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Step-by-Step Guide to Creating an AWS RDS Database Instance

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 Amazon Relational Database Service (AWS RDS) makes it easy to set up, operate, and scale a relational database in the cloud. Instead of managing servers, patching OS, and handling backups manually, AWS RDS takes care of the heavy lifting so you can focus on building applications and data pipelines. In this blog, we’ll walk through how to create an AWS RDS instance , key configuration choices, and best practices you should follow in real-world projects. What is AWS RDS? AWS RDS is a managed database service that supports popular relational engines such as: Amazon Aurora (MySQL / PostgreSQL compatible) MySQL PostgreSQL MariaDB Oracle SQL Server With RDS, AWS manages: Database provisioning Automated backups Software patching High availability (Multi-AZ) Monitoring and scaling Prerequisites Before creating an RDS instance, make sure you have: An active AWS account Proper IAM permissions (RDS, EC2, VPC) A basic understanding of: ...
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6 Advantages of Columnar Databases over Traditional RDBMS

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In traditional RDBMS, when a data source is accessed by multi users at single time, then database will go into deadlock state.

One of the advantages of a columnar model is that if two or more users want to use a different subset of columns, they do not have to lock out each other.

(Superior benefits for NoSQL Jobs)
        (Superior benefits for NoSQL Jobs)
This design is made easier because of a disk storage method known as RAID (redundant array of independent disks, originally redundant array of inexpensive disks), which combines multiple disk drives into a logical unit. Data is stored in several patterns called levels that have different amounts of redundancy. The idea of the redundancy is that when one drive fails, the other drives can take over. When a replacement disk drive in put in the array, the data is replicated from the other disks in the array and the system is restored.

The following are the various levels of RAID:

RAID 0 (block-level striping without parity or mirroring) has no (or zero) redundancy. It provides improved performance and additional storage but no fault tolerance. It is a starting point for discussion.

In RAID 1 (mirroring without parity or striping) data is written identically to two drives, thereby producing a mirrored set; the read request is serviced by either of the two drives containing the requested data, whichever one involves the least seek time plus rotational latency.

In RAID 10 (mirroring and striping) data is written in stripes across primary disks that have been mirrored to the secondary disks.

In RAID 2 (bit-level striping with dedicated Hamming-code parity) all disk spindle rotation is synchronized, and data is striped such that each sequential bit is on a different drive. Hamming-code parity is calculated across corresponding bits and stored on at least one parity drive. This theoretical RAID level is not used in practice.

In RAID 3 (byte-level striping with dedicated parity) all disk spindle rotation is synchronized, and data is striped so each sequential byte is on a different drive.

RAID 4 (block-level striping with dedicated parity) is equivalent to RAID 5 except that all parity data is stored on a single drive. In this arrangement, files may be distributed between multiple drives.

RAID 5, RAID 6, and other patterns exist; many of them are marketing terms more than technology. The goal is to provide fault tolerance of drive failures, up to n disk drive failures or removals from the array. This makes larger RAID arrays practical, especially for high-availability systems. While this is nice for database people, we get more benefit from parallelism for queries.

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