1.1 What is data engineering?

Data engineering is a specialized field of data management where developers focus on designing, building, and maintaining technology systems for data storage, processing, and analytics. Data engineers play a crucial role in ensuring that data flows from various sources to end-users, such as data analysts and data scientists, enabling informed decision-making and advanced analytics.

Key components of data engineering:

1. Data collection and ingestion: Data needs to be collected from various sources such as APIs, IoT devices, logs, and external data providers.

2. Data processing and transformation: Raw data often requires cleaning, aggregating, and transforming into a usable format for analysis.

3. Data integration and pipeline orchestration:

4. Data storage: Once data is collected, orchestrated, and transformed it needs to be stored in a manner that supports efficient retrieval and analysis.

5. Data quality and governance: Data quality measures involve validation, error handling, and monitoring, while governance encompasses policies for data access, privacy, and compliance.

6. Scalability and performance optimization: As data volumes grow, the infrastructure must scale to handle increased load without compromising performance. Optimization involves fine-tuning storage, processing, and retrieval mechanisms to ensure efficient operations.

Figure 1: Data orchestration flow

Data engineering at scale demands significant resources and can quickly outgrow manual processes commonly used by many businesses. This is where Mage becomes indispensable. Mage streamlines the data engineering process by facilitating efficient data collection, transformation, and pipeline orchestration. By automating complex workflows, enhancing operational efficiency, and ensuring scalability, Mage empowers organizations to manage large volumes of data with ease, maintain high data quality, and support robust analytics capabilities.

CODE TEST

-- Create a simple table for 311 requests
CREATE TABLE IF NOT EXISTS bronze_311_requests (
    unique_key VARCHAR(50),
    created_date VARCHAR(50),
    agency VARCHAR(10),
    complaint_type VARCHAR(100),
    borough VARCHAR(20),
    status VARCHAR(20)
);

-- Insert data from upstream block
INSERT INTO bronze_311_requests
SELECT 
    "Unique Key" as unique_key,
    "Created Date" as created_date,
    "Agency" as agency,
    "Complaint Type" as complaint_type,
    "Borough" as borough,
    "Status" as status
FROM {{ df_1 }};

-- Show a quick summary
SELECT COUNT(*) as total_records FROM bronze_311_requests;