Database Dictionary: 20+ database words every developer should know- Worknrby

Each one of us is a slave to technology and the never stopping technological revolution. It is amazing how we have been looking forward to technology like it is going to save earth from global warming or something.

Well whether it is good or bad, there is one thing for sure that we are dependent upon the technology and technology is derived from the data. The data is developed by programmers in the form of applications and websites, which makes our lives less messy and easier.

With 23 million software developers all over the world, software services are touching heights every day. But there are some important aspects which have been ignored and even the best of developers or programmers lack them. The knowledge of a programmer must be up to date because he has the key to the future.

Hence, feed your knowledge by paying attention to this list of 20+ important database terms:

1. Atomicity, Consistency, Isolation, Durability aka ACID:

ACID is used to define the model properties of the database transactions. This is known to be used for the SQL database.

2. Aggregate:

A group of domain objects which can be treated as a single unit. This is said to be ideal data storage on large distributed systems.

3. Apache Cassandra:

This is an open-source distributed database system which is commonly used for storing and managing big data across servers and can become a read-intensive database for large Business Intelligence Systems.

4. Apache Spark:

Yet again an open-source parallel processing framework that takes care of large-scale data analytics applications, real-time analytics, and processing workloads.

5. Basic Availability, Soft State, and Eventual Consistency aka BASE:

Commonly used for model properties of database transactions and especially for NoSQL databases which needs the unstructured data to be managed.

6. B-Tree:

In this form of data structure, all the terminal nodes are present at the same distance from the base and all the non-terminal nodes are present between n and 2n subtrees or pointers. This is used to optimize the systems that read and write large blocks of data.

7. Cloud-native database:

This is the kind of database which is built and runs on the cloud computing delivery.

8. Complex Event Processing:

The process is done to collect data from multiple streams to perform analysis and plan.

9. Database Clustering:

This is the process of connecting two or more servers and instances to a database to gain the advantage of fault tolerance, parallel processing, and load balancing.

10. Data lineage:

This is all about the information on where the data came from, where it moves or how it changes. This can also be used for address validation and debugging issues in databases.

11. Data mining:

Through this process, one discover patterns in large sets of data and can easily transform that information into an understandable format.

12. Database Management System aka DBMS:

A collection of software which facilitates management between the end-user and the data.

13. Data Warehouse:

A term used for the collection of computers which work together and appears to function as a single unit. Access to a central database, multiple copies of database or database on each computer is a must on unit falling under warehouse.

14. Dynamo DB:

This NoSQL database service from AWS with low latency which facilitates the storage and retrieving of data and can easily serve large amounts of the database.

15. Elastic Search:

This is a JAVA based search engine which is built under Apache Lucene. This can search and list files at near real-time. This is also used in indexing JSON documents.

16. Fault tolerance:

The term is used for defining the system’s ability to respond to hardware or software failure without disrupting other ongoing systems.

17. Graph Store:

Commonly comes handy when handling entities which have a huge number of relationships, like social graphs, tag systems or any link-rich domain. This can also be used for routing services.

18. Hadoop:

This is a derivation obtained from Apache Software which is developed for high scalability, data-intensive, and distributed computing. Its main function is to process large sets of data efficiently.

19. In-memory:

This term is used to describe data management tools which load data into RAM or flash memory instead of hard-disk.

20. Java Persistence API aka JPA:

Sort of a Java condition which helps in accessing, managing and persisting data between Java objects/classes and relational databases.

21. Key-value store:

This is used to store data in simple key-value pairs. Commonly used for storing lots of small, continuous and unstable reads and writes.

22. Lightning memory-mapped database aka LMDB:

This is a copy-on-write B-tree database but is fully transactional, ACID acquiescent, small in size and takes help of MVCC.

23. Multi-version concurrency control aka MVCC:

A popular way of handling situations where machines concurrently read and write to a database.

24. NoSQL:

This class of database system incorporates other means of querying outside the old SQL and usually does not use standard relational structures.

25. Structure Query Language:

This is a type of programming language which helps in managing and manipulating data and is also used in relational databases.

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