The Story of Online Data Warehouse

The faster your data warehousing solution runs, the higher would be the business demand related to the speed of new data availability in their reports. Over the last time I’ve seen a number of attempts to build up a cool thing called “online DWH” – a data warehouse that is almost in sync with data sources and has its data marts and reports dynamically updated as new data flows into it. This is a very great and powerful thing, but unfortunately its implementation is not as straightforward as the business wants it to be.


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Spark Architecture: Shuffle

This is my second article about Apache Spark architecture and today I will be more specific and tell you about the shuffle, one of the most interesting topics in the overall Spark design. The previous part was mostly about general Spark architecture and its memory management. It can be accessed here. The next one is about Spark memory management and it is available here.

Spark Shuffle Design

What is the shuffle in general? Imagine that you have a list of phone call detail records in a table and you want to calculate amount of calls happened each day. This way you would set the “day” as your key, and for each record (i.e. for each call) you would emit “1” as a value. After this you would sum up values for each key, which would be an answer to your question – total amount of records for each day. But when you store the data across the cluster, how can you sum up the values for the same key stored on different machines? The only way to do so is to make all the values for the same key be on the same machine, after this you would be able to sum them up.

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Spark DataFrames are faster, aren’t they?

Recently Databricks announced availability of DataFrames in Spark , which gives you a great opportunity to write even simpler code that would execute faster, especially if you are heavy Python/R user. In this article I would go a bit deeper than the publicly available benchmark results to show you how it really works.
stupid benchmarking

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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.

Measuring the Elephant Hadoop

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Modern Data Architecture Podcast

Great news! I have participated in a podcast recorded by Pivotal and published in our official blog. In this podcast I discuss the data architecture in general – how the things started, what was the main driver for its evolution and what we have now as a “modern data architecture”. Come and listen here:

Pivotal Podcast Modern Data Architecture

Text transcript of this talk is also available by the same URL

Hadoop vs MPP

Over the latest time I’ve heard many discussions on this topic. Also this is a very popular question asked by the customers with not much experience in the field of “big data”. In fact, I dislike this buzzword for ambiguity, but this is what the customers are usually coming to us with, so I got to use it.

Screen Shot 2015-07-13 at 12.41.07 PM

If we take a look 5 years back, that was the time when Hadoop was not an option for most of the companies, especially for the enterprises that ask for stable and mature platforms. At that very moment the choice was very simple: when your analytical database grow beyond 5-7 terabytes in size you just initiate an MPP migration project and move to one of the proven enterprise MPP solutions. No one heard about the “unstructured” data – if you got to analyze logs just parse them with Perl/Python/Java/C++ and load into you analytical DBMS. And no one heard about high velocity data – simply use traditional OLTP RDBMS for frequent updates and chunk them for insertion into the analytical DWH.

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