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

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