The following is a re-post from Bob Gourley of CTOVision.com.

The amount of data being created in governments is growing faster than humans can analyze. But analysis can solve tough challenges. Those two facts are driving the continual pursuit of new Big Data solutions. Big Data solutions are of particular importance in government. The government has special abilities to focus research in areas like Health Sciences, Economics, Law Enforcement, Defense, Geographic Studies, Environmental Studies, Bioinformatics, and Computer Security. Each of those area can be well served by Big Data approaches, and each has exemplars of solutions worthy of highlighting to the community.

The Government Big Data Solutions Award was established to help highlight some of the best innovation in the federal space. The 2012 award process solicited nominations from across federal, state and local governments. Nominations were evaluated based on how well submissions addressed three key factors:

This is the third article in a series about analyzing Twitter data using some of the components of the Apache Hadoop ecosystem that are available in CDH (Cloudera’s open-source distribution of Apache Hadoop and related projects). If you’re looking for an introduction to the application and a high-level view, check out the first article in the series.

In the previous article in this series, we saw how Flume can be utilized to ingest data into Hadoop. However, that data is useless without some way to analyze the data. Personally, I come from the relational world, and SQL is a language that I speak fluently. Apache Hive provides an interface that allows users to easily access data in Hadoop via SQL. Hive compiles SQL statements into MapReduce jobs, and then executes them across a Hadoop cluster.

In this article, we’ll learn more about Hive, its strengths and weaknesses, and why Hive is the right choice for analyzing tweets in this application.

Characterizing Data

Cloudera recently announced the general availability of CDH4.1, an update to our open-source, enterprise-ready distribution of Apache Hadoop and related projects. Among various components, Apache Mahout is a relatively recent addition to CDH (first added to CDH3u2 in 2011), but is already attracting increasing interest out in the field. 

Mahout started as a sub-project of Apache Lucene to provide machine-learning libraries in the area of clustering and classification. It later evolved into a top-level Apache project with much broader coverage of machine-learning techniques (clustering, classification, recommendation, frequent itemset mining etc.). 

In CDH4.1, Mahout is upgraded to upstream version 0.7. Several new changes are included in this release, and this post will briefly go over some of the interesting ones.

Outlier Removal Capability

A few weeks back, Cloudera announced CDH 4.1, the latest update release to Cloudera’s Distribution including Apache Hadoop. This is the first release to introduce truly standalone High Availability for the HDFS NameNode, with no dependence on special hardware or external software. This post explains the inner workings of this new feature from a developer’s standpoint. If, instead, you are seeking information on configuring and operating this feature, please refer to the CDH4 High Availability Guide.

Background

Since the beginning of the project, HDFS has been designed around a very simple architecture: a master daemon, called the NameNode, stores filesystem metadata, while slave daemons, called DataNodes, store the filesystem data. The NameNode is highly reliable and efficient, and the simple architecture is what has allowed HDFS to reliably store petabytes of production-critical data in thousands of clusters for many years; however, for quite some time, the NameNode was also a single point of failure (SPOF) for an HDFS cluster. Since the first beta release of CDH4 in February, this issue has been addressed by the introduction of a Standby NameNode, which provides automatic hot failover capability to a backup. For a detailed discussion of the design of the HA NameNode, please refer to the earlier post by my colleague Aaron Myers.

Limitations of NameNode HA in Previous Versions

As described in the March blog post, NameNode High Availability relies on shared storage - in particular, it requires some place in which to store the HDFS edit log which can be written by the Active NameNode, and simultaneously read by the Standby NameNode. In addition, the shared storage must itself be highly available — if it becomes inaccessible, the Active NameNode will no longer be able to continue taking namespace edits.

I am very pleased to announce the availability of Cloudera Manager 4.1. This release adds support for the Cloudera Impala beta release, and management and monitoring of key CDH features.

Here are the highlights of Cloudera Manager 4.1:

After a long period of intense engineering effort and user feedback, we are very pleased, and proud, to announce the Cloudera Impala project. This technology is a revolutionary one for Hadoop users, and we do not take that claim lightly.

When Google published its Dremel paper in 2010, we were as inspired as the rest of the community by the technical vision to bring real-time, ad hoc query capability to Apache Hadoop, complementing traditional MapReduce batch processing. Today, we are announcing a fully functional, open-sourced codebase that delivers on that vision – and, we believe, a bit more – which we call Cloudera Impala. An Impala binary is now available in public beta form, but if you would prefer to test-drive Impala via a pre-baked VM, we have one of those for you, too. (Links to all downloads and documentation are here.) You can also review the source code and testing harness at Github right now.

Impala raises the bar for query performance while retaining a familiar user experience. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time. Furthermore, it uses the same metadata, SQL syntax (Hive SQL), ODBC driver and user interface (Hue Beeswax) as Apache Hive, providing a familiar and unified platform for batch-oriented or real-time queries. (For that reason, Hive users can utilize Impala with little setup overhead.) The first beta drop includes support for text files and SequenceFiles; SequenceFiles can be compressed as Snappy, GZIP, and BZIP (with Snappy recommended for maximum performance). Support for additional formats including Avro, RCFile, LZO text files, and the Parquet columnar format is planned for the production drop.

Today we’re proud to announce a new addition to the Apache Hadoop ecosystem: Cloudera Impala, a parallel SQL engine that runs natively on Hadoop storage. The salient points are:

This is a guest post by Oliver Guinan, VP Ground Software, at Skybox Imaging. Oliver is a 15-year veteran of the internet industry and is responsible for all ground system design, architecture and implementation at Skybox.

One of the great promises of the big data movement is using networks of ubiquitous sensors to deliver insights about the world around us. Skybox Imaging is attempting to do just that for millions of locations across our planet.

Skybox is developing a low cost imaging satellite system and web-accessible big data processing platform that will capture video or images of any location on Earth within a couple of days. The low cost nature of the satellite opens the possibility of deploying tens of satellites which, when integrated together, have the potential to image any spot on Earth within an hour.

Hue is a Web-based interface that makes it easier to use Apache Hadoop. Hue 2.1 (included in CDH4.1) provides a new application on top of Apache Oozie (a workflow scheduler system for Apache Hadoop) for creating workflows and scheduling them repetitively. For example, Hue makes it easy to group a set of MapReduce jobs and Hive scripts and run them every day of the week.

In this post, we’re going to focus on the Workflow component of the new application.

Workflow Editor

Workflows consist of one or multiple actions that can be executed sequentially or in parallel. Each action will run a program that can be configured with parameters (e.g. output=${OUTPUT} instead of hardcoding a directory path) in order to be easily reusable.

Apache Pig is a platform for analyzing large data sets that provides a high-level language called Pig Latin. Pig users can write complex data analysis programs in an intuitive and compact manner using Pig Latin.

Among many other enhancements, CDH4.1, the newest release of Cloudera’s open-source Hadoop distro, upgrades Pig from version 0.9 to version 0.10. This post provides a summary of the top seven new features introduced in CDH4.1 Pig.

Boolean Data Type

Pig Latin is continuously evolving. As with other actively developed programming languages, more data types are being added to Pig. CDH4.1 adds the boolean type. The boolean type is internally mapped to the Java Boolean class, and the boolean constants ‘TRUE’ and ‘FALSE’ are case-insensitive. Here are some example uses of boolean type: