03 December 2013
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.
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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.
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- 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.
- Create a simple predictive analytics classification model in Java with Weka
Real-time classification of data (the goal of predictive analytics) relies on insight and intelligence based on historical data patterns. Learn how to use the Weka classification engine to create a simple classifier for programmatic use.
- Explore StreamsDev, your direct channel to the Streams development team
Find all the resources you need to develop with InfoSphere Streams, brought to you by the extended Streams development team. Doc, product downloads, SPL code examples, help, events, expert blogs it's all there. Plus a direct line to the developers. Get starting analyzing data in real time now.
- Real-time anomaly detection using the InfoSphere Streams TimeSeries Toolkit
Automate the detection of anomalies in time series data to monitor systems across the domains of cybersecurity, infrastructure, data center management, healthcare, and the environment.
- Build a sentiment analysis application with Node.js, Express, sentiment, and ntwitter
Use Node.js modules to build an app that analyzes public reaction on Twitter. The sample application makes use of popular Node.js modules and builds a structure that can be reused for future applications that need to be created quickly, using a mobile interface, to analyze large volumes of data.
This datagram quickly explains the nine levers that enable organizations to create value from an ever-growing volume of data from a variety of sources — value that results from insights derived and actions taken at every level of the organization. (2:27) | 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.