Data lifecycle management is the process of managing business information throughout its lifecycle, from requirements through retirement. The lifecycle crosses different application systems, databases and storage media. By managing information properly over its lifetime, organizations are better equipped to deliver competitive offerings to the market faster and support business goals with less risk.
How Data Lifecycle Management helps your enterprise
Data Lifecycle Management solutions these challenges by:
♦Reducing Costs: Lower infrastructure and capital costs, improve productivity, and reduce application defects during the development lifecycle.
♦ Reducing Risks: Reduce application down time, minimize service and performance disruptions, and meet data retention requirements.
♦ Promoting Business Agility: Improve time to market, increase application performance and improve quality of applications through realistic test data.
Big Data is not simply a huge pile of information. “Big Data describes datasets so large they become awkward to manage with traditional database tools at a reasonable cost.”
Big Data Can Work With Any Dataset. However, Big Data Shines When Dealing With Unstructured Data.
Some organizations — typically those with sufficient in-house IT skills to integrate multiple software products choose to implement only portions of an ERP system and develop an external interface to other ERP or stand-alone systems for their other application needs. For example, one may choose to use human resource management system from one vendor, and the financial systems from another, and perform the integration between the systems themselves.
List Data Types:
♦ Structured Data
♦ Semi-Structured Data
♦ Unstructured Data
Apache Hadoop™ was born out of a need to process an avalanche of big data.
Hadoop is basically a massively parallel, shared nothing, distributed processing algorithm.
“The Apache™ Hadoop® project develops open-source software for reliable, scalable, distributed computing.
“The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.”
Apache Cassandra is an open source distributed database management system designed to handle large amounts of data across many commodity servers, providing high availability with no single point of failure. Cassandra offers robust support for clusters spanning multiple data centers, with asynchronous master less replication allowing low latency operations for all clients.
We at ITTStar can support our clients in dealing with their data needs.
Maintain multiple database environments. Leverage analytics to make informed decisions across structured & unstructured data. Create dashboards to support business needs, optimize storage, maintenance and licensing costs by migrating rarely used data in an onshore, offshore & cloud environments. We are experts in Data Warehouse Management and Test Data Maintenance. We take appropriate controls in place to preventing the exposure of confidential data in both production and non-production environments.