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Big Data

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Put Your Big Data Together

How will you know how to put all of your data together? With a big data project, what you want to do with your structured and unstructured data indicates why you might choose one piece of technology over [more…]

The Evolution of Distributed Computing for Big Data

Behind all the important trends over the past decade, including service orientation, cloud computing, virtualization, and big data, is a foundational technology called [more…]

Distributed Computing Basics for Big Data

If your company is considering a big data project, it’s important that you understand some distributed computing basics first. There isn’t a single distributed computing model because computing resources [more…]

Performance and Big Data

Just having a faster computer isn’t enough to ensure the right level of performance to handle big data. You need to be able to distribute components of your big data service across a series of nodes. In [more…]

Classes of Big Data Analytics

Existing analytics tools and techniques will be very helpful in making sense of big data. The algorithms that are part of these tools, however, must be able to work with large amounts of potentially real-time [more…]

Big Data Applications

Custom and third-party applications offer an alternative method of sharing and examining big data sources. Although all the layers of the reference architecture are important in their own right, this layer [more…]

Big Data Virtualization Basics

Virtualization is ideal for big data because it separates resources and services from the underlying physical delivery environment, enabling you to create many virtual systems within a single physical [more…]

Challenges of Virtualization for Big Data

Virtualized big data environments need to be adequately managed and governed to realize cost savings and efficiency benefits. If you rely on big data services to solve your analytics challenges, you need [more…]

The Cloud in the Context of Big Data

Cloud computing is a method of providing a set of shared computing resources and is becoming increasingly important for your big data initiative. The cloud includes applications, computing, storage, networking [more…]

How to Make Use of the Cloud for Big Data

Clearly, the very nature of the cloud makes it an ideal computing environment for big data. So how might you use big data together with the cloud? Here are some examples: [more…]

Big Data and Polyglot Persistence

The term polyglot is borrowed and redefined for big data as a set of applications that use several core database technologies, and this is the most likely outcome of your implementation planning. The official [more…]

Big Data and the Origins of MapReduce

MapReduce is increasingly becoming useful for big data. In the early 2000s, some engineers at Google looked into the future and determined that while their current solutions for applications such as web [more…]

Functional vs. Procedural Programming Models for Big Data

When people talk of map and reduce in big data, they do so as operations within a functional programming model. Functional programming is one of the two ways that software developers create programs to [more…]

Why Should You Use Hadoop for Big Data?

Search engine innovators like Yahoo! and Google were faced with a bog data problem. They needed to find a way to make sense of the massive amounts of data that their engines were collecting. These companies [more…]

Build a Big Data Foundation with the Hadoop Ecosystem

As core components, Hadoop MapReduce and HDFS are constantly being improved and provide starting points for big data, but you need something more. Trying to tackle big data challenges without a toolbox [more…]

Hadoop Pig and Pig Latin for Big Data

The power and flexibility of Hadoop for big data are immediately visible to software developers primarily because the Hadoop ecosystem was built by developers, for developers. However, not everyone is [more…]

Hadoop Sqoop for Big Data

Sqoop (SQL-to-Hadoop) is a big data tool that offers the capability to extract data from non-Hadoop data stores, transform the data into a form usable by Hadoop, and then load the data into HDFS. This [more…]

Hadoop Zookeeper for Big Data

Hadoop’s greatest technique for addressing big data challenges is its capability to divide and conquer with Zookeeper. After the problem has been divided, the conquering relies on the capability to employ [more…]

The Evolution of Deployment Models in the Big Data Era

With the advent of big data, the deployment models for managing data are changing. The traditional data warehouse is typically implemented on a single, large system within the data center. The costs of [more…]

The Future of Data Warehouses in the Big Data Era

The data warehouse market has indeed begun to change and evolve with the advent of big data. In the past, it was simply not economical for companies to store the massive amount of data from a large number [more…]

Data Mining for Big Data

Data mining involves exploring and analyzing large amounts of data to find patterns for big data. The techniques came out of the fields of statistics and artificial intelligence [more…]

Big Data Streaming with a Public Policy Impact

Almost every area of a city has the capability to use big data, whether in the form of taxes, sensors on buildings and bridges, traffic pattern monitoring, location data, and data about criminal activity [more…]

Big Data Streaming in the Energy Industry

Reducing energy consumption, finding new sources of renewable energy, and increasing energy efficiency are all important big data goals for protecting the environment and sustaining economic growth. Large [more…]

Big Data Streaming in the Healthcare Industry

Big data is of enormous significance to the healthcare industry — including its use in everything from genetic research to advanced medical imaging and research on improving quality of care. While conducting [more…]

Put Big Data to Use

Text analytics can be used to help gain insight into data. So, what if the data is big data? That would mean that the unstructured data being analyzed is high volume, high velocity, or both. [more…]

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