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

TOP 6 BIG DATA TRENDS IN THE NEAR FUTURE

Big Data is a buzz word we all are familiar with now. But behind the buzz, there have been rapid developments which has changed business models and brought big data to the strategic foreground. 2016 has been a pretty eventful year for BigData and the future indicates promising. Let’s take a look at the top trends that will follow in the upcoming year:

1) CUSTOMER DIGITAL ASSISTANTS

One of the surprising trends we saw this year was the growing interests in Digital Assistants. The logic had been simple: If we could gather and process data to generate meaningful results, why do we need humans to convey them to customers?  The most devoted users are perhaps the gamers, who have fully accepted this technology in the likes of XBox One and Sony PS4. With advanced NLP and audio-recognition, mobile digital assistants like Cortana, Siri and Google Now are almost the must-haves today, and all signs indicate that digital assistants will play an even more important role in the upcoming year.

2) SIMPLER DATA ANALYSIS

Like many past years, data saw an unprecedented growth in volume and veracity. With this rate, the current data analysis techniques would soon be obsolete. However, the upcoming trend in 2017 might focus on simplifying the data analysis process, to an extent where even non-coders could easily analyze huge datasets. Giants like Microsoft and Salesforce are working upon it, while complementary tools to SQL like Spark will continue to make storage and access of data easier.

3) MACHINE LEARNING IS THE FUTURE

Not far ago, machine learning was considered purely a research field. For the benefit of all, this perception soon changed and today, machine learning has dedicated departments in numerous companies. For business purposes, the idea of machine learning is to serve as an extension to predictive analytics, thereby minimizing the work and maximizing the profits. This trend will continue to be one of the top business strategies in the future.

4) DATA-AS-A-SERVICE

Although it took a long, long time; but today, companies realize the importance of their data. This, in turn, is giving rise to an entire new business model of data-as-a-service (DaaS). With IBm's acquisition of The Weather Channel, more tech giants might realize that their data can, in fact, be converted into a profitable service.

5) THE TRANSITION OF BIG DATA to “ACTIONABLE DATA”

Big data will continue to face its existing challenges- the most prominent being the required manpower to handle the ever-increasing volume. Privacy concerns will also continue to haunt the general perception regarding the increased use of Big Data. Amidst all that is the new question: Why to worry about big data when most companies only use a fraction of it anyway? The answer to this question is giving rise to a new trend of "actionable data", data that is relevant to the business. It is entirely possible that big data may be replaced by actionable data in upcoming years.

6) INTERNET-OF-THINGS

One of the most revolutionary digital concepts of this century, IoT still fascinates masses, even if its application continues to face hurdles. But the rise and success of IoT is inevitable. With the rapid rate with which devices are becoming integral parts of our lives, IoT can provide us with unmeasured potential. While the initial cost of converting every device as a node in a vast, digital world is pretty high, it is estimated that IoT will grow by 30% in next 5 years, creating an economic value of $4-11 trillion by 2025.

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Google BigQuery- An externalized version of Dremel

So What is Google Big Query?? Its powerful Big Data analytics platform used by all types of organizations to run SQL-like queries against multiple terabytes of data in a matter of seconds. With this cloud based interactive query service we can handle web-sized amounts of data at blazing fast speed. 


Big Query (released in 2010)is actually the external or public implementation of one of the Google’s core technologies so-called Dremel .Big Query provides the features available in Dermel to third party conserving its unparalleled query performance. Both in fact share the same underlying architecture and performance characteristics. 

Big Query release made it possible to utilize the power of Dremel and to take advantage of Google’s massive computational infrastructure.

Let’s take a deeper look into power of Dremel… It is a query service that allows you to run SQL-like queries against very, very large data sets and get accurate results in mere seconds. You just need a basic knowledge of SQL to query extremely large datasets in an ad hoc manner.

Dermel runs through tens of thousands of servers simultaneously and makes it easy to analyse large amount of data such as a collection of web documents or a library of digital books or even the data describing millions of spam messages.

“According to Google’s paper, this has been used inside Google since 2006, with “thousands” of Googlers using it to analyse everything from the software crash reports for various Google services to the behavior of disks inside the company’s data centers”


The two core technologies that makes Dremel and BigQuery so fast is the Tree Architecture of Dremel And that the Data is stored in a Columnar Storage fashion in so doing, it gives very high compression ratio and scan throughput. 

So how to use data in Big Query or how to import data into Big Query:

• Upload your data to Google Cloud Storage
• Import the files to Big Query. Executed using command-line tool, Web UI or API, which can typically import roughly 100 GB within a half hour.

Other Important Features of Google Big Query:

• BigQuery is designed to handle structured data using SQL. Apart from SQL queries we can easily read and write data in Big Query via Cloud Dataflow, Spark, and Hadoop

• BigQuery provides extremely high cost effectiveness and full-scan performance for ad hoc queries and cost effectiveness compared to traditional data warehouse solutions and appliances.

• BigQuery is the best choice for ad hoc OLAP/BI queries that require results as fast as possible.

• BigQuery requires no capacity planning, provisioning, 24x7 monitoring or operations, nor does it require manual security patch updates. You simply upload datasets to Google Cloud Storage of your account, import them into Big Query, and let Google’s experts manage the rest.

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