Analytics
494 bites tagged Analytics — interview questions with model answers, and 60-second explainers.
Report Generation: Turning Raw Data into Human-Readable Documents
Report generators translate raw data into formatted documents for human eyes. They power business dashboards and sales summaries. The main footgun is forgetting the report is a stale snapshot, not the live data source itself.
Data Stewardship: The Librarian for Your Data
A data steward is the designated owner of a data asset, responsible for its quality and business value, not just its storage. This role is crucial where data is shared across teams, ensuring consistency.
Master Data Management (MDM): The Single Source of Truth
Master Data Management (MDM) creates a single source of truth for core business entities. It's used when departments have conflicting data (e.g., 'ACME Inc.' vs 'Acme Corp').
Data Governance: Corporate Management vs. Global Policy
Data governance operates at two scales: managing data within a company and setting data policy between nations. It applies to corporate data management and international internet governance.
Data Anonymization: Protecting Privacy by Removing PII
Data anonymization breaks the link between data and real people by removing personal identifiers. It’s used to share datasets for research or analytics while protecting privacy.
Data Catalog: The Library Card for Your Data
A data catalog is like a library card catalog for your company's data, telling you what exists, where it lives, and what it means. It helps analysts find trustworthy datasets and engineers trace the impact of schema changes. The footgun is letting it go stale.
Data Lineage: The Story of Your Data
Data lineage is a family tree for your data, showing its origins, transformations, and final destination. It's essential for debugging broken analytics and tracing errors to their source.
Data Quality: Is Your Data Fit for Purpose?
High-quality data is defined by its fitness for a specific purpose, not just its correctness. It must accurately represent the real world. This is critical for business planning or ML models.
Data Profiling: The First Step in Any Data Project
Data profiling creates a 'character sketch' of a dataset, revealing its structure, content, and quality. It's the first step in data warehousing or analytics to discover metadata and assess risks. The footgun is skipping it, leading to late-project surprises.
Data Cleansing: Fixing Your Data Before It Fails You
Data cleansing is quality control for your dataset, finding and fixing errors before they skew your analysis. It's a crucial first step in any data pipeline, from training an ML model to generating business reports. The footgun is assuming data is clean.
Reverse ETL: From Warehouse Insights to Operational Action
Reverse ETL pushes clean data from your central warehouse back into the operational tools business teams use daily. This powers sales with customer scores in their CRM or marketing with personalized segments, all from a single source of truth.
Data Vault Modeling: An Audit-First Data Warehouse
Data Vault modeling builds a warehouse like a financial ledger, where every entry is permanent and traceable. It excels at storing historical data from multiple systems for auditing.
Slowly Changing Dimensions (SCDs)
Slowly Changing Dimensions (SCDs) are how data warehouses handle history for attributes that change infrequently, like a customer's address. This ensures historical reports remain accurate. The footgun is overwriting old values, which corrupts past analysis.
Data Lakehouse: The 'Lake' Foundation
A data lake is a central repository that stores all your data—structured or raw—in its original format. It's used to hold raw source system copies, sensor data, and social feeds for later analysis, but can become a messy "data swamp" without governance.
Snowflake Schema: Trading Query Speed for Storage
A snowflake schema saves storage by normalizing a star schema's dimensions into smaller, related tables. It's used in data warehouses to reduce redundancy, but the extra joins required can slow down queries, making it a trade-off against a simpler star schema.
Star Schema: The Blueprint for Analytics Data
A star schema organizes analytics data with a central fact table (e.g., sales) surrounded by dimension tables (e.g., customers). It's built for fast queries in data warehouses. The footgun is normalizing dimensions, which negates its speed advantage.
Data Marts: Your Department's Slice of the Data Warehouse
Think of a data mart as a department's personal slice of the main data warehouse, containing only relevant data. This allows teams like Sales or Marketing to run faster, focused queries. The footgun is letting each team define shared terms differently.
Data Lake: Store Raw Data Now, Analyze It Later
A data lake is a central repository that holds vast amounts of raw data in its native format. This "store now, structure later" approach is ideal for machine learning on original, unfiltered source data.
Online Analytical Processing (OLAP)
OLAP databases are built to quickly answer complex, multi-dimensional questions, unlike transactional (OLTP) databases that handle individual records. They power business intelligence tools for sales and marketing analysis.
Data Warehouse: The Single Source of Truth for Analytics
A data warehouse is a central database optimized for analytics, not transactions. It integrates historical data from disparate sources like sales and marketing to create a single source of truth for business intelligence.
ELT: Load Raw Data First, Transform It Later
ELT pipelines load raw data directly into a data lake *before* any transformation. This speeds up ingestion and lets you figure out the data's structure later.
Event Data Pipelining: From Raw Events to Analytics
Treat data not as static tables but as a continuous stream of events. Event data pipelining builds the infrastructure to capture, process, and deliver this real-time flow for analytics or AI applications.
Customer Data Platform (CDP): Your Customer's Single Source of Truth
A Customer Data Platform (CDP) creates a single, persistent profile for each customer by unifying data from siloed sources. It's used for real-time personalization and AI-driven marketing. The footgun is confusing it with a CRM, which manages relationships.
Event Autocapture: Low-Effort Frontend Analytics
Event autocapture is like a security camera for your UI, recording all user interactions automatically. It's used in web analytics to capture clicks and page views with minimal setup, letting you analyze behavior without manually instrumenting every button.
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