Modern Data Stack Overview: Building Blocks for Data Product Managers (Phase 1- Post 2)

Making good use of weekend, let's move ahead with our third post in "Becoming a Data Product Manager" series. Today, we'll dive into something that might seem complex but is crucial for every data PM - the Modern Data Stack.

Before we dive in, here's something I wish someone had told me when I started: Understanding the modern data stack isn't about becoming a technical expert; it's about knowing how different pieces work together to create value for your users.

Let's break this down in a way that makes sense for product managers.

From Monolithic to Modern: Understanding the Shift

Let's start at the beginning. Not long ago, organizations used what we call "monolithic" data systems. Monolithic simply means a single, all-in-one platform that handled every aspect of working with data.

Think about older enterprise software systems. They often came from one vendor, required extensive setup, and were difficult to change once implemented. These systems handled everything from collecting data to storing it to creating reports – but they weren't particularly excellent at any single task.

The modern approach is fundamentally different. Instead of one system doing everything, we now use specialized tools that each focus on doing one thing exceptionally well. These tools are designed to work together, creating a more flexible and powerful system.

Resource: https://www.youtube.com/watch?v=fCARsxFBRjE

This shift matters for you as a Product Manager because it directly impacts what you can build, how quickly you can iterate, and how your product will handle growth.

Traditional Monolithic Systems: Oracle Data Integration Suite, IBM InfoSphere, SAP Data Services.

Modern Approach: Specialized tools that each excel at specific functions:

• Data Integration: Fivetran, Airbyte
• Storage: Snowflake, BigQuery
• Transformation: dbt, Airflow
• Analytics: Looker, Tableau

The Four Layers: A Step-by-Step Tour

Let me walk you through each layer of the modern data stack, explaining core concepts in plain language before introducing technical terms.

1. Getting Data In: The Collection & Integration Layer

The Basic Concept: This first layer solves a fundamental challenge: how do we get data from various sources into one central location? Think about all the places data might live – inside your product database, in third-party tools like Salesforce or Google Analytics, or coming from user actions in real-time.

Key Approaches Explained:

Batch Processing: This is like collecting the mail once a day. Data is gathered at scheduled intervals (hourly, daily, weekly) and moved in large chunks. For example, every night at midnight, yesterday's sales data might be copied from your transaction database to your analysis system.

Tools: Apache Airflow, AWS Glue.

Resource: For a clear explanation of batch processing, https://www.youtube.com/watch?v=ya4298V8Mqo

ETL (Extract, Transform, Load): This traditional approach consists of three steps: pulling data from sources (Extract), cleaning and restructuring it (Transform), and then putting it into your destination (Load). It's like harvesting vegetables from your garden, washing and chopping them in your kitchen, and then storing them in your refrigerator.

Tools: Informatica, Talend

ELT (Extract, Load, Transform): A newer approach that reverses the order: data is extracted and loaded first, then transformed where it lands. This is like bringing your vegetables inside and storing them immediately, then washing and preparing them only when you're ready to cook.

Tools :Fivetran, Stitch, Airbyte

Resource: For a comparison of ETL vs ELT, https://www.youtube.com/watch?v=_Nk0v9qUWk4

Stream processing: Unlike batch processing, stream processing handles data continuously as it's created. For example, when a user clicks on your website, that event is immediately sent to your analysis system.

Tools: Apache Kafka, Amazon Kinesis

Change Data Capture (CDC): This technique identifies and captures changes in your databases (new records, updates, deletions) and forwards only those changes to your destination. It's like having someone notify you only when something changes in your inventory rather than repeatedly counting everything.

Tools: Debezium, Oracle GoldenGate

Resource: To understand CDC in simple terms, https://www.youtube.com/watch?v=6PBspEkBlPU

Example: Let's consider Netflix. They need to understand user viewing habits to recommend content. Their integration layer:

• Captures what shows you watch, when you pause, and when you stop
• Collects this data from TVs, phones, tablets, and laptops
• Processes millions of viewing events every second
• Makes this data available for analysis almost immediately

2. Storing Data: The Storage Layer

Once data is collected, it needs a place designed for analysis rather than just record-keeping. This is very different from the databases that run your applications.

Resource: For an excellent overview of analytical vs operational databases, https://www.youtube.com/watch?v=0v5Xy-O4Jls

Key Approaches Explained:

Data Warehouse: A specialized database optimized for analysis and reporting. Unlike operational databases that process transactions, data warehouses are designed to answer complex questions across large datasets.

Tools: Snowflake, Amazon Redshift, Google BigQuery

Data Lake: A storage repository that holds a vast amount of raw data in its original format until needed. It's like having a large storage unit where you keep everything that might be valuable someday. Data lakes are especially useful for unstructured data like text, images, and videos.

Tools: Amazon S3, Azure Data Lake, Google Cloud Storage

Resource: For a clear comparison, watch "Data Warehouse vs Data Lake" https://www.youtube.com/watch?v=WgIbvkyY4mI&t=10s

Data Lakehouse: A newer approach that combines the best features of warehouses and lakes. It provides the structured organization of a warehouse with the flexibility of a lake. It's like having a library with both carefully cataloged books and a collection of diverse materials that haven't yet been fully processed.

Tools: Databricks, Apache Iceberg

Real-World Example: Let's look at Airbnb. They store:

• Booking history for millions of properties
• Detailed listing information and photos
• User profiles and preferences
• Reviews and messages

Their storage layer must handle both structured data (booking dates, prices) and semi-structured data (listing attributes, user preferences). It needs to support both historical analysis ("How have booking patterns changed over seasons?") and real-time features ("Is this property available tonight?").

3. Making Data Useful: The Transformation Layer

Raw data isn't immediately valuable. The transformation layer converts raw data into useful information by cleaning, combining, and restructuring it.

Resource: For an introduction to data transformation, https://www.youtube.com/watch?v=2GXw9c49gL4

Key Approaches Explained:

Data Cleaning: Removing or correcting errors and inconsistencies. This might include fixing typos, standardizing formats, or handling missing values. It's like sorting through a pile of receipts, making sure dates and amounts are correct before doing your expense report.

Data Modeling: Creating a structured representation of your data that reflects business concepts. For example, defining what a "customer" or an "active user" means in your specific context. It's like organizing information in a way that makes sense for your particular needs.

Tools: dbt, Dataform

Resource: For a beginner-friendly introduction to data modeling, https://www.youtube.com/watch?v=reHw8KChCHg

Aggregation: Combining individual data points into summary statistics. Instead of listing every transaction, you might calculate daily totals, averages, or counts. It's like summarizing a long document into key points.

Feature Engineering: Creating new data points derived from existing ones. For instance, calculating how many days since a customer's last purchase or converting a birth date into an age group. These derived values often provide more insight than the raw data.

Real-World Example: Spotify transforms listening data into valuable insights:

• They clean raw listening events (handling cases where connections drop mid-song)
• They calculate metrics like "song completion rate" and "skip patterns"
• They determine your favorite genres based on listening patterns
• They identify relationships between songs you like to power recommendations

4. Getting Insights to Users: The Consumption Layer

The final step is making data accessible and actionable for end users. This layer turns insights into features, visualizations, or reports that deliver value.

Key Approaches Explained:

Dashboards and Reporting: Visual displays of key metrics and trends. These give users a quick overview of performance or status. It's like having gauges on your car's dashboard that show speed, fuel level, and engine temperature at a glance.

Resource: For dashboard design best practices, https://www.youtube.com/watch?v=x-rDVXVwW9s

Self-Service Analytics: Tools that allow users to explore data and answer their own questions without requiring technical skills. It's like giving someone access to a well-organized library with a helpful guide, rather than requiring them to submit requests to a librarian.

Embedded Analytics: Integrating insights directly into applications where people already work. Instead of switching to a separate analytics tool, users see relevant data right within their workflow. It's like having weather information appear in your calendar app alongside your daily appointments.

Machine Learning Applications: Using data to make predictions or recommendations automatically. These range from product recommendations to fraud detection systems to automated decision-making tools.

Real-World Example: Zomato uses the consumption layer to power multiple features:

• Dashboards for restaurant owners showing order trends
• Real-time mapping for delivery drivers
• Personalized recommendations for customers
• Automated delivery time predictions

Putting It All Together: How the Layers Work as a System

These four layers don't operate in isolation – they form an interconnected system. Let's see how they work together through a practical example: a personalized shopping recommendation system.

The Data Journey:

1. Collection & Integration Customer purchase history is collected from the order database Browsing behavior is captured from the website and mobile app Inventory and pricing data is pulled from product systems All this data is brought together in a consistent format
2. Storage The raw event data is stored in a data lake for future flexibility Processed, structured data is loaded into a data warehouse Both historical patterns and real-time activities are maintained
3. Transformation Purchase and browsing data is cleaned and standardized Customer profiles are enriched with derived insights (favorite categories, price sensitivity) Product relationships are calculated based on purchase patterns Recommendation models are trained on historical data
4. Consumption Shoppers see personalized product recommendations in the app Marketing team accesses dashboards showing recommendation performance Product managers analyze A/B tests of different recommendation algorithms APIs deliver real-time recommendations as users browse

This complete flow – from raw data to valuable feature – is what makes modern data products powerful.

How to Apply This Knowledge as a PM

Now that you understand the modern data stack, how can you use this knowledge in your role?

Resource: https://www.youtube.com/watch?v=SsYmKtwCQL8

When Planning Features: Consider each layer's role in enabling your vision:

• What data sources will you need to access?
• Where and how will you store this data?
• What transformations will turn raw data into useful insights?
• How will users consume and act on these insights?

When Working with Engineering: Use this framework to ask better questions:

• "If we add this new data source, how will it flow through our system?"
• "What transformation logic do we need to create this new metric?"
• "How will this storage decision affect our ability to scale?"

When Evaluating Tools: Understand where each tool fits in the stack:

• Is this a collection tool, a storage solution, a transformation framework, or a consumption interface?
• How will it interact with our existing components?
• Does it solve our specific pain points at this layer?

Key Takeaways

1. The modern data stack is modular by design. This modularity allows you to select the best tool for each function and evolve components independently.
2. Each layer solves specific challenges. Understanding these challenges helps you identify where your current stack might be limiting your product.
3. The stack should grow with your needs. Start simple and add complexity only as required.
4. User needs should drive technology choices. Let product requirements determine your architectural decisions, not vice versa.

What's Next?

In our next post, we'll explore how to effectively lead data product development – from gathering requirements to working with data teams and measuring success.

What questions do you have about the modern data stack? What aspects would you like to understand better? Share in the comments!

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