Incrementally Write Data to Delta Lake in Azure Synapse Analytics | Azure Synapse Tutorial

Incrementally Write Data to Delta Lake in Azure Synapse Analytics

📘 Overview

Delta Lake provides ACID-compliant storage that enables scalable and reliable data lake solutions. With Apache Spark Pools in Azure Synapse Analytics, you can incrementally write data to Delta tables using merge operations or overwrite modes for upserts.

💡 Why Incremental Writes?

• Efficient handling of new or updated records
• Reduced cost and faster performance over full reloads
• Supports upsert (insert + update) logic

🛠️ Step-by-Step: Upsert to Delta Table

1. Load New Data

%%pyspark
new_data = [
    (1, "Alice", "2024-01-01"),
    (2, "Bob", "2024-01-02")
]
columns = ["id", "name", "modified_date"]
df_new = spark.createDataFrame(new_data, columns)

2. Write Base Delta Table (if not exists)

df_new.write.format("delta").mode("overwrite") \
    .save("abfss://container@account.dfs.core.windows.net/delta/customer")

3. Merge New Data (Incremental Write)

from delta.tables import DeltaTable

delta_table = DeltaTable.forPath(spark, "abfss://container@account.dfs.core.windows.net/delta/customer")

delta_table.alias("target").merge(
    df_new.alias("source"),
    "target.id = source.id"
).whenMatchedUpdateAll().whenNotMatchedInsertAll().execute()

📦 Notes

• You must import DeltaTable from the Delta Lake module
• The merge function ensures existing records are updated and new ones inserted
• Delta Lake auto-manages transaction logs for rollback and audit

✅ Best Practices

• Use partitioning if writing large volumes of data
• Track modified dates to avoid reprocessing old records
• Validate schema before merges to prevent errors

📈 Use Cases

• CDC (Change Data Capture) implementation
• Daily/Hourly incremental ingestion jobs
• Data warehouse staging layer with Delta Lake

📺 Watch the Video Tutorial

📚 Credit: Content created with the help of ChatGPT and Gemini.