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How do I migrate AWS Glue jobs to Databricks notebooks or workflows?

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• Jan 28, 2026 • Calculating...
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AWS Glue to Databricks Migration: A Comprehensive Playbook for Data Teams

You've built your data pipelines on AWS Glue, and it’s served you well. It's a powerful, serverless ETL service that gets the job done. But as your data team grows and your use cases become more complex, you might be feeling the friction. Limited developer tooling, challenges in debugging, and the desire for a more unified platform for both data engineering and data science are common drivers for looking at alternatives.

Databricks, with its collaborative notebooks, optimized Spark engine, and unified governance, often emerges as the next logical step. The promise is compelling: faster performance, a better developer experience, and a single platform for your entire data and AI lifecycle.

But the path from AWS Glue to Databricks isn't just a simple copy-and-paste. It’s a migration project that requires careful planning, technical translation, and a solid understanding of both platforms. This guide is your step-by-step playbook. We'll walk through the entire process, from initial discovery and planning to translating code, re-architecting orchestration, and troubleshooting the common errors you'll inevitably encounter.

Why Migrate from AWS Glue to Databricks?

Before diving into the "how," let's solidify the "why." Understanding the benefits helps justify the effort and keeps your team motivated.

  • Unified Analytics Platform: Databricks combines data engineering, data science, and machine learning on a single platform. This breaks down silos, allowing engineers and scientists to collaborate seamlessly on the same data and code.
  • Superior Developer Experience: The interactive, multi-language notebook environment in Databricks is a game-changer. It allows for rapid prototyping, easy debugging, built-in visualizations, and real-time collaboration—features that are less fluid in the Glue ecosystem.
  • Performance and Optimization: Databricks is built around the Databricks Runtime, a highly optimized version of Apache Spark. Features like Photon (a C++ vectorized execution engine), Delta Lake for ACID transactions on your data lake, and advanced caching mechanisms often lead to significant performance gains over standard Spark in Glue.
  • Simplified Orchestration and Governance: Databricks Workflows offer a more intuitive and powerful way to orchestrate complex, multi-task data pipelines compared to Glue Triggers and Workflows. Unity Catalog provides fine-grained, centralized governance for all your data and AI assets across clouds.
  • Cost Management and Visibility: With granular control over cluster configurations, auto-scaling policies, and detailed cluster usage reports, you can often achieve better cost-efficiency for your ETL workloads.

Phase 1: Discovery, Inventory, and Planning

A successful migration begins with a thorough understanding of what you're moving. You can't migrate what you don't know exists. The goal of this phase is to create a complete inventory of your AWS Glue assets and assess their complexity.

What to Look For:

  • Glue Jobs: The core ETL scripts. Note the language (PySpark or Scala), the script location in S3, and the Glue version.
  • Glue Triggers: How are jobs orchestrated? Are they on a schedule, triggered by an event (like a new file in S3), or chained together?
  • Glue Connections: How do your jobs connect to data sources (RDS, Redshift, etc.)? Document the connection types, VPCs, subnets, and security groups.
  • Glue Data Catalog: What tables and databases are your jobs using?
  • Dependencies: Identify custom libraries (JARs, Python files) and their versions.
  • IAM Roles: Which roles are associated with each job, and what permissions do they have?

Automating Your Inventory with a Python Script

Manually clicking through the AWS console to gather this information is tedious and error-prone. Instead, you can use the AWS SDK for Python (Boto3) to automate this process and generate a report. This script will give you a detailed inventory of your Glue jobs, their configurations, and their triggers.

Here is a Python script you can run on your local machine or an EC2 instance with the necessary IAM permissions to catalog your Glue environment.

    import boto3
    import pandas as pd
    import logging
    from datetime import datetime

    # Configure logging
    logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

    def get_aws_glue_inventory(region_name: str) -> pd.DataFrame:
        """
        Connects to AWS Glue in a specific region and gathers a detailed inventory
        of all Glue jobs and their associated triggers.

        Args:
            region_name: The AWS region to scan (e.g., 'us-east-1').

        Returns:
            A pandas DataFrame containing the detailed inventory.
        """
        logging.info(f"Starting Glue inventory scan for region: {region_name}")
        try:
            client = boto3.client('glue', region_name=region_name)
        except Exception as e:
            logging.error(f"Failed to create Boto3 client for region {region_name}. Error: {e}")
            return pd.DataFrame() # Return empty dataframe on client failure

        all_jobs_data = []

        # 1. Get all job names
        try:
            paginator = client.get_paginator('get_jobs')
            job_pages = paginator.paginate()
            job_names = [job['Name'] for page in job_pages for job in page['Jobs']]
            logging.info(f"Found {len(job_names)} jobs in {region_name}.")
        except Exception as e:
            logging.error(f"Failed to list Glue jobs. Error: {e}")
            return pd.DataFrame()

        # 2. Get detailed information for each job
        for job_name in job_names:
            try:
                logging.info(f"Fetching details for job: {job_name}")
                response = client.get_job(JobName=job_name)
                job = response['Job']

                job_details = {
                    'Region': region_name,
                    'JobName': job.get('Name'),
                    'Role': job.get('Role'),
                    'GlueVersion': job.get('GlueVersion'),
                    'WorkerType': job.get('WorkerType'),
                    'NumberOfWorkers': job.get('NumberOfWorkers'),
                    'CommandName': job.get('Command', {}).get('Name'), # e.g., glueetl, pythonshell
                    'PythonVersion': job.get('Command', {}).get('PythonVersion'),
                    'ScriptLocation': job.get('Command', {}).get('ScriptLocation'),
                    'DefaultArguments': str(job.get('DefaultArguments', {})),
                    'Connections': ', '.join(job.get('Connections', {}).get('Connections', [])),
                    'Timeout': job.get('Timeout'),
                    'MaxRetries': job.get('MaxRetries'),
                    'LastModifiedOn': job.get('LastModifiedOn').isoformat() if job.get('LastModifiedOn') else None,
                    'Triggers': [] # We will populate this next
                }

                # 3. Get triggers associated with this job
                trigger_paginator = client.get_paginator('get_triggers')
                trigger_pages = trigger_paginator.paginate()

                for page in trigger_pages:
                    for trigger in page['Triggers']:
                        # Check if any action in the trigger calls our current job
                        actions = trigger.get('Actions', [])
                        if any(action.get('JobName') == job_name for action in actions):
                            trigger_info = {
                                'TriggerName': trigger.get('Name'),
                                'TriggerType': trigger.get('Type'), # SCHEDULED, CONDITIONAL, ON_DEMAND
                                'TriggerState': trigger.get('State'), # CREATED, ACTIVATED, DEACTIVATED
                                'Schedule': trigger.get('Schedule', 'N/A')
                            }
                            job_details['Triggers'].append(trigger_info)

                # Convert trigger list to a more readable string format
                job_details['Triggers'] = str(job_details['Triggers']) if job_details['Triggers'] else 'None'

                all_jobs_data.append(job_details)

            except Exception as e:
                logging.error(f"Failed to get details for job {job_name}. Error: {e}")
                continue

        df = pd.DataFrame(all_jobs_data)
        logging.info(f"Successfully compiled inventory for {len(df)} jobs.")
        return df

    if __name__ == '__main__':
        # --- Configuration ---
        # List of AWS regions where your Glue jobs are deployed
        aws_regions = ['us-east-1', 'us-west-2'] 

        final_inventory = pd.DataFrame()

        for region in aws_regions:
            regional_df = get_aws_glue_inventory(region)
            if not regional_df.empty:
                final_inventory = pd.concat([final_inventory, regional_df], ignore_index=True)

        if not final_inventory.empty:
            # Save the inventory to a CSV file
            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
            output_filename = f'aws_glue_inventory_{timestamp}.csv'
            final_inventory.to_csv(output_filename, index=False)
            logging.info(f"Complete Glue inventory saved to: {output_filename}")
        else:
            logging.warning("No Glue jobs found or an error occurred. Inventory file was not created.")

How to Use the Script:

  1. Prerequisites: Make sure you have Python, Pandas, and Boto3 installed (pip install pandas boto3).
  2. AWS Credentials: Configure your AWS credentials. The easiest way is to use the AWS CLI (aws configure) to set up a profile with programmatic access. The IAM user or role running the script needs glue:GetJobs, glue:GetJob, glue:GetTriggers, and related read-only permissions.
  3. Configure Regions: Update the aws_regions list in the if __name__ == '__main__': block with the regions where your Glue jobs reside.
  4. Run the Script: Execute the script from your terminal: python your_script_name.py.
  5. Review the Output: A CSV file named aws_glue_inventory_YYYYMMDD_HHMMSS.csv will be created in the same directory. This file is your migration bible.

Sizing Up the Effort

With your inventory in hand, you can categorize your jobs to estimate the migration effort:

  • Low Complexity (Lift and Shift): Simple PySpark jobs that read from S3, perform standard DataFrame transformations, and write back to S3. These are prime candidates for a quick migration.
  • Medium Complexity (Refactor): Jobs using DynamicFrames, custom connectors, or specific Glue features like bookmarks. These will require code translation. Jobs with simple, scheduled triggers also fall here.
  • High Complexity (Re-architect): Jobs with complex dependencies (e.g., calling other AWS services like Lambda or Step Functions), intricate trigger chains, or reliance on Python shell jobs with heavy Boto3 logic. These may need to be completely re-designed as multi-task Databricks Workflows.

Your strategy should be to start with the "low complexity" jobs to build momentum and establish a migration pattern.

Phase 2: The Core Migration - Translating Glue Logic to Databricks

This is where the real work begins. We'll break down the key technical translations required to make your Glue scripts run on the Databricks platform.

From GlueContext and DynamicFrame to SparkSession and DataFrame

This is the most fundamental change. AWS Glue extends Spark with its own context and data structure.

  • GlueContext: A wrapper around the standard SparkContext. It provides helper methods for creating DynamicFrames.
  • DynamicFrame: A distributed collection of data similar to a Spark DataFrame, but with a key difference: each record is self-describing. This allows a DynamicFrame to handle messy data with inconsistent schemas and data types within the same column. While flexible, this can lead to unpredictable behavior and makes strongly-typed operations difficult.

Databricks uses the standard, open-source SparkSession and DataFrame. Migrating means converting your code to use these native Spark APIs.

Example: A Typical Glue Script

    # AWS Glue Script
    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

    # Initialization
    sc = SparkContext()
    glueContext = GlueContext(sc)
    spark = glueContext.spark_session
    job = Job(glueContext)
    args = getResolvedOptions(sys.argv, ['JOB_NAME'])
    job.init(args['JOB_NAME'], args)

    # Read data using a DynamicFrame
    datasource0 = glueContext.create_dynamic_frame.from_catalog(
        database="my_db",
        table_name="my_table",
        transformation_ctx="datasource0"
    )

    # A Glue-specific transform
    applymapping1 = ApplyMapping.apply(
        frame=datasource0,
        mappings=[("id", "string", "user_id", "long"), ("data", "string", "payload", "string")],
        transformation_ctx="applymapping1"
    )

    # Convert to DataFrame for some operations (if needed)
    df = applymapping1.toDF()
    df_transformed = df.withColumn("processed_at", current_timestamp())

    # Convert back to DynamicFrame to write
    dynamic_frame_write = DynamicFrame.fromDF(df_transformed, glueContext, "dynamic_frame_write")

    # Write data
    glueContext.write_dynamic_frame.from_options(
        frame=dynamic_frame_write,
        connection_type="s3",
        connection_options={"path": "s3://my-output-bucket/data/"},
        format="parquet"
    )

    job.commit()

The Migrated Databricks Notebook Equivalent

    # Databricks Notebook Cell
    # No special imports or context initialization needed. SparkSession is pre-defined as `spark`.

    # Read data directly into a Spark DataFrame
    # We assume the table is registered in the Databricks Metastore (e.g., via Unity Catalog)
    df = spark.read.table("my_catalog.my_db.my_table")

    # You can also read directly from S3 if needed
    # df = spark.read.format("parquet").load("s3://my-source-bucket/raw_data/")

    # Perform transformations using standard PySpark
    from pyspark.sql.functions import col, current_timestamp

    # The ApplyMapping transform is replaced by standard `select` and `cast`
    df_transformed = df.select(
        col("id").cast("long").alias("user_id"),
        col("data").cast("string").alias("payload")
    ).withColumn("processed_at", current_timestamp())

    # Write data using the DataFrameWriter API
    df_transformed.write.format("delta") \
        .mode("overwrite") \
        .save("s3://my-output-bucket/data/")

    # To write as a table for easy querying:
    # df_transformed.write.format("delta").mode("overwrite").saveAsTable("my_catalog.my_db.processed_table")

Key Takeaways:

  1. No Boilerplate: Databricks notebooks come with a pre-configured SparkSession named spark. You can remove all the GlueContext, Job.init, and job.commit boilerplate.
  2. Use DataFrame API: Replace glueContext.create_dynamic_frame with spark.read. Use df.write instead of glueContext.write_dynamic_frame.
  3. Translate Glue Transforms: Replace Glue-specific transforms like ApplyMapping, Relationalize, and Unbox with their standard Spark DataFrame API equivalents (e.g., select, cast, explode).
  4. Embrace Delta Lake: While you can write to Parquet, the default and recommended format in Databricks is Delta Lake. It provides ACID transactions, time travel, and performance optimizations out of the box.

Managing Libraries and Dependencies

In Glue, you specify Python libraries or JARs in the job definition. Databricks offers more flexible options.

  • Glue Approach:

    • --extra-py-files: For custom Python modules.
    • --extra-jars: For Java/Scala dependencies.
    • Python shell jobs use a deployment package.

  • Databricks Approach:

    1. Cluster Libraries: Install libraries directly onto the cluster. These are available to all notebooks running on that cluster. This is best for common, shared libraries. You can install from PyPI, Maven, or a location in DBFS/S3.
    2. Notebook-scoped Libraries: Use %pip install <library> at the top of a notebook. This installs the library only for the current notebook session. This is great for quick experiments or job-specific dependencies.
    3. Workspace Files/Repos: Store your custom Python modules (.py files) in Databricks Repos (backed by Git) or Workspace Files. Then you can import them directly in your notebook (e.g., from my_module import my_function). This is the best practice for managing your own code.

Best Practice: For migrated jobs, use a combination. Install common, stable libraries on the job cluster and manage your ETL-specific Python code using Databricks Repos for version control and CI/CD.

Connecting to Data Sources

Securely connecting to databases like RDS or Redshift is critical.

  • Glue Approach: You create a "Glue Connection" which stores the JDBC connection string, credentials (via AWS Secrets Manager), and network configuration (VPC, subnet, security group). You then attach this connection to the job.

  • Databricks Approach:

    1. Store Credentials in Databricks Secrets: Create a secret scope (either Databricks-backed or backed by AWS Secrets Manager) and store your JDBC connection string, username, and password as secrets.
    2. Retrieve Secrets in Notebook: Use dbutils.secrets.get(scope="<scope_name>", key="<key_name>") to fetch the credentials in your notebook. Never hardcode credentials.
    3. Network Configuration: Your Databricks workspace needs to be deployed within your VPC, and its security groups must have egress rules that allow traffic to the database's security group on the correct port (e.g., 5432 for PostgreSQL).
    4. Use IAM Instance Profiles: For connecting to AWS services like S3 or DynamoDB, attach an IAM role with the necessary permissions to your Databricks cluster via an instance profile. This is the most secure way to grant access without using access keys.

Example: Connecting to PostgreSQL

    # Databricks Notebook Cell

    # 1. Retrieve secrets securely
    jdbc_url = dbutils.secrets.get(scope="my-jdbc-scope", key="pg-url")
    db_user = dbutils.secrets.get(scope="my-jdbc-scope", key="pg-user")
    db_password = dbutils.secrets.get(scope="my-jdbc-scope", key="pg-password")

    # 2. Define connection properties
    connection_properties = {
      "user": db_user,
      "password": db_password,
      "driver": "org.postgresql.Driver"
    }

    # 3. Read data from PostgreSQL
    df_postgres = spark.read.jdbc(
        url=jdbc_url,
        table="public.my_source_table",
        properties=connection_properties
    )

    df_postgres.show()

    # 4. Write data to PostgreSQL
    # (Ensure the IAM role has permissions if writing data from S3, etc.)
    df_to_write.write.jdbc(
        url=jdbc_url,
        table="public.my_destination_table",
        mode="append",
        properties=connection_properties
    )

Phase 3: Orchestration - From Glue Triggers to Databricks Workflows

A one-to-one migration of your ETL logic is only half the battle. You also need to replicate the orchestration that runs your jobs in the correct sequence and on the right schedule.

  • Glue Triggers:

    • Scheduled: Runs on a cron-like schedule.
    • On-demand: Started manually.
    • Conditional/Event-driven: Triggered by the completion of other jobs or by an S3 event (via EventBridge). Chaining jobs together is done via Glue Workflows or by having one trigger start the next job.

  • Databricks Workflows:
    A much more powerful and visual orchestrator. A workflow is a Directed Acyclic Graph (DAG) of tasks. A task can be a notebook, a Python script, a Delta Live Tables pipeline, and more.

Mapping Glue Concepts to Databricks Workflows:

Glue Concept Databricks Workflows Equivalent
Single Scheduled Job A single-task workflow with a Notebook task, configured with a schedule.
Job triggered by S3 event Use the "File Arrival" trigger in a Databricks Workflow. This directly triggers the workflow when a new file lands in a specified S3 path.
Chain of jobs (Job A -> Job B) A multi-task workflow. Create a task for Job A's notebook and a task for Job B's notebook. Drag an arrow from Task A to Task B.
Conditional triggering (Job A succeeds -> B) This is the default behavior for task dependencies in Databricks Workflows. Task B will only run if Task A succeeds.
Job Parameters (--my-param) Each task in a workflow can be configured with parameters. These are passed to the notebook and can be retrieved using dbutils.widgets.
Glue Bookmarks Use Delta Lake tables. Delta's transaction log automatically tracks which files have been processed, providing exactly-once processing guarantees without manual bookmarking. For sources that don't support this, use Auto Loader's checkpointing mechanism.

Building a workflow is intuitive. In the Databricks UI, you create a new workflow, add a task (e.g., a notebook), point it to your migrated notebook, select a cluster, and set a schedule or trigger. For dependent jobs, you simply add another task and draw a dependency line between them.

Common Migration Pitfalls and How to Solve Them

No migration is without its challenges. Here are the most common errors you’ll face, grouped by category, with practical solutions.

Category 1: Connectivity and Security Errors

These errors usually happen when your Databricks cluster can't communicate with your data sources or destinations.

Error: S3_DOWNLOAD_FAILURE, Access Denied when reading/writing to S3

  • The Symptom: Your Spark job fails with a Java error stack trace that includes "Access Denied" or a similar permissions-related message when trying to access an S3 bucket.
  • Why it Happens: This is almost always an IAM permissions issue. The IAM role associated with your Databracks cluster (via its instance profile) does not have the required permissions (s3:GetObject, s3:PutObject, s3:ListBucket, s3:DeleteObject) for the S3 bucket you are trying to access.
  • How to Fix It:
    1. Identify the Role: In your cluster configuration, find the "Instance Profile" and note the IAM role it uses.
    2. Inspect the Policy: Go to the IAM console in AWS. Find that role and examine its attached policies.
    3. Verify Permissions: Ensure the policy grants the necessary S3 permissions for the specific bucket ARN (e.g., arn:aws:s3:::my-data-bucket/*). A common mistake is forgetting the /* for object-level access or missing s3:ListBucket on the bucket ARN itself (arn:aws:s3:::my-data-bucket).
    4. Check Bucket Policies: Verify that the S3 bucket policy itself isn't explicitly denying access to your cluster's IAM role.
    5. VPC Endpoints: If you are using a VPC endpoint for S3, ensure that the endpoint policy also allows access for your role.

Error: Job times out trying to connect to a database (RDS, Redshift)

  • The Symptom: Your JDBC read/write step hangs for a long time and eventually fails with a connection timeout error.
  • Why it Happens: This is a networking issue. Your Databricks cluster's nodes cannot establish a network path to the database.
  • How to Fix It:
    1. Security Groups: This is the most common culprit. The security group attached to your Databricks cluster workers must have an egress (outbound) rule that allows traffic to the database's IP address (or its security group ID) on the correct port (e.g., PostgreSQL: 5432, Redshift: 5439, MySQL: 3306).
    2. Database Ingress: Conversely, the security group attached to your database must have an ingress (inbound) rule that allows traffic from your Databricks workers' security group ID on that same port.
    3. Subnets and Route Tables: Ensure your Databricks cluster and your database are in the same VPC or in peered VPCs. Verify that the route tables for the cluster's subnets have a route to the database's subnets. If using a NAT Gateway for internet access, ensure the database is accessible from the NAT's Elastic IP if it's public, or via private routes if it's not.
    4. Network ACLs (NACLs): Less common, but check that your subnet's NACLs are not blocking the traffic. Remember NACLs are stateless, so you need to allow both inbound and outbound traffic.

Category 2: Code and Dependency Errors

These issues arise from the differences between the Glue and Databricks execution environments.

Error: NameError: name 'GlueContext' is not defined or ModuleNotFoundError: No module named 'awsglue'

  • The Symptom: Your notebook fails immediately, complaining that it can't find Glue-specific modules.
  • Why it Happens: You've copied your Glue script directly into a Databricks notebook without translating it. The awsglue library and its components (GlueContext, DynamicFrame) do not exist in the standard Databricks Runtime.
  • How to Fix It:
    1. Refactor the Code: This is the core migration task. Remove all references to awsglue and GlueContext.
    2. Use SparkSession: Replace glueContext.spark_session with the built-in spark object.
    3. Use DataFrame API: Convert all create_dynamic_frame, write_dynamic_frame, and ApplyMapping calls to their spark.read, df.write, and df.select().cast() equivalents, as shown in the examples earlier.

Error: Py4JError or java.lang.NoClassDefFoundError

  • The Symptom: A Spark job fails with a cryptic Java error, often mentioning a class that cannot be found.
  • Why it Happens: This is classic "JAR hell." It means a Java library (JAR file) your code depends on is missing from the cluster's classpath, or there's a version conflict with a library already included in the Databricks Runtime.
  • How to Fix It:
    1. Identify Dependencies: Review your original Glue job configuration. Look for any JARs specified in the --extra-jars parameter. These are your dependencies.
    2. Install on Cluster: Go to your Databricks cluster configuration, navigate to the "Libraries" tab, and click "Install New." Choose "Maven" and search for your required library (e.g., the PostgreSQL JDBC driver: org.postgresql:postgresql:42.2.18). Install it on the cluster.
    3. Check for Conflicts: The Databricks Runtime includes many libraries. Check the runtime release notes for your version to see what's included. If you're trying to install a different version of a library that's already present (like a different Spark connector version), you may cause conflicts. Try to use the versions compatible with your Databricks Runtime whenever possible.
    4. Upload Custom JARs: If your JAR is not in a public repository, you can upload it to DBFS or S3 and install it from that path.

Category 3: Performance and Cost Issues

Your job runs, but it's slow or expensive. This is an optimization problem.

Issue: Job is much slower or more expensive than it was in Glue.

  • The Symptom: A job that took 10 minutes in Glue now takes 30 minutes in Databricks, or your cloud bill spikes unexpectedly.
  • Why it Happens: A direct lift-and-shift often results in a sub-optimal cluster configuration. Glue's serverless model hides cluster sizing from you, but in Databricks, you have full control—and responsibility.
  • How to Fix It:
    1. Right-size Your Cluster: Don't just guess. Start with a reasonable general-purpose worker type (e.g., i3.2xlarge on AWS) and a small number of workers (e.g., min 2, max 8 with autoscaling). Use the Spark UI and Ganglia metrics in Databricks to observe CPU, memory, and disk utilization. If your CPUs are maxed out, you need more/bigger workers. If memory is the bottleneck, choose a memory-optimized instance type.
    2. Enable Autoscaling: Always use autoscaling for job clusters. This allows the cluster to scale up to handle heavy stages and then scale down to save costs during lighter ones.
    3. Choose the Right Runtime: Use a recent Databricks Runtime version with Photon enabled. Photon can dramatically accelerate SQL and DataFrame operations with no code changes.
    4. Optimize Your Spark Code: Performance isn't just about the cluster.
      • Partitioning: Ensure your data is partitioned correctly on S3/Delta Lake. Your reads and writes should use partition pruning.
      • Shuffles: Use the Spark UI to identify stages with large shuffles (data exchange between workers). This can indicate a need to re-think your joins or aggregations, perhaps by using broadcast joins for small tables.
      • Caching: If you re-use a DataFrame multiple times, use df.cache() to keep it in memory.

Category 4: Schema and Data Type Mismatches

These errors are related to how Glue and Spark interpret data schemas.

Issue: Migrated job fails on schema-on-read; fields are missing or have wrong types.

  • The Symptom: Your code fails with errors like AnalysisException: cannot resolve 'my_column' given input columns... or you get null values where you expect data.
  • Why it Happens: This often stems from losing the "flexibility" of DynamicFrame. A DataFrame requires a consistent schema. If your source data (e.g., a directory of JSON files) has evolving or inconsistent schemas, spark.read.json() might infer a schema based on a sample of files, and then fail when it encounters a file that doesn't match.
  • How to Fix It:
    1. Use Auto Loader: For ingesting files from cloud storage, Databricks Auto Loader is the best solution. It can be configured with schema evolution mode (.option("cloudFiles.schemaEvolutionMode", "rescue")) to handle schema changes gracefully. New or unexpected columns are captured in a "rescued data" column instead of failing the job.
    2. Explicitly Define Schema: Instead of relying on schema inference, define the schema yourself and provide it to the reader. This provides reliability but requires maintenance if the schema is expected to change.
    from pyspark.sql.types import StructType, StructField, StringType, LongType

    my_schema = StructType([
        StructField("user_id", LongType(), True),
        StructField("payload", StringType(), True)
    ])

    df = spark.read.schema(my_schema).json("s3://my-source-bucket/data/")


3.  **Leverage the Glue Catalog:** You can continue to use your AWS Glue Data Catalog as a metastore for Databricks. Configure your cluster to connect to it. This way, `spark.read.table("my_db.my_table")` will use the schema defined in Glue, providing consistency. However, the long-term goal should be to migrate to Unity Catalog for unified governance.

Migration Best Practices: A Summary Checklist

  1. Automate Discovery: Use the Boto3 script to create a comprehensive inventory. Don't migrate blind.
  2. Start Small: Begin with a handful of low-complexity jobs. Use them to establish a repeatable migration pattern and build team confidence.
  3. Use Version Control: Store your new Databricks notebooks and any helper modules in Databricks Repos, backed by a Git provider (GitHub, GitLab, etc.). This is non-negotiable for production code.
  4. Refactor, Don't Just Lift-and-Shift: Take the time to translate Glue-specific code into idiomatic PySpark. This will make your code more maintainable, performant, and future-proof.
  5. Embrace Databricks-Native Features:
    • Use Delta Lake as your default storage format.
    • Use Databricks Workflows for orchestration.
    • Use Auto Loader for incremental file ingestion.
    • Use Unity Catalog for governance and security.
  6. Secure Everything: Use Databricks Secrets for credentials and IAM instance profiles for access to AWS services. Never hardcode secrets.
  7. Optimize for the New Environment: Don't assume your Glue configurations are relevant. Right-size your clusters, enable autoscaling, and use Photon. Monitor performance with the Spark UI.
  8. Test Rigorously: Create a testing plan that verifies not just that the job runs, but that the output data is correct (e.g., row counts, checksums, value comparisons against the old pipeline).

Conclusion: Beyond a Simple Migration

Migrating from AWS Glue to Databricks is more than a technical task; it's a strategic move to empower your data team. By moving to a unified, collaborative, and high-performance platform, you're not just modernizing your ETL pipelines—you're accelerating your organization's entire data and AI journey.

The process requires a methodical approach: plan carefully, translate deliberately, and test thoroughly. While you will encounter errors, a systematic understanding of the differences between the two platforms—from security models and dependencies to execution context and orchestration—will equip you to solve them efficiently. By following the playbook laid out here, you can navigate the complexities of the migration and unlock the full potential of the Databricks Lakehouse Platform.

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