International Air Transport Association - IATA

 IATA stands for International Air Transport Association. It is a trade association of the world's airlines. IATA was founded in 1945 and is headquartered in Montreal, Canada.

IATA's mission is to represent the interests of the airline industry and to promote safe, secure, and sustainable air travel. IATA does this by developing standards and regulations for the airline industry, providing training and education, and advocating for the interests of airlines with governments and other organizations.

IATA is best known for its development of the IATA airline codes. These codes are three-letter codes that are used to identify airlines. For example, the IATA code for Qatar Airways is QR. IATA airline codes are used in a variety of applications, including flight booking, baggage tracking, and airport signage.

IATA also develops standards for a variety of other aspects of the airline industry, including passenger and cargo handling, aircraft maintenance, and fuel efficiency. IATA also provides training and education to airline employees on a variety of topics, including safety, security, and customer service.

IATA is a powerful voice for the airline industry. It works with governments and other organizations to advocate for policies that are beneficial to airlines. IATA also works to promote the safe and secure operation of the global air transportation system.

Here are some of the benefits of IATA:

• Standardization: IATA develops standards for a variety of aspects of the airline industry, which helps to ensure that airlines operate in a safe and efficient manner.
• Training and education: IATA provides training and education to airline employees on a variety of topics, which helps to improve the safety and security of air travel.
• Advocacy: IATA is a powerful voice for the airline industry, and it works with governments and other organizations to advocate for policies that are beneficial to airlines.
• Research and development: IATA conducts research and development on a variety of topics related to air travel, which helps to improve the safety and efficiency of the industry.

CDH migration Cloudera to AWS Databricks

Cloudera/Hortonworks to Azure Databricks for Pratt & Whitney, incorporating a bridging technique for the landing layer, AWS EventBridge for ingestion, and data quality/anomaly detection measures.

Pratt & Whitney – Data Lake Migration from Hortonworks (CDP) to Azure Databricks

Project Title:

Modernizing Pratt & Whitney’s Enterprise Data Lake – Hortonworks to Azure Databricks

Background:

As part of its digital transformation journey, Pratt & Whitney aimed to retire its legacy on-premise Hortonworks Data Platform (HDP) cluster, which was becoming increasingly costly to maintain and lacked elasticity for modern analytics needs. The strategic decision was made to migrate to Azure Databricks to leverage cloud scalability, Delta Lake reliability, and native ML/AI integration.

Key Objectives:

• Migrate historical and active workloads from Hortonworks (CDP) to Azure Databricks.
• Design a bridging strategy to synchronize landing zone data during the transition.
• Implement an ingestion framework leveraging AWS EventBridge for real-time and batch ingestion.
• Establish a Data Quality Framework including anomaly detection for data reliability and governance.

Migration Strategy Overview:

1. 

Data Lake Zones Re-Architecture:

Redesigned the data lake layers using the medallion architecture on Azure:

• Landing (Raw): Replication of on-premise ingestions.
• Bronze: Cleaned ingested data, including file validation and metadata enrichment.
• Silver: Curated and joined data for analytical access.
• Gold: Aggregated data for business-specific reporting and ML use cases.

2. 

Bridging Technique for Landing Layer:

To minimize disruption and ensure data continuity:

• A bi-directional sync bridge was built between the legacy Hadoop landing directory (HDFS) and Azure Data Lake Storage Gen2 using DistCp and Azure Data Factory pipelines.
• Kafka Connect was used as a buffer for real-time sync between HDFS and cloud landing zones to handle change data capture (CDC).
• A cutover window was defined, during which both systems were synchronized and reconciled for data integrity using hash-based row-level comparisons.

Ingestion Framework Using AWS EventBridge:

Although the target platform is Azure-based, Pratt & Whitney utilized AWS EventBridge to trigger and orchestrate ingestion events from its distributed sensor and application systems (historically hosted on AWS):

• Architecture Highlights:
• 
  
• AWS EventBridge captures data availability events from S3 and external sensors.
• Events are passed to a Lambda-based bridge that places payloads into Azure Storage Queues or Event Grid.
• Azure Functions consume these triggers to kick off Databricks Jobs or ADF Pipelines for downstream ingestion into the bronze layer.
• This provided a hybrid event-driven ingestion mechanism, bridging cloud ecosystems during the transition period.
• 
  

Data Quality & Anomaly Detection:

A modular data quality framework was implemented to monitor the integrity of data during and after ingestion.

Key Dimensions Measured:

• Completeness: Row counts and expected null checks.
• Accuracy: Rule-based validation using domain-specific thresholds.
• Timeliness: SLAs on file delivery and latency between event capture and ingestion.
• Uniqueness: Duplicate detection using hash-based keys.

Anomaly Detector:

• Built on Azure Machine Learning and integrated with Databricks Delta tables.
• Applied unsupervised models (Isolation Forest, Z-Score, Autoencoders) on time-series sensor and transactional datasets.
• Detected:
• 
  
• Missing sequences in batch loads
• Outliers in engine sensor readings
• Unexpected volume surges in operational data
• 
  
• Alerting integrated with Azure Monitor and Teams/Slack channels for near real-time notification.

Outcomes:

• Successfully migrated 120+ TB of data with less than 1-hour downtime.
• Reduced data ingestion latency by 40% via event-driven architecture.
• Enhanced data quality score from 72% to 95% (measured monthly).
• Enabled new ML pipelines for predictive maintenance leveraging curated gold data.

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