A couple of months ago I wrote a first post about
Microsoft Big Data – Introducing Windows Azure HDInsight. In this post I will delve a little deeper into the different components which are used in HDInsight. This is not an exhaustive list of components but it lists a number of components which you might encounter when working on your first big data project using
Microsoft Azure HDInsight.
- Ambari – provides provisioning, monitoring and management layer on top of Apache Hadoop clusters. It provides a web interface for easy management as well as a REST API.
- Flume – allows you to collect, aggregate and move large volumes of streaming data into HDFS in a fault tolerant fashion.
- HBase – provides NoSQL database functionality on top of HDFS. It is a columnar store, which provides fast access to large quantities of data. HBase tables can have billions of rows and these rows can have almost unlimited number of columns.
- HCatalog – provides a tabular abstraction on top of HDFS. Pig, Hive and Mapreduce use this layer to make it easier to work with files in Hadoop. HCatalog has been merged into the Hive project. Hive uses it kind of a like a master database. For more details check out Apache HCatalog – a table management layer that exposes Hive metadata to other Hadoop applications.
- Hive – allows you to perform data warehouse operations using HiveQL. HiveQL is a SQL like language and provides an abstraction layer on top of MapReduce. Hive allows you to use Hive tables to project a schema onto the data (schema on read). Through the use of HiveQL you can view your data as a table and create queries just as you would in a normal database with support for selects, filters, group by, equi-joins, etc…. Hive inherits schema and location information from HCatalog. Hive will act as a bridge to many BI products which expect tabular data. One of the recent developments around Hive is the Stinger initiative – its main aim is to deliver performance improvements while keeping SQL compatibility
- Kafka – is a fast, scalable, durable and fault-tolerant messaging system. It is commonly used together with Storm and HBase for stream processing, website activity tracking, metrics collection and monitoring or log aggregation. It is provides similar functionality as AMQP, JMS or Azure Event Hub
- Mahout – the goal of Mahout is build scalable machine learning libraries. The main machine learning use cases Apache Mahout support are recommender systems (people who buy x also buy y), classification (assigning data to discrete categories e.g. is a credit card transaction fraudelent or not) and clustering (grouping unstructured data without any training data). For more details take a look at Introducing Mahout (IBM)
- Oozie – enables you to create repeatable, dynamic workflows for tasks to be performed in a Hadoop cluster. An Oozie workflow can include Sqoop transfers, Hive jobs, HDFS commands, Mapreduce jobs, etc … Oozie will submit the jobs but Mapreduce will execute them. Oozie also has built-in callback and pollback mechanisms to check for the status of jobs
- Pegasus provides large scale graph mining capabilities by offering important graph mining algorithms such as degree calculation, pagerank calculation, random walk with restart (RWR), etc .. Most graph mining algorithms have limited scalability, they support up to millions of nodes. Pegasus billion-node graphs. Graphs (also referred to as networks) are everywhere in real life going from web pages, social networks, biological networks and many more… Finding patterns, rules etc within these networks allow you to rank web pages (or documents), measure viral marketing, discover disease patterns, etc … The details of Pegasus can be found in the white paper Pegasus: a peta-scale graph mining system – implementation and observations.
- Pig is developed to make data analysis on Hadoop easier. It is made up of two components: a high level scripting language (which is called Pig Latin but most people just reference it as Pig) and an execution environment. Pig Latin is a procedural language which allows you to build data flows, it contains a number of built in User Defined Functions (UDFs) to manipulate data. These UDFs allow you to ingest data from files, streams or other sources, make selections and transform the data. Finally Pig will store the results back into HDFS. Pig scripts are translated into a series of MapReduce jobs that are run on Apache Hadoop. Users can create their own functions or invoke code in other languages such as JRuby, Jython and Java. Pig will gives you more control and optimization over the flow of the data than Hive does.
- RHadoop – is a collection of R packages that allow users to manage and analyze data with Hadoop in R, including the creation of map-reduce jobs. Check out Step-by-step guide to setting up an R-Hadoop system and Using RHadoop to predict website visitors to get started with some hands-on examples.
- Storm – distributed real-time computation system, it supports a set of common stream analytics operations, provides guaranteed message processing with support for transactions. It was originally created by Nathan Marz (see History of Apache Storm and lessons learned) – the guy who cam up with the term Lambda architecture for a generic, scalable and fault tolerant data processing architecture.
- SQOOP – was built to transfer data from relational structured data stores (such as SQL Server, MySQL or Oracle) to Apache Hadoop and vice versa. Because Sqoop can handle database metadata, it is able to perform type-safe data movement using the data types specified in the metadata.
- Zookeeper – manages and store configuration information. It is responsible for managing and mediating conflicting updates across your Hadoop cluster.
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