Modernizing Data Science and Analytics in the Cloud While Minimizing Business Disruptions
Webinar Speaker: David Stodder, Senior Director of Research for BI, TDWI
Date: Thursday, January 19, 2023
Time: 12:00 p.m. PT, 3:00 p.m. ET
Modernization is business-critical—especially to take advantage of powerful cloud data platforms such as data lakehouses. With the growing importance of analytics, it is vital that you address the modernization and cloud migration of data management and analytics workloads together to avoid delays and disruptions that damage business. This means that along with the data, you need to ensure good migration of predictive models, statistics, visualizations, AI/ML algorithms, and any related programs and applications currently residing on legacy systems.
The problem is that most migrations involve significant manual work. Dependence on hand coding by teams of programmers often means that organizations spend too much time finding and fixing errors and inconsistencies. Manual work is typically not well documented, which makes troubleshooting, testing, and validation slow and tedious. Modernization becomes nearly impossible due to lack of profiling visibility into existing programs, applications, and workloads. You don’t know where to start. Costs quickly increase beyond what organizations had forecast for cloud migration.
Fortunately, automated tools and modern practices offer an alternative. Join this TDWI webinar to learn how you can accelerate cloud migration and modernization of data and analytics to take advantage of scalable and agile resources and meet pressing business demands for data science.
Topics to be discussed include:
- TDWI perspectives on pain points in cloud migration and modernization from legacy analytics and data systems and how technology trends are enabling faster and better solutions
- Why simple lift-and-shift cloud migration often leads to higher costs and unanticipated delays
- Analytics modernization: Ensuring that you meet objectives for migrating from legacy systems to cloud data platforms such as a data lakehouse
- How automated tools improve profiling so you understand what you have and can optimize cloud migration for better speed, accuracy, and transparency
- Future-proofing your modernization strategy for growth in workloads, data volumes, and business demands for faster analytics and AI/ML development
Satish has a distinguished background in data management, data science, and risk management. He has extensive experience in implementing enterprise risk solutions such as mortgage loss forecasting, model risk management, stress testing for CCAR, regulatory compliance, and healthcare risk adjustments using SAS. He has previously leveraged open source tools and dotData technology to implement automated data science (automated feature engineering, AutoML) use cases for businesses across different industries. Currently, he works as a solutions architect at Databricks, helping enterprises leverage cloud and lakehouse adoption via open source technologies such as Apache Spark, Delta, and MLFlow. He has also presented and published papers at various conferences on machine learning and programming and has co-authored a book on text analytics (Text Mining and Analysis: Practical Methods, Examples, and Case Studies using SAS).
Sunil Gowri has extensive experience of 13+ years in design, development, implementation, and support of cloud, big data, and business intelligence solutions. He specializes in big data governance and management and consumer data analytics with strong experience in cloud and on-premises ecosystems. He has also worked on the development and implementation of digital data analytics environments on Hadoop clusters with vast experience in insurance, healthcare, and finance domains. In the past, he successfully led the design and development of various predictive analytics data mart, enterprise data warehouse, data integration, and server migration projects. Currently at Impetus, he leads the migration of SAS analytics applications onto Azure Databricks cloud for various Fortune 500 companies, enhancing and automating their cloud transformation journeys.