ProjectPro

MAY 31, 2021

Ace your big data interview by adding some unique and exciting Big Data projects to your portfolio. This blog lists over 20 big data projects you can work on to showcase your big data skills and gain hands-on experience in big data tools and technologies.

Big Data 52

Big Data Coding Project Hadoop 52

Knowledge Hut

JANUARY 11, 2024

It uses complex machine learning algorithms to build meaningful and structured data. Data Science can be described as a domain that applies advanced analytics, statistics and scientific principle for extracting valuable information and deriving valuable conclusions from structured or unstructured data.

Finance 93

Finance Data Science Programming Language Machine Learning 93

ProjectPro

MARCH 19, 2015

Big Data NoSQL databases were pioneered by top internet companies like Amazon, Google, LinkedIn and Facebook to overcome the drawbacks of RDBMS. RDBMS is not always the best solution for all situations as it cannot meet the increasing growth of unstructured data.

NoSQL 49

NoSQL Big Data SQL Database-centric 49

ProjectPro

JULY 13, 2015

Large commercial banks like JPMorgan have millions of customers but can now operate effectively-thanks to big data analytics leveraged on increasing number of unstructured and structured data sets using the open source framework - Hadoop. JP Morgan has massive amounts of data on what its customers spend and earn.

Hadoop 52

Hadoop Big Data Data Analytics Banking 52

Knowledge Hut

DECEMBER 26, 2023

Analyzing and organizing raw data Raw data is unstructured data consisting of texts, images, audio, and videos such as PDFs and voice transcripts. The job of a data engineer is to develop models using machine learning to scan, label and organize this unstructured data.

Data Engineering 98

Data Engineering Data Engineer Engineering Pipeline-centric 98

Silectis

JANUARY 3, 2021

BI (Business Intelligence) Strategies and systems used by enterprises to conduct data analysis and make pertinent business decisions. Big Data Large volumes of structured or unstructured data. Big Query Google’s cloud data warehouse. Database A collection of structured data.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

ProjectPro

JANUARY 27, 2023

However, the vast volume of data will overwhelm you if you start looking at historical trends. The time-consuming method of data collection and transformation can be eliminated using ETL. You can analyze and optimize your investment strategy using high-quality structured data.

BI 52

BI ETL Tools Retail Healthcare 52

ProjectPro

JUNE 6, 2015

Business Intelligence (BI) combines human knowledge, technologies like distributed computing, and Artificial Intelligence, and big data analytics to augment business decisions for driving enterprise’s success. It replaced its traditional BI structure by integrating big data and Hadoop."-April So what is BI?

Business Intelligence 40

Business Intelligence Big Data BI Hadoop 40

AltexSoft

AUGUST 29, 2023

It is a versatile platform for exploring, refining, and analyzing petabytes of information that continually flow in from various data sources. Who needs a data lake? If the intricacies of big data are becoming too much for your existing systems to handle, a data lake might be the solution you’re seeking.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Knowledge Hut

DECEMBER 29, 2023

Data scientists may improve their knowledge and response to crucial business demands by opting to specialize in a subfield of their subject. It's possible they'll zero down on a certain data kind, like Big Data, or a computer language. It separates the hidden links and patterns in the data.

Data Science 98

Data Science Data Mining Deep Learning Programming Language 98

ProjectPro

JULY 27, 2021

In this blog, we have collated the frequently asked data engineer interview questions based on tools and technologies that are highly useful for a data engineer in the Big Data industry. that leverage big data analytics and tools. Preparing for data engineer interviews makes even the bravest of us anxious.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Edureka

FEBRUARY 9, 2023

In this blog on “Azure data engineer skills”, you will discover the secrets to success in Azure data engineering with expert tips, tricks, and best practices Furthermore, a solid understanding of big data technologies such as Hadoop, Spark, and SQL Server is required.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Mining 52

Knowledge Hut

SEPTEMBER 26, 2023

Microsoft Azure's Azure Synapse, formerly known as Azure SQL Data Warehouse, is a complete analytics offering. Designed to tackle the challenges of modern data management and analytics, Azure Synapse brings together the worlds of big data and data warehousing into a unified and seamlessly integrated platform.

Data Lake 52

Data Lake Database-centric Pipeline-centric Machine Learning 52

ProjectPro

DECEMBER 7, 2021

Data pipelines are a significant part of the big data domain, and every professional working or willing to work in this field must have extensive knowledge of them. Table of Contents What is a Data Pipeline? The Importance of a Data Pipeline What is an ETL Data Pipeline? What is a Big Data Pipeline?

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

ProjectPro

JANUARY 19, 2022

Read this blog till the end to learn more about the roles and responsibilities, necessary skillsets, average salaries, and various important certifications that will help you build a successful career as an Azure Data Engineer. The big data industry is flourishing, particularly in light of the pandemic's rapid digitalization.

Data Engineering 52

Data Engineering Data Engineer Engineering Scala 52

ProjectPro

OCTOBER 28, 2015

Apache Hadoop is synonymous with big data for its cost-effectiveness and its attribute of scalability for processing petabytes of data. Data analysis using hadoop is just half the battle won. Getting data into the Hadoop cluster plays a critical role in any big data deployment.

ETL Tools 52

ETL Tools Hadoop Relational Database Unstructured Data 52

ProjectPro

DECEMBER 17, 2021

are shifting towards NoSQL databases gradually as SQL-based databases are incapable of handling big-data requirements. Industry experts at ProjectPro say that although both have been developed for the same task, i.e., data storage, they vary significantly in terms of the audience they cater to.

Data Science 52

Data Science SQL NoSQL Programming Language 52

ProjectPro

AUGUST 11, 2021

“Data Lake vs Data Warehouse = Load First, Think Later vs Think First, Load Later” The terms data lake and data warehouse are frequently stumbled upon when it comes to storing large volumes of data. Is Snowflake a data lake or data warehouse? Is Hadoop a data lake or data warehouse?

Data Lake 52

Data Lake Data Warehouse Cloud Hadoop 52

Edureka

FEBRUARY 7, 2023

Azure Synapse Interview Questions – Analytics The interview questions and responses for azure data engineers for synapse analytics and stream analytics are covered in this section. 6) Which Azure service would you use to build a data warehouse? 14) What are Azure Databricks, and how are they unique from standard data bricks?

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

ProjectPro

OCTOBER 15, 2014

Table of contents Hive vs Pig What is Big Data and Hadoop? Not only this, few of the people are as well of the thought that Big Data and Hadoop are one and the same. What is Big Data and Hadoop? It is Hive that has enabled Facebook to deal with 10’s of Terabytes of Data on a daily basis with ease.

Hadoop 52

Hadoop Unstructured Data Java SQL 52

ProjectPro

SEPTEMBER 26, 2014

The toughest challenges in business intelligence today can be addressed by Hadoop through multi-structured data and advanced big data analytics. Big data technologies like Hadoop have become a complement to various conventional BI products and services.

Business Intelligence 40

Business Intelligence Hadoop BI Relational Database 40

ProjectPro

AUGUST 24, 2016

Table of Contents Need for HBase HBase –Understanding the Basics HBase Architecture Explained Components of Apache HBase Architecture HMaster Region Server Zookeeper Need for HBase Apache Hadoop has gained popularity in the big data space for storing, managing and processing big data as it can handle high volume of multi-structured data.

Architecture 40

Architecture IT Hadoop NoSQL 40

ProjectPro

SEPTEMBER 11, 2015

By 2018, the Big Data market will be about $46.34 The availability of skilled big data Hadoop talent will directly impact the market. Big Data and Hadoop are moving out of its experimental stage and Hadoop is continuously maturing after having completed 10 years. Big Data is not going to go away.

Hadoop 52

Hadoop Java BI Big Data 52

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. Scalability 4.Link

Hadoop 52

Hadoop Project Big Data Healthcare 52

AltexSoft

JULY 29, 2022

Depending on how you measure it, the answer will be 11 million newspaper pages or… just one Hadoop cluster and one tech specialist who can move 4 terabytes of textual data to a new location in 24 hours. Developed in 2006 by Doug Cutting and Mike Cafarella to run the web crawler Apache Nutch, it has become a standard for Big Data analytics.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

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. Which Big Data tasks does Spark solve most effectively? How does it work?

Big Data Tools 98

Big Data Tools Hadoop Big Data Database-centric 98

AltexSoft

MAY 14, 2021

Big Data enjoys the hype around it and for a reason. But the understanding of the essence of Big Data and ways to analyze it is still blurred. This post will draw a full picture of what Big Data analytics is and how it works. Big Data and its main characteristics. Key Big Data characteristics.

Big Data 78

Big Data Data Analytics IT NoSQL 78

AltexSoft

JUNE 26, 2023

Data collection is a methodical practice aimed at acquiring meaningful information to build a consistent and complete dataset for a specific business purpose — such as decision-making, answering research questions, or strategic planning. Key differences between structured, semi-structured, and unstructured data.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

U-Next

SEPTEMBER 7, 2022

Structuring data refers to converting unstructured data into tables and defining data types and relationships based on a schema. The data lakes store data from a wide variety of sources, including IoT devices, real-time social media streams, user data, and web application transactions.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Amazon Web Services 52

AltexSoft

MAY 12, 2023

In today’s data-driven world, organizations amass vast amounts of information that can unlock significant insights and inform decision-making. A staggering 80 percent of this digital treasure trove is unstructured data, which lacks a pre-defined format or organization. What is unstructured data?

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

AltexSoft

AUGUST 4, 2021

A data warehouse (DW) is a data repository that allows for storing and managing all the historical enterprise data, coming from disparate internal and external sources like CRMs, ERPs, flat files, etc. Initially, DWs dealt with structured data presented in tabular forms. The “cloud-y” future of data marts.

Data Lake 64

Data Lake Data Warehouse ETL Tools Business Intelligence 64

AltexSoft

NOVEMBER 10, 2021

Prior to the recent advances in data management technologies, there were two main types of data stores companies could make use of, namely data warehouses and data lakes. Data warehouse. Traditional data warehouse platform architecture. Unstructured and streaming data support.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 52

AltexSoft

SEPTEMBER 23, 2021

An ETL approach in the DW is considered slow, as it ships data in portions (batches.) The structure of data is usually predefined before it is loaded into a warehouse, since the DW is a relational database that uses a single data model for everything it stores. Data lake vs data hub.

Architecture 52

Architecture Data Lake Unstructured Data Data Warehouse 52

AltexSoft

AUGUST 22, 2022

What is data fabric? A data fabric is an architecture design presented as an integration and orchestration layer built on top of multiple disjointed data sources like relational databases , data warehouses , data lakes, data marts , IoT , legacy systems, etc.,

Architecture 52

Architecture Metadata Data Lake Machine Learning 52