Tuesday, May 30, 2023
HomeBig DataDeal with UPSERT knowledge operations utilizing open-source Delta Lake and AWS Glue

Deal with UPSERT knowledge operations utilizing open-source Delta Lake and AWS Glue

Many shoppers want an ACID transaction (atomic, constant, remoted, sturdy) knowledge lake that may log change knowledge seize (CDC) from operational knowledge sources. There may be additionally demand for merging real-time knowledge into batch knowledge. Delta Lake framework offers these two capabilities. On this put up, we talk about learn how to deal with UPSERTs (updates and inserts) of the operational knowledge utilizing natively built-in Delta Lake with AWS Glue, and question the Delta Lake utilizing Amazon Athena.

We study a hypothetical insurance coverage group that points business insurance policies to small- and medium-scale companies. The insurance coverage costs differ primarily based on a number of standards, reminiscent of the place the enterprise is positioned, enterprise sort, earthquake or flood protection, and so forth. This group is planning to construct an information analytical platform, and the insurance coverage coverage knowledge is likely one of the inputs to this platform. As a result of the enterprise is rising, lots of and hundreds of recent insurance coverage insurance policies are being enrolled and renewed each month. Subsequently, all this operational knowledge must be despatched to Delta Lake in near-real time in order that the group can carry out numerous analytics, and construct machine studying (ML) fashions to serve their clients in a extra environment friendly and cost-effective method.

Answer overview

The info can originate from any supply, however usually clients need to deliver operational knowledge to knowledge lakes to carry out knowledge analytics. One of many options is to deliver the relational knowledge by utilizing AWS Database Migration Service (AWS DMS). AWS DMS duties might be configured to repeat the complete load in addition to ongoing adjustments (CDC). The total load and CDC load might be introduced into the uncooked and curated (Delta Lake) storage layers within the knowledge lake. To maintain it easy, on this put up we decide out of the information sources and ingestion layer; the belief is that the information is already copied to the uncooked bucket within the type of CSV information. An AWS Glue ETL job does the mandatory transformation and copies the information to the Delta Lake layer. The Delta Lake layer ensures ACID compliance of the supply knowledge.

The next diagram illustrates the answer structure.
Architecture diagram

The use case we use on this put up is a few business insurance coverage firm. We use a easy dataset that accommodates the next columns:

  • Coverage – Coverage quantity, entered as textual content
  • Expiry – Date that coverage expires
  • Location – Location sort (City or Rural)
  • State – Title of state the place property is positioned
  • Area – Geographic area the place property is positioned
  • Insured Worth – Property worth
  • Enterprise Kind – Enterprise use sort for property, reminiscent of Farming or Retail
  • Earthquake – Is earthquake protection included (Y or N)
  • Flood – Is flood protection included (Y or N)

The dataset accommodates a pattern of 25 insurance coverage insurance policies. Within the case of a manufacturing dataset, it might comprise tens of millions of data.

200358,2023-01-05,City,WI,Midwest,928300,Workplace Bldg,N,N
100463,2023-03-27,City,NY,East,15480000,Workplace Bldg,Y,Y
100617,2023-03-27,City,VT,Northeast,8861500,Workplace Bldg,N,N
100580,2023-03-30,City,NH,Northeast,97920,Workplace Bldg,Y,Y
100503,2023-03-31,City,NJ,East,1761960,Workplace Bldg,N,N
100611,2023-04-25,City,NJ,East,1595500,Workplace Bldg,Y,Y

Within the following sections, we stroll by the steps to carry out the Delta Lake UPSERT operations. We use the AWS Administration Console to carry out all of the steps. Nevertheless, you may also automate these steps utilizing instruments like AWS CloudFormation, the AWS Cloud Growth Equipment (AWS CDK), Terraforms, and so forth.


This put up is concentrated in direction of architects, engineers, builders, and knowledge scientists who construct, design, and construct analytical options on AWS. We anticipate a fundamental understanding of the console, AWS Glue, Amazon Easy Storage Service (Amazon S3), and Athena. Moreover, the persona is ready to create AWS Identification and Entry Administration (IAM) insurance policies and roles, create and run AWS Glue jobs and crawlers, and is in a position work with the Athena question editor.

Use Athena question engine model 3 to question delta lake tables, later within the part “Question the complete load utilizing Athena”.

Athena QE V3

Arrange an S3 bucket for full and CDC load knowledge feeds

To arrange your S3 bucket, full the next steps:

  1. Log in to your AWS account and select a Area nearest to you.
  2. On the Amazon S3 console, create a brand new bucket. Be certain that the title is exclusive (for instance, delta-lake-cdc-blog-<some random quantity>).
  3. Create the next folders:
    1. $bucket_name/fullload – This folder is used for a one-time full load from the upstream knowledge supply
    2. $bucket_name/cdcload – This folder is used for copying the upstream knowledge adjustments
    3. $bucket_name/delta – This folder holds the Delta Lake knowledge information
  4. Copy the pattern dataset and reserve it in a file known as full-load.csv to your native machine.
  5. Add the file utilizing the Amazon S3 console into the folder $bucket_name/fullload.

s3 folders

Arrange an IAM coverage and position

On this part, we create an IAM coverage for the S3 bucket entry and a task for AWS Glue jobs to run, and in addition use the identical position for querying the Delta Lake utilizing Athena.

  1. On the IAM console, select Polices within the navigation pane.
  2. Select Create coverage.
  3. Choose JSON tab and paste the next coverage code. Substitute the {bucket_name} you created within the earlier step.
    "Model": "2012-10-17",
    "Assertion": [
            "Sid": "AllowListingOfFolders",
            "Action": [
            "Impact": "Permit",
            "Useful resource": [
            "Sid": "ObjectAccessInBucket",
            "Impact": "Permit",
            "Motion": [
            "Useful resource": "arn:aws:s3:::{bucket_name}/*"

  1. Title the coverage delta-lake-cdc-blog-policy and choose Create coverage.
  2. On the IAM console, select Roles within the navigation pane.
  3. Select Create position.
  4. Choose AWS Glue as your trusted entity and select Subsequent.
  5. Choose the coverage you simply created, and with two further AWS managed insurance policies:
    1. delta-lake-cdc-blog-policy
    2. AWSGlueServiceRole
    3. CloudWatchFullAccess
  1. Select Subsequent.
  2. Give the position a reputation (for instance, delta-lake-cdc-blog-role).

IAM role

Arrange AWS Glue jobs

On this part, we arrange two AWS Glue jobs: one for full load and one for the CDC load. Let’s begin with the complete load job.

  1. On the AWS Glue console, below Information Integration and ETL within the navigation pane, select Jobs. AWS Glue Studio opens in a brand new tab.
  2. Choose Spark script editor and select Create.

Glue Studio Editor

  1. Within the script editor, substitute the code with the next code snippet
import sys
from awsglue.utils import getResolvedOptions
from pyspark.sql.session import SparkSession
from pyspark.sql.sorts import *

## @params: [JOB_NAME]
args = getResolvedOptions(sys.argv, ['JOB_NAME','s3_bucket'])

# Initialize Spark Session with Delta Lake
spark = SparkSession 
.config("spark.sql.extensions", "io.delta.sql.DeltaSparkSessionExtension") 
.config("spark.sql.catalog.spark_catalog", "org.apache.spark.sql.delta.catalog.DeltaCatalog") 

#Outline the desk schema
schema = StructType() 

# Learn the complete load
sdf = spark.learn.format("csv").choice("header",True).schema(schema).load("s3://"+ args['s3_bucket']+"/fullload/")

# Write knowledge as DELTA TABLE
sdf.write.format("delta").mode("overwrite").save("s3://"+ args['s3_bucket']+"/delta/insurance coverage/")

  1. Navigate to the Job particulars tab.
  2. Present a reputation for the job (for instance, Full-Load-Job).
  3. For IAM Position¸ select the position delta-lake-cdc-blog-role that you simply created earlier.
  4. For Employee sort¸ select G 2X.
  5. For Job bookmark, select Disable.
  6. Set Variety of retries to 0.
  7. Below Superior properties¸ maintain the default values, however present the delta core JAR file path for Python library path and Dependent JARs path.
  8. Below Job parameters:
    1. Add the important thing --s3_bucket with the bucket title you created earlier as the worth.
    2. Add the important thing --datalake-formats  and provides the worth delta
  9. Preserve the remaining default values and select Save.

Job details

Now let’s create the CDC load job.

  1. Create a second job known as CDC-Load-Job.
  2. Comply with the steps on the Job particulars tab as with the earlier job.
  3. Alternatively, it’s possible you’ll select “Clone job” choice from the Full-Load-Job, this may carry all of the job particulars from the complete load job.
  4. Within the script editor, enter the next code snippet for the CDC logic:
import sys
from awsglue.utils import getResolvedOptions
from awsglue.context import GlueContext
from pyspark.sql.session import SparkSession
from pyspark.sql.capabilities import col
from pyspark.sql.capabilities import expr

## For Delta lake
from delta.tables import DeltaTable

## @params: [JOB_NAME]
args = getResolvedOptions(sys.argv, ['JOB_NAME','s3_bucket'])

# Initialize Spark Session with Delta Lake
spark = SparkSession 
.config("spark.sql.extensions", "io.delta.sql.DeltaSparkSessionExtension") 
.config("spark.sql.catalog.spark_catalog", "org.apache.spark.sql.delta.catalog.DeltaCatalog") 

# Learn the CDC load
cdc_df = spark.learn.csv("s3://"+ args['s3_bucket']+"/cdcload")

# now learn the complete load (newest knowledge) as delta desk
delta_df = DeltaTable.forPath(spark, "s3://"+ args['s3_bucket']+"/delta/insurance coverage/")

# UPSERT course of if matches on the situation the replace else insert
# if there is no such thing as a key phrase then create an information set with Insert, Replace and Delete flag and do it individually.
# for delete it has to run in loop with delete situation, this script don't deal with deletes.
final_df = delta_df.alias("prev_df").merge( 
supply = cdc_df.alias("append_df"), 
#matching on primarykey
situation = expr("prev_df.policy_id = append_df._c1"))
.whenMatchedUpdate(set= {
    "prev_df.expiry_date"           : col("append_df._c2"), 
    "prev_df.location_name"         : col("append_df._c3"),
    "prev_df.state_code"            : col("append_df._c4"),
    "prev_df.region_name"           : col("append_df._c5"), 
    "prev_df.insured_value"         : col("append_df._c6"),
    "prev_df.business_type"         : col("append_df._c7"),
    "prev_df.earthquake_coverage"   : col("append_df._c8"), 
    "prev_df.flood_coverage"        : col("append_df._c9")} )
.whenNotMatchedInsert(values =
#inserting a brand new row to Delta desk
{   "prev_df.policy_id"             : col("append_df._c1"),
    "prev_df.expiry_date"           : col("append_df._c2"), 
    "prev_df.location_name"         : col("append_df._c3"),
    "prev_df.state_code"            : col("append_df._c4"),
    "prev_df.region_name"           : col("append_df._c5"), 
    "prev_df.insured_value"         : col("append_df._c6"),
    "prev_df.business_type"         : col("append_df._c7"),
    "prev_df.earthquake_coverage"   : col("append_df._c8"), 
    "prev_df.flood_coverage"        : col("append_df._c9")

Run the complete load job

On the AWS Glue console, open full-load-job and select Run. The job takes about 2 minutes to finish, and the job run standing adjustments to Succeeded. Go to $bucket_name and open the delta folder, which accommodates the insurance coverage folder. You may be aware the Delta Lake information in it. Delta location on S3

Create and run the AWS Glue crawler

On this step, we create an AWS Glue crawler with Delta Lake as the information supply sort. After efficiently working the crawler, we examine the information utilizing Athena.

  1. On the AWS Glue console, select Crawlers within the navigation pane.
  2. Select Create crawler.
  3. Present a reputation (for instance, delta-lake-crawler) and select Subsequent.
  4. Select Add an information supply and select Delta Lake as your knowledge supply.
  5. Copy your delta folder URI (for instance, s3://delta-lake-cdc-blog-123456789/delta/insurance coverage) and enter the Delta Lake desk path location.
  6. Preserve the default choice Create Native tables, and select Add a Delta Lake knowledge supply.
  7. Select Subsequent.
  8. Select the IAM position you created earlier, then select Subsequent.
  9. Choose the default goal database, and supply delta_ for the desk title prefix. If no default database exist, it’s possible you’ll create one.
  10. Select Subsequent.
  11. Select Create crawler.
  12. Run the newly created crawler. After the crawler is full, the delta_insurance desk is on the market below Databases/Tables.
  13. Open the desk to test the desk overview.

You may observe 9 columns and their knowledge sorts. Glue table

Question the complete load utilizing Athena

Within the earlier step, we created the delta_insurance desk by working a crawler towards the Delta Lake location. On this part, we question the delta_insurance desk utilizing Athena. Notice that should you’re utilizing Athena for the primary time, set the question output folder to retailer the Athena question outcomes (for instance, s3://<your-s3-bucket>/query-output/).

  1. On the Athena console, open the question editor.
  2. Preserve the default choices for Information supply and Database.
  3. Run the question SELECT * FROM delta_insurance;. This question returns a complete of 25 rows, the identical as what was within the full load knowledge feed.
  4. For the CDC comparability, run the next question and retailer the ends in a location the place you may evaluate these outcomes later:
SELECT * FROM delta_insurance
WHERE policy_id IN (100462,100463,100475,110001,110002)
order by policy_id;

The next screenshot exhibits the Athena question consequence.

Query results from full load

Add the CDC knowledge feed and run the CDC job

On this part, we replace three insurance coverage insurance policies and insert two new insurance policies.

  1. Copy the next insurance coverage coverage knowledge and reserve it regionally as cdc-load.csv:
U,100463,2023-03-27,City,NY,East,1000000,Workplace Bldg,Y,Y
I,110001,2024-03-31,City,CA,WEST,210000,Workplace Bldg,N,N

The primary column within the CDC feed describes the UPSERT operations. U is for updating an current document, and I is for inserting a brand new document.

  1. Add the cdc-load.csv file to the $bucket_name/cdcload/ folder.
  2. On the AWS Glue console, run CDC-Load-Job. This job takes care of updating the Delta Lake accordingly.

The change particulars are as follows:

  • 100462 – Expiry date adjustments to 12/31/2024
  • 100463 – Insured worth adjustments to 1 million
  • 100475 – This coverage is now below a brand new flood zone
  • 110001 and 110002 – New insurance policies added to the desk
  1. Run the question once more:
SELECT * FROM delta_insurance
WHERE policy_id IN (100462, 100463,100475,110001,110002)
order by policy_id;

As proven within the following screenshot, the adjustments within the CDC knowledge feed are mirrored within the Athena question outcomes.
Athena query results

Clear up

On this answer, we used all managed companies, and there’s no value if AWS Glue jobs aren’t working. Nevertheless, if you wish to clear up the duties, you may delete the 2 AWS Glue jobs, AWS Glue desk, and S3 bucket.


Organizations are constantly excessive efficiency, cost-effective, and scalable analytical options to extract the worth of their operational knowledge sources in near-real time. The analytical platform needs to be able to obtain adjustments within the operational knowledge as quickly as they happen. Typical knowledge lake options face challenges to deal with the adjustments in supply knowledge; the Delta Lake framework can shut this hole. This put up demonstrated learn how to construct knowledge lakes for UPSERT operations utilizing AWS Glue and native Delta Lake tables, and learn how to question AWS Glue tables from Athena. You may implement your massive scale UPSERT knowledge operations utilizing AWS Glue, Delta Lake and carry out analytics utilizing Amazon Athena.


In regards to the Authors

 Praveen Allam is a Options Architect at AWS. He helps clients design scalable, higher cost-perfromant enterprise-grade functions utilizing the AWS Cloud. He builds options to assist organizations make data-driven choices.

Vivek Singh is Senior Options Architect with the AWS Information Lab workforce. He helps clients unblock their knowledge journey on the AWS ecosystem. His curiosity areas are knowledge pipeline automation, knowledge high quality and knowledge governance, knowledge lakes, and lake home architectures.



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