ProjectPro

NOVEMBER 7, 2014

A good hadoop big data resume might not be enough to get you selected but a bad hadoop big data resume is enough for rejection.Many big data professionals consider writing big data hadoop resume as an exercise in psychological warfare. Are you one among them? Do you want to move your big data hadoop resume from the slush pile to the "YES" pile ,then you must follow some important guidelines to ensure that your hadoop big data resume does not land into the "NO" pile of CV's.This article aims to p

Technology 52

Technology Hadoop Big Data Recruitment 52

ProjectPro

NOVEMBER 24, 2014

With a rapid pace in evolution of Big Data, its processing frameworks also seem to be evolving in a full swing mode. Hadoop (Hadoop 1.0) has progressed from a more restricted processing model of batch oriented MapReduce jobs to developing specialized and interactive processing models (Hadoop 2.0). With the advent of Hadoop 2.0, it is possible for organizations to create data crunching methodologies within Hadoop which were not possible with Hadoop 1.0 architectural limitations.

Hadoop 40

Hadoop Programming Big Data Unstructured Data 40

ProjectPro

NOVEMBER 11, 2014

Confused over which framework to choose for big data processing - Hadoop MapReduce vs. Apache Spark. This blog helps you understand the critical differences between two popular big data frameworks. Hadoop and Spark are popular apache projects in the big data ecosystem. Apache Spark is an improvement on the original Hadoop MapReduce component of the Hadoop big data ecosystem.

Hadoop 40

Hadoop Machine Learning Scala Big Data 40

ProjectPro

NOVEMBER 5, 2014

Hadoop is the way to go for organizations that do not want to add load to their primary storage system and want to write distributed jobs that perform well. MongoDB NoSQL database is used in the big data stack for storing and retrieving one item at a time from large datasets whereas Hadoop is used for processing these large data sets. For organizations to keep the load off MongoDB in the production database, data processing is offloaded to Apache Hadoop.

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MongoDB Hadoop NoSQL Big Data 40

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In Airflow, DAGs (your data pipelines) support nearly every use case. As these workflows grow in complexity and scale, efficiently identifying and resolving issues becomes a critical skill for every data engineer. This is a comprehensive guide with best practices and examples to debugging Airflow DAGs. You’ll learn how to: Create a standardized process for debugging to quickly diagnose errors in your DAGs Identify common issues with DAGs, tasks, and connections Distinguish between Airflow-relate

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