The Rise and Rule of Cassandra

The world of database is dark and full of terrors. We spent decades working happily upon relational databases, until realizing one day that relations are not enough. This paved way for NoSQL databases, or alternatively, any database that do not use tables. These new databases were shiny and cool, but could not match the massive power that Oracle and SQLServer wielded. This changed with the arrival of Cassandra.

When two scientists in Facebook in 2008 decided to build a database different from other NoSQL databases, they only wanted to have their own database. But little did they know of the impact they would have on the industry.

Cassandra was introduced with a sole objective: to solve the crisis of scalability. And it managed that beautifully. In fact, it has consistently been cited as the only NoSQL database that can take as many machines as could be added to it, without breaking a sweat. In 2012, a group of researchers from University of Toronto declared that as far as scalability goes, there is no match for Cassandra. But this has not been the only reason behind the vast popularity of Cassandra.

Cassandra prides itself as having no "single-point of failure", which implies that there is no single component whose failure can shut down the whole database. In a world where transactions are carried out every second, this feature of robustness is of vital importance. Many talk about decentralization, but nobody does it better than Cassandra.

But having a couple of advantages does not make you better, not in a world of ruthless competition. MongoDB, Redis and others would not be amused by a database who would take away their market with a couple of features. This is why Cassandra tried to achieve perfection. Its fault-tolerant and decentralized nature makes it extremely durable, thus being the perfect choice for those organizations who cannot afford to lose even an ounce of data. The throughput increase is linear with respect to growth in size, which makes it extremely desirable for databases which are growing constantly. After providing all these features, it is not a surprise that Cassandra is trusted by some of the biggest names in the industry, including CERN, eBay, Instagram, GoDaddy, Netflix and Reddit. In fact, Apple's deployment of Cassandra stores a whopping 10 PB of data across 75000 (and growing) nodes. Cassandra can give lessons on scalability to every other non-relational database.

That said, Cassandra is still not the most popular database around; it is not even the most popular NoSQL database right now. There are few inherent flaws that Cassandra needs to fix, including simplified deployment, simplified operational maintenance and an improved web interface, among other things. There is still the issue of low predictability of performance (which was partially reduced, but never solved) and the complexity of APIs in the client libraries which is nothing but unnecessary. But Cassandra is growing strong, and the time is not far when it will be a common name among all DB designers.

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.

How and Why To Bridge between SQL and NoSQL

SQL have for long now been the synonym of "database" for us. For any sort of data management, SQL had been our instinctive choice. However, the past decade saw the emergence of NoSQL which gave rise to a fierce competition of preferences.

  

What haunts the mind of every aspiring database developer today is the question of choice: To SQL or NoSQL. We want to keep in touch with the latest trends in the technology, but don't want the established technologies to slip away either. However, the most basic point that most people seem to miss is this: SQL and NoSQL are not competitors, and most certainly not antonyms of each other.

SQL or Structured Query Language is the most standard concept of database management systems today. SQL considers data to be stored in the form of tables called Relations, that consist of tuples and attributes. While this concept had been a hugely successful improvement over the data-storage systems present at that time, like flat files, things have changed today.

NoSQL came as a breath of fresh air in an industry that was rapidly changing. The world is going digital, and the digital world is messy. We can never predict the volume, variety or velocity of incoming data. The data, apart from being unpredictable, is also unstructured. Since relational databases are not inherently adept to handle them, something else was required. At the same time, distributed computing is all the rage today, because most businesses are moving towards the cloud. The expansion of relational databases cannot keep up with the pace; thus, NoSQL entered into the scene.

Why to migrate from SQL to NoSQL

Strictly speaking, NoSQL aims to do what SQL cannot. It is not based on relations and it may sometimes even fail to follow the ACID properties! But unlike what you have been taught, ACID properties, though really useful, are not the ultimate necessity. The ultimate necessity is fault tolerance, and NoSQL manages to achieve that anyway.

NoSQL cannot be defined in a single line, as there is no single definition. While all SQL-based databases follow strict guidelines that adhere to SQL-standards, NoSQL gives the databases a free rein. With so many lacks of standards, one might wonder: Are the reasons enough to migrate to NoSQL?

Yes, because we have only touched the crux of the importance of NoSQL in modern world. The two biggest reasons why NoSQL trumps over SQL are agility and scalability.

With the rapid changes that occur daily in the industry, being agile is the only way to survive. However, Relational databases couldn't ever hope to achieve that, with their rigid schemas and complex development. The aforementioned rapid changes are also met by growing size, which require rapid scalability. However, scalability was one aspect that was blatantly ignored in SQL (as it was made in a time when web and internet were non-existent). To cope up with these issues, NoSQL seems like our best bet.

Why to Bridge SQL and NoSQL

"Now that we know how NoSQL differs from SQL, the question arises: Why to bridge them? Why not adopt NoSQL altogether?   "

Simply, because NoSQL doesn't have the same penetration as SQL. A huge number of companies have their entire existing architecture based on relational databases, which would be quite a headache to change. But that doesn't mean that one has to remain stuck with SQL forever. The best option in such scenarios is to bridge the existing SQL framework with a NoSQL database. The benefit? To put it simple, it will bring out "the best of both worlds".

As far the "bridging" goes, there is no one, simple way to do that. The easiest way would be to use third-party drivers like easysoft, which provides ODBC-like bridging capabilities. However, as it comes from a third-party vendor, it might have its own security and licensing issues.

An alternative approach would be to develop languages that could extend SQL functionality to NoSQL databases. One example would be the N1QL, introduced by Couchbase Server, which extends SQL to JSON.

The ways to bridge the gap between these two technologies may differ and evolve; but we can all agree that co-existence of the two is best for the progress of industry.

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Read more on NO SQL- NOT ONLY SQL here - WhatisNoSQL

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

“ VoltDB ” - NewSQL DBMS For High Velocity Applications

VoltDB is a high performance scalable RDBMS and NewSQL Database primarily for Big Data, high velocity OLTP and Real-time analytics designed by Michael Stonebraker, Sam Madden, and Daniel Abadi.

It was one of the NewSQL databases first introduced as open source product in 2010.Demand for high scale and in memory databases(which means it depends mainly on main memory for storage) in the market has shaped great aperture for VoltDB.