Study Notes dlt Workshop: API, Warehouses, Data Lakes

#dataengineering #dezoomcamp #dlt #beginners

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1. Workshop Overview & Introduction

Workshop Focus:

– How to build robust, scalable, and self-managing data pipelines using the open source DLT library.

– Ingestion of data from various sources (especially REST APIs) into destinations such as data warehouses, data lakes, and lakehouses.
Instructor Background:

– Violetta (VLT) is a solutions engineer at DLT Hub.

– Experience spans data science and machine learning, which informs her approach to consolidating and normalizing diverse data sources.
Course Logistics:

– Materials, homework instructions, and community links are provided in the course repository and chat.

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2. Data Pipeline Fundamentals

Typical Pipeline Stages:
1. Collect/Extract: – Gathering data from varied sources (SQL databases, APIs, files, etc.).
2. Ingest/Load: – Moving the data into a storage layer (data lake, warehouse, or lakehouse).
3. Store/Compute/Consume: – Processing and making the data available for analytics, reporting, or further computation.
ETL/ELT Concepts:

– The traditional ETL (Extract, Transform, Load) process is recast here as extraction, normalization (transformation/cleaning), and loading.

– DLT handles the first three steps, focusing on efficient data ingestion and schema management.

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3. DLT: Overview and Key Features

What is DLT?

– An open source library designed for data ingestion.

– Simplifies connecting to many data sources (APIs, databases, cloud storage) and loading data into multiple destinations.
Key Capabilities:

– Extraction:

• Supports REST APIs (as well as file-based and database sources).

• Automatically handles pagination, rate limits, authentication, and retries.
– Normalization:

• Infers and applies schemas automatically.

• Flattens nested JSON structures (e.g., splitting nested objects and lists into child tables).

• Converts data types (e.g., strings to dates/numbers) and standardizes column naming.
– Loading:

• Connects to various destinations (local databases like DuckDB, cloud warehouses like BigQuery, or file systems for data lakes).

• Automates table creation and schema mapping based on destination requirements.
– Incremental Loading:

• Tracks state using special system tables (prefixed with underscore “_DLT”) to load only new or updated records.

• Supports both append (for immutable data) and merge (for data that may change over time) strategies.
– Error Handling & Performance:

• Built-in mechanisms for managing hardware limits, network issues, and retries (including exponential backoff).

• Options for multi-threaded and multi-process loading to boost throughput.
– Extensibility:

• Uses Python decorators (like @dlt.resource) to let you define custom data sources.

• Provides a simple streamlit-based UI (via “dlt show”) to inspect pipelines and metadata.

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4. Ingesting Data from APIs

API Types Covered:

– REST APIs (most common; often return JSON data)

– File-based APIs (returning bulk data in JSON/CSV)

– Database APIs (where SQL queries are behind an API)
Challenges with APIs:

– Rate Limits:

• APIs restrict the number of requests in a given time frame.

• Best practices include monitoring headers for rate limit info and using retry mechanisms.
– Pagination:

• Data is often returned in pages (e.g., 1000 records per page).

• Manual looping versus using DLT’s built-in pagination (generator-based to reduce memory usage).
– Authentication:

• Methods include API keys, OAuth tokens, and basic authentication.

• It is essential to secure credentials (e.g., via environment variables).
– Memory Management:

• Processing large volumes of data page by page (using generators) avoids memory overload.
– Error Handling:

• Handling intermittent network issues with retries and respect for “retry-after” headers.

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5. Data Normalization Process

Purpose:

– Convert raw, often nested and inconsistent, JSON data into a structured, relational format for analysis.
Key Steps in Normalization:

– Schema Inference:

• Automatically detect data types (timestamps, numbers, booleans) and assign them.
– Renaming & Standardization:

• Adjust column names to adhere to destination naming rules.
– Flattening Nested Data:

• Convert nested dictionaries into separate columns.

• Extract lists (e.g., passenger lists) into separate child tables.
– Data Type Conversion:

• Convert strings to appropriate types (e.g., ISO date strings, numeric types).
DLT’s Role:

– Automates the normalization process so you don’t have to write manual transformation logic for each API.

– Manages schema evolution if new fields are added or data types change over time.

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6. Loading Data to Various Destinations

Manual vs. DLT-Assisted Loading:

– Manual Approach:

• Requires explicit table creation, SQL schema definitions, and custom error handling.

• Tedious when dealing with multiple data sources and complex transformations.
– DLT-Assisted Loading:

• Abstracts away the manual schema management.

• Automatically creates tables in the destination (whether DuckDB, BigQuery, etc.).

• Integrates incremental loading and state tracking.
Destinations Demonstrated:

– Local Databases (e.g., DuckDB):

• Useful for prototyping and debugging.
– Cloud Data Warehouses (e.g., BigQuery):

• Requires proper credential management.

• DLT automatically maps internal data types to the warehouse-specific schema.
– Data Lakes:

• Data can be stored as files (e.g., Parquet) on local file systems or cloud storage (like S3).

• Emphasizes the need for proper partitioning and metadata management (using formats like Iceberg or Delta tables).

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7. Incremental Loading and State Management

Why Incremental Loading?

– Avoid reprocessing the entire dataset each time.

– Only load new or modified records.
How DLT Manages Incremental Loads:

– Uses a cursor (e.g., based on a timestamp field such as “trip_drop_off_datetime”) to track the last loaded record.

– Two modes:

• Append: Simply adds new records (ideal for immutable datasets).

• Merge: Updates existing records if data changes (suitable for stateful datasets).
State Tracking:

– DLT stores pipeline state and load metadata in internal system tables.

– These tables help ensure data consistency and prevent duplicate loads.

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8. Additional Features & Best Practices

Extending DLT:

– You can write custom data sources using the DLT resource decorator.

– Enrichment tasks (adding extra columns from other sources) are possible, although best practice is to load different sources separately and then join them later.
User Interface:

– DLT provides a lightweight streamlit-based UI (“dlt show”) to monitor pipelines, inspect data types, and view job metadata.
Community & Documentation:

– Extensive documentation is available to help with specific use cases (such as loading from Parquet files into BigQuery).

– Active community support (via Slack and public forums) is encouraged for troubleshooting and sharing projects.
Comparison with Other Tools:

– DLT vs. DBT:

• DLT focuses on data ingestion (extract, normalize, load), while DBT is used for transformation (SQL-based data modeling).

– DLT vs. Kafka:

• Kafka is a message broker for streaming data, whereas DLT provides richer features (e.g., schema management, normalization) for building complete ingestion pipelines.

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9. Summary & Takeaways

DLT’s Value Proposition:

– Simplifies the creation and management of data pipelines by automating extraction, normalization, and loading.

– Handles common challenges in API data ingestion (rate limits, pagination, authentication, memory issues) with built-in best practices.
Practical Implications:

– With DLT, you can focus on your data’s business logic rather than writing extensive boilerplate code.

– The framework’s ability to work with multiple destinations (cloud warehouses, local file systems, data lakes) makes it versatile for many modern data engineering projects.
Next Steps:

– Explore DLT documentation for detailed code examples and configuration options.

– Engage with the DLT community to share experiences and get help with advanced scenarios.

– Consider the provided homework tasks (e.g., rewriting a script using DLT to load Parquet files to BigQuery) to reinforce learning.