Latest news with #Autosys
India.com
16-05-2025
- Business
- India.com
Re-architecting Data Pipelines in Regulated Industries
From healthcare reimbursement to energy trading, the information flowing through regulatory pipelines has never been more complex—or more consequential. Compliance mandates such as FERC's five-minute settlement rules and the 340B drug-pricing program now demand granular lineage, near-real-time validation, and immutability. At the same time, cloud economics are reshaping how firms ingest, store, and serve terabytes of operational data. Against this backdrop, many organizations still rely on legacy PL/SQL routines or siloed PowerBuilder screens that struggle to keep pace with evolving audit trails. Industries that follow strict rules deal with huge amounts of data, and every bit of it needs to be saved and checked without any room for error. Whether it's fast-paced energy trades or pharmacy claims under programs like 340B, each record matters both legally and financially. Old systems that rely on PL/SQL scripts, scattered rules, or nightly file transfers often struggle to keep up with growing data loads, which can put both compliance and customer trust at risk. Modernization, therefore, demands not only cloud elasticity but forensic lineage, real-time monitoring, and ironclad audit trails. One engineer who has quietly mastered that balancing act is data specialist Naveen Kumar Siripuram. His career traces the path from Oracle partitions to BigQuery pipelines without ever losing auditability. A Career Built on Precision: Naveen Kumar Siripuram Naveen Kumar Siripuram entered this labyrinth in 2015, fresh from a master's program at Texas A&M. 'I was fascinated by the idea that a single mis-keyed FX deal could ripple through an entire settlement system,' he recalls. Over nine years he has examined that ripple from every angle—first at State Street Bank, then at utility giant NextEra Energy, and now at a leading U.S. healthcare provider. His toolkit spans Oracle partitioning, Autosys orchestration, Kafka streams, and most recently Google Cloud's BigQuery and Dataflow. Siripuram's early work on State Street's Wall Street System migration foreshadowed his pragmatic approach. To move high-frequency currency trades onto a Linux-based IORD cluster, he used PL/SQL table functions and bitmap indexes to shave report runtimes by 40 percent. 'My mandate was simple: nothing breaks during close of area,' he says. The discipline of monitoring Autosys event logs at 3 a.m. shaped his bias toward audit-friendly design. The Journalist's Lens: Why Method Matters As a reporter covering data-intensive sectors, I have seen many engineers equate progress with wholesale replacement. Siripuram stands out for weaving incremental change into entrenched processes. At Florida Blue he consolidated twelve rule-set screens into a single UI, but only after mapping each keyword to its actuarial intent. 'You can't refactor a claims engine unless you speak its dialect,' he tells me. His insistence on preparatory dev-analysis documents—unfashionable in some agile circles—reduced defect leakage during monthly BART releases to near zero. That same deliberation guided a two-petabyte Teradata-to-GCP migration he led for his current employer's rebates program. Rather than forklift the data warehouse, he converted BTEQ scripts into parameterized Dataform templates, using materialized views for the costliest joins. 'Partitioning and clustering are free compared to reprocessing stale claims,' Siripuram notes. Internal dashboards show compute spend down by a third, while query latency for pharmacists fell from minutes to seconds. Siripuram also underscores the human dimension. He keeps a Slack channel open with compliance analysts so they can flag anomalous NDC codes in near real time. 'If you wait for the nightly batch, the drug is already dispensed,' he points out. That feedback loop informed his decision to stage raw files in Cloud Storage before canonicalizing them in BigQuery—an architecture that supports ad-hoc SAS extracts without duplicating storage. Closing the Loop on Compliance-ready Data Stepping back, Siripuram's trajectory illustrates a pattern: design for traceability first, performance second, and cloud elasticity third. The order matters because regulated enterprises cannot afford data surprises. His PL/SQL schedulers at NextEra ensured hourly roll-ups met FERC reporting windows; his Airflow DAGs at the healthcare provider guarantee 340B accumulators stay within split-billing tolerances. 'A good pipeline,' he says, 'is one the auditor understands without me in the room.' Looking ahead, Siripuram is experimenting with TensorFlow models that forecast rebate liabilities based on seasonality and formulary shifts. Yet he remains wary of hype. 'Machine learning is useful only if the training data survives an FDA audit,' he cautions a reminder that innovation in these sectors is as much about governance as about code. These days, cloud migrations often grab all the attention, but Naveen Kumar Siripuram's path has been more low-key and more practical. His work shows that real progress starts by carefully looking at the data itself, whether it's coming from a turbine's SCADA system or a pharmacy claim. For teams working in tightly controlled environments, his step-by-step approach is a solid guide: respect what's already in place, move forward carefully, and keep a clear record of every change along the way.
India.com
29-04-2025
- Business
- India.com
Innovative Data Integration by Kishore Ande
Kishore Ande is a highly accomplished data integration expert based in the United States, with over 15 years of experience in ETL integration, business intelligence, and data warehousing. With a strong educational foundation, including a Master of Science in Electrical Engineering from California State University, Northridge (graduated April 2010), Kishore combines academic knowledge with extensive practical experience. His professional journey has been marked by significant contributions to major data integration projects, where he has honed his skills in Informatica, Stibo MDM, various SQL technologies, cloud platforms, and automation solutions. Q1: To what inspired you to choose this career in data integration and business intelligence? My academics in Electrical Engineering have much to do about fiber optics, which piqued my curiosity about how data moves and, more importantly, processes it. As a result of this curiosity, it became natural for me to want to look into ETL, data warehousing, and BI. I really enjoy being behind the scenes transforming raw data to information that can drive decision-making within the business. I enjoy solving complex problems and designing scalable solutions, and I love learning continuously in this fast-changing field. Q2: How do you approach the technical requirements elicitation process and what are the critical points to be observed? I emerge from a collaborative, vigorous process of exploring one's needs and challenges with stakeholders, analyzes current data systems-high complexity and quality, aligns technical dimensions within their business objectives, scoping scalability, performance and security, and nothing gets built without documentation and validation with stakeholders. Here, I concentrate much on solution building, being both technically feasible and delivering bottom-line value to your business. Q3: Please give an example of a difficult project you handled and how you managed through the barriers: I led a high-pressure insurance claims integration project that involved legacy mainframes and poor data quality and required early delivery. To handle this, It's implemented by phased rollout to compartmentalize the mazes. Automated processes in used under Autosys and Control-M. Setup daily stand-ups for issue resolution and strict data validation. This created a high-fidelity deliverable under tight timelines. Q4: What is the role of automation in your data integration approach? Automation forms the base of the work that I do. It improves the productivity and consistency of operations, scales with the volume of digital data, and frees up resources for strategic tasks. I schedule and monitor tasks and jobs using Autosys and Control-M, leaving automated tools to take on the big, complicated integrations, especially with costs on legacy systems. Q5: Best Practices Inclusion in ETL Development Work Modular reusable ETL components. While dealing with complete documentation, one must also bring into focus data lineage. Error trapping and very strong logging and validation. Query optimization and peer review. Version control and uniform coding standard. This would make the solution high-quality, maintainable, and scalable. Q6: What are the most common tools or technologies you use, and how do you keep up with new trends? I have a wide toolset that includes Informatica ETL, SQL databases like Oracle and MySQL, automation through Python and shell scripts, cloud platforms (AWS, GCP), and job schedulers such as Autosys and Control-M. To keep abreast of the latest happenings, I invest in various online courses, track industry-specific blogs, interact on GitHub and Stack Overflow, collaborate with peers, run experiments through proof of concepts, and attend conferences/workshops. It is through constant learning and hands-on experimentation that one stays updated. Q7: What strategies do you use to manage cross-team collaboration during complicated data integration projects? I emphasize the importance of clear communication, assign roles using the RACI matrices, and followed Agile methodologies (Scrum, Kanban) through tools like Jira. Building relationships-organizing workshops, dragging those technical concepts into the language of business, making visible documentation and dashboards-regular feedback sessions to keep all stakeholders aligned and agile. Q8: What would you recommend to a person trying to walk into the field of data integration? Get his SQL and databases down well, learn every ETL process that he can get practical at, learn his Python or shell scripting for automation, get to know cloud platforms (AWS, GCP, Azure), develop some acumen for business, and look to improve communication. So, curiosity and continuous learning, quality of data, and networking with professionals would go far. A mix between technical strength and business understanding is a key factor in success. Q9: Generally, how would you deal with data quality challenges in integration projects? There are clear standards for early data profiling and data quality metrics. At the same time, I tend to apply validation rules and automated monitoring. Resolving any identified issues is closely done with the data stewards while documenting the resolutions made. Trying to do it right at the start builds trust and cuts down the later effort of having to redo. Q10: If you cast your gaze far into the future, where do you see yourself in the company? What steps are you taking or planning to take in order to achieve this? My vision for the future is to drive and deliver data strategies for enterprises through cloud competency enhancement, AI/ML training, and data governance knowledge enhancement. I am concentrating on business skills, communication, and leadership abilities in order to bridge the gap between technology and business needs. I am for automation-build error-free and failure-tolerant workflows that increase speed and reliability to allow teams to focus on strategy work. About Kishore Ande Kishore Ande is a data integration specialist with a penchant for designing efficient automated data solution processes with sound educational background inputs through electrical engineering. With expertise in ETL development, business intelligence, and data warehousing; Kishore possesses a remarkable ability to put together and manage technical integration projects in a very agile manner across the retail and insurance industries. His stack-wise technical expertise includes Informatica, all varieties of SQL Technologies, cloud platforms-Amazon Web Services, Google Cloud Platform, real-time processing, AI/ML, and automation tools. During his professional life, he has carried out the entire life cycle of management of complex data integration projects with special emphasis on product data management, pricing systems, and vendor management specifically in retail and insurance industries. He balances technical skills and leadership qualities to give varied value proposition among different business environments.
