Hadoop and Spark are two of the most used platforms when handling Big Data, each implementing various technologies that can work separately and together. Consequently, anyone trying to compare one to the other can be missing the larger picture.

Like any technology, both Hadoop and Spark have their benefits and challenges. But the fact is that more and more organizations are implementing both of them, using Hadoop for managing and performing big data analytics and Spark for ETL and SQL batch jobs across large datasets, processing of streaming data from sensors, IoT, or financial systems, and machine learning tasks.

What are their differences? Is it better to choose one over the other? Can they actually work together?

Iker Gómez, Big Data engineer, will approach these issues analyzing several real-life case studies in which he has been involved. Iker comes from Pamplona, where he studied Computer Science Engineering, and has been linked to Big Data since he began his professional career, working for one of the most important betting houses in the UK. After returning to Spain, he worked at LUCA, Telefonica’s Big Data unit, and World Wide Mobility, where he was in charge of analyzing data in the mobility industry. If you want to get to know more, he also runs the podcast “Bigdateame”, where he shares his insights into this topic.

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