Solving Big Data Disaster with Cloud Computing

Posted on: July 4th, 2016 by nexstepaz No Comments

If you are a law firm that has a reasonably efficient Information Technology management framework, then it would be safe to say that you have data that is continuously growing.  This data growth can be traced not only on the influx of new client information, but more so with data redundancy.

Where does data redundancy come from?  This is usually the result of production data caused by analytics, backup, disaster recovery, and other similar protocols.  So how do you solve the big data disaster?

Data Virtualization

It is estimated that in 2013 alone, many companies have spent more than $44 billion in the management of redundant data copies.  About 85% of these companies invested in hardware storage solutions and around 65% in storage software.  These however are far from being considered as cost-effective solutions to the bloating of data.

How can data redundancy be addressed?  One perspective is the virtualization of data copies.  For many companies, including law firms, this has proven to be an effective data management solution.

This is done in combination with network utilization optimization and efficient data handling.  Short recovery becomes possible through the use of less storage and bandwidth.

To take this further, virtual data pipeline can be used as a fundamental data management solution.  The virtualized copies become time-specific and should they need to be restored, the files will merely be extracted and analyzed based on a recovery point defined by the user.

With the recovered data directly mounted on the server, data movement is eliminated allowing for faster and immediate recovered data.

Data Handling

Another solution to bloating data and avoiding associated disasters is efficient data handling.  This describes how the collections, management, and delivery of data can be done as efficiently and effectively as possible through the virtual data pipeline.

Once the snapshot has been created, only the changed block will be captures using an incremental-forever principle.  This leaves the collected data at its most efficient state for tracking and transfer changes.  By using the native format in storage, the need to create and restore data from backup is eliminated.

The administrator can set SLAs that will define the frequency of snapshots, type of memory storage, and location and retention policies.  This will include the replication to remote cloud servers.  The creation of the SLA allows for the connection with any virtual machine or application to capture data.

The requirement to generate virtual copies is to have a master copy of the data that will be used for production.  This allows for the creation of an indefinite number of virtual copies that can be available for daily use, testing, analysis, or whatever process needed in the production environment.  The master copy can be mirrored to a remote cloud server as part of a disaster recovery procedure.

Increased Data Virtualization

Why is there an increase in data virtualization?  This can be traced to the fact that data virtualization is the next reasonable step after having a computer server.  The infrastructures are also reasonably easy to manage and are considered extremely cost-effective due to its demand-oriented model.

More importantly for law firms and other industries, data virtualization matches the realistic needs of today that require management of big data using fewer resources.  Its proven efficiency have contributed to the increase and extended the ability to protect and manage data for server efficiency.

The increase in data virtualization is also attributed to its less bandwidth requirements and the possibility of instant restoration.  Platforms that accelerate data management while reducing data center complexity have made distributed environments in the cloud more accessible.

Contact NexStep, cloud computing for small to medium law firms to store and create law data back up to your important data and avoid any data problems.


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