Anandaganesh Balakrishnan
American Water, Principal Software Engineer
Philadelphia, Pennsylvania, United States
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Anandaganesh Balakrishnan has 15+ years of experience in data engineering, data virtualization, database development, infrastructure development, and data analytics. He held leadership roles spanning diverse industries across Banking, Trading, Biotech, Real Estate, and Utilities.
He currently leads the development and optimization of data virtualization infrastructure and data engineering strategies. He supports application developers, data products team, database developers, data scientists, and other key stakeholders on data initiatives. He ensures optimal data delivery architecture by benchmarking different tools' capabilities and performance. His current focus is AI on unstructured data, large language models, Generative AI, self-service data analytics, and data catalogs.
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Low latency Hybrid Data Pipeline Architecture for Trading and productivity improvements using AI
I will discuss the challenges faced and solutions implemented in architecting data engineering solutions for a trading desk. The key challenges included improving the efficiency of transaction data maintenance in an Oracle database and storing historical data for advanced analytics on the Cloudera Hadoop platform.
To address these, I optimized data structures within the Oracle database and developed data engineering frameworks to migrate terabytes of historical data into Cloudera Hadoop. This included creating data products in Hadoop for sub-second latency queries over large datasets.
Key actions included:
1) Restructuring Oracle tables for better performance.
2) Collaborating with the trading team to strategize data product development.
3) Domain-driven data engineering design for trading.
4) Designing innovative data pipelines and frameworks for efficient data handling.
5) Creating a hybrid low-latency data architecture for real-time and historical data analytics.
6) Technologies used included Sqoop, Hive, Spark, Kafka, HBase, Druid, Unix scripting, and Python.
The results were significant: improved query performance by 20%, faster data access times (10 times faster than Oracle), and reduced P&L calculation times from 45 minutes to less than 5 minutes. The novel hybrid data architecture tailored for the trading desk facilitated faster historical data processing via Hadoop. It alleviated the computational burden for transaction data queries on the Oracle database by 70%, facilitating better trading decisions and increased profitability.
The talk will go through some key architectural considerations, optimization methodologies, and data pipeline strategies for hybrid data architecture.
I will also add content on how AI can enable latency Hybrid Data Pipeline Architecture for Trading to improve productivity.
Navigating the Minefield: Mitigating Risks of AI in Data Cataloging and Documentation
Artificial intelligence (AI) significantly enhances the efficiency of data cataloging and documentation, automating many tasks in managing data products and datasets. However, the integration of AI introduces various risks:
Bias in Metadata Generation: AI-driven systems may generate biased or skewed metadata if the underlying models are trained on biased data or flawed algorithms. This can affect data tagging, categorization, and decision-making processes.
Quality and Accuracy Issues: AI may fail to capture or describe complex data nuances accurately, leading to data tagging or metadata errors. Such inaccuracies can be especially problematic in critical sectors like healthcare or finance.
Lack of Contextual Understanding: AI might not fully grasp the broader context of the data, missing regional variations or changes in data relevance, which could make the data seem outdated or irrelevant.
Security and Privacy Concerns: AI in data cataloging might inadvertently reveal sensitive information or create security vulnerabilities, exposing more data than intended.
Dependency and Overreliance: Heavy reliance on AI for data management could make organizations less inclined to utilize human expertise, which is vital for handling complex or sensitive data.
Scalability and Performance Issues: Although AI can efficiently process large data volumes, scalability challenges may arise as data grows in volume and complexity, potentially slowing down data processing and cataloging.
Ethical and Legal Compliance: AI must adhere to ethical standards and legal regulations like GDPR or HIPAA. Non-compliance due to AI inaccuracies can result in serious legal and financial consequences.
A combination of technical measures, robust governance frameworks, and human oversight is essential to address these risks.
Strategies include:
Implementing bias mitigation techniques.
Enhancing data quality assurance.
Strengthening contextual understanding.
Prioritizing security and privacy.
Reducing dependency on AI.
Optimizing scalability.
Ensuring ethical and legal compliance.
Fostering continuous education and robust AI governance.
These measures help ensure that AI in data management is efficient and trustworthy.
Low latency Hybrid Data Pipeline Architecture for Trading
I will discuss the challenges faced and solutions implemented in architecting data engineering solutions for a trading desk. The key challenges included improving the efficiency of transaction data maintenance in an Oracle database and storing historical data for advanced analytics on the Cloudera Hadoop platform.
To address these, I optimized data structures within the Oracle database and developed data engineering frameworks to migrate terabytes of historical data into Cloudera Hadoop. This included creating data products in Hadoop for sub-second latency queries over large datasets.
Key actions included:
1) Restructuring Oracle tables for better performance.
2) Collaborating with the trading team to strategize data product development.
3) Domain-driven data engineering design for trading.
4) Designing innovative data pipelines and frameworks for efficient data handling.
5) Creating a hybrid low-latency data architecture for real-time and historical data analytics.
6) Technologies used included Sqoop, Hive, Spark, Kafka, HBase, Druid, Unix scripting, and Python.
The results were significant: improved query performance by 20%, faster data access times (10 times faster than Oracle), and reduced P&L calculation times from 45 minutes to less than 5 minutes. The novel hybrid data architecture tailored for the trading desk facilitated faster historical data processing via Hadoop. It alleviated the computational burden for transaction data queries on the Oracle database by 70%, facilitating better trading decisions and increased profitability.
The talk will go through some key architectural considerations, optimization methodologies, and data pipeline strategies for hybrid data architecture.
AI-powered Data Observability in Data Engineering
AI-powered data observability marks a transformative approach in data engineering, focusing on the advanced monitoring, management, and comprehension of an organization's data health. This method employs artificial intelligence (AI) and machine learning (ML) algorithms to automate issue detection and diagnosis, ensuring data quality, reliability, and trustworthiness. Essential aspects of this integration include Automated Anomaly Detection, Predictive Analytics, Root Cause Analysis, Data Quality Scoring, and Real-time Monitoring. These features collectively identify and promptly address data discrepancies, analyze historical data patterns to predict future issues and evaluate data quality across various dimensions, ensuring immediate and effective data management.
Adopting AI in data observability yields significant benefits such as increased operational efficiency, enhanced data quality, reduced system downtime, improved decision-making capabilities, and considerable cost savings. These advantages stem from reducing manual monitoring requirements, maintaining high data quality crucial for analytical processes, rapid issue resolution, and providing high-quality data to support strategic business decisions.
However, successfully implementing AI-powered data observability necessitates considering factors like integrating with existing data systems, customizing and tuning AI models according to specific data environments and business needs, and providing adequate training for teams. Given the growing complexity and pivotal role of data environments in business operations, AI's role in data observability is poised for expansion, promising innovative solutions for ensuring data integrity and enhancing business value.
Implementing AI-powered data observability in data engineering requires adherence to several best practices to enhance the effectiveness of data system monitoring, diagnosis, and health assurance. These practices aim to bolster data quality and operational efficiency and achieve superior business outcomes. Key strategies include:
- Setting clear objectives and measurable KPIs aligned with business goals.
- Comprehensive monitoring of the data ecosystem in real time.
- Leveraging advanced anomaly detection techniques through machine learning for precise issue identification.
Additionally, automating root cause analysis, ensuring the scalability and flexibility of the observability solution, and prioritizing data quality management are crucial. Encouraging cross-functional collaboration, addressing privacy and security concerns, and maintaining a continuous evaluation and improvement cycle are also vital. By embracing these practices, organizations can effectively leverage AI-powered data observability for proactive data management, minimizing operational risks, and facilitating informed decision-making based on high-quality data.
Multi-Engine Data Platform Architecture for Data Virtualization
The talk will delve into the Multi-Engine Data Virtualization Framework, an innovative approach to enhance data virtualization by leveraging the capabilities of various data platforms.
International Conference on Machine Learning and Artificial Intelligence Upcoming
The aim of the ICMLAI-2024 is to promote quality research and real-world impact in an atmosphere of true international cooperation between scientists and engineers by bringing together again the world class researchers, International Communities and Industrial heads to discuss the latest developments and innovations in the fields of Machine Learning and Artificial Intelligence.
AI Tech Symposium Upcoming
Topic: Retrieval Augmented Generation (RAG) and LLMOps for Database Tasks Automation and Data Architecture Selection
Panel Talk: Designing Modern AI Systems
Automating Database Tasks: RAG can automate various database tasks such as query generation, data cleaning, data transformation, statistics gathering, data cataloging, and even database schema design. By leveraging the power of pre-trained language models and retrieval mechanisms, RAG can understand natural language queries or commands and generate SQL queries or Python scripts to perform the required tasks on the database.
Data Architecture Selection: When designing or selecting a data architecture for a specific project or use case, there are numerous factors to consider, such as scalability, performance, data consistency, and cost-effectiveness. RAG can assist in this process by analyzing the requirements and constraints provided by the user and retrieving relevant information from a vast repository of knowledge. It can then generate recommendations or even design proposals for the most suitable data architecture, considering factors like relational databases, NoSQL databases, data lakes, data warehouses, and distributed computing frameworks.
By combining the power of natural language understanding, information retrieval, and generative capabilities, RAG can significantly streamline and enhance the efficiency of database-related tasks and data architecture selection processes. Additionally, it can adapt and improve over time as it learns from user interactions and feedback, making it a valuable tool for automating and optimizing data-related workflows.
World Data Summit 2024
Topic: Navigating the Double-Edged Sword: Advantages, Risks, and Governance of AI in Data Engineering
The presentation delves into the dual-edged nature of AI in data platforms, data engineering, and big data analytics, highlighting its transformative potential and the array of risks it introduces. It categorizes the primary concerns into several key areas:
Bias and Ethical Concerns
Data Privacy and Security
Quality and Accuracy of AI Models
Dependency and Over-reliance
Interpretability and Transparency
Compliance and Legal Risks
Resource Intensiveness
Model Drift and Maintenance
The presentation concludes with a call to adopt comprehensive AI governance strategies to navigate these challenges. These include implementing ethical AI frameworks, ensuring model transparency, maintaining rigorous data privacy standards, and fostering a multidisciplinary approach to AI system development and management. By taking these steps, organizations can mitigate the risks associated with AI, harnessing its capabilities more responsibly and effectively in Data Engineering and Big Data Space.
Anandaganesh Balakrishnan
American Water, Principal Software Engineer
Philadelphia, Pennsylvania, United States
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