The first blog post of mine is accepted to official Pivotal blog! Feel free to comment and share your opinion on the subject:
Tag Archives: HDFS
Spark Architecture Video
This is the talk I made on Java Day Kiev 2015. It was a great conference after all
Hadoop on Remote Storage
The question regarding running Hadoop on a remote storage rises again and again by many independent developers, enterprise users and vendors. And there are still many discussions in community, with completely opposite opinions. I’d like to state here my personal view on this complex problem.
MPP vs Hadoop Talk
Today I had a great talk at the Hadoop User Group Ireland meetup in Dublin, and it was an adapted and refactored version of the article on the same subject, MPP vs Hadoop. Here are the slides:
Feel free to comment and share your opinion on this subject
Apache HAWQ Architecture Talk
Finally I have translated my talk from Highload++ 2015 conference in Moscow into English, so now you can enjoy the fresh information about the Apache HAWQ internals!
If you’d like to download the slides, you can find them here: HAWQ Architecture HL++ 2015 Moscow
Hadoop Cluster Sizing
When you are completely ready to start your “big data” initiative with Hadoop, one of your first questions would be related to the cluster sizing. What is the right hardware to choose in terms of price/performance? How much hardware you need to handle your data and your workload? I will do my best to answer these questions in my article.
Hadoop Cluster Backup
Over the time working with enterprise customers, I repeatedly hear the question regarding the Hadoop cluster backup. It is a very reasonable question from the customer standpoint as they know that the backup is the best option to protect themselves from the data loss, and it is a crucial concept for each of the enterprises. But this question should be treated with care because when interpreted in a wrong way it might lead to huge investments from the customer side, that in the end would be completely useless. I will try to highlight the main pitfalls and potential approaches that would allow you to work out the best Hadoop backup approach, which would fulfill your needs.
Spark Architecture
Edit from 2015/12/17: Memory model described in this article is deprecated starting Apache Spark 1.6+, the new memory model is based on UnifiedMemoryManager and described in this article
Over the recent time I’ve answered a series of questions related to ApacheSpark architecture on StackOverflow. All of them seem to be caused by the absence of a good general description of the Spark architecture in the internet. Even official guide does not have that many details and of cause it lacks good diagrams. Same for the “Learning Spark” book and the materials of official workshops.
In this article I would try to fix this and provide a single-stop shop guide for Spark architecture in general and some most popular questions on its concepts. This article is not for complete beginners – it will not provide you an insight on the Spark main programming abstractions (RDD and DAG), but requires their knowledge as a prerequisite.
This is the first article in a series. The second one regarding shuffle is available here. The third one about new memory management model is available here.
Virtualized Hadoop
Hadoop is becoming crucial tool in big data strategy for any enterprise. The more important it becomes, the more companies start to propose their solutions utilizing its open-source power. Today I will talk about virtualized Hadoop, one of the modern branches of the current market.
Let’s start from the virtualization itself. It begins its story in 60s, but the broader adoption of the virtualization in enterprises started approximately 10 years ago. What is the purpose for virtualization in enterprises? Continue reading