Aws Glue To Snowflake

How to Optimize Data Pipelines from AWS Glue to Snowflake for Scalability and Cost Efficiency

AWS Glue and Snowflake together provide a robust platform for ingesting, transforming, and analyzing data at scale. However, the simple ETL path—Glue job to JDBC upsert into Snowflake—doesn’t hold up as data sets approach tens or hundreds of terabytes. Cost spikes, cluster bottlenecks, and IO inefficiencies happen fast.

Below: practical strategies for controlling costs and ensuring throughput when integrating AWS Glue (tested against AWS Glue 4.0+) with Snowflake (tested with Snowflake Enterprise, v7.x).

Partitioning: Foundation for Efficient Processing

Code first, diagnose why later. Here’s how partitioning saves time and credits:

import sys
from awsglue.context import GlueContext
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext

args = getResolvedOptions(sys.argv, ['JOB_NAME', 'process_date'])
sc = SparkContext()
glueContext = GlueContext(sc)

# Only pull relevant partition from Glue Data Catalog table
sales = glueContext.create_dynamic_frame.from_catalog(
    database="sales_db",
    table_name="daily_sales",
    push_down_predicate=f"process_date='{args['process_date']}'"
)

Above: processing daily partitions cuts both cumulative runtime and per-job memory allocation.

Partitioning applies on both ends—source and target. In Snowflake, clustering keys like (sale_date, region) accelerate query pruning. That said, Snowflake partitions are managed internally; clustering helps, but don’t burn credits on RECLUSTER for small tables.

Typical gotcha:
Glue Data Catalog partition keys must be registered in AWS Glue, else push_down_predicate simply scans everything. Missing partitions are the top source of surprise 30x AWS bill increases.

Bulk Loads vs. JDBC Inserts

JDBC is clean for tiny trickles. For real volume, switch to bulk-loading Parquet or ORC directly to S3, then ingest with Snowflake’s COPY INTO for parallelism.

Minimalist two-stage pipeline architecture:

• Glue ETL job: write partitioned Parquet files to S3, e.g., s3://my-bucket/transformed/sale_date=2023-06-01/part-001.snappy.parquet
• Out-of-band loader: Lambda, Airflow, or native Snowflake external stage triggers COPY INTO.

COPY INTO myschema.sales
FROM @my_stage/transformed/sale_date=2023-06-01/
FILE_FORMAT = (type = 'parquet' compression = 'snappy');

Side note: Parquet with snappy compression generally yields the fastest loads with minimal tuning for Snowflake (see Snowflake file best practices).

If you see warning Load failed due to file size too small, batch Parquet output so files are at least 100 MB each. An easy way: set Spark partition count in Glue using .repartition(n).

Tuning Glue Jobs: DPU and Bookmarks

Over-allocating Glue DPUs wastes budget, under-allocating extends wall clock runtime and drives indirect charges (S3, CloudWatch, Snowflake idle). Typical starting points:

Test minimal DPU settings; scale up if Spark log shows repeated OOM errors.

Bookmarks: always enable for incremental ETL jobs. Bookmarks prevent duplicate loads, especially with append-only data. In Python:

from awsglue.job import Job

job = Job(glueContext)
job.init(args['JOB_NAME'], args)

# downstream ETL .. 
job.commit()  # Bookmarks auto-managed

Known issue: Bookmarks do not work if job logic rewrites earlier partitions out of sequence. Stick to forward-only, append models.

Pushdown Filtering: Keep Data Movement Local

Always use pushdown predicates when pulling partitions from S3 via Glue Catalog:

frame = glueContext.create_dynamic_frame.from_catalog(
    database="my_db",
    table_name="event_data",
    push_down_predicate="year='2023' AND month='06'"
)

Practical note: pushdown filtering only works if partition columns are present in the Glue Catalog table definition. Otherwise, the predicate is silently ignored—check Spark logs for No partition predicate applied, an easy-to-miss sign.

Snowflake Warehouse Scaling: Automation Required

Bulk loads swamp under-sized warehouses and burn credits on idle. For ETL ingestion:

• Use X-Small or Small warehouses per job, with AUTO_SUSPEND = 60 and AUTO_RESUME = TRUE.
• For parallelism across days/regions, multi-cluster warehouses can unblock deadlocks but double spend if not closely monitored.
• Activate Resource Monitors; set at 80% usage for alerting.

-- Monitor setup (adjust thresholds per environment)
CREATE RESOURCE MONITOR etl_credit_monitor
  WITH CREDIT_QUOTA = 500
  TRIGGERS ON 80 PERCENT DO NOTIFY;

Bulk ingest off peak. Snowflake historically offers better I/O during region traffic lulls (e.g., after 0200 UTC for us-east-1), but monitor your own workload for local maxima.

Pipeline Overview Table

Additional Practicalities

• Schema evolution: Glue and Snowflake handle minor schema adds (columns) transparently, but type changes require manual migration.
• Error patterns: If COPY INTO fails with File not found or Stage file is empty, suspect stale S3 object listings or write delays out of Glue.
• Alternative: Consider Snowpipe for near-real-time loads, but costs and operational overhead typically exceed batch COPY for large daily volumes.

Pipe design matters more than code optimizations. Most wasted dollars come from missed partitioning and lack of incremental bookmarks. Lock down partition logic, bulk-load patterns, and automate monitoring—performance and cost predictability will follow. There’s no silver bullet, but following these conventions shields pipelines from the usual scale-induced surprises.