- June 01, 2016
Data wrapping -- in this case, bundling data and analytics services with products -- may entice more companies to become data businesses. A panel at an MIT symposium considered some best practices for doing so.
- May 19, 2016
New cloud apps seem ready-made for NoSQL. This may cause Oracle to put more focus on its Oracle NoSQL database, which is often overlooked amid a crush of NoSQL contenders.
- May 16, 2016
In an interview, consultant Lakshmi Randall foresees changes in how data management is organized and executed as the overall data landscape shifts due to the adoption of big data systems.
- April 29, 2016
Surging big data is changing data modeling techniques, including schema creation. The word from Enterprise Data World 2016: Data pros must adjust.
- April 29, 2016
Open source data engineering has become a way of life at e-commerce leader eBay, says the company's Debashis Saha. Kylin is one of the tools that has resulted.
Sponsored by Intel - Remember what it felt like when you got your first solid state drive (SSD)? Back in 2008, laptops with a hard disk drive (HDD) felt unresponsive – they lagged and nearly ground to a halt when the virus scanner started accessing the HDD. Then Intel launched the X-25M SSD and laptops felt much more responsive. Gone was the lag. Nobody noticed when the virus scanner ran. See More
Sponsored by Intel - Businesses are excited about artificial intelligence (AI) and the benefits it offers, but like so many new technologies, that potential can be wasted if you don’t know where to begin the journey to production-ready AI. See More
Sponsored by Intel - The cloud is a natural fit for CIOs that wish to evaluate artificial intelligence (AI) because it lends itself to proof of concept (POC) and experimentation without the need for buying hardware that might ultimately not be required. See More
Sponsored by Intel - Artificial intelligence (AI) and machine learning are generating interest from IT leaders eager to discover how the technologies can help their business meet the constant demands of driving down costs, achieving growth, deriving insights from data, making decisions faster and improving efficiency. See More
- April 22, 2016
A new view on hybrid data architectures, in which data lakes and warehouses coexist, emerged at EDW 2016. The hybrid approach has implications for data design, skills and planning.
- April 19, 2016
Running a Hadoop cluster in the data center isn't for the weak. But several new tools aim to give IT operations teams a closer look into what's going on inside Hadoop-based big data systems.
- April 13, 2016
Pivotal Software dropped out of the Hadoop distribution business in favor of reselling the Hortonworks version of the big data framework -- and the market consolidation moves may not be over.
- April 01, 2016
Moving streams of data is a must in many modern applications. As a result, streaming analytics systems with Spark Streaming, Kafka and other components are coming to the big data forefront.
- March 31, 2016
At Strata + Hadoop World 2016, Hadoop co-creator Doug Cutting said the core of the distributed processing framework is likely to see its position at the center of big data systems diminish.
- March 25, 2016
Nowadays, the term unstructured data pops up everywhere. It owes its popularity for a large part to the success of big data, to successful technologies such as NoSQL and Hadoop, and to formats such ...
- March 24, 2016
The Strata + Hadoop World conference focuses on big data management and analytics technologies, in particular the Hadoop distributed processing framework and Spark processing engine.
- March 16, 2016
Because of growing data demands, and the need to nimbly scale up and down, a startup social networking platform chose a Redis Labs NoSQL database management system running on AWS.
- March 02, 2016
Looking to better balance system stability and innovation, Hadoop distribution provider Hortonworks will follow two release 'cadences' for different component sets in its HDP package.
- February 29, 2016
Its collection of big-data processing features is priming the Apache Spark architecture for wider deployment. One key trait: Spark performance outpaces MapReduce in many Hadoop use cases.