Challenges in Big Data & Languages to Crunch It

Big data is now a crucial means to gain a competitive edge and remain ahead of their peers. 


Challenges in Big Data and Languages to Crunch It

Offering a seamless customer experience is the primary challenge that most business organizations all over the globe are grappling with. Today’s customers look for personalized services without compromising on anything and also expect their service providers to educate them through various channels that they choose to use. Implementing successful customer centric services require clear understanding of customer preferences, needs and behavior and relating these with the ongoing operational transactions. Big Data Analytics is the emerging trend and is the key enabler in helping business organizations attain this.
With the advent of the internet and use of various mobile devices, huge quantities of data can now be easily harvested and used to optimize the business processes. Within such huge information lies insights that are extremely useful and beneficial and effective. Big data is thus an emerging technique used to capture, store, process, analyze and visualize huge quantities of data that enable the decision making and process optimization.

Discussing The Key Challenges of Big Data:

Utilizing and understanding big data:  Most organizations that deal with huge data often face a challenge of identifying and understanding the data that needs to be used based on the companies’ strategy and tactics. These analysis need to be performed on an ongoing basis since the data keeps on changing at an increased rate and experts also develop a bigger appetite for more available information.

Security and privacy considerations: Considering the volume and the complexity of big data, it is definitely a challenging task for organizations to attain a reliable grasp on content and to capture, store and secure it adequately. Since customer data has to be kept confidential and should not be accessed by any unauthorized parties, the cost of any data breach can be enormous. Companies that function globally should also take care of significant differences in the privacy laws and even regulatory functions.

Emerging technologies Most of the technologies that are used to analyze big data are new to organizations; it is essential to learn the latest techniques at a fast pace and engage with different service providers and partners. Entering the world of big data needs a perfect balance of understanding the business needs and using the huge information for the growth of the company by using the latest technologies.

Cloud based solutions: Latest business software applications have emerged where the company data is stored and managed in data centers that can be accessed from across the globe. The solutions may range from ERP, Document management, CRM, business intelligence to many others and cloud solutions offer companies with flexibility and cost savings, but raises issues that are related to data security and the overall management of Big Data paradigm.

The need of data analyst: Mining big data from the organizations is the first step, but organizations need to hire IT and data analyst to analyze the data so that the reports can be used to make critical decisions related to the growth of the company.

Languages Used for Crunching Big Data:

There are computer programming languages that are used to create algorithms or tools that can crunch through when structured or unstructured data are thrown at them. Certain languages that have proved to do the task better includes:

1. Python: This is an open source language that is used to work with complicated and large data. It offers great flexibility and is comparatively easy to learn.

2. Scala: Scala is a language that runs on JVM and is a perfect blend of functional and object oriented paradigms. It is a language that drives both Kafka and Spark.

3. Java: Java and Java based frameworks are considered to be the skeleton of the Silicon Valley tech companies and are the foundational language for all data engineering infrastructures. In case you need to build large systems, Java is the best.

4. Hadoop and Hive: There are some important Java based tools that are used to meet the enormous needs for data processing. Hadoop, a Java based framework is used for batch processing and it pairs well with Hive, which is a query based framework that is widely used.

5. Kafka and Storm: If you are in need of rapid and real time analytics, then Kafka is best as it is an ultra-fast query messaging system. All the big companies use Kafka and Storm for real time processing that may be slow, but super accurate.