Task 1: 160 words with references critical reply to topic

MongoDB in Action, Second Edition: Covers MongoDB version 3.0 is the title of the eBook that I summarized in this discussion

The first Chapter is the introductory and a brief history of MongoDB is brief but worth recounting, how it was born out of a much more ambitious project. In mid-2007, a startup in New York City called 10gen began work on a platform-as-a-service (PaaS), composed of an application server and a database, that would host web applications and scale them as needed.

Like Google's App Engine, 10gen's platform was designed to handle the scaling and management of hardware and software infrastructure automatically, freeing developers to focus solely on their application code. 10gen ultimately discovered that most developers didn't feel comfortable giving up so much control over their technology stacks, but users did want 10gen's new database technology. This led 10gen to concentrate its efforts solely on the database that became MongoDB.

The chapter also define MongoDB as a database management system designed to rapidly develop web applications and internet infrastructure.
And the biggest reason developers use MongoDB isn't because of its scaling strategy, but because of its intuitive data model. MongoDB stores its information in documents rather than rows. What's a document? Here's an example:
{
_id: 10,
username: 'peter',
email: 'pbbakkum'
}

Then second Chapter tells how MongoDB's shell, how it provides all the features that you'd expect of such a tool; it allows you to examine and manipulate data and administer the database server itself. MongoDB's shell differs from others, however, in its query language. Instead of employing a standardized query language such as SQL, you interact with the server using the JavaScript programming language and a simple API. This means that you can write JavaScript scripts in the shell that interact with a MongoDB database. If you're not familiar with JavaScript, rest assured that only a superficial knowledge of the language is necessary to take advantage of the shell, and all examples in this chapter will be explained thoroughly

I have heard before a portion of the language used in these two Chapter before and in general occasions, however as it is my first time to Learn about the MongoDB and NoSQL database; it is unfamiliar to me to peruse about introducing MongoDB and some topic the chapter two discussed.

Example:

• Using CRUD operations in the MongoDB shell

• Building indexes and using explain()

• Understanding basic administration

• Getting help.

I will like to take in more about NoSQL innovations since I found from this lesson that they're adding another new measurement to data administration, and their data adaptability and versatility is convenient when dealing with a high volume of data.

I would prescribe this eBook as an asset for this class on the grounds that other than the way that it will acquaint understudies with the MongoDB and NoSQL database display; it will likewise furnish them with intensive data about the MongoDB database and its components.

Task 2: 160 words with references critical reply to topic

The technology that I chose was Cassandra which is a column-oriented NOSql database. Column oriented systems store data in columns rather than rows. The structure of this type of model aids in decreasing disk access as compared to relational models. Cassandra was developed with Java by Apache Software in 2008.

It uses key-value stores and column-stores to create a dynamic schema that is highly available, persistent, highly scalable and tolerant to partitions. Cassandra is found in banking and finance applications, social networking and data analysis applications and is used by companies such as Twitter, eBay and Adobe.

Five Resources I would recommend:

Cassandra Documentation.

Vivek, M. (2014) Beginning Apache Cassandra Development. Apress Publishing. E-book.

Datastax Academy. A Brief Introduction to Apache Cassandra.

Germain, Jack (2017). Get Started with Apache Cassandra Database in the Cloud. Medium Corporation.

Saxena, S. (2015). Real-time Analytics with Storm and Cassandra. Packt Publishing.

Task 3: 160 words with references critical reply to topic

Couchbase is a software system created for NoSQL document-oriented database. As the name would suggest, couchbase is perfect for storing full documents that are not schema bound. In comparison to traditional database systems, couchbase's data modeling is quite different. While in traditional relational database systems, schemas have to be created and the data is bound to the schema, relationships are created as constraints between tables in order to allow join statements when querying data from different tables. On the other hand, quite different from the traditional data modeling technic is Couchbase which allows more fluidity and easier process when storing data.

According to Couchbase (n.d.), "a document-oriented database contains documents, which are records that describe the "schema" of the data in the document, as well as the actual data. Documents can be as complex as you choose; you can use nested data to provide additional sub-categories of information about your object. You can also use one or more documents to represent a real-world object. The following example compares a conventional table with document-based objects."

In a nutshell, a document, which is on the list as a type of unstructured data, can be easily stored and retrieved in a Couchbase database. Another important additional factor that must be added is the ability to store JSON documents. JSON means JavaScript Object Notation. As summarized, "JSON documents enable you to benefit from all new Couchbase features, such as indexing and querying; they also to provide a logical structure for more complex data and enable you to provide logical connections between different records" (Couchbase, n.d).

Task 4: 160 words with references critical reply to topic

Couchbase Server supports a range of high availability and disaster recovery strategies depending on an organization's needs, standard operating procedures, and budget. Most recovery strategies rely on a multi-pronged approach of maximizing availability, increasing redundancy within and across data centers, and performing regular backups.

Availability: Couchbase Server delivers key high availability features such as zero downtime administration and maintenance, built-in data redundancy, and automatic failover.

Factors that increase system uptime and availability include: Number of replicas; Number of racks or availability zones; and, Number of clusters.

Rack zone awareness helps protect against multi-node failure events by separating active data and its replicas across groups which can then be mapped such that they occupy different racks, zones, or VM hosts. This is still within the context of a single Couchbase Server cluster, and the entire active data set is still evenly distributed across all nodes or groups.

Data Recovery: Recovery is performed after a server node is failed over and before rebalance operations. Either Delta or Full recovery can be specified.

The process for re-adding a server involves:

1. Fail over the node using either the graceful or hard failover method. Graceful failover is recommended.

2. After the node is failed over, specify whether to use delta or full recovery.

3. Perform maintenance operations on the node.

4. Rebalance the cluster. During the rebalance, the same server node is added back to the cluster using the specified recovery method.

With "full recovery mode", the data files are removed from the failed over server node and then, during rebalance, the node is populated with new data files (active and replica vBuckets). Full recovery removes the node's data prior to the rebalance and delta recovery schedules the node's existing data to be re-used.

Full recovery characteristics:

• Data files are removed from the server node.

• Indexes must be rebuilt on the server node that is being re-added.

• A working set of documents is restored for the vBuckets that are being moved.

• Use when the data size is smaller than the bucket quotas. In this case, moving data over the network and onto disk (full recovery) may be faster than warming up data files (delta recovery).