Over the past several years, Hadoop has been accepted as the most popular big data solution—but are there alternatives and better approaches?

As cloud solutions and other open-source technologies continue to mature, it’s important to question and reevaluate the role Hadoop’s core components have in modern environments when solving new challenges in big data.

In this video, Danil Zburivsky, Director of Big Data and Data Science at Pythian, talks about the rapidly changing state of building big data systems on Hadoop based on the problems and opportunities he sees first-hand with our big data customers.

Watch this video to learn more about the realities and challenges of adopting Hadoop today, including:

• Trends around adopting Hadoop and migrating off Hadoop 

• Hadoop in the cloud vs on-premise

• Skipping Hadoop and going straight to the cloud

• Rising importance of standalone open-source tools like Spark and Kafka

About the Speaker

As Director of Big Data and Data Science at Pythian, Danil leads a team of big data architects and data scientists that help customers worldwide to achieve their most ambitious goals when it comes to large scale data platforms. He is recognized for his expertise in architecting, and building and supporting large mission-critical data platforms using MySQL, Hadoop and MongoDB. Danil is a popular speaker at industry events, and has authored a book titled Hadoop Cluster Deployment.