Latest news with #ShinoyVengaramkodeBhaskaran
Forbes
6 days ago
- Business
- Forbes
Securing The Future: How Big Data Can Solve The Data Privacy Paradox
Shinoy Vengaramkode Bhaskaran, Senior Big Data Engineering Manager, Zoom Communications Inc. As businesses continue to harness Big Data to drive innovation, customer engagement and operational efficiency, they increasingly find themselves walking a tightrope between data utility and user privacy. With regulations such as GDPR, CCPA and HIPAA tightening the screws on compliance, protecting sensitive data has never been more crucial. Yet, Big Data—often perceived as a security risk—may actually be the most powerful tool we have to solve the data privacy paradox. Modern enterprises are drowning in data. From IoT sensors and smart devices to social media streams and transactional logs, the information influx is relentless. The '3 Vs' of Big Data—volume, velocity and variety—underscore its complexity, but another 'V' is increasingly crucial: vulnerability. The cost of cyber breaches, data leaks and unauthorized access events is rising in tandem with the growth of data pipelines. High-profile failures, as we've seen at Equifax, have shown that privacy isn't just a compliance issue; it's a boardroom-level risk. Teams can wield the same technologies used to gather and process petabytes of consumer behavior to protect that information. Big Data engineering, when approached strategically, becomes a core enabler of robust data privacy and security. Here's how: Big Data architectures allow for precise access management at scale. By implementing RBAC at the data layer, enterprises can ensure that only authorized personnel access sensitive information. Technologies such as Apache Ranger or AWS IAM integrate seamlessly with Hadoop, Spark and cloud-native platforms to enforce fine-grained access control. This is not just a technical best practice; it's a regulatory mandate. GDPR's data minimization principle demands access restrictions that Big Data can operationalize effectively. Distributed data systems, by design, traverse multiple nodes and platforms. Without encryption in transit and at rest, they become ripe targets. Big Data platforms like Hadoop and Apache Kafka now support built-in encryption mechanisms. Moreover, data tokenization or de-identification allows sensitive information (like PII or health records) to be replaced with non-sensitive surrogates, reducing risk without compromising analytics. As outlined in my book, Hands-On Big Data Engineering, combining encryption with identity-aware proxies is critical for protecting data integrity in real-time ingestion and stream processing pipelines. You can't protect what you can't track. Metadata management tools integrated into Big Data ecosystems provide data lineage tracing, enabling organizations to know precisely where data originates, how it's transformed and who has accessed it. This visibility not only helps in audits but also strengthens anomaly detection. With AI-infused lineage tracking, teams can identify deviations in data flow indicative of malicious activity or unintentional exposure. Machine learning and real-time data processing frameworks like Apache Flink or Spark Streaming are useful not only for business intelligence but also for security analytics. These tools can detect unusual access patterns, fraud attempts, or insider threats with millisecond latency. For instance, a global bank implementing real-time fraud detection used Big Data to correlate millions of transaction streams, identifying anomalies faster than traditional rule-based systems could react. Compliance frameworks are ever-evolving. Big Data platforms now include built-in auditability, enabling automatic checks against regulatory policies. Continuous Integration and Continuous Delivery (CI/CD) for data pipelines allows for integrated validation layers that ensure data usage complies with privacy laws from ingestion to archival. Apache Airflow, for example, can orchestrate data workflows while embedding compliance checks as part of the DAGs (Directed Acyclic Graphs) used in pipeline scheduling. Moving data to centralized systems can increase exposure in sectors like healthcare and finance. Edge analytics, supported by Big Data frameworks, enables processing at the source. Companies can train AI models on-device with federated learning, keeping sensitive data decentralized and secure. This architecture minimizes data movement, lowers breach risk and aligns with the privacy-by-design principles found in most global data regulations. While Big Data engineering offers formidable tools to fortify security, we cannot ignore the ethical dimension. Bias in AI algorithms, lack of transparency in automated decisions and opaque data brokerage practices all risk undermining trust. Thankfully, Big Data doesn't have to be a liability to privacy and security. In fact, with the right architectural frameworks, governance models and cultural mindset, it can become your organization's strongest defense. Are you using Big Data to shield your future, or expose it? As we continue to innovate in an age of AI-powered insights and decentralized systems, let's not forget that data privacy is more than just protection; it's a promise to the people we serve. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Forbes
25-03-2025
- Business
- Forbes
Big Data Engineering: The Fuel Powering AI In The Digital Age
Shinoy Vengaramkode Bhaskaran, Senior Big Data Engineering Manager, Zoom Communications Inc. As a data engineering leader with over 15 years of experience designing and deploying large-scale data architectures across industries, I've seen countless AI projects stumble, not because of flawed algorithms but because the underlying data pipelines were weak or chaotic. These real-world struggles inspired me to write the book, Hands-On Big Data Engineering: From Architecture to Deployment, to guide companies on building scalable, AI-ready data systems. This article explores key insights from Hands-On Big Data Engineering, discussing why data engineering is critical in the AI-driven era, how enterprises can harness it for innovation and what the future holds for AI-driven data architectures. AI is reshaping industries from finance and healthcare to e-commerce and logistics. However, the real driving force behind AI's success is data. A 2024 study by MIT Technology Review Insights and Snowflake found that 78% of companies feel at least somewhat unequipped to deploy generative AI at scale, with weak data strategies being the prevailing issue. A 2024 Rand report also found that inadequate data infrastructure is a major factor in AI projects failing. In today's digital economy, data isn't just fuel—it's the foundation. Big Data and AI are deeply interconnected: Data fuels AI models, and AI enhances data processing. AI's effectiveness depends on three key aspects of Big Data: AI models thrive when trained on vast datasets. Platforms like Netflix process petabytes of user data weekly to improve recommendations. Similarly, the automotive industry relies on terabytes of sensor data to train autonomous vehicles. Handling such scale requires distributed storage systems like HDFS and cloud object stores, paired with scalable frameworks like Apache Spark. AI relies on diverse data types: structured transactional logs, semi-structured JSON and unstructured images, videos and social posts. Predictive healthcare models combine structured electronic health records (EHR) data with unstructured doctor notes and medical images. Data engineers build pipelines to unify these sources, often using Apache NiFi and schema evolution techniques. AI models in fraud detection and predictive maintenance rely on real-time data. Financial institutions process transactions within milliseconds to detect fraud before payments are completed. This speed depends on streaming tools like Apache Kafka, paired with Apache Flink for fast processing—the backbone of modern real-time data architectures. Simply having data isn't enough. AI's performance depends on data quality, structure and accessibility—all enabled by strong data engineering. While data science and AI get the spotlight, data engineering is the unsung hero behind successful AI systems. Let's explore why data engineering is the backbone of AI: Enterprises collect data from IoT devices, social platforms and legacy systems. The challenge is integrating them into high-quality, unified datasets. For example, healthcare providers often struggle to merge legacy EHR data with wearable device data. This is why data engineers built ETL pipelines to clean, normalize and unify this data, addressing: • Data Inconsistency: Incomplete or inaccurate records bias models. • Data Integration: Structured and unstructured data must coexist in AI-ready formats. • Scalability: Pipelines must adapt as new data sources emerge. Relational databases were never designed for AI-scale workloads. The shift to cloud-native systems, Hadoop and Spark reflects the need for massive parallel processing. One retailer I worked with reduced recommendation engine training time by 90% by switching from a relational database to Apache Spark, leveraging in-memory distributed computing. AI-driven systems rely on continuous real-time data. Fraud detection pipelines often process millions of events per second using Kafka for ingestion and Flink for processing. Without this infrastructure, AI models would miss critical patterns and signals. Data engineering must embed compliance directly into pipelines, especially under GDPR, CCPA and HIPAA. Core practices include: • Encryption (TLS, AES-256) for data in transit and at rest. • Anonymizing personally identifiable information (PII) before exposing data to models. • Role-based access control (RBAC) to restrict unauthorized access. These are not optional—they're essential for lawful, ethical AI. AI is also transforming data engineering itself. Several trends are accelerating this shift: Traditionally, cleaning data has been a manual, time-consuming task. Today, AI tools like Google Cloud's Dataprep automate anomaly detection, deduplication and schema validation—freeing engineers to focus on higher-value work. Companies adopting these tools must invest in training staff and adjusting governance processes to trust AI-driven quality control. Machine learning models rely heavily on well-chosen features. In modern MLOps workflows, feature stores help to identify optimal features, reducing the time it takes to prepare datasets for training. The challenge here is ensuring explainability—if AI chooses the features, humans still need to understand why. Rather than static ETL jobs, AI is now used to predict peak data loads and automatically scale pipeline resources accordingly. This DataOps approach ensures efficiency, but it requires advanced observability tools to monitor and fine-tune. With the rise of IoT, more data is processed at the edge (closer to where it's generated). Companies are embedding lightweight AI models directly into sensors, allowing devices to filter, clean and analyze data before sending it to the cloud. However, this raises new challenges around distributed model management and ensuring consistent results across devices. AI is only as good as the data it learns from. That data doesn't magically arrive in the right shape. It takes skilled data engineers to design ingestion pipelines, enforce data quality, scale infrastructure and ensure compliance. Organizations that invest in strong data engineering today will have a competitive advantage in future AI innovation. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
