Configure the Data Lakes AWS Environment

The instructions below will guide you through the process required to configure the environment required to begin loading data into your Segment Data Lake. For a more automated process, see Set Up Segment Data Lakes.

As a best practice, Segment recommends that you consult with your network and security teams before you configure your EMR cluster.

Step 1 - Create a VPC and an S3 bucket

In this step, you’ll create a Virtual Private Cloud (VPC) to securely launch your AWS resources into and an S3 bucket that will store both the intermediate and final data.

To create a VPC, follow the instructions outlined in Amazon’s documentation, Create and configure your VPC.

To create an S3 bucket, see Amazon’s Create your first S3 bucket instructions.

After you create an S3 bucket, configure a lifecycle rule for the bucket and set it to expire staging data after 14 days. For instructions on configuring lifecycle rules, see Amazon’s documentation, Setting lifecycle configuration on a bucket.

Apply the following lifecycle settings to your staging data:

• Expire after: 14 days
• Permanently delete after: 14 days
• Clean up incomplete multipart uploads: after 14 days

Step 2 - Configure an EMR cluster

Segment requires access to an EMR cluster to perform necessary data processing. For best results, start with a small cluster and add more compute resources as required.

Configure the hardware and networking configuration

1. In the AWS console, navigate to Services > Analytics > EMR.
2. Click Create Cluster. On the Create Cluster - Quick Options page, click Go to advanced options.
3. In Advanced Options, on Step 1: Software and Steps, select both the emr-5.33.0 release and the following applications:
   • Hadoop 2.10.1
   • Hive 2.3.7
   • Hue 4.9.0
   • Spark 2.4.7
   • Pig 0.17.0
4. In the AWS Glue Data Catalog settings, select the following options:
   • Use for Hive table metadata
   • Use for Spark table metadata

5. Select Next to proceed to Step 2: Hardware. 6.In the Networking section, select a Network (the VPC you created in Step 1) and EC2 Subnet for your EMR instance.

   Creating the cluster in a private subnet is more secure, but requires additional configuration. Creating the cluster in a public subnet leaves it accessible from the Internet, but requires less up front configuration.

   If you create clusters in public subnets, you can configure strict security groups to prevent unauthorized inbound EMR cluster access. See Amazon’s document, Amazon VPC Options - Amazon EMR for more information.

6. In the Cluster Nodes and Instances section, create a cluster that includes the following on-demand nodes:
   • 1 master node
   • 2 core nodes
   • 2 task nodes

   Each node should meet or exceed the following specifications:

   • Instance type: mx5.xlarge
   • Number of vCores: 4
   • Memory: 16 GiB
   • EBS Storage: 64 GiB, EBS only storage

   For more information about configuring cluster hardware and networking, see Amazon’s documentation, Configure Cluster Hardware and Networking.

7. Click Next to proceed to Step 3: General Cluster Settings.

Configure logging

1. On Step 3: General Cluster Settings, configure logging to use the same S3 bucket you configured as the destination for the final data. Once configured, logs are assigned a new prefix and separated from the final processed data.

2. Add a new key-value pair to the Tags section, a vendor key with a value of segment. The IAM policy uses this tag to provide Segment access to submit jobs in the EMR cluster.

3. Click Next to proceed to Step 4: Security.

Secure the cluster

1. On Step 4: Security, in the Security Options section, create or select an EC2 key pair.
2. Choose the appropriate roles in the EC2 instance profile.
3. Expand the EC2 security groups section and select the appropriate security groups for the Master and Core & Task types.
4. Select Create cluster.

Step 3 - Create an Access Management role and policy

The following steps provide examples of the IAM Role and IAM Policy.

IAM role

Create a segment-data-lake-role for Segment to assume. The trust relationship document you attach to the role will be different depending on your workspace region.

IAM role for Data Lakes:

Attach the following trust relationship document to the role to create a segment-data-lake-role role for Segment:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Sid": "",
      "Effect": "Allow",
      "Principal": {
        "AWS": [
          "arn:aws:iam::595280932656:role/segment-datalakes-production-access"
        ]
      },
      "Action": "sts:AssumeRole",
      "Condition": {
        "StringEquals": {
          "sts:ExternalId": [
            "WORKSPACE_ID"
          ]
        }
      }
    }
  ]
}

IAM policy

Add a policy to the role created above to give Segment access to the relevant Glue databases and tables, EMR cluster, and S3.

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Action": [
                "elasticmapreduce:TerminateJobFlows",
                "elasticmapreduce:RunJobFlow",
                "elasticmapreduce:DescribeStep",
                "elasticmapreduce:DescribeCluster",
                "elasticmapreduce:CancelSteps",
                "elasticmapreduce:AddJobFlowSteps"
                "elasticmapredue:AddTags"
            ],
            "Effect": "Allow",
            "Resource": "*",
            "Condition": {
                "StringEquals": {
                    "elasticmapreduce:ResourceTag/vendor": "segment"
                }
            }
        },
                {
            "Sid": "",
            "Effect": "Allow",
            "Action": [
                "glue:UpdateTable",
                "glue:UpdatePartition",
                "glue:GetTables",
                "glue:GetTableVersions",
                "glue:GetTableVersion",
                "glue:GetTable",
                "glue:GetPartitions",
                "glue:GetPartition",
                "glue:DeleteTableVersion",
                "glue:DeleteTable",
                "glue:DeletePartition",
                "glue:CreateTable",
                "glue:CreatePartition",
                "glue:CreateDatabase",
                "glue:BatchGetPartition",
                "glue:BatchDeleteTableVersion",
                "glue:BatchDeleteTable",
                "glue:BatchDeletePartition",
                "glue:BatchCreatePartition"
            ],
            "Resource": [
                "arn:aws:glue:$REGION:$YOUR_ACCOUNT:table/*",
                "arn:aws:glue:$REGION:$YOUR_ACCOUNT:database/default",
                "arn:aws:glue:$REGION:$YOUR_ACCOUNT:database/*",
                "arn:aws:glue:$REGION:$YOUR_ACCOUNT:catalog"
            ]
        },
        {
            "Effect": "Allow",
            "Action": "*",
            "Resource": [
                "arn:aws:s3:::$BUCKET_NAME/*",
                "arn:aws:s3:::$BUCKET_NAME"
            ]
        },
        {
            "Effect": "Allow",
            "Action": [
                "athena:*"
            ],
            "Resource": [
                "*"
            ]
        },
        {
            "Sid": "",
            "Effect": "Allow",
            "Action": "iam:PassRole",
            "Resource": [
                "arn:aws:iam::$ACCOUNT_ID:role/EMR_DefaultRole",
                "arn:aws:iam::$ACCOUNT_ID:role/EMR_AutoScaling_DefaultRole",
                "arn:aws:iam::$ACCOUNT_ID:role/EMR_EC2_DefaultRole"
            ]
        }
    ]
}

Debugging

Segment requires access to the data and schema for debugging data quality issues. The modes available for debugging are:

• Access the individual objects stored in S3 and the associated schema to understand data discrepancies
• Run an Athena query on the underlying data stored in S3
  • Ensure Athena uses Glue as the data catalog. Older accounts may not have this configuration, and may require some additional steps to complete the upgrade. The Glue console typically displays a warning and provides a link to instructions on how to complete the upgrade. The warning reads:
    Upgrade to the AWS Glue Data Catalog
    To use the AWS Glue Data Catalog with Amazon Athena and Amazon Redshift Spectrum, you must upgrade your Athena Data Catalog to the AWS Glue Data Catalog. Without the upgrade, tables and partitions created by AWS Glue cannot be queried with Amazon Athena or Redshift Spectrum. Start the upgrade in the Athena console.
  • An easier alternative is to create a new account that has Athena backed by Glue as the default.

Updating EMR clusters

You can update your existing Data Lake destination to EMR version 5.33.0 by creating a new v5.33.0 cluster in AWS and associating it with your existing Data Lake. After you update the EMR cluster, your Segment Data Lake continues to use the Glue data catalog you initially configured.

When you update an EMR cluster to 5.33.0, you can participate in AWS Lake Formation, use dynamic auto-scaling, and experience faster Parquet jobs.

Prerequisites

• An EMR cluster up to Version 7
• An existing Segment Data Lakes destination

Procedure

1. Open your Segment app workspace and select the Data Lakes destination.
2. On the Settings tab, select the EMR Cluster ID field and replace the existing ID with the ID of your v5.33.0 EMR cluster. For help finding the cluster ID in AWS, see Amazon’s View cluster status and details. You don’t need to update the Glue Catalog ID, IAM Role ARN, or S3 Bucket name fields.
3. Click Save.
4. In the AWS EMR console, view the Events tab for your cluster to verify it is receiving data.

You can delete the old EMR cluster from AWS after the following conditions have been met:

• You have updated all Data Lakes to use the EMR cluster
• A sync has successfully completed in the new cluster
• Data is synced into the new cluster
• There are no ongoing jobs in the old cluster