Overview of Mongo DB 3.4 : New Features

Mongo DB has been wildly popular ever since its introduction for plenty of reasons. The biggest one was because it got rid of the Object-Relation Mapping to a large extent, which had been the source of trouble of programmers for years. Even today, it is the 5th most popular database. However, the graph of popularity of Mongo DB decreased somewhat over the years, due to introduction of more advanced and simplified NoSQL databases. This might change with the release of Mongo DB 3.4, released late last year. According to the company, they seek to attain a "digital transformation" with this release.

The clear message that the company gave with this release was that it is aiming to simplify the life of large enterprises that have depended upon Mongo DB for long now. Like Python, Mongo DB is aiming to evolve so that it alone suffices for tasks that earlier required multiple technologies. Since we have seen this formula succeeding more than once, we have to admit this is a very smart move from the company.

Graph support was the need of the hour in Mongo DB for quite some time now. Taking more than 3 years to become a reality, it is arguably the biggest addition in the new version. While it does not seem to pose any threat to established graph databases like Neo4J, the graph support is sure to simplify things for its existing users. This feature is sure to have large impact, as it will facilitate companies to explore hitherto doubted avenues like Deep Analytics, Internet of Things and Artificial Intelligence. This would be further aided by Atlas, Mongo DB's database cloud service released earlier last year.

Ecommerce websites working upon Mongo DB had toiled hard for long to provide decent search functionality to its customers. This ends with the faceted navigation feature, which uses filters to narrow down the query results. This ensures faster and more relevant search results. Also, a read-only mode was introduced that could expose the information of an application while preventing any modification. Another huge feature was the creation of Geo-distributed Mongo DB zones, which deals with the problem of data sovereignty and solves it by providing tagging via a higher abstraction of “zones”.

The release also had few things in store for the regular users. The new SQL interface is sure to greatly ease things for the users who have struggled for long to import their SQL code into Mongo. Mongo DB also introduced the ($switch) operator, which greatly simplifies complex branching, while making it more readable. Like the popular "switch" expression, it tests a number of cases, executing only the one that turns out to be true. Another addition was the ($reduce) operator, that could reduce the results of multiple arrays into a single expression.

Apart from this, there has been a whole array of other additions, whose actual importance would only be realized in the long run. This includes elastic clustering, tunable consistency and enhanced DBA.

Overall, this release has been quite impressive and an instant success. Mongo DB has made its intention very clear: It is here to stay and win. With this, other NoSQL providers like Redis and Cassandra as well as established SQL players like MySQL and Oracle will have to up their game.

7 Top Big Data Tools for Enterprise Developers

Big Data is now a critical technology utilized for leveraging data to enable better decision-making. Developers today have a wide variety of choice in picking a tool for their needs – open source or proprietary.

A through assessment of the existing data structure and the business requirements is essential for developers to identify the right tool. Predominant data formats, existing database types, available budget and the desired output analytics are some important factors. A few of the top tools that can be considered by developers are given below:

1.   MongoDB

This is an open source platform that that is heavily document oriented allowing for full-fledged indexing. Users can index any attribute and can also scale the data horizontally. Its a cross-platform tool that allows developers great control over final results

Read more about this at Mongo DB-Features         

2.   SAP HANA

A proprietary platform from tech giant SAP, HANA offers in-memory data storage that speeds up data processing and delivers more insightful data. It is flexible in handling all forms of data including spatial data, graph data and text data, at real time, making it a powerful though an expensive tool for holistic data analytics.

3.   Google Charts

A free open source platform from Google, this has a wide range of capabilities to handle data off a website. Primarily used for visualization, it can be plugged into a website with a simple JavaScript code. Developers can use it to create dashboards, carry out data management tasks, pull data from an external database, among other tasks

4.   Hadoop

Hadoop is the Big Data tool that everyone has been talking about. An open-source framework, it can handle, store and process massive amounts of data. Due to a distributed computing model, the data processing is fast and powerful. The tool is highly flexible and scalable, making it an ideal choice to leverage Big Data analytics for enormous data sets

Read more about this at Hadoop-Features

5.   Spark

Spark is an highly popular open-source data processing platform and is said to be the most active Apache open source project under Big Data. It is an extremely fast – 100x faster than Hadoop- and flexible platform, enabling analyses all forms of structured and unstructured data. Its advantages include use of multiple languages and accessibility to other databases. 

6.   Splice Machine

Splice Machine is a SQL-on-Hadoop database that analyses data in real time. The tool allows developers to utilize standard SQL on it, giving it flexibility and making it easier to use.

Splice Machine which is also ACID-compliant product is available on a freemium basis and has a list price annual license fee of $5,000 per node.

7.   Splunk

It’s a popular Advanced IT Search Tool used by many companies which derives information from machine data. To make it more lucid, Splunk has the ability to search, monitor and analyze through all machine generated data such as log data generated by applications, servers, and network devices across an organization.

This product further indexes structured as well as unstructured data and helps in diagnosing the problems, making it easy for administrators, business analysts and managers to detect requisite information.

Read more at Splunk-Features

Advantages of MongoDB over RDBMS

The Advantages of MongoDB over RDBMS are

• No schema migrations. Since MongoDB is schema-free, your code defines your schema.
• Number of fields, content and size of the document can be different from one document to another.
• Tuning using indexes

• Ease of scale-out: MongoDB is easy to scale by adding commodity hardware

Features of Mongo DB

Mongo DB is one of newest database introduced by 10gen .Mongo DB is an open source, on the whole a document oriented Database system and is a part of NoSQL family of database. Despite the fact that it’s not a relational database it has some of the imperative features of RDBMS and has got implausible speed. This DB is used in Projects like Unique Identification Authority of India (UIDAI) , MTV networks and many others

Instead of storing data in tables here rows are replaced by Documents (basic unit of Data in Mongo DB just like a ROW in RDMS) and Collections (collection is a group of documents.) which allow representing complex relationships. It can manage huge amount of data and can load data across a cluster. Mongo DB can perform some features which relational database cannot do.

Below are some of the Features of Mongo DB:

• Mongo DB supports Map reduce and Aggregation Tools
• Java Scripts are used instead of Procedures
• Mongo DB is a schema less Database
• Most Importantly Mongo DB supports secondary indexes and geospatial indexes.
• Simple to Administer the Mongo DB in cases of failures
• Mongo DB designed to provide High Performance
• MongoDB stores files of any size without complicating your stack.

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Please go through our latest post TOP 6 BIG DATA TRENDS IN THE NEAR FUTURE