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Published on Jul 23, 2018
Would you like to see Big Data usecases implemented on Spark? Are you working with Big Data projects already and you are considering introducing Spark to your technology stack? Would you like to know what Spark is good at and what parts of Spark are tricky? First I would like to provide an overview of multiple Spark use cases in various areas. The number of use cases described will be broad enough so it is likely that the audience will be able to find similarities to projects they are working on and see how they can use Spark to solve problems and bring value to the company. The second part of the presentation will be focused on technical challenges which need to be solved when introducing Spark to your ecosystem. Spark has a nice and relatively intuitive API. It also promises high performance for crunching large datasets. It’s really easy to write an app in Spark. Unfortunately, the nice API might be misleading and make us forget that we are implementing a distributed application. For that reason it’s easy to write one which doesn’t perform the way you would expect or just fails for no obvious reason. I will show in a nutshell all the lessons I have learned over 3 years of experience with Spark. It will give you an overview of what to expect and help you to avoid making mistakes typically made by Spark newbies. We will emphasize what you should know about your data in order to write efficient Spark jobs and what the most important configuration tweaks and optimization techniques are which will come in handy when implementing Spark based solutions.