17 December 2013
Learn how to set up OpenVPN — an open source implementation of VPN server and VPN client software published under the GNU General Public License — on a connected client to enable secure access to the Hadoop cluster.
Version 3.2 is available now. Find out what's
InfoSphere Streams Quick Start Edition puts real-time analytic processing at your fingertips. Now you can analyze massive data volumes quickly (even in real time) and turn data into insight you can use to make better decisions. InfoSphere Streams can quickly ingest, analyze, and correlate information as it arrives from thousands of real-time sources. Try out the newest stream computing software — free to download, quick to start.
Download InfoSphere Streams Quick Start Edition
- Big data architecture and patterns, Part 5
Using patterns based on three fraud-detection scenarios, learn how a big data solution can address the complexity of analyzing large volumes of varied data across many data sources.
- Building flexible apps from big data sources
InfoSphere BigInsights makes it easier to manage and run big data jobs through a simple REST API and Jaql interface to Hadoop. Examine how these systems work together to give you a rich basis for capturing data and provide an interface to get the information back out again.
- The world of interactive media systems and applications
The all-digital media world allows for a much wider range of artist and creative developer participation, including the consumer as an interactive participant. Anyone who has a creative mind, some computer skills, and patience can join this new creative digital culture.
- Process big data with Big SQL in InfoSphere BigInsights
Run complex queries on non-tabular data and query it with the SQL-like language Big SQL, which lets you import and process large-volume data sets.
- The grammar of graphics in VizJSON
VizJSON is a markup language for describing charts to a rendering engine such that the engine can interpret the data and display it in a chart. Learn the benefits of using a common approach, industry-wide.
- Explore the advanced analytics platform, Part 3: Analyze unstructured text using patterns
Combine simple design patterns and text analytics to find insight in unstructured text documents.
- R and the world of data
In projects as diverse as bioinformatics to Google Maps, the R programming language is a powerful tool for using statistics to improve decisions, interpret data, and mine data for hidden treasures. Read on to learn more.
- Big data architecture and patterns, Part 4
Learn the basic atomic patterns that describe the typical approaches for consuming, processing, accessing, and storing big data. Apply them to composite patterns that fit the context and scope of your big data situation.
- Oozie workflow scheduler for Hadoop
Big data in its raw form rarely satisfies the Hadoop developer's data requirements for performing data processing tasks. Let Apache Oozie help automate the process of preprocessing data using different types of workflow, which can be reused.
- IBM Accelerator for Machine Data Analytics, Part 6
Learn how to collect, integrate, and analyze Hadoop logs produced by InfoSphere BigInsights with the help of a new log monitoring and analysis function that aggregates log files and stores them over time.
The concern about consumer data privacy has never been higher. For example, 86 percent of Americans are concerned with data collection from Internet browsing and how it is used, and 70 percent of Europeans are concerned about the reuse of their personal data. (14:37) | Watch the video
InfoSphere BigInsights supports many query tools to summarize data and perform ad-hoc queries. This article describes three use cases using JAQL, Hive, and BigSQL against a sample data set and compares the language characteristics of each so you can learn how to select the one that best suits your working environment.
IBM big data platform capabilities
Hadoop-based analytics: Store any data type in the low-cost, scalable Hadoop engine to reduce the cost of processing and analyzing massive volumes of data.
Stream computing: Continuously analyze massive volumes of streaming data with sub-millisecond response times to take action in real time.
Text analytics: Analyze textual content to uncover hidden meaning and insight in unstructured information.
Accelerators: Deploy pre-packaged analytical and industry-specific software modules to extract value from big data.
Application development: Develop text analytics applications with toolkits and tools, including an extensive library of extractors you can customize and extend.