An Easy Approach to ETL with Apache Spark – Visually prepare, integrate, and transform data as it arrives
Mar 08, 2019 | 10:00 am PT / 1:00 pm ET
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:
- A modern approach to ETL
- Advantages of choosing Apache Spark to transform legacy ETL processes
- Easy adoption and integration of Spark based ETL
- Writing production grade Spark ETL jobs visually
- Deploying applications on-premise and in the cloud
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.
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.
Build Spark-based ETL Workflows on Cloud in Minutes
Jun 14, 2019 | 10:00 am PT / 1:00 pm ET