Why mysql is not used on big data?

Hi everyone,

I try to check database management system which is most used or which is most valueable in the world. I found a website like compare the databases for general structure.

I compared them on a link, you can check below, but i didnt understood to why mysql is not used in big data. If anyone can answer this question, you can contact me on about-contact page.


By the way, if you want to follow rank of databases you can catch on below link;



Talih Bayram

Different Types of Databases Top 12

1.0 Relational Databases

This is the most common of all the different types of databases. In this, the data in a relational database is stored in various data tables. Each table has a key field which is used to connect it to other tables. Hence all the tables are related to each other through several key fields. These databases are extensively used in various industries and will be the one you are most likely to come across when working in IT.

Examples of relational databases are Oracle, Sybase and Microsoft SQL Server and they are often key parts of the process of software development. Hence you should ensure you include any work required on the database as part of your project when creating a project plan and estimating project costs.

2.0 Operational Databases

In its day to day operation, an organisation generates a huge amount of data. Think of things such as inventory management, purchases, transactions and financials. All this data is collected in a database which is often known by several names such as operational/ production database, subject-area database (SADB) or transaction databases.

An operational database is usually hugely important to Organisations as they include the customer database, personal database and inventory database ie the details of how much of a product the company has as well as information on the customers who buy them. The data stored in operational databases can be changed and manipulated depending on what the company requires.

3.0 Database Warehouses

Organisations are required to keep all relevant data for several years. In the UK it can be as long as 6 years. This data is also an important source of information for analysing and comparing the current year data with that of the past years which also makes it easier to determine key trends taking place. All this data from previous years are stored in a database warehouse. Since the data stored has gone through all kinds of screening, editing and integration it does not need any further editing or alteration.

With this database ensure that the software requirements specification (SRS) is formally approved as part of the project quality plan.

4.0 Distributed Databases

Many organisations have several office locations, manufacturing plants, regional offices, branch offices and a head office at different geographic locations. Each of these work groups may have their own database which together will form the main database of the company. This is known as a distributed database.

5.0 End-User Databases

There is a variety of data available at the workstation of all the end users of any organisation. Each workstation is like a small database in itself which includes data in spreadsheets, presentations, word files, note pads and downloaded files. All such small databases form a different type of database called the end-user database.

6.0 External Database

There is a sea of information available outside world which is required by an organisation. They are privately-owned data for which one can have conditional and limited access for a fortune. This data is meant for commercial usage. All such databases outside the organisation which are of use and limited access are together called external database.

7.0 Hypermedia Database

Most websites have various interconnected multimedia pages which might include text, video clips, audio clips, photographs and graphics. These all need to be stored and “called” from somewhere when the webpage if created. All of them together form the hypermedia database.

Please note that if you are creating such a database from scratch to be generous when creating a project plan, detailed when defining the business requirements documentation (BRD) and meticulous in your project cost controls. I have seen too many projects where the creation of one of these databases has caused scope creep and an out of control budget for a project.

8.0 Navigational Database

Navigational database has all the items which are references from other objects. In this, one has to navigate from one reference to other or one object to other. It might be using modern systems like XPath. One of its applications is the air flight management systems.

9.0 In-Memory Database

An in-memory databases stores data in a computer’s main memory instead of using a disk-based storage system. It is faster and more reliable than that in a disk. They find their application in telecommunications network equipments.

10.0 Document-Oriented Database

A document oriented database is a different type of database which is used in applications which are document oriented. The data is stored in the form of text records instead of being stored in a data table as usually happens.

11.0 Real-Time Database

A real-time database handles data which constantly keep on changing. An example of this is a stock market database where the value of shares change every minute and need to be updated in the real-time database. This type of database is also used in medical and scientific analysis, banking, accounting, process control, reservation systems etc. Essentially anything which requires access to fast moving and constantly changing information.

Assume that this will require much more time than a normal relational database when it comes to the software testing life cycle, as these are much more complicated to efficiently test within normal time frames.

12.0 Analytical Database

An analytical database is used to store information from different types of databases such as selected operational databases and external databases. Other names given to analytical databases are information databases, management databases or multi-dimensional databases. The data stored in an analytical database is used by the management for analysis purposes, hence the name. The data in an analytical database cannot be changed or manipulated.

Different Types of Databases Top 12 – Tip

Of the different types of databases, relational is the most common and includes such well known names as Oracle, No-SQL, Couchbase, Hadoop, Sybase and SQL Server. However as a project manager you need to be prepared for anything, hence why having a high level view of the different databases is useful particularly when managing a software development life cycle. Regarding the remainder, you will hear a great deal about database warehouses. This is a highly specialized area which involves mining the data produced to generate meaningful trends and reports for senior management to act upon.

I used referenced web site for this post : my-project-management-expert.com

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What are the different types of databases?


The different types of databases include operational databases, end-user databases, distributed databases, analytical databases, relational databases, hierarchical databases and database models. Databases are classified according to their type of content, application area and technical aspect. For instance, a deductive database combines logic programming with a relational database, while a graph database uses graph structures to represent and store information.


Other types of databases include hypertext databases, mobile databases, parallel databases, active databases, cloud databases, in-memory databases, spatial databases, temporal databases, real-time databases, probabilistic databases and embedded databases.

A database is an organized collection of data. Its primary function is to interact with a database management system to capture and analyze data. A database management system is a software system designed to allow the creation, querying and administration of databases. Some popular database management systems include PostgreSQL, MySQL, Microsoft SQL Server, Oracle, IBM DB2 and SAP.

Databases are designed to operate large amounts of information by inputting, storing, retrieving and managing it. They are set up in a way that allows users to easily and intuitively gain access to all the information. A database management maintains the integrity and security of stored data. It is also used for data recovery, in case of system failure.


What is Database (DB) ?

(1) Often abbreviated DB, a database is basically a collection of information organized in such a way that a computer program can quickly select desired pieces of data. You can think of a database as an electronic filing system.

Traditional databases are organized by fields, records, and files. A field is a single piece of information; a record is one complete set of fields; and a file is a collection of records. For example, a telephone book is analogous to a file. It contains a list of records, each of which consists of three fields: name, address, and telephone number.


An alternative concept in database design is known as Hypertext. In a Hypertext database, any object, whether it be a piece of text, a picture, or a film, can be linked to any other object. Hypertext databases are particularly useful for organizing large amounts of disparate information, but they are not designed for numerical analysis.

To access information from a database, you need a database management system (DBMS). This is a collection of programs that enables you to enter, organize, and select data in a database.

(2) Increasingly, the term database is used as shorthand for database management system. There are many different types of DBMSs, ranging from small systems that run on personal computers to huge systems that run on mainframes.

I used referenced web site for this post : webopedia.com

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Why Data Integration ? – The Importance of Data Integration

Almost every Chief Information Officer (CIO) has the goal of integrating their organization’s data. In fact the issue of data integration has risen all the way to the Chief Financial Officer
(CFO) and Chief Executive Officer (CEO) level of a corporation. A key question is why is data integration becoming so important to so many C-level executives? There are several key reasons driving
this desire:

  • Provide IT Portfolio Management
  • Reduce IT Redundancy
  • Prevent IT Applications Failure

Provide IT Portfolio Management

Over the years I have had the opportunity to perform dozens of data warehousing assessments. During these assessments I always ask the client how much they spend annually on data warehousing. The
majority of companies and government organizations cannot give a relatively good estimate on what they actually spend. In order to manage these and any other costly information technology (IT)
initiatives it is critical to measure each one of them. However, it is impossible to measure them when most companies do not understand them (see Figure 1: “How To Manage IT”). This is
where IT Portfolio Management enters the picture.




Figure 1: How To Manage IT


IT portfolio management refers to the formal process for managing IT assets. An IT asset is software, hardware, middleware, IT projects, internal staff, applications and external consulting. Like
every newer discipline, many companies that have started their IT portfolio management efforts have not done so correctly. I would like to list out some of the keys to building successful IT
portfolio management applications.

By properly managing their IT portfolio it allows the corporation to see which projects are proceeding well and which are lagging behind. In my experience, almost every large company has a great
deal of duplicate IT effort occurring (see later section on “Reduce IT Redundancy”). This happens because the meta data is not accessible. At my company we have a couple of large
clients whose primary goal is to remove these tremendous redundancies, which translates into tremendous initial and ongoing IT costs.
Reduce IT Redundancy

CIO is commonly defined as Chief Information Officer; however, there is another possible meaning to this acronym; Career Is Over. One of the chief reasons for this is that most IT departments are
“handcuffed” in needless IT redundancy that too few CIOs are willing and capable of fixing.

There are several CIO surveys that are conducted annually. These surveys ask “what are your top concerns for the upcoming year”. Regardless of the survey you look at “data
integration” will be high on the list. Now data integration has two facets to it. One is the integration of data across disparate systems for enterprise applications. The second is the
integration/removal of IT redundancies. Please understand that some IT redundancy is a good thing. For example, when there is a power outage and one of your data centers is non-operational you need
to have a backup of these systems/data. However, when I talk about IT redundancies I am addressing “needless” IT redundancy. Meaning, IT redundancy that only exists because of
insufficient management of our IT systems. I was working with a Midwestern insurance company that, over a four year span had initiated various decision support efforts. After this four year period
they took the time to map out the flow of data from their operational systems, to their data staging areas and finally to their data mart structures. What they discovered was Figure 2:
“Typical IT Architecture”.


Figure 2: Typical IT Architecture

What is enlightening about Figure 2 is that when I show this illustration during a client meeting or at a conference keynote address the typical response that I receive from the people is
“Where did you get a copy of our IT architecture?” If you work at a Global 2000 company or any large government entity, Figure 2 represents an overly simplified version of your IT
architecture. These poor architecture habits create a litany of problems including:

  • Redundant Applications/Processes/Data
  • Needless IT Rework
  • Redundant Hardware/Software

Redundant Applications/Processes/Data

It has been my experience working with large government agencies and Global 2000 companies that needlessly duplicate data is running rampant throughout our industry. In my experience the typical
large organization has between 3 – 4 fold needless data redundancy. Moreover, I can name multiple organizations that have literally hundreds of “independent” data mart
applications spread all over the company. Each one of these data marts is duplicating the extraction, transformation and load (ETL) that is typically done centrally in a data warehouse. This
greatly increases the number of support staff required to maintain the data warehousing system as these tasks are the largest and most costly data warehousing activities. Besides duplicating this
process, each data mart will also copy the data as well requiring further IT resources. It is easy to see why IT budgets are straining under the weight of all of this needless redundancy.

Needless IT Rework

During the requirements gathering portion of one of our meta data management initiatives I had an IT project manager discuss the challenges that he is facing in analyzing one of the
mission-critical legacy applications that will feed the data warehousing application that his team has been tasked to build. During our interview he stated, “This has to be the twentieth time
that our organization is analyzing this system to understand the business rules around the data.” This person’s story is an all too common one as almost all organizations reinvent the
IT wheel on every project. This situation occurs because usually separate teams will typically build each of the IT systems and since they don’t have a Managed Meta Data Environment (MME),
these teams do not leverage the other’s standards, processes, knowledge, and lessons learned. This results in a great deal of rework and reanalysis.

Redundant Hardware/Software

I have discussed a great deal about the redundant application and IT work that occurs in the industry. All of this redundancy also generates a great deal of needless hardware and software
redundancy. This situation forces the enterprise to retain skilled employees to support each of these technologies. In addition, a great deal of financial savings is lost, as standardization on
these tools doesn’t occur. Often a software, hardware, or tool contract can be negotiated to provide considerable discounts for enterprise licenses, which can be phased into. These economies
of scale can provide tremendous cost savings to the organization.

In addition, the hardware and software that is purchased is not used in an optimal fashion. For example, I have a client that has each one of their individual IT projects buy their own hardware. As
a result, they are infamous for having a bunch of servers running at 25% capacity.

From the software perspective the problem only gets worse. While analyzing a client of mine I had asked their IT project leaders what software vendors have you standardized on? They answered
“all of them!” This leads to the old joke “What is the most popular form of software on the market? Answer…Shelfware!” Shelfware is software that a company purchases
and winds up never using and it just sits on the shelf collecting dust.

Prevent IT Applications Failure

When a corporation looks to undertake a major IT initiative, like a customer relationship management (CRM), enterprise resource planning (ERP), data warehouse, or e-commerce solution their
likelihood of project failure is between 65% – 80%, depending on the study referenced. This is especially alarming when we consider that these same initiatives traditionally have executive
management support and cost many millions of dollars. For example, I have one large client that is looking to roll out a CRM system (e.g. Siebel, Oracle) and an ERP system (e.g. SAP, PeopleSoft)
globally in the next four years. Their initial project budget is over $125 million! In my opinion they have a 0% probability of delivering all of these systems on-time and on-budget. Consider this,
when was that last time that you’ve seen an ERP or CRM initiative being delivered on time or on budget?

When we examine the causes for these projects failure several themes become apparent. First, these projects did not address a definable and measurable business need. This is the number one reason
for project failure, data warehouse, CRM, MME, or otherwise. As IT professionals we must always be looking to solve business problems or capture business opportunities. Second, the projects that
fail have a very difficult time understanding their company’s existing IT environment and business rules. This includes custom applications, vendor applications, data elements, entities, data
flows, data heritage and data lineage.

MME’s Focus On Data Integration

Many of these Global 2000 companies and large government organizations are targeting MME technology to assist them in identifying and removing existing application and data redundancy. Moreover,
many companies are actively using their MME to identify redundant applications through analysis of the data. These same companies are starting IT application integration projects to merge these
overlapping systems and to ensure that future IT applications do not proliferate needless redundancy.

If your organization can reduce their applications, processes, data, software and hardware, lowers the likelihood for IT project failure and speeds up the IT development life-cycle, then clearly it
will greatly reduce a company’s IT expenditures. For example, I have a large banking client that asked my company to analyze their IT environment. During this analysis we discovered that they
have a tremendous amount of application and data redundancy. Moreover, I had figured out that they have over 700 unique applications. I then compared this client to a bank that is more than twice
there size; however, this larger bank has a world class MME and uses it to properly manage their systems. As a result, they have less than 250 unique applications. Clearly the bank with more than
700 applications has a great deal of needless redundancy as compared to a bank that is more than twice their size and has less than 250 applications. Interestingly enough the bank that has less
than 250 applications and has a world-class MME is also 14 times more profitable than the bank maintaining over 700 applications. It doesn’t seem like a very far stretch to see that the less
profitable bank would become much more profitable if they removed this redundancy.

I used referenced web site : tdan.com

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