Latest news with #Hadoop
Forbes
30-05-2025
- 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?
International Business Times
28-05-2025
- Business
- International Business Times
Revolutionizing Media & Entertainment: The Industry Leading Approach By Raghavendra Sridhar
Unlock the Future of Media with AI, Multi-Cloud, and Big Data-Powered by Expertise Step into the next era of media and entertainment, where innovation meets impact. With 20 years of pioneering expertise, Raghavendra Sridhar is at the forefront of transforming the industry, harnessing the power of artificial intelligence, multi-cloud solutions, and big data to drive growth, engagement, and operational excellence. AI-Driven Personalization: Captivate Every Audience AI-powered strategies deliver hyper-personalized content recommendations and marketing campaigns that keep viewers engaged and coming back for more. By analyzing user behavior and preferences, his solutions enable streaming platforms and publishers to: Serve up tailored content and ads, boosting viewer satisfaction and loyalty Increase subscription renewals and drive millions in additional revenue Automate content production, reducing time-to-market and cutting costs Just like industry leaders Netflix and Spotify, AI systems predict trends and personalize the user journey, ensuring brands stay always ahead of the curve. These solutions not only elevate the viewing experience but also empower content creators to experiment with new formats and storytelling techniques, confident that data-driven insights will guide their creative decisions. Multi-Cloud Mastery: Scale Without Limits Break free from single-cloud constraints. multi-cloud architectures empower media companies to: Achieve global scalability and reliability by leveraging AWS, Google Cloud, Azure, and more Optimize costs and avoid vendor lock-in Enhance disaster recovery and streamline operations for seamless content delivery This flexibility ensures businesses can expand its reach, reduce overhead, and deliver services more efficiently-fueling both growth and profitability. With multi cloud, companies can rapidly deploy new features, adapt to changing viewer demands, and ensure uninterrupted access to content, regardless of audience location. Big Data Brilliance: Turn Insights into Revenue In today's data-driven world, industry experts like Raghavendra unlock the full potential of big data technologies like Hadoop, Spark, and NoSQL to: Micro-segment audiences for precision ad targeting and increased ROI Analyze vast viewer datasets to inform content creation and marketing strategies Identify new market opportunities and boost advertising revenue With actionable insights available, organizations can make smarter decisions, create more engaging content, and maximize every revenue stream. Big data analytics also enable real-time performance monitoring, allowing rapid optimization and continuous improvement. Continuous Innovation: Stay Ahead, Always A relentless pursuit of innovation ensures businesses are equipped with the latest tools and methodologies. Emerging technologies are integrated seamlessly to keep operations agile and offerings cutting edge. "Innovation, Executed with Expertise"- this is the guiding principle. Raghavendra is constantly evaluating advancements in AI, cloud, and analytics, ensuring his clients benefit from early adoption and sustained competitive advantage. Whether it's implementing next-gen recommendation engines or integrating AI-powered automation into production workflows, a forward-thinking approach keeps businesses future ready. Why Choose This Approach? Proven Impact: Millions in additional revenue generated for leading media brands Millions in additional revenue generated for leading media brands End-to-End Solutions: From data strategy to AI implementation and cloud optimization From data strategy to AI implementation and cloud optimization Future-Ready: Solutions designed to evolve with industry trends and consumer behaviors A consultative approach ensures that every solution is tailored to unique business needs, fostering long term partnerships and delivering measurable results. Shape the Future of Media & Entertainment Don't just keep up lead the transformation. With expertise in AI, multi cloud, and big data, businesses can: Enhance customer experiences Optimize workflows Accelerate growth and profitability Experience the power of innovation. Elevate the media business with cutting-edge technology solutions. "Results Through Revolutionary Tech. Elevating Industry Standards." Let Raghavendra Sridhar help unlock new levels of success in the media and entertainment industry where data-driven innovation meets creative excellence.
Time Business News
01-05-2025
- Business
- Time Business News
Top Challenges in Data Science Today and How to Overcome Them
You must have heard data science is continuously making headlines in the newspapers and magazines. It is impacting every field of our lives. From driving insights to innovations, it is amazing to see how data science is truly transforming everything around us. Data science domain is changing rapidly and is complex to deal with. Thus, it provides its own challenges and issues to deal with. Solving these problems requires skills which can be gained through free online data science courses. Several free or paid data science certification courses have made it easier to upskill. However, some practical challenges still remain. By reading this article, you will explore three top data science challenges and their solutions. Data science is transforming industries, but it also has some challenges. Professionals must overcome these challenges to use data science to its maximum potential. Now, in this part of the article, I will mention three main challenges in data science. I'm also going to tell you about their solutions. Do you know that in 2020, we generated 64.2 ZB of data, more than the number of detectable stars in the cosmos? Not only this, but experts predict that these figures will continue to rise. By the end of 2024, we are expected to generate 14 ZB data. Hence, we generate a huge amount of data daily. Organisations often find it challenging to manage and efficiently process such big datasets. Many conventional tools are insufficient to deal with such datasets, which are larger than terabytes or petabytes. This results in bottlenecks and inefficiencies. Another challenge is processing and getting insights from such huge datasets on time. This process requires scalable infrastructure and mechanisms. To solve this problem, companies should use distributed computing frameworks like Apache Spark and Hadoop. These platforms efficiently handle big data by breaking huge datasets into smaller chunks. Then, these are processed in parallel across many nodes. Apache Spark has its in-memory processing capabilities. This allows it to deliver faster results. On the other hand, Hadoop has robust data storage because of its HDFS (Hadoop Distributed File System). Therefore, companies can better scale their data processing and analysis on time by using these frameworks. The second most common data science challenge is poor data quality and integrity. This issue can derail or delay even the most advanced analytics projects. Why so? Because missing values, duplicate entries, and inconsistent formats lead to false predictions. This, in turn, generates wrong insights, which can hamper the project. If companies fail to ensure data quality and integrity, then their decisions will be based on unreliable information. It will further impact their reputation and trust among the public. It can also lead a company to face legal or regulatory challenges. The solution to the second problem is ensuring robust data cleaning and validation pipelines. These two things are foremost important to maintain data quality and integrity. Companies use tools like Pandas to handle these issues. It is a Python library that allows efficient manipulation and cleaning of structured data. Another thing is using automated ETL (Extract, Transform, Load) processes to streamline these workflows. ETL tools automate repetitive tasks such as removing duplicates and standardising formats. Moreover, real-time data validation systems can be used to prevent errors even before they occur. These kinds of systems flag errors at the source. Thus saving time and resources. The third challenge in data science is bridging the talent and skills gap. The demand for data science is growing, but there's also a shortage of skilled professionals. Many organisations find it difficult to find candidates who have both technical and domain-specific expertise. This mismatch in skills is evident during placement in colleges and universities. Beginner-level professionals find it challenging to crack the interview process. The education industry has not been able to cope with the demands of the dynamic data science industry. This gap can slow innovation and limit the impact of data science in the long run. Cross-functional collaboration among different teams and departments should be promoted to address the talent gap challenge. Diverse teams should be created wherein domain experts will work with data scientists. This can significantly reduce the gap between technical and industry-specific knowledge. Additionally, AutoML (Automated Machine Learning) tools like and Google AutoML should be adopted in the industry. It will allow non-technical stakeholders to contribute to data science projects. That, too, without using extensive programming skills. Moreover, businesses should also invest in upskilling programs for their existing employees. Companies should encourage employees to enrol in online data science courses if some employees find it challenging to join offline courses. These courses will allow them to learn crucial skills in machine learning, data visualisation, and statistical modelling. Trusted and reputed platforms like offer online data science courses which allow you to learn at your own pace. If you are worried about the cost or huge tuition fees, the institution also provides free data science courses to learn without worrying about financial constraints. Data science is a booming industry and affects every aspect of our lives. However, there are several challenges which come across while implementing it. It is essential to solve these issues to unlock the true potential of data science. Professionals should embrace diverse tools and techniques to solve such challenges. Moreover, professionals should enrol in online data science courses to upskill themselves for flexible learning. For those worried about cost issues, several platforms like offer free online data science courses for beginners and professionals. TIME BUSINESS NEWS
India.com
27-04-2025
- Business
- India.com
Innovation in Big Data Engineering: A Conversation with Bharath Thandalam Rajasekaran
Bharath Thandalam Rajasekaran (File) Bharath Thandalam Rajasekaran, a distinguished software engineer specializing in big data applications and cloud architecture, has established himself as a leader in the tech industry. With a Master's in Information Management from the University of Maryland and a unique combination of degrees in Information Technology and Psychology, Bharath brings a multifaceted perspective to solving complex data challenges. His expertise spans Hadoop ecosystems, cloud platforms, and data analytics, backed by prestigious certifications, including AWS Security Specialty and Solutions Architect. Q1: What made you enter and pursue a specialization in big data and cloud technologies? A: The sheer possibility of solving complex data challenges at an enormous scale drew me into big data and cloud technologies. I had worked with various data systems throughout my career and had the opportunity to witness some organizations struggling with managing and deriving value from massive datasets. The sheer thrill offered by being able to turn raw data into actionable insights and build scalable solutions capable of processing petabytes of information really lured me. Q2: Can you describe a special project that posed challenges to your approach to the problem? A: One of the tough ones was migrating a legacy data pipeline to a cloud environment. The challenge was scaling from a single-country operation to 15 countries in three months. The focus was not only on technical realization but also on ensuring that the architecture could stand up to a ten-fold increase in data volume and yet sustain performance. The project made me appreciate how important it is to design for scale from day 1. Q3: What are your processes in designing efficient data processing systems? A: I am methodical in my approach and start with thoroughly understanding the data flow and business requirements. For example, when building a scalable streaming application capable of ingesting large datasets, I focused on optimizing the complex processing logic as well as the supporting infrastructure components. A combination of EMR, Athena, and Airflow were used to help build systems capable of dealing with multi-petabyte data volumes efficiently while ensuring low-maintenance and cost. Q4: How does automation fit into your development process? A: Automation is really the most important quality and productivity factor in a big data system. I have also developed several CI/CD processes using Jenkins, Git, and Maven, which decreased build turnaround time by 25 percent. I also believe that besides deployment, to automate monitoring and alerting systems for example implementing Datadog alerting which reduced our mean time to resolution by 30 percent. Q5: How do you manage to be up to date with rapidly changing technologies? A: Learning never stops; it is in this field that I regularly do certifications-I have AWS and MAPR certifications-and actually engaging with emerging technologies. But more importantly, I'm a strong believer in applied learning. Each project turns out to be an opportunity to evaluate and experiment with new tools and methodologies that would improve our solutions. Q6: What advice would you give to budding data engineers? A: Build your basic foundation well to someday be able to learn and adapt to new technologies. Middleware by core distributed systems principles of data processing should not be forgotten. In addition, soft skills play an important role. Because of this, translate a technical concept to a non-technical audience. Q7: How do you think data systems can be made secure and trusted? A: Security and reliability should not be an add-on. Rather, they should percolate from the bottom. My AWS Security Specialty certification has helped a lot in putting stringent security measures across all levels of the architecture. Regular monitoring, automated testing, and data governance best practices are key characteristics of any system I build. Q8: What do you think the future will be for big data engineering? A: The future appears to be more connected, automated, and intelligent. We're also seeing a lot more development around cloud-native and serverless solutions. I think machine learning and AI will become crucial because they will have an increasing portion of the job when it comes to processing and analyzing data, making it important for data engineers to be aware of those technologies. Q9: What are you methods for forming and leading high-performance technical teams in your opinion? A: Building teams focuses on matching technical skills with a collaborative spirit. I cultivate an environment that encourages knowledge sharing and liberates people to innovate. While I have led many technical teams, from my experience, when expectations are clear and communicated, and regular feedback is given with enough context about the product or service under change, outcomes are smoother. I further emphasize documentation and knowledge transfer for sustainable team practices. Q10: What do you consider your most important professional achievement, and what did you learn from this? A: The other major achievement would be designing and implementing a scalable infrastructure platform to manage multi-petabyte data volumes for ingestion, aggregation, and analytics on Hadoop. What made this achievement so meaningful was not just the technical complexity of the challenge we solved, but also how it changed the organization's life in being able to glean insight out of its data. That project taught me many valuable lessons, such as, the importance of early-stage architectural decisions; the necessity of comprehensive error handling across distributed systems and a balancing act between performance and maintainability. Furthermore, it cemented my view that the best technical solutions are those that truly facilitate business success. About Bharath Thandalam Rajasekaran A seasoned software engineer with over a decade of experience, Bharath has effectively delivered innovative solutions in the world of big data and cloud computing. With multi-industry and technology exposure, his experience proves him as an expert in building scalable, efficient systems that solve complex data problems. Bharath's unique combination of education in technology and psychology enables him to look from a distinctive point of view while creating user-oriented technical solutions.
Yahoo
09-03-2025
- Business
- Yahoo
The résumé a software engineer used to land a cybersecurity job at Microsoft
Ankit Masrani shared the résumé that landed him a Microsoft role building security infrastructure. Prior to Microsoft, Masrani studied IT, got a Master's in computer science, and worked at AWS. He said data experience and security knowledge are needed to transition to cybersecurity. Breaking into the cybersecurity field can be a challenge for some — but 36-year-old Ankit Masrani stumbled into it. The Seattle-based Microsoft employee told Business Insider that while he had plans to become a software engineer, he didn't expect to work in the security space. Now, he develops sovereignty controls for the tech giant's security platform, ensuring sensitive customer information remains within geographic boundaries. After studying information technology in college and working in roles building software systems, Masrani came to the US to get a Master's degree in computer science. After completing a six-month co-op internship at AWS while he was in school, he converted to a full-time employee, where he focused on securing data and networks until he felt the need for a change. "To be honest, it was very tiring," Masrani said about his six and a half years at AWS. "And I wanted a change in my job to try something different." Masrani said his final project before joining Microsoft involved building a customer-managed key encryption feature, which required research into best practices for data security. He said he found the work "really interesting" and began exploring teams focused on data governance and security. He said working alongside engineers who were truly "passionate" about their work was a top priority for him. Here's the résumé he used to get his job at Microsoft, where he started on Microsoft's Purview security team as a senior software engineer. Now, he's a principal software engineer working on Microsoft's Security Platform. Masrani said he applied by going to the company site and didn't have any references. He said if he were to apply again today, he might not include such a lengthy education section because people would probably focus on his 10 years of experience. When he was a year or two out of school, though, he said he thinks it helped him get interviews. Masrani came into the role with a background in IT, computer science, and data experience — all of which are recommended routes to enter the field, according to industry veterans. Masrani's pivot wasn't drastic, but he said certain skill sets are needed to transition from general software engineering to the security side. As a software engineer building cybersecurity services, Masrani said he handles large volumes of security logs, user activity data, and threat intelligence data. Masrani said he isn't "actively doing security threat hunting" but is building services for a platform that does. Masrani said experience with big data technologies like Hadoop, an open source framework that processes large amounts of data for applications, is important for learning how to build data pipelines. He added that machine learning and anomaly detection is also useful for working on security product services. Masrani also recommends experience with cloud services like AWS or Microsoft Azure to understand scalable data processing. "Storage is very important since cloud services are leveraged everywhere from small to large software systems," Masrani said. Masrani also said security knowledge is necessary to pivot to the cybersecurity sector. Masrani said safety protocols and data processing guidelines are often specific to regions. He said domain knowledge around data governance and other security products is important, as well as familiarity with regulations, such as the General Data Protection Regulation. He said it's also important to know fundamentals around data encryption, network security, and application security. "Any handling of customer data must be done in a safe and secure manner," Masrani said. "Having knowledge of best practices for handling data is very important. Read the original article on Business Insider
