Linear regression through Apache Zeppelin: how to visualize your PySpark backend using Angular and D3

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Few month ago I had a chance to visit one technological summit driven by HortonWorks which was related to the evolution of Hadoop. There was a great number of amazing sessions held by different speakers who were describing their experience in working with Big Data. But one very important thing which I’ve noticed there was the fact that different projects and frameworks at some point were always somehow intersecting with another scope of problems called Machine Learning. This is probably one of the most exciting and interesting category of tasks solved by Big Data. That is why I decided to start covering this topic in my articles and today I’m going to tell you about one basic Machine Learning algorithm called Linear Regression.

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Accessing Kerberized Hadoop cluster using Ranger security policies and native APIs

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Security always played important role in every informational system. For each particular solution before actual implementation we first need to carefully design its protection layers by means of different techniques like authorization, authentication or encryption.  But sometimes at the early start developers don’t pay much attention to this topic and concentrate their efforts on the functional aspects of their applications. This is what has happened to Hadoop.

In my previous article about security in world of Big Data I’ve already given a high-level overview of this model. Now I want to share some experience about how to work with Hadoop services on the low level straight from the source code. We will create new principle in Hadoop environment, then we will give him permissions in Ranger and will use him from java application to access the services remotely.

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Apache Spark – first steps in getting familiar with your data

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The world of Big Data provides a wide variety of tools for organizing large amounts of data within a single place. But after we’ve pushed everything in place, usually we start seeking for the options of getting different benefits from this information. We involve statisticians and data experts for the investigation of our data and they start applying certain techniques to detect different features of such information. As a result these features will allow us to build some useful data models which will help us to improve the quality of our products. For example we can build the recommendations for our customers about which products they could like or dislike or even more we can identify the best characteristics for our new products relying on the current demands of our clients. Hadoop provides a set of tools for doing such operations and at this moment Spark is probably one of the best options which fits the demands of data specialists who work within Big Data realm. In this article I want to give the overview of this product and to show how to use it in HortonWorks Hadoop distribution sandbox.

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Getting data into HBase and consuming it through native shell and Hive

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If you followed my previous articles, probably at this stage you should have common understanding of primary components of Hadoop ecosystem and basics of distributed calculations. But in order implement performant processing we first need to prepare a strong data foundation for it. Hadoop provides a number of solutions for this purpose and HBase data store is one of the best products in this ecosystem which allows you to organize big amounts of data in a single place. In this article I want to tell about some techniques of working with HBase – how to import the data, how to read it through native API and how to simplify its consumption through another Hadoop product called Hive.

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Coordination of distributed applications through Zookeeper

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When I started to work in scope of Big Data, one of the most challenging things for me was to understand the distributed nature of Hadoop applications. Usually most part of software developers think about their products in terms of single program components which are represented by standalone applications. Every program is an independent monolith unit which is located in own machine, runs in its own process and responsible for custom range of tasks. Big data introduces another level of composition, where every single program can be distributed across the nodes of the cluster as set of standalone services driven by some master service. The development of such model creates new challenges related to the synchronization these services and handling the consistent state of the overall application. This problem is common for every distributed system and instead of reinventing custom solution for each particular product of Hadoop family, community created a universal tool called Zookeeper. In this article I want to give you overview of this application and show some examples of working with it.

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Consuming OpenCV through Hadoop Storm DRPC Server from .NET

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In previous article I gave a basic overview of Hadoop Storm framework and showed how to use it in order to perform some simple operations like word counting and persistence of the information in real time. But Storm service can cover much wider scope of tasks. The scalable and dynamic nature of this product allows to wrap most complicated algorithms and distribute their handling among different machines of the Hadoop cluster. Computer vision is a good candidate for such type of functionality. This term covers a large range of tasks related to the processing of graphical data and performing such operations upon it like objects detection, motion tracking or face recognition. As you can imaging these procedures can be quite expensive and wrapping them within some scalable parallel processing model could significantly increase the end capacity of the potential solutions. You can find a good example of such application called Amazon Recognition in the list of official Amazon services. In this article I want to show you how to build similar products using Hadoop Storm framework and open-source Computer Vision libraries.

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Perfect Storm – real-time data streaming from .NET through Kafka to HBase, HDFS and Hive

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In my previous articles I tried to give the overview of primary Hadoop services responsible for storing the data. With their help we can organize information into some common structures and perform operations upon them through such tools like MapReduce jobs or more high-level abstractions like Hive SQL or HBase querying language. But before doing this we certainly need to somehow put our data inside the cluster. The simplest way would be a copying of the information between the environments and  performing a set of commands from different service-related CLIs  to do the import or launching some bash scripts which can partly automate this work. But it would be great if we could have some common tool which would allow us to define different workflows for such processes so that single units of information could be imported, transformed, aggregated or passed through some algorithm before actual preservation. Such type of framework certainly should be scalable and should follow the general requirements of distributed environment. In Hadoop we have such tool called Storm and from my point of view this product is probably one of the most interesting and exciting parts of Big Data ecosystem. In this article I want to give you its overview and to share my experience of using it.

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HBase as primary NoSql Hadoop storage

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“Three admins once went to noSql bar, but a little while walked away from there as they could not find a table” – says one popular joke. The statement is arguable from my point of view, but it undercover the general idea – differences of approaches of treating the data. Most of developers usually start their career from working with some RDBMS products like Oracle or MSSQL server. These systems keep the data in some normalized format represented by a sets of tables and relations between them. In many cases this model ideally suits to accomplish the data-related requirements of many products. But sometimes such structure brings certain overheads which negatively effects the end performance of the application. In order to overcome these restrictions an alternative NoSQL approach for keeping and managing the data was invented. There are a lot of different implementations of this technique and each of them targets to resolve some individual tasks, but from Hadoop perspective HBase is probably one of the most commonly used NoSQL database which provides a strong and reliable mechanism for managing huge amounts of data across distributed environment. In this article I want to describe the general idea of this product and show some examples of working with it.

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Structuring Hadoop data through Hive and SQL

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In this article I would like to start getting you acquainted with the Hadoop services which can heavily simplify the process of working with the data within the cluster. We’ve already played around the fundamental part of Hadoop – HDFS. But as you could have noticed it is really quite complicated to work with the data in such way especially taking into consideration that every new operation upon the data would require another MapReduce job which should be properly implemented, tested, deployed, executed and verified. Besides MapReduce approach follows batch processing model, meanwhile some solutions could relay on the option of real-time data access which is beyond the scope of this framework. In some cases certainly this technique should be applied, for example to implement some complicated operations with data but the most part of scenarios usually relate to some basic and generic operations like searching, grouping or aggregating the information. And in order to simplify such functionality Hadoop community invented several solutions which help end users to get to the higher level of abstraction and to start using more simple mechanisms for querying the data. One of them called Hive.

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Hadoop security overview

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If you ask me what is the post complicated part of Hadoop configuration, I will say that it is security. From early start of development of this product the main efforts were focused on making a stable distributed framework and security was not the priority of that time. The base assumption was that system would work as a part of some trusted network environment and simple security model would be sufficient to cover the requirements of that period. But by the time Hadoop evolved and the problems of more complicated security challenges started to play more and more important role. Especially is became a sharp question once Big Data started to drive into the side cloud computing. So the integration of Kerberos protocol became the first serious step made in this direction. After authentication part logically community started to solve the problems related to authorization. According to basic security model most part of the services worked with custom Access Control Lists (ACL) and the general idea was to localize their management in a single place. Cloudera invented Senrty product and HortonWorks proposed alternative in view of Ranger application. Later on security components were improved with other features like support of encryption, protection of RESTful endpoints, integration with Active Directory and other. In this article I want to give the general overview of primary parts of Hadoop security model.

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