AWS Data Engineer Associate (DEA-C01) Certification Guide

The AWS Data Engineer Associate certification validates your ability to design and implement data pipelines on AWS. This guide covers essential concepts and services for the DEA-C01 exam.

Who Should Get This Certification?

• Data engineers building pipelines on AWS
• ETL developers
• Data analysts working with AWS services
• Big data professionals

Exam Overview

Aspect	Details
Exam Code	DEA-C01
Duration	130 minutes
Questions	65 questions
Passing Score	720/1000
Format	Multiple choice, multiple response
Cost	$150 USD

Exam Domains

Domain 1: Data Ingestion and Transformation (34%)

• Design data ingestion patterns
• Transform and process data
• Orchestrate data pipelines

Domain 2: Data Store Management (26%)

• Choose appropriate data stores
• Model data for analytics
• Manage data lifecycle

Domain 3: Data Operations and Support (22%)

• Automate data workflows
• Monitor data pipelines
• Ensure data quality

Domain 4: Data Security and Governance (18%)

• Implement data security
• Apply data governance
• Ensure compliance

Key AWS Data Services

Amazon S3 Data Lake

Data Lake Architecture:
┌─────────────────────────────────────────────────────────┐
│                    S3 Data Lake                          │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐     │
│  │    Raw      │  │  Processed  │  │   Curated   │     │
│  │   Layer     │──│    Layer    │──│    Layer    │     │
│  │  (Bronze)   │  │  (Silver)   │  │   (Gold)    │     │
│  └─────────────┘  └─────────────┘  └─────────────┘     │
│        │                │                │              │
│        ▼                ▼                ▼              │
│  ┌─────────────────────────────────────────────┐       │
│  │           AWS Glue Data Catalog              │       │
│  └─────────────────────────────────────────────┘       │
└─────────────────────────────────────────────────────────┘

AWS Glue ETL Job

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 awsglue.dynamicframe import DynamicFrame

args = getResolvedOptions(sys.argv, ['JOB_NAME', 'source_path', 'target_path'])

sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session
job = Job(glueContext)
job.init(args['JOB_NAME'], args)

# Read from source
datasource = glueContext.create_dynamic_frame.from_options(
    connection_type="s3",
    connection_options={
        "paths": [args['source_path']],
        "recurse": True
    },
    format="json"
)

# Transform data
def transform_record(record):
    record['processed_date'] = datetime.now().isoformat()
    record['full_name'] = f"{record['first_name']} {record['last_name']}"
    return record

transformed = Map.apply(
    frame=datasource,
    f=transform_record
)

# Apply mapping
mapped = ApplyMapping.apply(
    frame=transformed,
    mappings=[
        ("id", "string", "customer_id", "string"),
        ("full_name", "string", "name", "string"),
        ("email", "string", "email", "string"),
        ("processed_date", "string", "processed_at", "timestamp")
    ]
)

# Write to target
glueContext.write_dynamic_frame.from_options(
    frame=mapped,
    connection_type="s3",
    connection_options={
        "path": args['target_path'],
        "partitionKeys": ["processed_at"]
    },
    format="parquet"
)

job.commit()

Amazon Kinesis Data Streams

import boto3
import json
from datetime import datetime

kinesis = boto3.client('kinesis')

def put_records(stream_name, records):
    """
    Put multiple records to Kinesis
    """
    kinesis_records = []

    for record in records:
        kinesis_records.append({
            'Data': json.dumps(record),
            'PartitionKey': record.get('user_id', str(datetime.now().timestamp()))
        })

    response = kinesis.put_records(
        StreamName=stream_name,
        Records=kinesis_records
    )

    return response

# Lambda consumer for Kinesis
def lambda_handler(event, context):
    for record in event['Records']:
        # Decode and parse
        payload = base64.b64decode(record['kinesis']['data'])
        data = json.loads(payload)

        # Process record
        process_event(data)

    return {'statusCode': 200}

Amazon Redshift

-- Create optimized table
CREATE TABLE sales_fact (
    sale_id BIGINT IDENTITY(1,1),
    product_id INT NOT NULL ENCODE az64,
    customer_id INT NOT NULL ENCODE az64,
    sale_date DATE NOT NULL ENCODE az64,
    quantity INT ENCODE az64,
    amount DECIMAL(10,2) ENCODE az64,
    region VARCHAR(50) ENCODE lzo
)
DISTSTYLE KEY
DISTKEY (product_id)
SORTKEY (sale_date, product_id);

-- Load from S3
COPY sales_fact
FROM 's3://my-bucket/sales/'
IAM_ROLE 'arn:aws:iam::123456789012:role/RedshiftRole'
FORMAT AS PARQUET;

-- Materialized view for analytics
CREATE MATERIALIZED VIEW daily_sales AS
SELECT
    sale_date,
    region,
    SUM(amount) as total_sales,
    COUNT(*) as transaction_count,
    AVG(amount) as avg_sale
FROM sales_fact
GROUP BY sale_date, region;

Apache Spark on EMR

from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.sql.types import *

spark = SparkSession.builder \
    .appName("DataProcessing") \
    .enableHiveSupport() \
    .getOrCreate()

# Read data
df = spark.read \
    .option("header", "true") \
    .option("inferSchema", "true") \
    .csv("s3://my-bucket/raw/events/")

# Transform
transformed = df \
    .withColumn("event_date", to_date("timestamp")) \
    .withColumn("hour", hour("timestamp")) \
    .filter(col("event_type").isin(["click", "purchase", "view"])) \
    .groupBy("event_date", "hour", "event_type") \
    .agg(
        count("*").alias("event_count"),
        countDistinct("user_id").alias("unique_users"),
        sum("value").alias("total_value")
    )

# Write partitioned output
transformed.write \
    .mode("overwrite") \
    .partitionBy("event_date") \
    .parquet("s3://my-bucket/processed/events/")

Data Pipeline Orchestration

Step Functions Workflow

{
  "Comment": "ETL Pipeline",
  "StartAt": "ExtractData",
  "States": {
    "ExtractData": {
      "Type": "Task",
      "Resource": "arn:aws:states:::glue:startJobRun.sync",
      "Parameters": {
        "JobName": "extract-job"
      },
      "Next": "TransformData"
    },
    "TransformData": {
      "Type": "Task",
      "Resource": "arn:aws:states:::glue:startJobRun.sync",
      "Parameters": {
        "JobName": "transform-job"
      },
      "Next": "DataQualityCheck"
    },
    "DataQualityCheck": {
      "Type": "Task",
      "Resource": "arn:aws:lambda:invoke",
      "Parameters": {
        "FunctionName": "data-quality-check"
      },
      "Next": "QualityGate"
    },
    "QualityGate": {
      "Type": "Choice",
      "Choices": [
        {
          "Variable": "$.quality_passed",
          "BooleanEquals": true,
          "Next": "LoadData"
        }
      ],
      "Default": "NotifyFailure"
    },
    "LoadData": {
      "Type": "Task",
      "Resource": "arn:aws:states:::glue:startJobRun.sync",
      "Parameters": {
        "JobName": "load-job"
      },
      "End": true
    },
    "NotifyFailure": {
      "Type": "Task",
      "Resource": "arn:aws:states:::sns:publish",
      "Parameters": {
        "TopicArn": "arn:aws:sns:us-east-1:123456789012:alerts",
        "Message": "Data quality check failed"
      },
      "End": true
    }
  }
}

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Conclusion

The AWS Data Engineer Associate certification validates your expertise in building data pipelines and analytics solutions on AWS. Master the key services like Glue, Kinesis, Redshift, and EMR to pass the exam and advance your data engineering career.