Databricks Azure Data Factory UC4 Job delta optimization and diverse strategies for ETL process and Data sourcing work flow process strategies

To load data from S3 files into Delta tables, transform them, and create an audit Delta table version using PySpark, we can follow these steps:

Steps:

1. Set Up Configurations: Connect PySpark with AWS S3.

2. Load Data from S3: Read the files into a DataFrame.

3. Transform Data: Apply minimal transformations based on your requirements.

4. Write Data to Delta Tables: Write the transformed data to a Delta table.

5. Create an Audit Delta Table: Add metadata (e.g., version, timestamps, operations) for auditing purposes.

Prerequisites:

PySpark is configured with Delta Lake.

AWS credentials are accessible (either in the environment or through a credentials file).

Delta Lake libraries are included in the PySpark environment.

---

Example Code:

from pyspark.sql import SparkSession

from pyspark.sql.functions import current_timestamp, lit

# Initialize Spark session with Delta Lake support

spark = SparkSession.builder \

    .appName("S3 to Delta with Audit") \

    .config("spark.jars.packages", "io.delta:delta-core_2.12:2.4.0") \

    .config("spark.sql.extensions", "io.delta.sql.DeltaSparkSessionExtension") \

    .config("spark.sql.catalog.spark_catalog", "org.apache.spark.sql.delta.catalog.DeltaCatalog") \

    .getOrCreate()

# Step 1: Configure S3 Access

spark._jsc.hadoopConfiguration().set("fs.s3a.access.key", "YOUR_AWS_ACCESS_KEY")

spark._jsc.hadoopConfiguration().set("fs.s3a.secret.key", "YOUR_AWS_SECRET_KEY")

spark._jsc.hadoopConfiguration().set("fs.s3a.endpoint", "s3.amazonaws.com")

# Step 2: Read data from S3

s3_path = "s3a://your-bucket/your-folder/"

input_df = spark.read.format("csv").option("header", "true").load(s3_path)

# Step 3: Apply minimal transformations

# Example: Add an ingestion timestamp

transformed_df = input_df.withColumn("ingestion_time", current_timestamp())

# Step 4: Write transformed data to a Delta table

delta_table_path = "s3a://your-bucket/delta/your-table/"

transformed_df.write.format("delta").mode("overwrite").save(delta_table_path)

# Step 5: Create an Audit Delta Table

# Add versioning and metadata for auditing

audit_df = transformed_df.withColumn("operation", lit("INSERT")) \

                         .withColumn("version", lit(1)) # Increment this for subsequent writes

audit_table_path = "s3a://your-bucket/delta/audit-your-table/"

audit_df.write.format("delta").mode("overwrite").save(audit_table_path)

# Step 6: Register Delta Tables (optional, for querying in SQL)

spark.sql(f"CREATE TABLE IF NOT EXISTS your_table USING DELTA LOCATION '{delta_table_path}'")

spark.sql(f"CREATE TABLE IF NOT EXISTS audit_your_table USING DELTA LOCATION '{audit_table_path}'")

# Verify data

spark.sql("SELECT * FROM your_table").show()

spark.sql("SELECT * FROM audit_your_table").show()

---

Explanation of Key Steps:

1. Reading from S3:

spark.read.format("csv"): Adjust this format to match your file type (e.g., parquet, json).

2. Minimal Transformations:

Add metadata like ingestion_time for tracking data ingestion.

3. Writing to Delta:

Use write.format("delta") to save data as Delta tables.

Specify the mode (overwrite for initial load, append for incremental loads).

4. Audit Table:

Add columns like operation and version for tracking operations and versions.

5. Delta Table Registration:

Register Delta tables in the Spark catalog for SQL-based querying.

---

Key Points:

Delta Lake automatically manages ACID transactions and versioning.

For incremental loads, you can use the MERGE operation in Delta Lake to upsert data.

To explore Delta table versions, use:

DESCRIBE HISTORY your_table;

Optimization 

Optimization Strategies for High-Volume Delta Table Workflows with UC4 Jobs and Workflow Automation

1. Optimization Strategies for High Volume

When the data volume grows, optimizing the ETL pipeline and analytics processes becomes critical. Here’s a systematic approach:

---

1.1 Data Partitioning and Bucketing

Partition Delta tables by high-cardinality columns such as date or region.

transformed_df.write.partitionBy("date").format("delta").mode("overwrite").save(delta_table_path)

Use bucketing for low-cardinality columns to reduce shuffle overhead.

---

1.2 Incremental Loads

Only load data that has changed using Delta Lake's Merge or Append operations.

Example:

delta_table.alias("target").merge(

    incremental_data.alias("source"),

    "target.id = source.id"

).whenMatchedUpdateAll().whenNotMatchedInsertAll().execute()

---

1.3 Data Skipping and Z-Ordering

Use Z-Ordering to colocate frequently queried columns in storage.

delta_table.optimize().executeZOrderBy("query_column")

---

1.4 Optimize Table Compaction

Periodically compact small Delta files into larger files.

delta_table.optimize().executeCompaction()

---

1.5 Use Delta Cache

Enable Delta Cache for frequently accessed tables in Spark.

spark.conf.set("spark.databricks.io.cache.enabled", "true")

---

1.6 Query Optimization

Use filter and select to minimize data scanned during queries.

df = spark.read.format("delta").load(delta_table_path).filter("date = '2024-11-18'").select("id", "value")

---

1.7 Handle Schema Evolution

Enable schema evolution for smooth updates.

df.write.option("mergeSchema", "true").format("delta").mode("append").save(delta_table_path)

---

1.8 Automate Cleanup

Automate deletion of older Delta versions using VACUUM for storage optimization.

spark.sql("VACUUM delta_table RETAIN 7 HOURS")

---

2. Using UC4 Jobs and Workflows for Optimization

UC4 Workflow Design:

Create modular UC4 jobs for the ETL process (e.g., Extract, Transform, Load, Audit).

Define workflows to manage dependencies and execution order:

Trigger Frequency: Hourly/Daily, depending on data arrival patterns.

Failure Handling: Retry or alert mechanisms.

Dynamic Parameters: Pass runtime parameters like date or region.

Example Workflow Steps:

1. Step 1: Load new data from S3 to the Delta staging table.

2. Step 2: Apply transformations and load into the final Delta table.

3. Step 3: Update the Audit table with metadata.

4. Step 4: Trigger data analytics jobs.

---

3. Per Hourly/Per Day Table Partitioning and Frequency

Partition Strategy:

Partition Delta tables by hour or day for granular updates.

transformed_df.write.partitionBy("date", "hour").format("delta").mode("overwrite").save(delta_table_path)

Hourly Workflow in UC4:

1. Step 1: Schedule data ingestion hourly.

2. Step 2: Run incremental loads.

3. Step 3: Trigger analytics jobs for the past hour.

Daily Workflow in UC4:

1. Step 1: Consolidate hourly partitions into daily partitions.

2. Step 2: Trigger daily analytics and reporting.

---

4. Minimizing Time for Data Analytics

Quick Time Execution Strategies:

Pre-Aggregation: Compute daily/hourly aggregates during ETL.

hourly_agg_df = input_df.groupBy("hour").agg({"value": "sum"})

hourly_agg_df.write.format("delta").mode("overwrite").save(agg_table_path)

Materialized Views: Use Delta views for pre-computed analytics.

CREATE TABLE daily_aggregates USING DELTA AS

SELECT date, SUM(value) AS total_value FROM delta_table GROUP BY date;

Query Optimization: Use indexing and caching.

Parallelism: Leverage Spark's parallelism for distributed computations.

---

5. Monitoring and Audit Delta Table

Audit Table Design:

Add metadata like operation, timestamp, and user.

audit_df = transformed_df.withColumn("operation", lit("INSERT")) \

                         .withColumn("timestamp", current_timestamp()) \

                         .withColumn("user", lit("ETL_Job"))

audit_df.write.format("delta").mode("append").save(audit_table_path)

UC4 Job Monitoring:

Use UC4's built-in monitoring and alerting features for job success/failure.

---

This design ensures scalability, automation, and quick execution of high-volume workflows.