Understanding Data Platform Projects – A Primer for Project & Program Managers
📊 Understanding Data Platform Projects – A Primer for Project & Program Managers
As businesses evolve into data-driven organizations, data platform projects are becoming increasingly common. For PMs/PgMs who haven't worked in this space before, here's a quick primer to help you get familiar with the key concepts, components, and terminologies.
🔍 What is a Data Project?
A data project focuses on collecting, processing, storing, and delivering data to support decision-making, analytics, and product features. It’s not about building user-facing apps—it’s about enabling data flows, quality, insights, and governance.
Examples include:
Building a centralized data warehouse
Creating a customer 360° view
Enabling real-time analytics or dashboards
Developing a machine learning pipeline
🧰 What is Data Engineering?
Data Engineering is the backbone of any data platform project. It involves:
Ingesting data from multiple sources (APIs, databases, files, etc.)
Cleaning and transforming the data (ETL/ELT)
Moving it into storage systems (like data lakes or warehouses)
Making it available for consumption by analysts, data scientists, or other systems
Think of data engineers as the plumbers of the data world—making sure data flows efficiently, reliably, and securely.
📦 What is a Data Product?
The rectangle above shows a data product.
A Data Product is a curated, trustworthy, and reusable dataset or insight that serves a specific business need.
Examples:
A customer segmentation dataset for marketing
A sales performance dashboard
A recommendation engine input dataset
Data products are owned, versioned, and maintained like software products.
🧱 Key Layers of a Data Product / Platform
Data Ingestion Layer
Pulls data from various sources (CRM, ERP, logs, APIs, etc.)Data Storage Layer
Stores raw and processed data (e.g., Data Lakes, Data Warehouses)Data Transformation Layer
Cleans, joins, filters, and reshapes the data using pipelines (ETL/ELT)Semantic/Business Logic Layer
Defines KPIs, metrics, and business rules (used by BI tools)Consumption Layer
Dashboards, APIs, machine learning models, or data apps that use the processed dataData Governance & Security Layer
Ensures compliance, data quality, lineage, access controls, and auditability
🛡️ What is Data Governance?
Data Governance ensures that data is:
Accurate
Secure
Compliant with policies (e.g., GDPR, HIPAA)
Well-documented and easily discoverable
Key aspects include:
Data Catalogs (e.g., Alation, Collibra)
Data Lineage (track origin and changes)
Access Control & Policies
Quality Rules & Monitoring
📦Data Product Details
A data product is a high-quality, reusable dataset or data service that delivers value to end-users—such as analysts, business teams, or downstream systems—just like a software product.
It is not just raw data; it’s data that is:
Curated
Governed
Reliable
Purpose-built
Discoverable & usable
Think of it as the final output of your data platform pipelines that supports business decision-making or operational processes.
🧠 Examples of Data Products
🧱 Key Characteristics of a Data Product
A true data product follows these principles:
🧩 Where Do Data Products Fit in the Architecture?
Data products usually live in the Gold layer of the Medallion Architecture, and are exposed via:
BI dashboards
Data marts
APIs or data services
ML pipelines
Data marketplace/catalogs
🎯 Why Data Products Matter
Shift from “data as a byproduct” to “data as a product”
Promotes accountability and trust in data
Enables self-service analytics
Reduces dependency on IT and data engineering
Improves data reusability
One product, multiple consumers (dashboards, ML models, reports)
Scales with business
New domains or teams can plug into existing data products instead of building from scratch
🚧 Common Pitfalls When Delivering Data Products
🥉🥈🥇 Stages of Data: Bronze, Silver & Gold Explained for Project Managers
In modern data platforms, especially those following medallion architecture (popular in Delta Lake and Lakehouse models), data is processed and organized into three core layers: Bronze, Silver, and Gold.
These stages reflect the level of refinement, trust, and usability of data as it moves through the platform.
🥉 Bronze Layer – Raw / Ingested Data
What it is:
This layer contains raw, unprocessed data ingested from various sources like databases, APIs, flat files, logs, and streaming platforms.
Key Characteristics:
Data is stored as-is, with minimal or no transformation
May include duplicates, nulls, or inconsistent formats
Primarily used for auditing, backup, and replay purposes
Useful for data exploration and lineage tracking
Purpose: Act as the source of truth, untouched, useful for audit and reprocessing
Characteristics:
Schema-on-read (flexible)
Minimal validation or transformation
Often large and semi-structured (JSON, CSV)
Example:
Customer sign-up logs from the website in their original format, with all columns including noise or junk data
Project Implication:
Ensure scalable and secure ingestion pipelines with metadata tracking
🥈 Silver Layer – Cleansed / Structured Data
What it is:
This layer consists of cleaned and standardized data, typically after applying transformation rules such as joins, filters, deduplication, and type casting.
Key Characteristics:
Data quality improves (nulls removed, data types aligned)
Applied business logic begins (e.g., mapping country codes to names)
Used by analysts and data scientists for deeper exploration
Purpose: Create a trusted, query-ready foundation for analysis and modeling
Characteristics:
Applied data quality rules
Standardized schema and formats
Joins across tables, null handling, type casting
Example:
Cleaned customer data with valid email addresses, duplicate accounts removed, and all timestamps standardized
Project Implication:
Coordinate closely with business/data SMEs to define transformation rules; implement data quality checks
🥇 Gold Layer – Curated / Business-Ready Data
What it is:
This is the final, most refined layer, containing aggregated and domain-specific data products. It supports business intelligence, analytics, and ML models.
Key Characteristics:
Tailored to specific business use cases (sales, marketing, operations)
High trust, high usability datasets
Often consumed via dashboards, reports, and APIs
Purpose: Serve business users, dashboards, and downstream systems
Characteristics:
Highly reliable and fast for querying
Often used in BI tools and ML models
Built with stakeholder-defined KPIs
Example:
Monthly sales revenue by product category and region, enriched with customer segmentation
Project Implication:
Align gold-layer design with business stakeholders; measure adoption and value of the data products
🔁 How Medallion Architecture Supports Data Projects
As a PM, using this architecture allows you to:
Phase the delivery: You can deliver Bronze/Silver early and Gold iteratively.
Isolate issues: Data quality problems can be fixed at the Silver layer without touching raw data.
Standardize pipelines: Create reusable ETL patterns across use cases.
Improve stakeholder confidence: Gold datasets are always vetted and production-grade.
🔄 OLTP vs. 📊 OLAP – Key Concepts for Data Platform PMs
When managing data platform projects, it's important to understand the distinction between OLTP (Online Transaction Processing) and OLAP (Online Analytical Processing) systems. These two serve very different purposes and require different data architectures, processing strategies, and expectations.
🔄 What is OLTP? (Online Transaction Processing)
OLTP systems are designed to handle day-to-day business transactions quickly and reliably. These are the systems your business operations depend on.
Examples:
Banking applications
E-commerce checkout systems
Inventory management systems
CRM applications
Key Characteristics:
Handles a large number of short, atomic transactions
Supports real-time inserts, updates, and deletes
Data is highly normalized (to reduce redundancy)
Prioritizes speed and consistency
Technology Examples:
MySQL, PostgreSQL, Oracle DB, SQL Server
Project Implications for PMs:
OLTP systems are data sources in data projects
Data ingestion pipelines must extract data without affecting live operations
Often require CDC (Change Data Capture) mechanisms for real-time sync
📊 What is OLAP? (Online Analytical Processing)
OLAP systems are designed for data analysis, reporting, and decision support. These systems process aggregated and historical data, often derived from OLTP systems.
Examples:
Sales performance dashboards
Customer lifetime value analysis
Trend forecasting
Key Characteristics:
Handles complex queries on large datasets
Data is often denormalized for fast querying
Supports slice-and-dice, drill-down, roll-up analysis
Optimized for read-heavy workloads
Technology Examples:
Snowflake, Amazon Redshift, Google BigQuery, Azure Synapse, Apache Druid
Project Implications for PMs:
OLAP is often the end product of your data pipelines
Business users rely on OLAP systems for insights and reporting
Performance and latency must be tuned for query efficiency, not write speed
🏗️ Architecture of a Modern Data Platform – A PM’s Guide
A data platform architecture is the foundation that supports the ingestion, processing, storage, and consumption of data across the organization. It brings together various tools, layers, and design principles to deliver trusted, scalable, and usable data products.
Here's a high-level breakdown of a modern data architecture:
📥 1. Data Sources (Upstream Systems)
These are the origin points of your data. Examples include:
OLTP systems (CRM, ERP, POS, web apps)
Third-party APIs (weather, market data)
Logs, IoT streams, spreadsheets
Flat files (CSV, Excel), SFTP sources
PM Tip:
Define source systems early and plan for secure access + refresh cadence (real-time, hourly, daily, etc.)
🚰 2. Data Ingestion Layer
This layer collects data from the source systems into the data platform.
Ingestion Types:
Batch (e.g., daily file loads)
Streaming (e.g., Kafka, real-time logs)
Change Data Capture (CDC) for real-time updates
Common Tools:
Apache NiFi, Fivetran, Airbyte, Kafka, AWS Glue
PM Tip:
Watch for performance bottlenecks and source system impacts during ingestion.
🧼 3. Data Storage Layer
Once ingested, data is stored in stages:
Bronze → Silver → Gold
Bronze (Raw Layer): As-is data
Silver (Cleaned Layer): Structured, deduplicated, joined
Gold (Curated Layer): Aggregated, business-consumable datasets
Storage Types:
Data Lake: S3, ADLS, GCS (for raw and semi-structured data)
Data Warehouse: Snowflake, Redshift, BigQuery (for structured, query-ready data)
PM Tip:
Clarify data retention, backup, and archival policies early in planning.
🔄 4. Data Processing / Transformation Layer
This layer handles ETL (Extract, Transform, and Load) or ELT pipelines to convert raw data into meaningful formats.
Common Tools:
Apache Spark, dbt, Dataflow, Airflow, Azure Data Factory
Tasks Performed:
Data cleaning
Business rule application
Joining multiple datasets
Creating KPIs/metrics
PM Tip:
Map transformations to business logic; involve domain SMEs during development.
🔍 5. Semantic / Business Logic Layer
This is where business logic is centralized—so analysts and BI tools use consistent metrics (e.g., revenue, active users).
Examples:
Looker semantic models, dbt models, Power BI datasets
PM Tip:
Helps avoid "multiple versions of the truth" in reports—centralize this early.
📊 6. Consumption Layer (BI & ML)
This is where users interact with the data via tools and apps.
Consumers Include:
Dashboards (Power BI, Tableau, Looker)
Reports and ad-hoc queries
ML pipelines and models
APIs for other apps or clients
PM Tip:
Plan training or enablement sessions—dashboards are only useful if people can interpret and trust them.
🔐 7. Data Governance & Security Layer
Ensures your platform meets compliance, security, and data quality standards.
Functions:
Role-based access control (RBAC)
Data catalogs and lineage tracking
Auditing and compliance logging
Data quality rules and alerting
Tools:
Collibra, Alation, Unity Catalog, Great Expectations
PM Tip:
Prioritize governance from the start—it’s hard to retro-fit later.
🔁 8. Monitoring & Orchestration Layer
Ensures data jobs run as expected and issues are detected early.
Tools:
Airflow, Dagster, Prefect (orchestration)
Grafana, Datadog (monitoring)
PM Tip:
Use alerts and dashboards to track data freshness, pipeline health, and failures.
🧱 Summary Architecture Diagram (Text Format)
+-----------------------+
| Source Systems |
| (CRM, APIs, Logs) |
+----------+------------+
|
[Ingestion Layer]
|
+-------v--------+
| Raw Storage | <== Bronze
+-------+--------+
|
[Transformation Layer]
|
+-------v--------+
| Cleaned Storage| <== Silver
+-------+--------+
|
[Business Logic Layer]
|
+-------v--------+
| Curated Data | <== Gold
+-------+--------+
|
+------------+--------------+
| BI Tools / ML / APIs etc. |
+---------------------------+
⚖️ Why OLTP and OLAP Should Be Separate in a Data Architecture
In any data-driven organization, separating OLTP (Online Transaction Processing) and OLAP (Online Analytical Processing) systems is a best practice for both performance and architectural clarity.
This separation ensures that operational efficiency and analytical capability can coexist without compromising each other.
🔄 1. They Serve Different Purposes
Why separate?
You don’t want analytics queries slowing down your live order processing, or transaction issues impacting dashboards.
⚙️ 2. Different Workload Patterns
OLTP: Write-heavy, lots of small transactions (e.g., placing an order, updating inventory)
OLAP: Read-heavy, large-scale complex queries (e.g., “What was the month-over-month growth?”)
Why separate?
Combining them leads to resource contention—reporting queries can slow down business-critical operations.
📐 3. Different Data Models
OLTP: Highly normalized for write efficiency and data integrity
OLAP: Often denormalized for query performance (e.g., star/snowflake schemas)
Why separate?
What’s efficient for storing orders and customers separately (OLTP) is inefficient for summarizing trends (OLAP).
⚠️ 4. Risk of Performance Bottlenecks
If OLTP and OLAP share the same database or infrastructure:
A slow dashboard refresh can block incoming transactions
High-volume transactions can delay reporting refreshes
Why separate?
Ensures high availability and scalability on both fronts.
🔐 5. Different Data Retention & Volume Needs
OLTP only needs recent data (e.g., last 30 days for operations)
OLAP often stores years of data for trends, predictions, audits
Why separate?
Reduces storage and performance strain on OLTP systems.
🧩 6. Enables Better Scalability
OLTP systems scale vertically (e.g., bigger servers, higher IOPS)
OLAP systems scale horizontally (e.g., distributed query engines)
Why separate?
You can scale each system based on its usage profile without over-provisioning.
🔁 7. Enables a Robust Data Pipeline Architecture
Separating OLTP and OLAP enables the creation of dedicated ingestion pipelines, data quality checks, transformation logic, and semantic layers—without touching live operational systems.
PM Insight:
This enables better governance, reusability, and visibility across the data lifecycle.
✅ Summary – Key Benefits of Separation
🔧 Real-World Analogy
Think of it like a kitchen (OLTP) vs. a restaurant review dashboard (OLAP):
The kitchen must operate fast, reliably, and consistently to serve customers (OLTP).
The dashboard helps management analyze popular dishes, customer feedback trends, and supply chain performance (OLAP).
You don’t want customers waiting for food because someone is running a quarterly report!
🧰 Sample Tech Stack for a Modern Data Platform
A typical data platform is made up of multiple layers, each with a set of tools and technologies for data ingestion, storage, processing, governance, and consumption.
1️⃣ Data Ingestion Layer
Purpose: Bring data from source systems (OLTP, APIs, files, streaming) into the platform.
2️⃣ Data Storage Layer
Purpose: Store raw and processed data at scale.
3️⃣ Data Processing & Transformation Layer
Purpose: Clean, transform, and structure data (ETL/ELT pipelines).
4️⃣ Semantic / Business Logic Layer
Purpose: Apply business definitions and KPIs to transformed data.
Tools:
dbt (data build tool)
LookML (Looker modeling layer)
Power BI Datasets
Tableau Data Models
5️⃣ Data Consumption Layer
Purpose: Expose data to users via dashboards, APIs, notebooks, and ML models.
6️⃣ Data Governance, Security & Cataloging
Purpose: Manage data access, quality, lineage, and compliance.
7️⃣ Monitoring & DevOps
Purpose: Monitor pipelines, data health, and platform performance.
Tools:
Grafana, Prometheus, CloudWatch, Datadog
CI/CD: GitHub Actions, GitLab CI/CD, Jenkins
Terraform, Pulumi (for infra as code)
dbt Cloud / dbt Core for deployment & testing
✅Platform Hosting Options
🪷Other Supporting Details
🔧 1. Data Platform Roles & Responsibilities
Help PMs understand who does what.
🎯 Why include this: PMs must manage these roles, coordinate tasks, and resolve dependencies.
🧪 2. Data Quality Dimensions
Highlight the importance of data quality and how to monitor it.
Key dimensions:
Accuracy – Is the data correct?
Completeness – Are all required fields present?
Timeliness – Is the data fresh?
Consistency – Is it standardized across sources?
Validity – Is data within expected formats/ranges?
🎯 Why include this: PMs should track quality KPIs and know when data is "ready for use."
📊 3. BI & Reporting Layer
How data is consumed by business users.
Include:
Common BI tools (Power BI, Tableau, Looker)
Embedded analytics vs. self-service
Real-time vs scheduled dashboards
Row-level security (RLS), dashboard governance
🎯 Why include this: PMs often get judged on dashboard delivery timelines and usability.
⚙️ 4. Orchestration & Scheduling
Managing pipelines and data jobs.
Tools: Airflow, Dagster, Prefect
Concepts: DAGs, dependencies, retries
Use cases: Scheduling daily refresh, triggering downstream tasks
🎯 Why include this: Helps PMs identify potential delays and bottlenecks in pipeline runs.
📦 5. Data Cataloging & Discoverability
Make data usable and discoverable by business users.
Include:
Metadata management
Data lineage tools
Tools: Alation, Collibra, Unity Catalog
🎯 Why include this: PMs can proactively reduce “where is my data?” queries.
🧠 6. Machine Learning & Advanced Analytics Layer (optional, depending on scope)
If your platform supports ML use cases, mention:
Feature stores
Model training & tracking
Data drift monitoring
Tools: MLflow, SageMaker, Vertex AI
🎯 Why include this: Clarifies what infra/data setup is needed to support ML.
🛡️ 7. Security, Privacy & Compliance
Critical for regulated industries (e.g., finance, healthcare).
Include:
Role-based access control (RBAC)
Data masking & encryption
PII detection & anonymization
Audit logging
GDPR, HIPAA, ISO considerations
🎯 Why include this: PMs must plan secure environments and coordinate with InfoSec.
📅 8. Typical Phases of a Data Platform Project
Break it down like a delivery roadmap:
Discovery & stakeholder alignment
Data source inventory & access setup
Ingestion pipelines (Bronze)
Data transformations & validations (Silver)
Data product definition (Gold)
BI/ML layer build
Governance setup
User onboarding, training & adoption
Monitoring, automation, support
🎯 Why include this: Helps PMs structure sprints and deliverables.
📈 9. KPIs & Success Metrics for PMs
What defines success for the PM in a data platform project?
Examples:
% of critical data sources integrated
Time to deliver first dashboard
Data freshness SLAs met
Number of active data products used by business
User adoption rate
🎯 Why include this: Aligns PM goals with business value.
🧩 10. Challenges & Pitfalls to Watch For
Forewarned is forearmed. Common issues:
Scope creep (“just one more dataset”)
Lack of clear data ownership
Delayed access to source systems
Unclear data definitions (leading to conflicting reports)
Data quality gaps due to upstream changes
Gold layer built without Silver being stable
🎯 Why include this: Helps PMs proactively mitigate risks and set realistic expectations.
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