Constructing end-to-end information lineage for one-time and complicated queries utilizing Amazon Athena, Amazon Redshift, Amazon Neptune and dbt

One-time and complicated queries are two frequent situations in enterprise information analytics. One-time queries are versatile and appropriate for fast evaluation and exploratory analysis. Complicated queries, alternatively, check with large-scale information processing and in-depth evaluation based mostly on petabyte-level information warehouses in large information situations. These complicated queries usually contain information sources from a number of enterprise programs, requiring multilevel nested SQL or associations with quite a few tables for extremely subtle analytical duties.

Nevertheless, combining the information lineage of those two question sorts presents a number of challenges:

1. Range of information sources
2. Various question complexity
3. Inconsistent granularity in lineage monitoring
4. Totally different real-time necessities
5. Difficulties in cross-system integration

Furthermore, sustaining the accuracy and completeness of lineage info whereas offering system efficiency and scalability are essential concerns. Addressing these challenges requires a rigorously designed structure and superior technical options.

Amazon Athena gives serverless, versatile SQL analytics for one-time queries, enabling direct querying of Amazon Easy Storage Service (Amazon S3) information for speedy, cost-effective instantaneous evaluation. Amazon Redshift, optimized for complicated queries, offers high-performance columnar storage and massively parallel processing (MPP) structure, supporting large-scale information processing and superior SQL capabilities. Amazon Neptune, as a graph database, is right for information lineage evaluation, providing environment friendly relationship traversal and complicated graph algorithms to deal with large-scale, intricate information lineage relationships. The mixture of those three providers offers a robust, complete answer for end-to-end information lineage evaluation.

Within the context of complete information governance, Amazon DataZone gives organization-wide information lineage visualization utilizing Amazon Net Providers (AWS) providers, whereas dbt offers project-level lineage by mannequin evaluation and helps cross-project integration between information lakes and warehouses.

On this publish, we use dbt for information modeling on each Amazon Athena and Amazon Redshift. dbt on Athena helps real-time queries, whereas dbt on Amazon Redshift handles complicated queries, unifying the event language and considerably lowering the technical studying curve. Utilizing a single dbt modeling language not solely simplifies the event course of but additionally mechanically generates constant information lineage info. This strategy gives sturdy adaptability, simply accommodating modifications in information buildings.

By integrating Amazon Neptune graph database to retailer and analyze complicated lineage relationships, mixed with AWS Step Capabilities and AWS Lambda features, we obtain a totally automated information lineage technology course of. This mix promotes consistency and completeness of lineage information whereas enhancing the effectivity and scalability of the whole course of. The result’s a robust and versatile answer for end-to-end information lineage evaluation.

Structure overview

The experiment’s context entails a buyer already utilizing Amazon Athena for one-time queries. To raised accommodate large information processing and complicated question situations, they intention to undertake a unified information modeling language throughout totally different information platforms. This led to the implementation of each Athena on dbt and Amazon Redshift on dbt architectures.

AWS Glue crawler crawls information lake info from Amazon S3, producing a Knowledge Catalog to help dbt on Amazon Athena information modeling. For complicated question situations, AWS Glue performs extract, remodel, and cargo (ETL) processing, loading information into the petabyte-scale information warehouse, Amazon Redshift. Right here, information modeling makes use of dbt on Amazon Redshift.

Lineage information unique information from each elements are loaded into an S3 bucket, offering information help for end-to-end information lineage evaluation.

The next picture is the structure diagram for the answer.

Some vital concerns:

This experiment makes use of the next information dictionary:

The lineage information generated by dbt on Athena contains partial lineage diagrams, as exemplified within the following pictures. The primary picture reveals the lineage of name_basics in dbt on Athena. The second picture reveals the lineage of title_crew in dbt on Athena.

The lineage information generated by dbt on Amazon Redshift contains partial lineage diagrams, as illustrated within the following picture.

Referring to the information dictionary and screenshots, it’s evident that the entire information lineage info is extremely dispersed, unfold throughout 29 lineage diagrams. Understanding the end-to-end complete view requires vital time. In real-world environments, the state of affairs is usually extra complicated, with full information lineage doubtlessly distributed throughout lots of of information. Consequently, integrating an entire end-to-end information lineage diagram turns into essential and difficult.

This experiment will present an in depth introduction to processing and merging information lineage information saved in Amazon S3, as illustrated within the following diagram.

Conditions

To carry out the answer, it is advisable to have the next conditions in place:

• The Lambda perform for preprocessing lineage information will need to have permissions to entry Amazon S3 and Amazon Redshift.
• The Lambda perform for setting up the directed acyclic graph (DAG) will need to have permissions to entry Amazon S3 and Amazon Neptune.

Answer walkthrough

To carry out the answer, comply with the steps within the subsequent sections.

Preprocess uncooked lineage information for DAG technology utilizing Lambda features

Use Lambda to preprocess the uncooked lineage information generated by dbt, changing it into key-value pair JSON information which can be simply understood by Neptune: athena_dbt_lineage_map.json and redshift_dbt_lineage_map.json.

1. To create a brand new Lambda perform within the Lambda console, enter a Operate identify, choose the Runtime (Python on this instance), configure the Structure and Execution position, then click on the “Create perform” button.

2. Open the created Lambda perform and on the Configuration tab, within the navigation pane, choose Surroundings variables and select your configurations. Utilizing Athena on dbt processing for example, configure the atmosphere variables as follows (the method for Amazon Redshift on dbt is analogous):
   • INPUT_BUCKET: data-lineage-analysis-24-09-22 (change with the S3 bucket path storing the unique Athena on dbt lineage information)
   • INPUT_KEY: athena_manifest.json (the unique Athena on dbt lineage file)
   • OUTPUT_BUCKET: data-lineage-analysis-24-09-22 (change with the S3 bucket path for storing the preprocessed output of Athena on dbt lineage information)
   • OUTPUT_KEY: athena_dbt_lineage_map.json (the output file after preprocessing the unique Athena on dbt lineage file)

3. On the Code tab, within the lambda_function.py file, enter the preprocessing code for the uncooked lineage information. Right here’s a code reference utilizing Athena on dbt processing for example (the method for Amazon Redshift on dbt is analogous). The preprocessing code for Athena on dbt’s unique lineage file is as follows:

The athena_manifest.json, redshift_manifest.json, and different information used on this experiment might be obtained from the Knowledge Lineage Graph Building GitHub repository.

import json
import boto3
import os

def lambda_handler(occasion, context):
    # Arrange S3 shopper
    s3 = boto3.shopper('s3')

    # Get enter and output paths from atmosphere variables
    input_bucket = os.environ['INPUT_BUCKET']
    input_key = os.environ['INPUT_KEY']
    output_bucket = os.environ['OUTPUT_BUCKET']
    output_key = os.environ['OUTPUT_KEY']

    # Outline helper perform
    def dbt_nodename_format(node_name):
        return node_name.cut up(".")[-1]

    # Learn enter JSON file from S3
    response = s3.get_object(Bucket=input_bucket, Key=input_key)
    file_content = response['Body'].learn().decode('utf-8')
    information = json.masses(file_content)
    lineage_map = information["child_map"]
    node_dict = {}
    dbt_lineage_map = {}

    # Course of information
    for merchandise in lineage_map:
        lineage_map[item] = [dbt_nodename_format(child) for child in lineage_map[item]]
        node_dict[item] = dbt_nodename_format(merchandise)

    # Replace key names
    lineage_map = {node_dict[old]: worth for previous, worth in lineage_map.objects()}
    dbt_lineage_map["lineage_map"] = lineage_map

    # Convert consequence to JSON string
    result_json = json.dumps(dbt_lineage_map)

    # Write JSON string to S3
    s3.put_object(Physique=result_json, Bucket=output_bucket, Key=output_key)
    print(f"Knowledge written to s3://{output_bucket}/{output_key}")

    return {
        'statusCode': 200,
        'physique': json.dumps('Athena information lineage processing accomplished efficiently')
    }

Merge preprocessed lineage information and write to Neptune utilizing Lambda features

1. Earlier than processing information with the Lambda perform, create a Lambda layer by importing the required Gremlin plugin. For detailed steps on creating and configuring Lambda Layers, see the AWS Lambda Layers documentation.

As a result of connecting Lambda to Neptune for setting up a DAG requires the Gremlin plugin, it must be uploaded earlier than utilizing Lambda. The Gremlin package deal might be obtained from the Knowledge Lineage Graph Building GitHub repository.

2. Create a brand new Lambda perform. Select the perform to configure. To the not too long ago created layer, on the backside of the web page, select Add a layer.

Create one other Lambda layer for the requests library, much like the way you created the layer for the Gremlin plugin. This library can be used for HTTP shopper performance within the Lambda perform.

3. Select the not too long ago created Lambda perform to configure. Hook up with Neptune by Lambda to merge the 2 datasets and assemble a DAG. On the Code tab, the reference code to execute is as follows:

import json
import boto3
import os
import requests
from botocore.auth import SigV4Auth
from botocore.awsrequest import AWSRequest
from botocore.credentials import get_credentials
from botocore.session import Session
from concurrent.futures import ThreadPoolExecutor, as_completed

def read_s3_file(s3_client, bucket, key):
    strive:
        response = s3_client.get_object(Bucket=bucket, Key=key)
        information = json.masses(response['Body'].learn().decode('utf-8'))
        return information.get("lineage_map", {})
    besides Exception as e:
        print(f"Error studying S3 file {bucket}/{key}: {str(e)}")
        elevate

def merge_data(athena_data, redshift_data):
    return {**athena_data, **redshift_data}

def sign_request(request):
    credentials = get_credentials(Session())
    auth = SigV4Auth(credentials, 'neptune-db', os.environ['AWS_REGION'])
    auth.add_auth(request)
    return dict(request.headers)

def send_request(url, headers, information):
    strive:
        response = requests.publish(url, headers=headers, information=information, timeout=30)
        response.raise_for_status()
        return response.textual content
    besides requests.exceptions.RequestException as e:
        print(f"Request Error: {str(e)}")
        if hasattr(e.response, 'textual content'):
            print(f"Response content material: {e.response.textual content}")
        elevate

def write_to_neptune(information):
    endpoint="https://your neptune endpoint identify:8182/gremlin"
    # change along with your neptune endpoint identify

    # Clear Neptune database
    clear_query = "g.V().drop()"
    request = AWSRequest(technique='POST', url=endpoint, information=json.dumps({'gremlin': clear_query}))
    signed_headers = sign_request(request)
    response = send_request(endpoint, signed_headers, json.dumps({'gremlin': clear_query}))
    print(f"Clear database response: {response}")

    # Confirm if the database is empty
    verify_query = "g.V().rely()"
    request = AWSRequest(technique='POST', url=endpoint, information=json.dumps({'gremlin': verify_query}))
    signed_headers = sign_request(request)
    response = send_request(endpoint, signed_headers, json.dumps({'gremlin': verify_query}))
    print(f"Vertex rely after clearing: {response}")
    
    def process_node(node, youngsters):
        # Add node
        question = f"g.V().has('lineage_node', 'node_name', '{node}').fold().coalesce(unfold(), addV('lineage_node').property('node_name', '{node}'))"
        request = AWSRequest(technique='POST', url=endpoint, information=json.dumps({'gremlin': question}))
        signed_headers = sign_request(request)
        response = send_request(endpoint, signed_headers, json.dumps({'gremlin': question}))
        print(f"Add node response for {node}: {response}")

        for child_node in youngsters:
            # Add baby node
            question = f"g.V().has('lineage_node', 'node_name', '{child_node}').fold().coalesce(unfold(), addV('lineage_node').property('node_name', '{child_node}'))"
            request = AWSRequest(technique='POST', url=endpoint, information=json.dumps({'gremlin': question}))
            signed_headers = sign_request(request)
            response = send_request(endpoint, signed_headers, json.dumps({'gremlin': question}))
            print(f"Add baby node response for {child_node}: {response}")

            # Add edge
            question = f"g.V().has('lineage_node', 'node_name', '{node}').as('a').V().has('lineage_node', 'node_name', '{child_node}').coalesce(inE('lineage_edge').the place(outV().as('a')), addE('lineage_edge').from('a').property('edge_name', ' '))"
            request = AWSRequest(technique='POST', url=endpoint, information=json.dumps({'gremlin': question}))
            signed_headers = sign_request(request)
            response = send_request(endpoint, signed_headers, json.dumps({'gremlin': question}))
            print(f"Add edge response for {node} -> {child_node}: {response}")

    with ThreadPoolExecutor(max_workers=10) as executor:
        futures = [executor.submit(process_node, node, children) for node, children in data.items()]
        for future in as_completed(futures):
            strive:
                future.consequence()
            besides Exception as e:
                print(f"Error in processing node: {str(e)}")

def lambda_handler(occasion, context):
    # Initialize S3 shopper
    s3_client = boto3.shopper('s3')

    # S3 bucket and file paths
    bucket_name="data-lineage-analysis" # Substitute along with your S3 bucket identify
    athena_key = 'athena_dbt_lineage_map.json' # Substitute along with your athena lineage key worth output json identify
    redshift_key = 'redshift_dbt_lineage_map.json' # Substitute along with your redshift lineage key worth output json identify

    strive:
        # Learn Athena lineage information
        athena_data = read_s3_file(s3_client, bucket_name, athena_key)
        print(f"Athena information dimension: {len(athena_data)}")

        # Learn Redshift lineage information
        redshift_data = read_s3_file(s3_client, bucket_name, redshift_key)
        print(f"Redshift information dimension: {len(redshift_data)}")

        # Merge information
        combined_data = merge_data(athena_data, redshift_data)
        print(f"Mixed information dimension: {len(combined_data)}")

        # Write to Neptune (together with clearing the database)
        write_to_neptune(combined_data)

        return {
            'statusCode': 200,
            'physique': json.dumps('Knowledge efficiently written to Neptune')
        }
    besides Exception as e:
        print(f"Error in lambda_handler: {str(e)}")
        return {
            'statusCode': 500,
            'physique': json.dumps(f'Error: {str(e)}')
        }

Create Step Capabilities workflow

1. On the Step Capabilities console, select State machines, after which select Create state machine. On the Select a template web page, choose Clean template.

2. Within the Clean template, select Code to outline your state machine. Use the next instance code:

{
  "Remark": "Every day Knowledge Lineage Processing Workflow",
  "StartAt": "Parallel Processing",
  "States": {
    "Parallel Processing": {
      "Sort": "Parallel",
      "Branches": [
        {
          "StartAt": "Process Athena Data",
          "States": {
            "Process Athena Data": {
              "Type": "Task",
              "Resource": "arn:aws:states:::lambda:invoke",
              "Parameters": {
                "FunctionName": "athena-data-lineange-process-Lambda", ##Replace with your Athena data lineage process Lambda function name
                "Payload": {
                  "input.$": "$"
                }
              },
              "End": true
            }
          }
        },
        {
          "StartAt": "Process Redshift Data",
          "States": {
            "Process Redshift Data": {
              "Type": "Task",
              "Resource": "arn:aws:states:::lambda:invoke",
              "Parameters": {
                "FunctionName": "redshift-data-lineange-process-Lambda", ##Replace with your Redshift data lineage process Lambda function name
                "Payload": {
                  "input.$": "$"
                }
              },
              "End": true
            }
          }
        }
      ],
      "Subsequent": "Load Knowledge to Neptune"
    },
    "Load Knowledge to Neptune": {
      "Sort": "Activity",
      "Useful resource": "arn:aws:states:::lambda:invoke",
      "Parameters": {
        "FunctionName": "data-lineage-analysis-lambda" ##Substitute along with your Lambda perform Identify
      },
      "Finish": true
    }
  }
}

3. After finishing the configuration, select the Design tab to view the workflow proven within the following diagram.

Create scheduling guidelines with Amazon EventBridge

Configure Amazon EventBridge to generate lineage information every day throughout off-peak enterprise hours. To do that:

1. Create a brand new rule within the EventBridge console with a descriptive identify.
2. Set the rule kind to “Schedule” and configure it to run as soon as every day (utilizing both a set price or the Cron expression “0 0 * * ? *”).
3. Choose the AWS Step Capabilities state machine because the goal and specify the state machine you created earlier.

Question leads to Neptune

1. On the Neptune console, choose Notebooks. Open an current pocket book or create a brand new one.

2. Within the pocket book, create a brand new code cell to carry out a question. The next code instance reveals the question assertion and its outcomes:

%%gremlin -d node_name -de edge_name
g.V().hasLabel('lineage_node').outE('lineage_edge').inV().hasLabel('lineage_node').path().by(elementMap())

Now you can see the end-to-end information lineage graph info for each dbt on Athena and dbt on Amazon Redshift. The next picture reveals the merged DAG information lineage graph in Neptune.

You’ll be able to question the generated information lineage graph for information associated to a particular desk, akin to title_crew.

The pattern question assertion and its outcomes are proven within the following code instance:

%%gremlin -d node_name -de edge_name
g.V().has('lineage_node', 'node_name', 'title_crew')
  .repeat(
    union(
      __.inE('lineage_edge').outV(),
      __.outE('lineage_edge').inV()
    )
  )
  .till(
    __.has('node_name', inside('names', 'genre_titles', 'titles'))
    .or()
    .loops().is(gt(10))
  )
  .path()
  .by(elementMap())

The next picture reveals the filtered outcomes based mostly on title_crew desk in Neptune.

Clear up

To wash up your assets, full the next steps:

1. Delete EventBridge guidelines

# Cease new occasions from triggering whereas eradicating dependencies
aws occasions disable-rule --name <rule-name>
# Break connections between rule and targets (like Lambda features)
aws occasions remove-targets --rule <rule-name> --ids <target-id>
# Take away the rule fully from EventBridge
aws occasions delete-rule --name <rule-name>

2. Delete Step Capabilities state machine

# Cease all working executions
aws stepfunctions stop-execution --execution-arn <execution-arn>
# Delete the state machine
aws stepfunctions delete-state-machine --state-machine-arn <state-machine-arn>

3. Delete Lambda features

# Delete Lambda perform
aws lambda delete-function --function-name <function-name>
# Delete Lambda layers (if used)
aws lambda delete-layer-version --layer-name <layer-name> --version-number <model>

4. Clear up the Neptune database

# Delete all snapshots
aws neptune delete-db-cluster-snapshot --db-cluster-snapshot-identifier <snapshot-id>
# Delete database occasion
aws neptune delete-db-instance --db-instance-identifier <instance-id> --skip-final-snapshot
# Delete database cluster
aws neptune delete-db-cluster --db-cluster-identifier <cluster-id> --skip-final-snapshot

5. Observe the directions at Deleting a single object to wash up the S3 buckets

Conclusion

On this publish, we demonstrated how dbt permits unified information modeling throughout Amazon Athena and Amazon Redshift, integrating information lineage from each one-time and complicated queries. Through the use of Amazon Neptune, this answer offers complete end-to-end lineage evaluation. The structure makes use of AWS serverless computing and managed providers, together with Step Capabilities, Lambda, and EventBridge, offering a extremely versatile and scalable design.

This strategy considerably lowers the educational curve by a unified information modeling technique whereas enhancing growth effectivity. The tip-to-end information lineage graph visualization and evaluation not solely strengthen information governance capabilities but additionally supply deep insights for decision-making.

The answer’s versatile and scalable structure successfully optimizes operational prices and improves enterprise responsiveness. This complete strategy balances technical innovation, information governance, operational effectivity, and cost-effectiveness, thus supporting long-term enterprise development with the adaptability to fulfill evolving enterprise wants.

With OpenLineage-compatible information lineage now usually accessible in Amazon DataZone, we plan to discover integration prospects to additional improve the system’s functionality to deal with complicated information lineage evaluation situations.

When you have any questions, please be happy to go away a remark within the feedback part.

Concerning the authors

Nancy Wu is a Options Architect at AWS, accountable for cloud computing structure consulting and design for multinational enterprise prospects. Has a few years of expertise in massive information, enterprise digital transformation analysis and growth, consulting, and undertaking administration throughout telecommunications, leisure, and monetary industries.

Xu Feng is a Senior Business Answer Architect at AWS, accountable for designing, constructing, and selling trade options for the Media & Leisure and Promoting sectors, akin to clever customer support and enterprise intelligence. With 20 years of software program trade expertise, at present targeted on researching and implementing generative AI and AI-powered information options.

Xu Da is a Amazon Net Providers (AWS) Associate Options Architect based mostly out of Shanghai, China. He has greater than 25 years of expertise in IT trade, software program growth and answer structure. He’s obsessed with collaborative studying, information sharing, and guiding neighborhood of their cloud applied sciences journey.