Executing analytical queries on massive data volumes with traditional databases and batch ETL processes is complex, expensive, and time-consuming.
Open-source distributed computing technologies like Apache Spark and Hadoop provide an efficient and cost-effective data processing paradigm. Apache Spark enables end-to-end ETL workflows for incessant data streaming from heterogeneous sources and overcomes the constraints imposed by legacy ETL processes. Explore how Apache Spark provides a powerful and efficient approach to ETL. Join our upcoming webinar where experts at Impetus talk about:
Technical Product Manager, StreamAnalytix
Saurabh is a Technical Product Manager at StreamAnalytix where he leads multiple engineering and R&D efforts. He is one of the early team members who bootstrapped StreamAnalytix. He is responsible for analyzing complex engineering and business challenges for clients, managing and developing a roadmap and providing an appropriate solution which can be incorporated in the product. He brings a unique blend of business acumen and technical knowledge that help clients achieve their objectives. His areas of expertise include big data, advanced analytics and cloud computing.
Senior Product Engineer , StreamAnalytix
Aman is a Senior Software Engineer responsible for analyzing complex business use cases, implementation challenges, and devising appropriate solutions for StreamAnalytix. His areas of expertise include big data, cloud computing, and machine learning.