Writing Streaming Data to Sinks in PySpark — Console, Parquet, Snowflake

At NeoMart, streaming data is only valuable when it reaches dashboards, storage, or analytics systems:

• Console → quick debugging
• Parquet → persistent storage for batch + streaming queries
• Snowflake → real-time analytics and reporting

Structured Streaming provides a unified API to write data safely and efficiently.

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1. Writing to Console (Quick Debugging)

Input Data:

+--------+-----------+------+
|order_id|customer_id|amount|
+--------+-----------+------+
|O1001   |C101       |250   |
|O1002   |C102       |150   |
|O1003   |C103       |300   |
+--------+-----------+------+

Code:

query_console = df_parsed.writeStream \
    .outputMode("append") \
    .format("console") \
    .option("truncate", "false") \
    .start()

query_console.awaitTermination()

Console Output Example:

+--------+-----------+------+
|order_id|customer_id|amount|
+--------+-----------+------+
|O1001   |C101       |250   |
|O1002   |C102       |150   |
|O1003   |C103       |300   |
+--------+-----------+------+

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2. Writing to Parquet (Persistent Storage)

Input Data: Same as above

Code:

query_parquet = df_parsed.writeStream \
    .outputMode("append") \
    .format("parquet") \
    .option("path", "/tmp/neomart_orders") \
    .option("checkpointLocation", "/tmp/checkpoints_parquet") \
    .start()

query_parquet.awaitTermination()

Output Example (Files Created in /tmp/neomart_orders):

/tmp/neomart_orders/
 ├── part-00000-xxxx.snappy.parquet
 ├── part-00001-xxxx.snappy.parquet
 └── part-00002-xxxx.snappy.parquet

Reading back Parquet files:

spark.read.parquet("/tmp/neomart_orders").show()

Output:

+--------+-----------+------+
|order_id|customer_id|amount|
+--------+-----------+------+
|O1001   |C101       |250   |
|O1002   |C102       |150   |
|O1003   |C103       |300   |
+--------+-----------+------+

---

3. Writing to Snowflake (Cloud Analytics)

Input Data: Same as above

Code:

sf_options = {
    "sfURL": "account.snowflakecomputing.com",
    "sfDatabase": "NEOMART_DB",
    "sfSchema": "PUBLIC",
    "sfWarehouse": "COMPUTE_WH",
    "sfRole": "SYSADMIN",
    "sfUser": "USER",
    "sfPassword": "PASSWORD"
}

query_snowflake = df_parsed.writeStream \
    .format("snowflake") \
    .options(**sf_options) \
    .option("dbtable", "ORDERS_STREAM") \
    .option("checkpointLocation", "/tmp/checkpoints_snowflake") \
    .outputMode("append") \
    .start()

query_snowflake.awaitTermination()

Snowflake Output Example:

SELECT * FROM ORDERS_STREAM;

+---------+------------+------+
|ORDER_ID |CUSTOMER_ID |AMOUNT|
+---------+------------+------+
|O1001    |C101        |250   |
|O1002    |C102        |150   |
|O1003    |C103        |300   |
+---------+------------+------+

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4. Choosing OutputMode

Mode	Use Case
append	Only new rows, no updates (Kafka, Parquet, Snowflake)
complete	Full aggregation results (console, dashboards)
update	Only changed rows since last batch (stateful aggregations)

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5. Best Practices

✔ Always set checkpointLocation → prevents data loss ✔ Use append mode for streaming writes to Parquet/Kafka/Snowflake ✔ Optimize batch size with .trigger(processingTime='10 seconds') ✔ Monitor sinks for backpressure or failures ✔ Test with console sink before writing to production sinks

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Summary

NeoMart’s streaming pipelines efficiently write data to:

• Console → debugging & testing
• Parquet → scalable, persistent storage
• Snowflake → real-time analytics

Structured Streaming ensures reliable, exactly-once delivery, combining performance, fault-tolerance, and flexibility.

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Next Topic → Introduction to MLlib — Pipelines, Transformers, and Estimators