon S3 and Redshift. Testing end to end chains is crucial.
5. Create Data Mapings
Map source interfaces and attributes to destinations identifying where transformations are needed across areas like:
- Structure conversions like arrays to rows
- Translations like discrete values to descriptions
- Encryption, tokenization or data masking
- Validations checks and filtering
Rules and lookup tables help drive this logic consistently while code handles programmatic translations.
6. Set Load Frequency and Recovery
Configure job scheduling aligned to SLAs while building in failure handling such as retries, dead letter queues and notifications. Test degraded performance to right size underlying resources like Spark clusters.
While the wide range tools can ease configuration, architect end-to-end flows deliberately, especially once pipelines mature to scale.
Now that we have a handle on key integration considerations, let’s pivot to equally pivotal elements around usage management, cost optimization and governance to ensure your solutions remain performant, efficient and secure over time.
Managing, Optimizing and Governing Your Deployments
With data platforms live satisfying business demands, the work isn’t over. You have living systems now that require careful oversight as usage evolves to prevent unexpected costs or performance impacts down the line.
Here are proactive measures to bake in upfront:
Usage Monitoring and Alarming
Get visibility into consumption trends leveraging capabilities like:
- CloudWatch custom metrics on usage dimensions
- S3 bucket metrics on storage distribution
- Redshift Query Analyzer on long running SQL
- Cost Explorer budgets with alerts
Continuous monitoring allows responding to patterns before they escalate by right sizing or throttling capacity.
Tagging Standards
Establish formats early encompassing details like environments, applications, compliance levels and business units. Tags allow automation around actions such as:
- Setting IAM resource permissions
- Routing logs and metrics
- Allocating costs
- Lifecycle transitions
Make tagging relentless habit not an afterthought.
Workload Isolation
Set quotas on consumption by usage types with guardrails like:
- Redshift WLM queue controls per user/group
- S3 bucket policies around permissible operations
- Performance Insights to monitor SQL interference
Protect production workloads from impacting each other.
There are so many other facets around data lifecycle management, DR preparedness, and security hardening that enable analytics environments to scale efficiently. I’m happy to explore suitable governance models and archpatterns that adapt over time as your needs evolve. Reach out!
Now that we have covered a ton of ground on techniques and tactics, let’s shift to some industry examples of what modern analytics outcomes can look like.
Real World Data Lake and Warehouse Use Cases
While we covered a wide span of technical considerations, discussing tangible implementations makes patterns more concrete.
Here are a few composite examples of how AWS enables analytics innovation across sectors:
Media & Entertainment
A media conglomerate centralized decades of audience and viewership data from across video content, TV programming and filmed entertainment assets onto a petabyte scale AWS data lake. This powers real-time dashboards predicting viewership trends while optimizing content licensing revenue through machine learning algorithms.
Higher Education
A university system migrated its on-premises data warehouse to AWS Redshift enabling more interactive analysis across student enrollment/registration datasets, financial aid programs and graduation tracking. This allows administrators to improve curriculum planning while increasing student success rates.
Healthcare
A biotech firm leverages AWS analytics and data lake capabilities to ingest and analyze complex genomic datasets and molecular simulations to accelerate drug discovery efforts through AI/ML. Redshift enables analyzing research results and clinical trials while achieving compliance readiness.
The art of the possible is truly unlimited provided the foundation enables consolidating data into an accurate, accessible, and actionable form.
In this extensive guide, we covered:
- The business impact possible through data mastery
- Comparing data warehouses and lakes
- Evaluating analytics service options with AWS
- Architecting for scale, security and accessibility
- Implementing future-proof data pipelines
- Optimizing investments and consumption
- Industry examples applying analytics
The technologies and techniques discussed aim to provide a framework as you plan building out advanced cloud analytics capability. Please reach out if you need any guidance tailoring an approach for your unique requirements. With the right foundations, data can drive material growth by informing directions, decisions and differentiations.
