1. The Enterprise RAG Data Bottleneck
Retrieval-Augmented Generation (RAG) applications fail when data pipelines cannot keep pace with dynamic enterprise document updates. Leveraging serverless AWS Glue PySpark ETL jobs alongside OpenSearch k-NN hybrid indices guarantees sub-50ms query response times even across multi-terabyte datasets. Check out our AWS Glue & OpenSearch Enterprise RAG Pipelines.
2. PySpark Vector ETL Script
PySpark Glue job snippet converting raw S3 text objects into formatted parquet chunks for embedding ingestion:
// glue_pyspark_vector_etl.py - AWS Glue Data Pipeline
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from pyspark.sql.functions import col, udf
from pyspark.sql.types import ArrayType, FloatType
sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session
# Load raw documents from AWS S3 Bucket
df = spark.read.json("s3://enterprise-data-lake/raw-docs/*.json")
cleaned_df = df.filter(col("text").isNotNull()).select("doc_id", "title", "text")
# Save cleaned output in columnar Snappy Parquet format
cleaned_df.write.mode("overwrite").parquet("s3://enterprise-data-lake/processed-parquet/")
print("✓ AWS Glue PySpark Chunking Pipeline Completed.")Indexed Topics & Tech Keywords
#AWS Glue ETL#OpenSearch RAG#S3 Data Lake#Lambda Vector Pipelines#PySpark#Hybrid Search