Machine Learning based Fuzzy Matching using AWS Glue ML Transforms
By: Engineering Zenofai
Tags: Cloud Computing, Machine Learning, Spark, AWS, Software Development

Machine Learning Transforms in AWS Glue

AWS Glue provides machine learning capabilities to create custom transforms to do Machine Learning based fuzzy matching to deduplicate and cleanse your data. For this we are going to use a transform named FindMatches. The FindMatches transform enables you to identify duplicate or matching records in your dataset, even when the records do not have a common unique identifier and no fields match exactly. This will not require writing any code or knowing how machine learning works. For more details about ML Transforms, please go through the docs.

Creating a Machine Learning Transform with AWS Glue

This article walks you through the actions to create and manage a machine learning (ML) transform using AWS Glue. I assume that you are familiar with using the AWS Glue console to add crawlers and jobs and edit scripts. You should also be familiar with finding and downloading files on the Amazon Simple Storage Service (Amazon S3) console.

In case you are just starting out on AWS Glue, I have explained how to create an AWS Glue Crawler and Glue Job from scratch in one of my earlier articles.

The source data used in this blog is a hypothetical file named customers_data.csv. A second file, label_file.csv, is an example of a labeling file that contains both matching and nonmatching records used to teach the transform.

Step 1: Crawl the Data using AWS Glue Crawler

At the outset, crawl the source data from the CSV file in S3 to create a metadata table in the AWS Glue Data Catalog. I created a crawler pointing to the source location (s3://bucketname/data/ml-transform/customers/).

In case you are just starting out on the AWS Glue crawler, I have explained how to create one from scratch in one of my earlier articles. If you run this crawler, it creates a customers table in the specified database (ml-transform).

Step 2: Add a Machine Learning Transform

Next, add a machine learning transform that is based on the schema of your data source table created by the above crawler.

Choose Worker type and Maximum capacity as per the requirements.

3. For Data source, choose the table that was created in the earlier step. In this, the table named customers in database ml-transform.

4. For Primary key, choose the primary key column for the table, email.

Step 3: How to Teach Your Machine Learning Transform

Next, teach the machine learning transform using the sample labeling file.

You can’t use a machine language transform in an extract, transform, and load (ETL) job until its status is Ready for use. To get your transform ready, you must teach it how to identify matching and non-matching records by providing examples of matching and non-matching records. To teach your transform, you can Generate a label file, add labels, and then Upload label file.

For this article, the label file I have used is label_file.csv

On the AWS Glue console, in the navigation pane, choose ML Transforms.

Choose the earlier created transform, and then choose Action, Teach.

If you don’t have the label file, choose I do not have labels, you can Generate a label file, add labels, and then Upload label file.

If you have the label file, choose I have labels, then choose Upload labelling file from S3.

Choose an Amazon S3 path to the sample labeling file in the current AWS Region. (s3://bucketname/data/ml-transform/labels/label_file.csv) with the option to overwrite existing labels. The labeling file must be located in S3 in the same Region as the AWS Glue console.

When you upload a labeling file, a task is started in AWS Glue to add or overwrite the labels used to teach the transform how to process the data source.

Step 4: Estimate the Quality of ML Transform

What is Labeling?

The act of labeling is creating a labeling file (such as in a spreadsheet) and adding identifiers, or labels, into the label column that identifies matching and non-matching records. It is important to have a clear and consistent definition of a match in your source data. AWS Glue learns from which records you designate as matches (or not) and uses your decisions to learn how to find duplicate records.

Next, you can estimate the quality of your machine learning transform. The quality depends on how much labeling you have done.

On the AWS Glue console, in the navigation pane, choose ML Transforms .

. Choose the earlier created transform, and choose the Estimate quality tab. This tab displays the current quality estimates, if available, for the transform.

Choose Estimate quality to start a task to estimate the quality of the transform. The accuracy of the quality estimate is based on the labeling of the source data.

to start a task to estimate the quality of the transform. The accuracy of the quality estimate is based on the labeling of the source data. Navigate to the History tab. In this pane, task runs are listed for the transform, including the Estimating quality task. For more details about the run, choose Logs. Check that the run status is Succeeded when it finishes.

Step 5: Create and run a Job with ML Transform

In this step, we use your machine learning transform to add and run a job in AWS Glue. When the transform is Ready for use, we can use it in an ETL job.

On the AWS Glue console, in the navigation pane, choose Jobs.

Choose Add job.

In case you are just starting out on AWS Glue ETL Job, I have explained how to create one from scratch in one of my earlier articles.

For Name , choose the example job in this tutorial, ml-transform .

, choose the example job in this tutorial, . Choose an IAM role that has permission to access Amazon S3 and AWS Glue API operations.

that has permission to access Amazon S3 and AWS Glue API operations. For ETL language , choose Spark 2.2, Python 2. Machine learning transforms are currently not supported for Spark 2.4.

, choose Spark 2.2, Python 2. Machine learning transforms are currently not supported for Spark 2.4. For Data source , choose the table created in Step 1 . The data source you choose must match the machine learning transform data source schema.

, choose the table created in . The data source you choose must match the machine learning transform data source schema. For Transform type, choose to Find matching records to create a job using a machine learning transform.

For Transform , choose transform created in step 2, the machine learning transform used by the job.

, choose transform created in step 2, the machine learning transform used by the job. For Create tables in your data target, choose to create tables with the following properties.

Data store type — Amazon S3

Format — CSV

Compression type — None

Target path — The Amazon S3 path where the output of the job is written (in the current console AWS Region)

Choose Save job and edit script to display the script editor page. The script looks like the following. After you edit the script, choose Save.

import sys

from awsglue.transforms import *

from awsglue.utils import getResolvedOptions

from pyspark.context import SparkContext

from awsglue.context import GlueContext

from awsglue.job import Job

from awsglueml.transforms import FindMatches

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

glueContext = GlueContext(sc)

spark = glueContext.spark_session

job = Job(glueContext)

job.init(args['JOB_NAME'], args)

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datasource0 = glueContext.create_dynamic_frame.from_catalog(database = "ml_transforms", table_name = "customers", transformation_ctx = "datasource0")

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resolvechoice1 = ResolveChoice.apply(frame = datasource0, choice = "MATCH_CATALOG", database = "ml_transforms", table_name = "customers", transformation_ctx = "resolvechoice1")

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findmatches2 = FindMatches.apply(frame = resolvechoice1, transformId = "eacb9a1ffbc686f61387f63", transformation_ctx = "findmatches2")

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datasink3 = glueContext.write_dynamic_frame.from_options(frame = findmatches2, connection_type = "s3", connection_options = {"path": "s3:/<bucket-name>/data/ml-transforms/output/"}, format = "csv", transformation_ctx = "datasink3")

job.commit() sc = SparkContext()glueContext = GlueContext(sc)spark = glueContext.spark_sessionjob = Job(glueContext)job.init(args['JOB_NAME'], args)## @type : DataSource## @args : [database = "ml_transforms", table_name = "customers", transformation_ctx = "datasource0"]## @return : datasource0## @inputs : []datasource0 = glueContext.create_dynamic_frame.from_catalog(database = "ml_transforms", table_name = "customers", transformation_ctx = "datasource0")## @type : ResolveChoice## @args : [choice = "MATCH_CATALOG", database = "ml_transforms", table_name = "customers", transformation_ctx = "resolvechoice1"]## @return : resolvechoice1## @inputs : [frame = datasource0]resolvechoice1 = ResolveChoice.apply(frame = datasource0, choice = "MATCH_CATALOG", database = "ml_transforms", table_name = "customers", transformation_ctx = "resolvechoice1")## @type : FindMatches## @args : [transformId = "eacb9a1ffbc686f61387f63", emitFusion = false, survivorComparisonField = " ", transformation_ctx = "findmatches2"]## @return : findmatches2## @inputs : [frame = resolvechoice1]findmatches2 = FindMatches.apply(frame = resolvechoice1, transformId = "eacb9a1ffbc686f61387f63", transformation_ctx = "findmatches2")## @type : DataSink## @args : [connection_type = "s3", connection_options = {"path": "s3://bucket-name/data/ml-transforms/output/"}, format = "csv", transformation_ctx = "datasink3"]## @return : datasink3## @inputs : [frame = findmatches2]datasink3 = glueContext.write_dynamic_frame.from_options(frame = findmatches2, connection_type = "s3", connection_options = {"path": "s3:/ /data/ml-transforms/output/"}, format = "csv", transformation_ctx = "datasink3")job.commit()

Choose Run job to start the job run. Check the status of the job in the jobs list. When the job finishes, in the ML transform, History tab, there is a new Run ID row added of type ETL job.

Navigate to the Jobs, History tab. In this pane, job runs are listed. For more details about the run, choose Logs. Check that the run status is Succeeded when it finishes.

Step 6: Verify Output Data from Amazon S3 in Amazon Athena

In this step, check the output of the job run in the Amazon S3 bucket that you chose when you added the job. You can create a table in the Glue Data catalog pointing to the output location, just like the way we crawled the source data in Step 1. You can then query the data in Athena.

However, the Find matches transform adds another column named match_id to identify matching records in the output. Rows with the same match_id are considered matching records.

If you don’t find any matches, you can continue to teach the transform by adding more labels.

Thanks for the read and look forward to your comments

This story is authored by PV Subbareddy. Subbareddy is a Big Data Engineer specializing on AWS Big Data Services and Apache Spark Ecosystem.