Greek Users Of Business Intelligence Apps

cloud engineer

A cloud engineer is an IT professional responsible for any technological duties associated with cloud computing, including design, planning, management, maintenance and support.

The cloud engineer position can be broken into multiple roles, including cloud software engineer, cloud security engineer, cloud systems engineer and cloud network engineer. Each position focuses on a specific type of cloud computing, rather than the technology as a whole. Companies that hire cloud engineers are often looking to deploy cloud or further their cloud understanding and technology.

Job listings on Dice.com seek cloud engineers with at least three to five years’ experience with cloud — including open source technology, software development, system engineering, scripting languages and multiple cloud provider environments. Additionally, cloud engineers must have a background building or designing web services in the cloud.

Cloud engineers need to be familiar with programming languages including Java, Python and Ruby. Many companies looking to hire cloud engineers seek experience with OpenStack, Linux, Amazon Web Services, SoftLayer, Rackspace, Google cloud, Microsoft Azure and Docker. Experience with APIs, orchestration, automation, DevOps and databases like NoSQL are also important.

A cloud engineer should have a Bachelor of Science degree in computer science, engineering or another related field, but some companies prefer a Master of Science degree. Additional certifications may be required.

ensemble modeling

Ensemble modeling is the process of running two or more related but different analytical models and then synthesizing the results into a single score or spread in order to improve the accuracy of predictive analytics and data mining applications.

In predictive modeling and other types of data analytics, a single model based on one data sample can have biases, high variability or outright inaccuracies that affect the reliability of its analytical findings. Using specific modeling techniques can present similar drawbacks. By combining different models or analyzing multiple samples, data scientists and other data analysts can reduce the effects of those limitations and provide better information to business decision makers.

One common example of ensemble modeling is a random forest model. This approach to data mining leverages multiple decision trees, a type of analytical model that’s designed to predict outcomes based on different variables and rules. A random forest model blends decision trees that may analyze different sample data, evaluate different factors or weight common variables differently. The results of the various decision trees are then either converted into a simple average or aggregated through further weighting.

Ensemble modeling has grown in popularity as more organizations have deployed the computing resources and advanced analytics software needed to run such models. In addition, the emergence of Hadoop and other big data technologies has led businesses to store and analyze greater volumes of data, creating increased potential for running analytical models on different data samples.

Disaster Recovery as a Service

Disaster Recovery as a Service (DRaaS) is the replication and hosting of physical or virtual servers by a third-party to provide failover in the event of a man-made or natural catastrophe.

Typically, DRaaS requirements and expectations are documented in a servel-level agreement (SLA) and the third-party vendor provides disaster recovery failover to a cloud environment, either through a contract or pay-per-use basis. In the event of an actual disaster, an offsite vendor will be less likely than the enterprise itself to suffer the direct and immediate effects of that disaster, allowing the provider to implement the DRP even in the event of the worst-case scenario: a total or near-total shutdown of the affected enterprise.

DRaaS can be especially useful for small to mid-size businesses that lack the necessary expertise to provision, configure and test an effective disaster recovery plan. Using DRaaS also means the organization doesn’t have to invest in — and maintain — their own off-site DR environment. An additional benefit is that RaaS contracts can be flexible as the business’ needs change. The downside, of course, is that the business must trust that the DRaaS service provider can implement the plan, in the event of a disaster, and meet the defined recovery time and recovery point objectives.

data warehouse

A data warehouse is a federated repository for all the data that an enterprise’s various business systems collect. The repository may be physical or logical.

Data warehousing emphasizes the capture of data from diverse sources for useful analysis and access, but does not generally start from the point-of-view of the end user or knowledge worker who may need access to specialized, sometimes local databases. The latter idea is known as the data mart.

There are two approaches to data warehousing, top down and bottom up. The top down approach spins off data marts after the complete data warehouse has been created. The bottom up approach builds the data marts first and then combines them into a single, all-encompassing data warehouse.

Typically, a data warehouse is housed on an enterprise mainframe server or increasingly, in the cloud. Data from various online transaction processing (OLTP) applications and other sources is selectively extracted for use by analytical applications and user queries.

The term data warehouse was coined by William H. Inmon, who is known as the Father of Data Warehousing. Inmon described a data warehouse as being a subject-oriented, integrated, time-variant and non-volatile collection of data that supports management’s decision-making process.