Learning Center

Guides

Data Catalog: Components, Challenges & 5 Critical Best Practices

A data catalog is a centralized repository that provides an organized inventory of data assets within an organization.

Data Engineering with Python: 4 Libraries + 5 Code Examples

Data engineering is the practice of designing, building, and maintaining the infrastructure necessary for collecting, storing, and processing large-scale data.

Data Pipeline

Data Pipeline Frameworks: Key Features & 10 Tools to Know in 2025

A data pipeline framework is a structured system that enables the movement and transformation of data within an organization.

Data Catalog

Automated Data Lineage vs. Manual Tracking: A Practical Comparison

Compare automated data lineage vs. manual tracking across accuracy, coverage, and root-cause speed, with honest guidance on when each approach makes sense.

Data Platform

Data Orchestration Tools: 10 Key Features & 10 Platforms to Know

Data orchestration tools manage data workflows, automating the movement and transformation of data across different systems.

Data Build Tool

How dbt Snapshots Work, Quick Tutorial & Best Practices

dbt snapshots are a mechanism within dbt (data build tool) designed to track and preserve changes in data over time, specifically for tables that are mutable or have Slowly Changing Dimensions (SCD) Type 2. They allow users to maintain a historical record of how rows within a table evolve.

Data Pipeline

ETL Pipelines: 5 Key Components and 5 Critical Best Practices

An ETL (extract, transform, load) pipeline is a data processing system that automates the extraction of data from various sources.

Data Build Tool

dbt Unit Testing: Why You Need Them, Tutorial & Best Practices

dbt unit tests are a feature within dbt that enable the validation of a dbt model's SQL logic in isolation, using a small, controlled set of static input data

Data Catalog

Data Lineage for Compliance: GDPR, HIPAA, and What Auditors Want

GDPR and HIPAA auditors don't want dashboards. Learn how column-level lineage built into your pipeline architecture produces audit-ready evidence on demand.

Data Quality

5 Things You Need to Achieve Data Visibility

Data visibility refers to how accessible, understandable, and useful data is within an organization.

Data Platform

Data Platform Buyer's Guide: What to Evaluate

Learn how to evaluate a modern data platform. This buyer's guide covers declarative architecture, developer experience, and true cost of ownership.

Data Build Tool

Ultimate Guide to dbt Macros in 2025: Syntax, Examples & Pro Tips

dbt (data build tool) macros are reusable, parameterized SQL and Jinja templates to automate repetitive SQL operations within a dbt project.

Data Pipeline

Data Pipelines on AWS: 12 Services & How to Use Them Effectively

AWS provides an ecosystem of services to support data pipelines at every stage—from data ingestion and transformation to storage, analysis, and monitoring.

Data Platform

Data Observability Buyer's Guide: 8 Questions to Ask

A data observability buyer's guide for evaluating platforms beyond table scanning. Learn the 8 questions to ask to avoid compute taxes and alert fatigue.

Data Quality

Data Observability in 2025: Pillars, Pros/Cons & Best Practices

Data observability refers to the ability to fully understand the health and state of data in an organization.

Data Build Tool

dbt SQL Models: the Basics and Building Your First dbt Model

SQL models in dbt are SQL files that define transformations in a data warehouse, each creating a view or table from a SELECT statement.

Data Build Tool

dbt Lineage in Action: 3 Use Cases, Challenges and Best Practices

Data lineage refers to the end-to-end tracking of data as it moves through systems, from its origin to its final destination.

Data Quality

Data Reliability: Challenges, Measurement & Best Practices

Data reliability refers to the consistency and dependability of data over time.

Data Engineering

Data Engineering Tools: Types, Features, and 10 Essential Tools

Data engineering tools are software applications and platforms that assist in building, managing, and optimizing data pipelines.

Data Engineering

A 6-Step Data Engineering Workflow and 6 Ways to Optimize It

A data engineering workflow involves a series of structured steps for data management, from data acquisition to applications for organizational data users.

Data Platform

Data Quality Platform: Benefits, Key Features, and 10 Tools to Know

Data quality platforms work by automating the processes involved in identifying and correcting data errors. This automation reduces manual effort and minimizes the risk of human error.

Data Pipeline

Data Pipeline Architecture: 5 Design Patterns with Examples

Data pipelines architecture automates the collection, processing, and transfer of data from various sources to destinations for analysis or storage.

Data Pipeline

Data Pipelines with Python: 6 Frameworks & Quick Tutorial

A data pipeline is a series of processes that move data from one system to another.

Data Build Tool

ETL Tools: Key Features and 10 Tools to Know in 2025

ETL (Extract, Transform, Load) tools are software solutions that help organizations manage and process data from multiple sources.

Data Build Tool

dbt Python: Running Transformations with Python on Data Warehouses

A dbt Python model is a type of transformation within the dbt (data build tool) ecosystem that lets developers write business logic using Python, instead of SQL.

Data Quality

Data Quality Checks: How to Test 6 Data Quality Dimensions

Data quality testing involves evaluating data to ensure it meets specific standards for accuracy, completeness, consistency, and more.

Data Build Tool

Working with dbt Seeds: Quick Tutorial & Critical Best Practices

dbt (data build tool) seeds are static CSV files stored within your dbt project that are loaded into your analytics warehouse as database tables.

Data Platform

Data Orchestration Simplified: Process, Capabilities, and Strategies

Data orchestration refers to the automated coordination and management of data movement and data processing across different systems and environments

Data Pipeline

10 Data Ingestion Tools to Know in 2025: Open Source + Paid

Ingestion capabilities are important to collect structured, semi-structured, and unstructured data. By ensuring data arrives in a consistent, well-organized manner, organizations can eliminate bottlenecks associated with data processing.