Data migration

Data migration is the process of selecting, preparing, extracting, and transforming of the existing stored digital information data and permanently transferring it from one storage system or computing environment to another.

The transfer can occur between different formats or different computer systems. The migration ends only when the old system, database, or environment is shut down. Data migration ensures that data is successfully and securely transferred to another application, storage system or cloud. Although moving data from one platform to another can be risky and costly, it also provides an organization with numerous benefits.

There are four types of data migrations:
*Storage migration
*Database migration
*Application migration
*Cloud migration
*Business process migration

In storage migration, the process of moving data off existing arrays into more modern ones that enable other systems to access it. Offers significantly faster performance and more cost-effective scaling while enabling expected data management features such as cloning, snapshots, and backup and disaster recovery.

Database migration is used for moving an entire database from one vendor to another, or to upgrade the software currently being used for the database.

Business process of migration is driven by mergers and acquisitions, business optimization, or reorganization to address competitive challenges or enter new markets. All these changes may require the transfer of business applications and databases with data on customers, products, and operations to the new environment.
Data migration

Data integration

Data Integration is the process of integrating data from multiple sources and probably have a single view over all these sources and answering queries using the combined information. Once the data integrated, so that they form a unified new whole and give users the illusion of interacting with one single information system.

The reason for integration is twofold:
*First, given a set of existing information systems, an integrated view can be created to facilitate information access and reuse through a single information access point.
*Second, given a certain information need, data from different complementing information systems is to be combined to gain a more comprehensive basis to satisfy the need.

There is a manifold of applications that benefit from integrated information. For instance, in the area of business intelligence (BI), integrated information can be used for querying and reporting on business activities, for statistical analysis, online analytical processing (OLAP), and data mining in order to enable forecasting, decision making, enterprise-wide planning, and, in the end, to gain sustainable competitive advantages.

For customer relationship management (CRM), integrated information on individual customers, business environment trends, and current sales can be used to improve customer services.

Integration can be physical or virtual:
• Physical: Coping the data to warehouse
• Virtual: Keep the data only at the sources

The most complex and difficult part of integrating data is transforming data into a common format. Understanding the data to be combined and understanding (and possibly defining) the structure of the combined data requires both a technical and business understanding of the data and data structures in order to define how the data needs to be transformed.
Data integration

Data mining

Data Mining, also popularly known as Knowledge Discovery in Databases (KDD), refers to the nontrivial extraction of implicit, previously unknown and potentially useful information from data in databases. While data mining and knowledge discovery in databases (or KDD) are frequently treated as synonyms, data mining is actually part of the knowledge discovery process.

Statisticians were the first to use the term “data mining.” Originally, “datamining” or “data dredging” was a derogatory term referring to attempts toextract information that was not supported by the data.

Data mining is a multidisciplinary field, drawing work from areas including database technology, machine learning, statistics, pattern recognition, information retrieval, neural networks, knowledge-based systems, artificial intelligence, high-performance computing, and data visualization.

Data mining derives its name from the similarities between searching for valuable information in a large database and mining rocks for a vein of valuable ore. Both imply either sifting through a large amount of material or ingeniously probing the material to exactly pinpoint where the values reside.

The essential difference between the data mining and the traditional data analysis (such as query, reporting and on-line application of analysis) is that the data mining is to mine information and discover knowledge on the premise of no clear assumption.

Data mining is highly useful in the following domains:
*Market Analysis and Management
*Corporate Analysis & Risk Management
*Fraud Detection
Apart from these, data mining can also be used in the areas of production control, customer retention, science exploration, sports, astrology, and Internet Web Surf-Aid.

Stages of the Data Mining Process
1. Data gathering
2. Data cleansing
3. Feature extraction
4. Pattern extraction and discovery
5. Visualization of the data.
6. Evaluation of results
Data mining

Secure Socket Layer (SSL)

The internet is a packet-switching network. This means that for two hosts to communicate, they must packetize their data and submit it to router with the destination address prepended to each packet.

Secure Socket Layer is used for protection of data in transit that encompasses all network services using TCP/IP to support typical tasks of communication between the clients and the servers.

It is enable client-server applications to communicate in a way that is designed to prevent eavesdropping, tampering and message forgery.

Secure Socket Layer (SSL) is an encryption technology that runs on top of TCP/IP and below application level protocols, such as HTTP. Secure Socket Layer uses a cryptographic system that uses two keys to encrypt data – a public key known to everyone and a private or secret key known only to the recipient of the message.

Digital Certificate is used for the authentication of web server to the client server. Authentication here means that the web server confirms that it is legitimate/genuine to the client server.

SSL ensures the security of data transported and routed though HTTP. Secure Socket Layer was originally developed by Netscape Communications Corp and RSA Data Security, Inc. and it first appeared in a Netscape Navigator in 1994.
Secure Socket Layer (SSL)

In-memory database

In-memory databases do not use any file to store any kind of information, the databases and their logs are stored entirely in the main memory. So, the database operations are superfast. When the application closes an in-memory database, the database is eliminated from the main memory.

In-memory database management systems have the potential to reduce the execution time of complex operational analytical queries to the order of seconds while executing business transaction in parallel.

Since in-memory database utilize the server’s main memory as primary storage location, the size, cost and access speed provided by main memory components are vitally important.

With the help of data compression, today’s standard server systems comprise sufficiently large main memory volumes to accommodate the entire operation data of all companies.

Main memory, as the primary storage location is, nevertheless, becoming increasingly attractive as a result of the decreasing cost/size ratio.

The In-Memory Database (IMDB) technology has proven to have major capabilities for analyzing big enterprise and medical data, e.g., to identify relevant patient data and to protect markets from injecting pharmaceutical counterfeits.
In memory database