A newly popular unit of data storage in the big data era is the petabytes. Petabytes, which is a billion gigabytes, is equivalent to one petabyte (1 trillion gigabytes). It is the largest available storage space available for computer storage today. It is available in Terabytes, Exabytes and Terabytes. These units of measurement are the same as the Gb, Tb, Fb and Kb used in the big data era.
There are a high value and a wide range of uses for these units of measurement. The biggest benefit of these is that they are increasingly challenging to deal with and hard to make. There are several different technologies that are used in making data. Data has to be stored in large RLP or relational databases, which is a form of clustered database. This type of database structure is the biggest challenge in the RDBMS arena today.
A two-page ref, or two-bit numerical value, is an incremental change to a row in a database. One of the biggest advantages of an RDBMS is the ability to create them automatically. They can be efficiently managed using a planner and this is the biggest question mark in an RDBMS. Creating them is increasingly challenging task. They are very costly in terms of both time and money.
Metrics which are measured against the performance of the system is another challenge. There are different ways of measuring metrics and the best way to measure them is through performance counters. Performance counters can be efficiently managed by the data warehouses. They make them increasingly challenging task and even harder to achieve. The biggest advantage of a data warehouse is that it makes analytics easier. Big data warehouses are increasingly common in both the small and large scale data analytics systems.
In a nutshell, a large amount of analytics can be processed on one machine. It is not possible for smaller machines to do so. However, the key advantage is that the entire process of analytics is integrated into the main database server. It is possible to run it on a laptop if needed but the main advantage is that the whole process is done in a virtual environment, in a cloud hosting environment. Cloud hosting allows the same processes to be run across many machines.
The biggest challenge that new data warehouses face is the evolution from traditional relational databases to the cloud model. They will have to deal with the issues of high latency, batching, latency due to heavy workloads, concurrency, failures, partitions, redundancy, and read-only inputs/output signals. New analytics techniques such as the parallel data warehouse or the in-memory analytics will have to be developed.
The other challenge that faces the industry is how to deal with extreme workloads and the heavy workload of managing the analytics environment. New solutions are required that allow the companies to scale up and reduce the input of the analytical workload. This will be a solution that address the concurrency challenges, and some of these solutions might include capabilities for streaming analytics and real time processing. If the workload is reduced, the company is able to improve its capital expenditure. The solution which can give the most benefit to the organization is a combination of streaming analytics and the old school data warehouse.
As mentioned earlier, there is a lot of work ahead of the data scientists. They have to solve concurrency, streaming, and the mapreduce issues. They will also have to provide an answer to the questions which have to do with providing the amortization savings. This will help them gain the competitive advantage. A data scientist who has worked with this project previously will have an idea about the current challenges.