A backup is a copy of the data vital for comprehensive recovery in case of software or hardware failures. There are different backup types including normal, copy, incremental and differential as well as various strategies suitable for certain scenarios. In case of hosting industry routine approach is about full automation of backup processes.
As an alternative to MongoDB, Cassandra is highly available and uses a column-orientated approach. It is a database that was developed by Facebook, particularly with the goal of enabling inbox searches. It is utilised by both Facebook and Twitter, and it is also very popular wherever a degree of logging is required, including in financial services.
The Cassandra database makes use of a replication model that is in a Dynamo-style and as a result does not have a single point of failure. It uses a SQL-like language for queries called Cassandra Query Language or CQL. It can be used alongside Linux, Unix and Windows and many different languages.
Top features of Cassandra
- Cassandra scales linearly and offers very quick response times
- It supports the ACID principle
- Cassandra supports Hadoop and MapReduce
- It offers solid flexibility when it comes to distributing data
- Cassandra is very scalable and uses peer to peer architectural principles
Pros and Cons of Cassandra Database
Developers like Cassandra because it is very scalable and because there is no single point of failure in the database. Its multi-DC replication capabilities is also attractive, while developers also like the Cassandra integrates with other applications based on JVM.
However, Cassandra has limited support for aggregations and developers find that it does not offer predictable performs. It also lacks support for ad-hoc queries.
DB2 is a Relational Database Management System (RDBMS). IBM introduced it in 1983, to run on its Multiple Virtual Storage (MVS) mainframe platform.
Its name refers to the change from the hierarchical database model (incredibly popular at the time) to the new relational model. While DB2 was originally created to function on IBM mainframe platforms only, it was later ported to the following operating systems: Windows, UNIX, and Linux.
DB2 is a key component in the IBM information management portfolio: this database engine offers a wealth of features and high performance. And it has the capacity to handle data in large amounts and serve many users at the same time.
How Does DB2 Work?
A relational database allows for a declarative data model, and makes it accessible through queries. IBM built the now standard Structured Query Language (SQL) for that purpose.
SQL is a simple language similar to English. With this, users can create tables, and manipulate the internal data. Numerous table entries (known as records) may be inserted, deleted, and updated by various users, all at the same time, using specific SQL commands.
In the ‘90s, a form of DB2 known as LUV (Linux, Unix, Windows) was released. This marked the database’s porting to smaller-type computing platforms. DB2 runs on a wide variety of platforms, covering mainframes and large distributed platforms to PCs on a smaller scale.
DB2 Express-C is a free version with similarities to the open-source MySql, though it’s not open-source. It’s available to the developer community.
DB2 may be administered via a GUI or a command-line prompt. Its highly advanced security and features have contributed to it becoming a popular database in today’s software industry.
There are several processes core to the way computers and software work, and managing databases is one of these core processes. Every company of size uses a database, and very large companies use incredibly large, complicated databases. Now, to a large degree, database management can be facilitated by a DBMS – or a database management solution. Nonetheless, managing databases still require human involvement. Sometimes it just requires a system administrator to complete a few tasks, but at other time database management requires the involvement of a specialist.
Database administrators are core to running a database
In short, database administrators are the people responsible for managing databases. Every database administrator ( DBA ) can be involved in a number of activities which manipulate databases. This includes security control, testing and running databases and archiving when necessary – all to do with the database environment.
It’s a key role because almost all the information companies have are now stored in a database. Of course, there are different ways to store data in database, so as a result there are different approaches to data handling and different types of DBAs. Vendors that supply database software also supply certification for DBAs, and provide courses so that DBAs can educated themselves.
What are typical DBA tasks?
Now, every DBA will have a different set of tasks because it really depends on their individual job roles and indeed the way in which a company decides to apply IT policies. It also depends on the way the DBMS they make use of work. However, it is key for DBAs to be able to think logically, and to be excellent at problem solving. Teamwork is important too because large databases require a lot of DBAs and assistants, while DBAs also need to be able to work well with colleagues who do not have any technology training.
Some of the work that a DBA does is similar to the work that system administrators do, supplementing their duties. Think the configuration of hardware and software alongside the installation of new versions of a DBMS. These are all key points to take care of because a database will work well only if the DBMS is up to date. Updates are particularly key because it ensure that the latest security patches and bug fixes are installed – not doing so can cause issues down the line.
Analysing data and managing security
DBAs are also responsible for managing the security of a database as well as data analysis. DBAs must control the security of the DBMS by ensuring only the right people have credentials, removing and adding users and by managing the quotes for a database. Regularly checking for security issues are also core duties. DBAs also need to analyse the content of a database to ensure that it is efficiently stored, and to check that the use of indexes are optimal – or just to enable the execution of “Parallel Queries” if necessary.
As much as DBAs are mainly responsible for the logical and the physical aspects of managing a database, DBAs will also get involved in the designing of databases. For example, DBAs can recommend to developers how a DBMS should be configured in order to avoid future database performance issues. DBAs can also contribute to data modelling, so that the layout of the database is efficient and optimal. Finally, DBAs also help to analyse and set up new databases.
A database management system (DBMS) is created for defining, manipulating, retrieving, and managing data within a database. Typically, a DBMS will manipulate the data, data format, field names, file structure, and record structure. It defines rules, too, for the validation and manipulation of said data.
These systems are set up on certain data handling concepts, as database administration practices continue to evolve. The earliest databases were only capable of handling single pieces of data that had been formatted specially.
But the more advanced systems available today are capable of handling less-formatted data of various types, and of connecting them in more sophisticated ways.
How a Database Management System Works
Database management system models have evolved significantly over the years. And that’s crucial in understanding the way in which different database management systems work.
The earliest database management system types primarily comprised hierarchy and network models:
- in the hierarchy model, each component or node functions as part of a child/parent relationship with another component or node
- In the network model, an individual component may have numerous relationships: in essence, a single node can “multicast” connections
But these models have been overtaken by a relational database over time.
A relational database model has individual components with attributes linked to their respective identities via a database table design. Within an individual database table, the rows and columns include the identities and attributes in such a way that allows for the use of standard structured query language (SQL) in gathering various types of information on these models.
However, a newer concept — NoSQL — has gained recognition since. Those with expertise in the subject claim that the best way to understand NoSQL is to rephrase it so that it means “not only SQL” instead. More specifically, NoSQL can be used broadly to describe systems that are beyond the standard SQL and relational database models.
It’s also worth noting that NoSQL is a far more abstract term than the conventional relational database. NoSQL can be considered “not relational”, in the traditional sense at least. One well-known NoSQL database management system type is the object-oriented database model.
Instead of comprising relational tables, database systems utilize object designs to work with the attributes and identities covered above.
Amongst the considerations for NoSQL database design are the degree of normalization, or data structuring, that takes place, and the way in which this is handled by the database system. Engineers need to explore tools for consistency and data resolution throughout the system. This is important to achieve uniformity, and for fixing a number of correlation problems.
Other database management system model types include the graph database model, in which graph models are utilized for semantic queries, as well as the entity-relational model. Both serve as additional alternatives to standard relational database design.
Newer database management system types may be utilized where a data center has a broad disparity of data that is formatted differently, or relatively unformatted (“raw”), to handle. In these cases, records will not be normalized as per the conventional method.
This, and other kinds of advances, have made the world of database management systems more complicated. And they have increased the value that experienced database engineers and administrators for modern systems have.
A DBMS interface enables a user to enter queries into a database without the need for the query language. A DBMS interface may be a web client, a smartphone app, or a local client running on a desktop computer.
How Does a DBMS Interface Work?
A DBMS is designed for storing data and responding to queries via a query language (e.g. SQL). A DBMS interface provides users with a way to query data without using the query language, as this can be complex and confusing.
The standard process for this involves creating a form presenting the query types that users can make. Due to MySQL’s popularity, web-based forms have become particularly common, though the traditional approach has been local desktop apps.
However, mobile applications may be made too. These interfaces offer users a simpler, more friendly way to access data instead of using the command line.
Essentially, Elasticsearch is a search engine which is enterprise-grade yet open-source, while also being distributed in nature and easy to scale. The database has a very extensive API and delivers searches which are very fast, key for data discovery apps. It is a very popular search engine for enterprise use and organizations including the Guardian, Wikipedia and GitHub make use of Elasticsearch as their search engine.
Top features of Elasticsearch:
- Elasticsearch is incredibly scalable and can accommodate petabytes of date in any combination of structured or unstructured data
- Multi-tenancy setups are supported
- Search is fully scalable and yet also nearly real-time in nature
Developers enjoy the fact that Elasticsearch uses a restful API and that it is schema-free, yet document-orientated. Of course, the powerful full-text search abilities are a major benefit too. However Elasticsearch does not include support for multi-language and in rare cases throws up a Asplit-brainn problem.
Oracle is one of the most popular databases, and IBM answered Oracle’s 11g edition with IBM DB2, available in both Windows and Linux editions. Yes, DB2 can run on your choice of Linux, Unix or Windows mainframes and is indeed an ideal choice if you operate in an IBM host environment.
The UDB version for IBM DB2 is version 10.5, which includes BLU acceleration – the code name for this setup is “Kepler”. DB2 has had several releases, the latest edition is DB2 Express-C but this edition cannot be used in environments that require high availability. Think replication, shared disk clustering or active-passive setups.
Top features of IBM DB2 v 11.1
- The latest edition extends the application of native encryption
- It also adds enhancements to the use of environments
- 1 includes a more simplified installation and deployment process
- Native encryption is also included in version 11.1
The included Task Scheduler allows database admins to run multiple jobs when the database starts up. Importantly, DB2 supports both NoSQL and SQL database models. It can be hosted in the cloud, on a physical service or as a hybrid solution. Exit codes and error codes can trigger jobs. DB2 is an excellent database but the costs can be prohibitive for smaller organizations, while the basic support package is valid for only three years.
The term “index” has a number of definitions in computing: it may refer to a website’s index directory listing, a search engine’s index, or a database index.
And this variation means that “index” can be a complex term for newcomers to grasp. But we’re here to help with this simple guide.
What does index actually mean?
Index is a universal term applied to information organized as a structured list. You might find this in the back of a nonfiction book (e.g. an encyclopedia): the aim is to help you find the details you’re looking for quickly and easily.
An index serves a similar role in computing and IT: bringing users a summary of items found within a data container, for easy reference.
But as there are various data container types, “index” can be used in the following contexts:
A website index serves two functions: it’s a directory of the folders and files under a specific domain when no website has been made for it, or it’s a file for a website’s first page.
If you have registered your domain but still have no website for it yet, you’ll find a white page titled “Index of” when you open it in a browser. This lists the domain’s default main directory.
Uploading the contents of your future website to the directory (as files and folders), without making an index.html file, will mean that you keep seeing the same page. The only difference will be that every file and directory uploaded to the domain will be shown online.
The page will contain each directory or file’s name, as well as other details (relating to the upload date, size, a description). The index provides an overview of a website’s contents, even if the site isn’t ready to welcome visitors yet.
If you want to switch the /index of your new site with the page you want to display online upon typing your domain into the browser, you’ll need an index.html file. This is your website’s “face”: it features all the information presented on the homepage.
Its name is taken from the fact that the file provides visitors with an overview of the website’s reason for existing and contents, offering links for navigation (navigation menu) and visual references (banners) to the site’s internal pages.
Due to the dynamic sites server technology, using server-side scripts (such as Perl, PHP, ASP, etc.) and the latest HTTP servers (e.g. Apache web server), an index file may appear in numerous forms. These include index.shtml, index.php, default.asp, and more.
The index file’s actual extension depends on the server-side script that serves as the backbone of the website. For PHP-based sites, the file will be titled index.php: this allows for dynamic content to be utilized on the index page.
Databases have indexes too, as they’re responsible for the storage and management of dynamic sites’ content.
A database index represents a data structure intended to boost a database table’s operational speed. You can build your index with database columns, in which you need to specify names for all key fields that define the structure of the table.
Certain advanced databases (such as MySQL, PostgreSQL) enable more advanced indexing according to specific predefined conditions.
What does a database index look like?
CREATE INDEX part_of_name ON customer (name(10));
Search engine index
Indexing is responsible for your website’s presence on a search engine result page. The contents will be scanned by a search engine bot when it detects the site, and these are entered into the database of websites that have been scanned already (the index).
Search engine (or web) indexing’s main purpose is to ensure that users can find content (text, audio, video, pictures) they’re looking for with the utmost accuracy. This revolves around keywords: when you enter certain ones into a search engine, related results will be returned within seconds.
This is because the search bot has scanned the documents already and organized them into an ordered list, based on keyword relevance. As no index has been made in advance, the search engine will need to perform a live scan of the pages to determine their rank — a process that could take hours.
As much as SQL is a common way to build a database, NoSQL database is popular for a variety of reasons. MongoDB keeps some of the important SQL characteristics such as queries and indexes. MongoDB links with several languages such as Scala, Clojure and Java – all in all, MongoDB can cope with more languages than its closes NoSQL competitor, Cassandra.
Developers that execute dynamic queries will particularly like MongoDB, which is also great for defining indexes. It uses a very flexible database structure that can be extended and modified more easily that the typical SQL database – for example, a database schema can be written in MongoDB without any database downtime.
You can use MongoDB alongside OS X, Linux and Windows but the size of MongoDB databases are limited to 2.5GB for 32-bit operating systems. It uses MMAPv1 and WiredTiger.
Top features of MongoDB
- MongoDB is known for its top performance
- You can active auto-sharding with MongoDB
- MongoDB can be run across multiple servers
- Full support for replication using Master-Slave
- jSON style documents is the way data is stored
- You can index your fields of choice in your documents
- Data is placed in shards to load balancing is built-in
- Searching using expressions is fully supported
- You can easily fix problems in MongoDB when it fails
Pros and Cons of MongoDB
Admins will like the fact that MongoDB is so easy to set up while the parent company, MongoDB Inc., can offer professional support to enterprise clients. It’s a high-speed database which supports ad-hoc queries, and it is also both schema-less and horizontally scalable.
Unfortunately, with MongoDB you cannot perform joins and you will find that it takes a lot of storage space. Document nesting is restricted, and you can find that MongoDB uses a lot of RAM.