ETL Load Phase and Strategies

• The Load phase in ETL involves inserting transformed data into the target system, such as a Data Warehouse, Data Lake, or an Analytical Database.
• This phase ensures efficient data storage and optimization for querying and analytics.
• The Load stage delivers transformed data into its new home, the target system.
• Writes transformed data to the target system reliably and efficiently, making it ready for action.

Types of Data Loading Methods

• Data loading depends on system requirements, data size, and performance needs.
• Full Load: Overwrites all existing data, used for initial loading or periodic refresh, as in loading historical sales data.
• Incremental Load: Loads only new or changed records, suitable when source data changes frequently.
• Batch Load: Processes data in scheduled batches (e.g., hourly, daily), used when near real-time data is not needed, such as loading website traffic logs.
• Real-Time Load: Continuously loads new data in near real-time, required when real-time updates are needed.
• Example: Loading sales data every second.

Load Strategies

• Full Load overwrites existing data and is used for initial loading or historical data refresh.
• Full Load can be slow for large datasets.
• Example Full Load into Data Warehouse:
  • TRUNCATE TABLE sales_data;
  • INSERT INTO sales_data (order_id, customer_id, total_amount, order_date) SELECT order_id, customer_id, total_amount, order_date FROM staging_sales;
• Incremental Load loads only new or updated records.
• Incremental Load is faster and more efficient for large datasets.
• Incremental Load requires a timestamp, primary key, or Change Data Capture (CDC).
• Example Incremental Load using updated_at timestamp:
  • INSERT INTO sales_data (order_id, customer_id, total_amount, order_date) SELECT order_id, customer_id, total_amount, order_date FROM staging_sales WHERE updated_at > (SELECT MAX(updated_at) FROM sales_data);
• Batch Load loads data at scheduled intervals (e.g., hourly, daily).
• Batch Load is suitable for large volumes of data that do not need real-time updates.
• Example of Batch Load: Batch Processing in Apache Airflow:
  • from airflow import DAG
  • from airflow.operators.python import PythonOperator
  • The code snippet provided demonstrates how to load sales data in batch mode using Apache Airflow.
• Real-Time (Streaming) Load loads data continuously, using event-driven architectures like Kafka or AWS Kinesis.
• Real-Time (Streaming) Load is suitable for IoT, stock market, or fraud detection systems.
• Example of Real-Time Streaming Load implemented using Kafka and Spark:
  • from pyspark.sql import SparkSession
  • spark = SparkSession.builder.appName("RealTimeLoad").getOrCreate()
  • df = spark.readStream.format("kafka").option("subscribe", "sales_topic").load()
  • df.writeStream.format("snowflake").option("table", "sales_data").start()

Load Approaches in Data Warehousing

• Append-Only Load adds new records without modifying existing data, like storing log files.
• Upsert (Insert + Update) updates existing records or inserts them if not found, as with customer profile updates.
• Uses the SQL MERGE INTO statement

Slowly Changing Dimensions (SCD)

• SCD tracks historical changes in data, like updating product prices while keeping history.
• Example of Upsert Load (Insert + Update)
  • MERGE INTO sales_data AS target USING staging_sales AS source ON target.order_id = source.order_id WHEN MATCHED THEN UPDATE SET target.total_amount = source.total_amount WHEN NOT MATCHED THEN INSERT (order_id, customer_id, total_amount, order_date) VALUES (source.order_id, source.customer_id, source.total_amount, source.order_date);

Data Validation During Load

• Ensure data integrity and accuracy before loading.
• Use checksums, row counts, and constraints for data validation.
• Validating Data Before Load:
  • SELECT COUNT(*) FROM staging_sales; -- Count rows before loading
  • SELECT COUNT(*) FROM sales_data; -- Count rows after loading
  • SELECT * FROM sales_data WHERE total_amount < 0; -- Check for invalid amounts

Challenges in Data Loading

• Performance Issues: Large data loads can slow down systems.
• Duplicate Data: Improper handling can cause duplicate entries.
• Data Integrity Issues: Missing foreign keys or constraints.
• Real-Time Latency: Delays in streaming data pipelines.

Solutions to Real-Time Latency

• Use indexes and partitioning for faster queries.
• Optimize load using bulk insert instead of row-by-row processing.
• Implement retry mechanisms.

Load Performance Optimization Techniques

• Bulk Insert: Load large datasets in chunks instead of row-by-row.
• Indexing: Improve query speed on large tables.
• Partitioning: Store data in separate partitions (e.g., by month).
• Compression: Reduce storage size for faster queries.
• Optimized Bulk Insert:
  • COPY INTO sales_data FROM '/staging/sales.csv' FILE_FORMAT = (TYPE = 'CSV', SKIP_HEADER = 1);

Tools for Data Loading

• SQL-Based: PostgreSQL, MySQL, Snowflake
• Cloud-Based: AWS Redshift, BigQuery, Azure Synapse
• ETL Tools: Apache Airflow, Informatica, Talend
• Streaming Tools: Apache Kafka, AWS Kinesis, Spark Streaming

ETL Load Pipeline

• Step 1: Extract Data from MySQL statement: SELECT * FROM orders WHERE order_date >= NOW() - INTERVAL 1 DAY;
• Step 2: Transform Data in Python using pandas.
• Step 3: Load into Snowflake using COPY INTO command.
• Copy into statement COPY INTO sales_data FROM @my_stage/orders_transformed.csv FILE_FORMAT = (TYPE = 'CSV', SKIP_HEADER = 1);

Key Considerations and Techniques in the Load Stage

• Database Type (Relational, NoSQL, Columnar) with its own loading quirks.
• Schema Design and how it dictates how the data needs to be loaded.
• Performance considerations with some systems needing super fast data loading.
• Concurrency: Can the system handle many loads happening at once?
• Full Load deletes everything and loads all the data fresh, which is good for initial data loads.
• Incremental Load loads only the new or changed data since the last time, and is more efficient for regular updates.
• Delta Load loads only additions, modifications, and deletions, requiring tracking changes.
• Snapshot Load loads a complete picture of the data at a specific point if it's different from before.
• Trickle Feed loads data in small bits or even one by one as it comes in, frequently used for real-time data.

Data Integrity and Quality techniques

• Constraints & Validation ensuring the loaded data follows the rules of the target system (like having a unique ID).
• Error Handling to determine what happens when something goes wrong during loading.
• Transaction Management ensures important data is either all loaded successfully, or none is loaded.

Performance Optimization techniques

• Batch Processing enables loading data in groups instead of one at a time is much faster.
• Indexing ensures the target system has the right "shortcuts" to speed up loading and later querying.
• Parallel Loading enables doing multiple loads at the same time if the system allows.
• Optimized Writes uses the most efficient commands the target database offers for writing data.

Auditing and Logging considerations

• Tracking Loads to keep records of when data was loaded, how much, and if there were any issues.
• Data Lineage for understanding where the data came from and how it got to the target system (including the load process).
• The load stage is the final step where the transformed data lands into its new home.
• Goal of load stage is to reliably and efficiently write data to its target.