Big Data

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Manage Virtualization for Big Data

Virtualization separates resources and services from the underlying physical delivery environment, enabling you to create many virtual systems within a single physical system. One of the primary reasons [more…]

Big Data Cloud Deployment Models

Two key cloud models are important in the discussion of big data — public clouds and private clouds. Cloud computing is a method of providing a set of shared computing resources that include applications [more…]

Why the Cloud is Imperative for Big Data

Numerous combinations of deployment and delivery models exist for big data in the cloud. For example, you can utilize a public cloud IaaS or a private cloud IaaS. So, what does this mean for big data and [more…]

Big Data Cloud Providers

Cloud providers come in all shapes and sizes and offer many different products for big data. Some are household names while others are recently emerging. Some of the cloud providers that offer IaaS services [more…]

Warnings for Big Data Cloud Users

Warning! Cloud-based services can provide an economical solution to your big data needs, but the cloud has its issues. It’s important to do your homework before moving your big data there. Here are some [more…]

Nonrelational Databases in a Big Data Environment

Nonrelational databases do not rely on the table/key model endemic to RDBMSs (relational database management systems). In short, specialty data in the big data world requires specialty persistence and [more…]

Key-Value Pair Databases in a Big Data Environment

By far, the simplest of the NoSQL (not-only-SQL) databases in a big data environment are those employing the key-value pair (KVP) model. KVP databases do not require a schema [more…]

Document Databases in a Big Data Environment

You find two kinds of document databases for big data projects. One is often described as a repository for full document-style content. The other is a database for storing document components for permanent [more…]

Columnar Databases in a Big Data Environment

Columnar databases can be very helpful in your big data project. Relational databases are row oriented, as the data in each row of a table is stored together. In a columnar, or column-oriented database [more…]

Graph Databases in a Big Data Environment

The fundamental structure for graph databases in big data is called “node-relationship.” This structure is most useful when you must deal with highly interconnected data. Nodes and relationships support [more…]

Spatial Databases in a Big Data Environment

Spatial databases can be an important tool in your big data project. Spatial data itself is standardized through the efforts of the Open Geospatial Consortium [more…]

How to Use MapReduce for Big Data

MapReduce is a software framework that is ideal for big data because it enables developers to write programs that can process massive amounts of unstructured data in parallel across a distributed group [more…]

How to Optimize MapReduce Tasks

Aside from optimizing the actual application code with MapReduce for big data projects, you can use some optimization techniques to improve the reliability and performance. They fall into three categories [more…]

Hadoop Distributed File System (HDFS) for Big Data Projects

The Hadoop Distributed File System is a versatile, resilient, clustered approach to managing files in a big data environment. HDFS is not the final destination for files. Rather, it is a data service that [more…]

Hadoop MapReduce for Big Data

To fully understand the capabilities of Hadoop MapReduce, it’s important to differentiate between MapReduce (the algorithm) and an implementation of MapReduce [more…]

Manage Big Data Resources and Applications with Hadoop YARN

Job scheduling and tracking for big data are integral parts of Hadoop MapReduce and can be used to manage resources and applications. The early versions of Hadoop supported a rudimentary job and task tracking [more…]

Store Big Data with HBase

HBase is a distributed, nonrelational (columnar) database that utilizes HDFS as its persistence store for big data projects. It is modeled after Google BigTable and is capable of hosting very large tables [more…]

Mine Big Data with Hive

Hive is a batch-oriented, data-warehousing layer built on the core elements of Hadoop (HDFS and MapReduce) and is very useful in big data. It provides users who know SQL with a simple SQL-lite implementation [more…]

Big Data Analysis and the Data Warehouse

You will find value in bringing the capabilities of the data warehouse and the big data environment together. You need to create a hybrid environment where big data can work hand in hand with the data [more…]

Modify Business Intelligence Products to Handle Big Data

Traditional business intelligence products weren’t really designed to handle big data, so they may require some modification. They were designed to work with highly structured, well-understood data, often [more…]

Big Data Cloud Delivery Models

A number of cloud delivery models exist for big data. Try talking to those with experience to figure out which type of delivery model is best for your big data initiative. [more…]

Big Data Analytics Solutions Vendors

A number of vendors on the market today support the growing need with big data solutions for your business. Here is a listing of a few solutions that you may find interesting: [more…]

Analysis and Extraction Techniques for Big Data

In general, text analytics solutions for big data use a combination of statistical and Natural Language Processing (NLP) techniques to extract information from unstructured data. NLP is a broad and complex [more…]

Text Analytics Tools for Big Data

Here is an overview of some of the players in the text analysis big data market. Some are small while others are household names. Some call what they do [more…]

Different Approaches to Big Data Analysis

In many cases, big data analysis will be represented to the end user through reports and visualizations. Because the raw data can be incomprehensively varied, you will have to rely on analysis tools and [more…]

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