Today one of most wanted data Analytic platforms and Big Data career is Apache Hadoop framework. Hadoop is designed in accordance to Google GFS paper and aims to be the most scalable and effective data Analytic platform and Map/Reduce implementation.
Hadoop was initially developed by Yahoo, then opensourced and donated to ASF. Now there are number of Hadoop variation, Apache, CDH etc.
NetAngelS offers set of Hadoop operations services, included , but limited to:
Planning and design of Hadoop cluster.
Installation and support of any size Hadoop clusters.
Installation and support of any of Hadoop ecosystem components
Capacity planning of Hadoop clusters
Fine tuning of Hadoop clusters for specified Map/Reduce tasks
Fine tuning of HBase clusters
Hadoop security planning
Hadoop High-Availability planning and installation
Monitoring of Hadoop Ecosystem components
Consultation about Hadoop Ecosystem components
Installation and support of 3-rd party Hadoop ecosystem components
Hadoop in the Enterprise
Hadoop gives a great opportunity to enterprises and to organizations of several spheres, to discover and practice new data analysis and several techniques, which were previously impractical and useless for performance, for inappropriate cost and complicated technological reasons. For these purposes, Hadoop’s impact in this movements grows day by day, becoming more and more serous, it is increasingly becoming a popular option to process, store and analyze huge volumes of semi-structured, unstructured, or raw data that often comes from disparate data sources.
Tom White, one of the most popular gurus of Hadoop, put it in his book Hadoop: The Definitive Guide, “The good news is that Big Data is here. The bad news is that we are struggling to store and analyze it.”
How and when we can gain more exact advantage of Hadoop?
The most important strength of Hadoop is its’ proven cost-effective scalability in leveraging ordinary hardware. It supports the processing of all types of data – it does not depends on the data is structured, semi-structured or unstructured – and the open extensibility of Hadoop enables developers to enlarge and append it with specialized capabilities for a wide range of applications.
Many companies are looking on Hadoop as an extension to their environments to grip the volume, speed and frequency, and variety of Big Data. As a result, Hadoop adoption will grow – in a recent survey of large-scale data users, more than half of the respondents stated that they are considering Hadoop within their environment.
How Hadoop improves and enhanses the Data Integration
Hadoop has not come to replace the existing data bases. Instead, Hadoop grows them by enabling the additional, ore convenient processing of large volumes of data, so existing data bases can focus on the best they can do. Data integration has a fundamental and basic role for organizations, that want to combine Hadoop with data from multiple systems to realize penetration of business insights not otherwise possible. The Informatica Platform allows companies, to leverage Hadoop within a hybrid environment in order to take advantage of the unique strengths of each technology, and maximize performance of the overall environment.
Data Integration Platform for Hadoop
Hadoop as well, as any emerging technology, has its’ specific challenges too. Its’ data integration platform, which is the one, of the most open, unified and comprehensive one, enables organizations to take full advantage of Hadoop by providing the following capabilities:
Universal data access – Organizations will use Hadoop to store and process a variety of diverse data sources and often face challenges in combining and processing all relevant data. A data integration platform helps organizations to achieve ease and reliability of pre- and post-processing of data into and out of Hadoop.
Data analysis and exchange – Hadoop excels at storing a diversity of data, but the capability of deriving of meanings and make sense of it across all pertinent data types is a major challenge.
Managing metadata. Hadoop lacks metadata management, without which, the outcomes of projects are think, and may be cut up from incompatibility and poor visibility. This platform supplies full metadata management capabilities, with data lineage and auditability, and promotes standardization.
Mixed workload management. Hadoop is not able to manage mixed workloads. A data integration platform gives an opportunity for integration of data sets from Hadoop and other transaction sources, in order to implement real-time business intelligence and analytics.
The optimization and re-use of existing resources. The data integration platform supports the reuse-age of IT resources within multi-projects.
Interoperability in remain architecture. It is very important to simplify and rationalize Hadoop and incorporate Hadoop as part of the extended environment. A data integration platform’s capabilities for universal data access and transformation support the addition of Hadoop as part of an end-to-end analytics and data processing cycle that helps bridge the gap between Hadoop and your existing IT investment.
A variety of Hadoop projects, including those requiring metadata management, mixed workloads, resource optimization, and interoperability can benefit from a platform approach to data integration. A platform approach to data integration can help you can take full advantage of the data processing power of Hadoop and exploit the proven capabilities of an open, neutral, and complete platform for integrating data.
Informatica for Hadoop
NetAngels is uniquely positioned to help you get more from your Hadoop investments and leverage existing data integration and ETL skill sets. With theNetAngelS you can:
Achieve ease and reliability of pre- &-post-processing of data into and out of Hadoop
Improve productivity for extracting greater value from unstructured data sources – images, texts, binaries, industry standards, etc.
Drive metadata-driven auditability
Promote governance, trust and security over siloed activities with Hadoop deployments
Combine flexibility with high data processing power
Manage mixed workloads and concurrency with high throughput