Posted on

5 Essential Diagnostic Views to Fix Hive Queries

A perpetual debate rages about the effectiveness of a modern-day Data Analyst in a Distributed Computing environment. Analysts are used to SQL’s returning answers to their questions in short order. The RDBMS user is often unable to comprehend the root-cause when queries don’t return results for multiple hours. The opinions are divided, despite broad acceptance of the fact that Query Engines such as Hive and Spark are complex for the best engineers. At Acceldata, we see full TableScans run on multi-Tera Byte tables to get a count of rows, which to say the least is taboo in the Hadoop world. What results is a frustrating conversation between Cluster Admins and Data Users, which is devoid of data that is hard to collect. It is also a fact that data needs conversion into insights to make business decisions. More importantly, the value in Big Data needs to be unlocked without delays.

From here we start from the point where the Hadoop Admin/Engineer is ready to unravel the scores of metrics and interpret the reasons for poor performance and taking resources away from the cluster causing:

Posted on

Data Science and the Growing Importance of Professional Certifications

Data scientists are among the most sought-after technical talent today. According to Glassdoor, Data Scientist has been the top role in the US for four consecutive years and is increasingly identified as an essential component for business growth. As the demand for data science talent increases and the importance of the role is understood, the need to formalize the profession arises. What exactly is the role of a data scientist and why is professional certification for data scientists growing in importance? 

You may also like: 10 Steps to Become a Data Scientist.

The Role of a Data Scientist

Data scientists work with enterprise leaders and key decision-makers to solve problems by preparing, analyzing, and understanding data to deliver insight, predict emerging trends, and provide recommendations to optimize results. The impact these professionals have varies by industry. For example, in healthcare, data scientists are using cognitive computing technologies to help support doctors to deliver personalized and precision medicine.  

Posted on

Why Every Organization Needs a Data Analyst

Data-driven decisions make the world go round

There is so much hype around the data scientist role these days that when a company needs a specialist to get some insights from data, their first inclination is to look for a data scientist. But is that really the best option? Let’s see how the roles of data scientists and data analysts differ and why you may want to hire an analyst before any other role.

You may also like: Five Must Read Books to Become a Successful Data Analyst.

Data Scientist or Data Analyst

So, what’s the difference between data scientists and data analysts? The definitions of these roles can vary, but it’s usually believed that a data scientist combines three key disciplines — data analysis, statistics, and Machine Learning. Machine learning involves the process of data analysis to learn and generate analytical models that can perform intelligent action on unseen data, with minimal human intervention. With such expectations, it’s clear that three-in-one is better than one-in-one, and data scientists become more desired by companies.