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.
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