Iceberg/Hudi/Delta on EMR

1. Introduction

In this lesson, we will explore how to use Apache Iceberg, Apache Hudi, and Delta Lake on Amazon EMR (Elastic MapReduce) for data engineering tasks. These technologies are designed to manage large datasets, providing capabilities for schema evolution, time travel, and ACID transactions.

2. Key Concepts

3. Setup & Configuration

To get started with Iceberg, Hudi, or Delta on EMR, follow these steps:

1. Launch an Amazon EMR cluster with Spark and the desired libraries (Iceberg, Hudi, Delta).
2. Configure the EMR cluster to use the appropriate file system (S3 or HDFS).
3. Install necessary dependencies via bootstrap actions or custom scripts.

3.1 Example of launching an EMR Cluster

aws emr create-cluster --name "Iceberg-Hudi-Delta-Cluster" \
    --release-label emr-6.5.0 \
    --applications Name=Spark Name=Hudi Name=Iceberg Name=DeltaLake \
    --ec2-attributes KeyName=myKey --instance-type m5.xlarge \
    --instance-count 3 --use-default-roles
        

4. Data Management

Once your EMR cluster is set up, you can start managing your data.

4.1 Writing Data with Iceberg

import org.apache.iceberg.spark.SparkSchemaUtil
import org.apache.spark.sql.SparkSession

val spark = SparkSession.builder()
    .appName("IcebergExample")
    .getOrCreate()

val df = spark.read.format("csv").load("s3://path/to/your/data.csv")
df.write.format("iceberg").mode("overwrite").save("iceberg_table_name")
        

4.2 Upserting Data with Hudi

import org.apache.hudi.DataSourceWriteOptions
import org.apache.hudi.HoodieSparkSqlWriter

df.write.format("hudi")
    .option(DataSourceWriteOptions.TABLE_TYPE_OPT_KEY, DataSourceWriteOptions.COW_TABLE_TYPE_OPT_VAL)
    .option(DataSourceWriteOptions.RECORDKEY_FIELD_OPT_KEY, "record_id")
    .option(DataSourceWriteOptions.PRECOMBINE_FIELD_OPT_KEY, "timestamp")
    .mode("overwrite")
    .save("hudi_table_name")
        

4.3 Reading Data with Delta Lake

val deltaDf = spark.read.format("delta").load("s3://path/to/delta-table")
deltaDf.show()
        

5. Best Practices

• Use partitioning wisely to optimize query performance.
• Regularly vacuum your Delta Lake to remove obsolete files.
• Monitor and optimize your EMR cluster resources based on workload.

6. FAQ