ProjectPro

JUNE 6, 2025

Hadoop Datasets: These are created from external data sources like the Hadoop Distributed File System (HDFS) , HBase, or any storage system supported by Hadoop. RDDs provide fault tolerance by tracking the lineage of transformations to recompute lost data automatically. a list or array) in your program.

Hadoop 68

Hadoop Metadata Java Datasets 68

ProjectPro

JUNE 6, 2025

There are three steps involved in the deployment of a big data model: Data Ingestion: This is the first step in deploying a big data model - Data ingestion, i.e., extracting data from multiple data sources. Data Processing: This is the final step in deploying a big data model.

Big Data 40

Big Data Hadoop Relational Database AWS 40

ProjectPro

JUNE 6, 2025

Table of Contents What is Real-Time Data Ingestion? For this example, we will clean the purchase data to remove duplicate entries and standardize product and customer IDs. They also enhance the data with customer demographics and product information from their databases. Apache NiFi With over 4.1k

Data Ingestion 40

Data Ingestion Kafka Google Cloud AWS 40

ProjectPro

JUNE 6, 2025

DataFrames are used by Spark SQL to accommodate structured and semi-structured data. Apache Spark is also quite versatile, and it can run on a standalone cluster mode or Hadoop YARN , EC2, Mesos, Kubernetes, etc. Trino is a distributed query tool for effectively querying large volumes of data.

Big Data 49

Big Data Project Metadata Programming Language 49

ProjectPro

JUNE 6, 2025

Microsoft offers Azure Data Lake, a cloud-based data storage and analytics solution. It is capable of effectively handling enormous amounts of structured and unstructured data. Therefore, it is a popular choice for organizations that need to process and analyze big data files.

Data Lake 40

Data Lake Amazon Web Services Data Warehouse Big Data 40

ProjectPro

JUNE 6, 2025

The datasets are usually present in Hadoop Distributed File Systems and other databases integrated with the platform. Hive is built on top of Hadoop and provides the measures to read, write, and manage the data. Spark SQL, for instance, enables structured data processing with SQL.

Hadoop 45

Hadoop Java Big Data Tools Big Data 45

ProjectPro

JUNE 6, 2025

Big data , Hadoop, Hive —these terms embody the ongoing tech shift in how we handle information. It's not just theory; it's about seeing how this framework actively shapes our data-driven world. Hive is a data warehousing and SQL-like query language system built on top of Hadoop.

Big Data 40

Big Data Architecture Project Hadoop 40

ProjectPro

JUNE 6, 2025

Snowflake provides data warehousing, processing, and analytical solutions that are significantly quicker, simpler to use, and more adaptable than traditional systems. Snowflake is not based on existing database systems or big data software platforms like Hadoop. Let us now understand the Snowflake data storage layer in detail.

Architecture 56

Architecture IT Data Warehouse Amazon Web Services 56

ProjectPro

JUNE 6, 2025

Before diving into the how, let's briefly discuss why learning Apache Spark is worthwhile: High Performance: Spark offers in-memory processing, which makes it significantly faster than traditional disk-based data processing systems like Hadoop MapReduce. Master concepts like shuffling, data partitioning, and lineage.

Programming Language 40

Programming Language Scala Hadoop Big Data 40

ProjectPro

JUNE 6, 2025

FAQs on Data Engineering Skills Mastering Data Engineering Skills: An Introduction to What is Data Engineering Data engineering is the process of designing, developing, and managing the infrastructure needed to collect, store, process, and analyze large volumes of data.

Data Engineer 49

Data Engineer Data Engineering Engineering Hadoop 49

ProjectPro

JUNE 6, 2025

In 2024, the data engineering job market is flourishing, with roles like database administrators and architects projected to grow by 8% and salaries averaging $153,000 annually in the US (as per Glassdoor ). These trends underscore the growing demand and significance of data engineering in driving innovation across industries.

Data Engineer 59

Data Engineer Data Engineering Project Engineering 59

ProjectPro

JUNE 6, 2025

Below are some big data interview questions for data engineers based on the fundamental concepts of big data, such as data modeling, data analysis , data migration, data processing architecture, data storage, big data analytics, etc. Structured data usually consists of only text.

Data Engineer 40

Data Engineer Data Engineering Engineering Hadoop 40

ProjectPro

JUNE 6, 2025

An ETL (Extract, Transform, Load) Data Engineer is responsible for designing, building, and maintaining the systems that extract data from various sources, transform it into a format suitable for data analysis, and load it into data warehouses, lakes, or other data storage systems.

Data Engineer 40

Data Engineer Data Engineering Engineering ETL Tools 40

ProjectPro

JUNE 6, 2025

A data warehouse is a data storage system that collects data from various sources to provide meaningful business insights. It is like a central location where quality data from multiple databases are stored. Amazon Redshift is a fully-managed cloud data warehouse solution offered by Amazon.

Data Warehouse 40

Data Warehouse Data Mining Google Cloud PostgreSQL 40

ProjectPro

JUNE 6, 2025

Types of activities: Data Movement : Process of copying data from one data repository to another. Data Transformation : Refine data before transferring it to destination viz., HDInsight (Hive, Hadoop , Spark), Azure Functions, Azure Batch, Machine Learning, Data Lake Analytics.

Data Engineer 40

Data Engineer Data Engineering Engineering Hospitality 40

ProjectPro

JUNE 6, 2025

Furthermore, BigQuery supports machine learning and artificial intelligence, allowing users to use machine learning models to analyze their data. BigQuery Storage BigQuery leverages a columnar storage format to efficiently store and query large amounts of data. The equality operators equal (=), not equal (!=

Bytes 40

Bytes Google Cloud Data Warehouse Cloud Storage 40

ProjectPro

JUNE 6, 2025

When it comes to data ingestion pipelines, PySpark has a lot of advantages. PySpark allows you to process data from Hadoop HDFS , AWS S3, and various other file systems. PySpark SQL and Dataframes A dataframe is a shared collection of organized or semi-structured data in PySpark.

Big Data 40

Big Data Data Process Process Kafka 40

ProjectPro

JUNE 6, 2025

These AWS resources offer the highest level of usability and are created specifically for the performance optimization of various applications using content delivery features, data storage, and other methods. AWS Redshift Amazon Redshift offers petabytes of structured or semi-structured data storage as an ideal data warehouse option.

AWS 40

AWS Data Engineer Data Engineering Engineering 40

ProjectPro

JUNE 6, 2025

Big data is often characterized by the seven V's: Volume , Variety , Velocity, Variability, Veracity, Visualization, and Value of data. Big data engineers leverage big data tools and technologies to process and engineer massive data sets or data stored in data storage systems like databases and data lakes.

Big Data 45

Big Data Big Data Skills Hadoop Scala 45

ProjectPro

JUNE 6, 2025

Relational Databases Relational databases form the backbone of modern data storage and management systems, powering various applications across industries. Gaming Platforms: DynamoDB is an ideal solution for building gaming platforms with features like player data storage, session history, and leaderboards.

AWS 40

AWS Database Amazon Web Services MySQL 40

ProjectPro

JUNE 6, 2025

You can easily connect to multiple data sources, manipulate data, and load it into different data storage systems using Python. This makes it an ideal choice for ETL developers, data engineers , and data analysts, even those without a strong programming background.

Python 40

Python ETL Tools Data Warehouse Programming Language 40

ProjectPro

JUNE 6, 2025

Check out the ProjectPro repository with unique Hadoop Mini Projects with Source Code to help you grasp Hadoop basics. Understanding of continuous data protection methods. Familiarity with Snowflake Cloud Data Platform, SQL queries, and database design. Knowledge of handling unstructured and semi-structured data.

Certification 40

Certification Data Pipeline Hadoop AWS 40

ProjectPro

JUNE 6, 2025

The service provider's data center hosts the underlying infrastructure, software, and app data. Azure Redis Cache is an in-memory data storage, or cache system, based on Redis that boosts the flexibility and efficiency of applications that rely significantly on backend data stores. Define table storage in Azure.

BI 63

BI Cloud Computing SQL Database 63

ProjectPro

JUNE 6, 2025

Web Server Log Processing In this project, you'll process web server logs using a combination of Hadoop, Flume, Spark, and Hive on Azure. Starting with setting up an Azure Virtual Machine, you'll install necessary big data tools and configure Flume agents for log data ingestion.

Project 64

Project Transportation Datasets Data Pipeline 64

ProjectPro

JUNE 6, 2025

. · Tableau also provides a data blending facility. Which Tableau data types are preferable while dealing with structured data? We can prefer using Text (string) values and numerical values as the two popular data types while dealing with structured data in Tableau.

BI 40

BI Database-centric SQL Software Engineering 40

AltexSoft

JUNE 7, 2021

Hadoop and Spark are the two most popular platforms for Big Data processing. They both enable you to deal with huge collections of data no matter its format — from Excel tables to user feedback on websites to images and video files. What are its limitations and how do the Hadoop ecosystem address them? What is Hadoop.

Big Data Tools 98

Big Data Tools Hadoop Big Data Database-centric 98

ProjectPro

JUNE 4, 2015

All the components of the Hadoop ecosystem, as explicit entities are evident. All the components of the Hadoop ecosystem, as explicit entities are evident. The holistic view of Hadoop architecture gives prominence to Hadoop common, Hadoop YARN, Hadoop Distributed File Systems (HDFS ) and Hadoop MapReduce of the Hadoop Ecosystem.

Hadoop 52

Hadoop Architecture IT Java 52

ProjectPro

MARCH 15, 2016

Hadoop is beginning to live up to its promise of being the backbone technology for Big Data storage and analytics. Companies across the globe have started to migrate their data into Hadoop to join the stalwarts who already adopted Hadoop a while ago. Hadoop runs on clusters of commodity servers.

Hadoop 40

Hadoop Retail Banking Healthcare 40

ProjectPro

NOVEMBER 14, 2015

Big data has taken over many aspects of our lives and as it continues to grow and expand, big data is creating the need for better and faster data storage and analysis. These Apache Hadoop projects are mostly into migration, integration, scalability, data analytics, and streaming analysis. Data Migration 2.

Hadoop 52

Hadoop Project Big Data Healthcare 52

Cloudera

SEPTEMBER 15, 2022

It was designed as a native object store to provide extreme scale, performance, and reliability to handle multiple analytics workloads using either S3 API or the traditional Hadoop API. Structured data (such as name, date, ID, and so on) will be stored in regular SQL databases like Hive or Impala databases.

Systems 87

Systems Hadoop Metadata Telecommunication 87

Knowledge Hut

DECEMBER 21, 2023

To establish a career in big data, you need to be knowledgeable about some concepts, Hadoop being one of them. Hadoop tools are frameworks that help to process massive amounts of data and perform computation. You can learn in detail about Hadoop tools and technologies through a Big Data and Hadoop training online course.

Hadoop 52

Hadoop Big Data NoSQL Unstructured Data 52

Knowledge Hut

MAY 2, 2024

To store and process even only a fraction of this amount of data, we need Big Data frameworks as traditional Databases would not be able to store so much data nor traditional processing systems would be able to process this data quickly. But, in the majority of cases, Hadoop is the best fit as Spark’s data storage layer.

Hadoop 96

Hadoop Scala Java Datasets 96

Cloudera

OCTOBER 3, 2018

First, remember the history of Apache Hadoop. Google built an innovative scale-out platform for data storage and analysis in the late 1990s and early 2000s, and published research papers about their work. The two of them started the Hadoop project to build an open-source implementation of Google’s system.

Hadoop 75

Hadoop Cloud Data Storage Machine Learning 75

ProjectPro

JUNE 6, 2025

With the help of ProjectPro’s Hadoop Instructors, we have put together a detailed list of big data Hadoop interview questions based on the different components of the Hadoop Ecosystem such as MapReduce, Hive, HBase, Pig, YARN, Flume, Sqoop , HDFS, etc. What is the difference between Hadoop and Traditional RDBMS?

Hadoop 40

Hadoop Big Data Metadata Java 40

ProjectPro

AUGUST 21, 2015

With the help of ProjectPro’s Hadoop Instructors, we have put together a detailed list of big data Hadoop interview questions based on the different components of the Hadoop Ecosystem such as MapReduce, Hive, HBase, Pig, YARN, Flume, Sqoop , HDFS, etc. What is the difference between Hadoop and Traditional RDBMS?

Hadoop 40

Hadoop Big Data Metadata Java 40

AltexSoft

MAY 14, 2021

A growing number of companies now use this data to uncover meaningful insights and improve their decision-making, but they can’t store and process it by the means of traditional data storage and processing units. Key Big Data characteristics. And most of this data has to be handled in real-time or near real-time.

Big Data 78

Big Data Data Analytics IT NoSQL 78

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.

Hadoop 40

Hadoop Scala Machine Learning Java 40